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Entropy, Volume 26, Issue 5 (May 2024) – 83 articles

Cover Story (view full-size image): Information theory has found applications in diverse disciplines due to its solid foundation in probability theory. However, data often doesn’t follow specific probability distributions, complicating direct applications of information-theoretic concepts. Non-parametric methods can estimate these quantities from data, but which existing method is best? We evaluated different estimation methods practically by measuring the relative error of their estimates across different uni- and multivariate distributions and sample sizes. We also considered each estimator’s hyperparameters and implementation challenges. Through synthetic case studies, we show the behavior of different methods and highlight the advantages of nearest neighbor-based estimation. View this paper
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14 pages, 601 KiB  
Article
Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen–Shannon Distance
by Luciano Zunino
Entropy 2024, 26(5), 432; https://doi.org/10.3390/e26050432 - 20 May 2024
Cited by 1 | Viewed by 1225
Abstract
Taking into account the complexity of the human brain dynamics, the appropriate characterization of any brain state is a challenge not easily met. Actually, even the discrimination of simple behavioral tasks, such as resting with eyes closed or eyes open, represents an intricate [...] Read more.
Taking into account the complexity of the human brain dynamics, the appropriate characterization of any brain state is a challenge not easily met. Actually, even the discrimination of simple behavioral tasks, such as resting with eyes closed or eyes open, represents an intricate problem and many efforts have been and are being made to overcome it. In this work, the aforementioned issue is carefully addressed by performing multiscale analyses of electroencephalogram records with the permutation Jensen–Shannon distance. The influence that linear and nonlinear temporal correlations have on the discrimination is unveiled. Results obtained lead to significant conclusions that help to achieve an improved distinction between these resting brain states. Full article
(This article belongs to the Section Entropy and Biology)
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Figure 1
<p>Mean and standard deviation (as error bar) of the PJSD estimations with <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>∈</mo> <mo>{</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>6</mn> <mo>}</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> from an ensemble of 100 independent realizations with length <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math> data for the mixed model with parameter <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mi>PJSD</mi> <mo>(</mo> <msub> <mi>P</mi> <mi>X</mi> </msub> <mo>,</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>S</mi> <mi>h</mi> </mrow> </msub> </msub> <mo>)</mo> </mrow> </semantics></math> (blue curve), <math display="inline"><semantics> <mrow> <mi>PJSD</mi> <mo>(</mo> <msub> <mi>P</mi> <mi>X</mi> </msub> <mo>,</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>F</mi> <mi>T</mi> </mrow> </msub> </msub> <mo>)</mo> </mrow> </semantics></math> (red curve), <math display="inline"><semantics> <mrow> <mi>PJSD</mi> <mo>(</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>S</mi> <mi>h</mi> </mrow> </msub> </msub> <mo>,</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>F</mi> <mi>T</mi> </mrow> </msub> </msub> <mo>)</mo> </mrow> </semantics></math> (green curve), and <math display="inline"><semantics> <mrow> <mi>PJSD</mi> <mo>(</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>S</mi> <mi>h</mi> </mrow> </msub> </msub> <mo>,</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>S</mi> <mi>h</mi> </mrow> </msub> </msub> <mo>)</mo> </mrow> </semantics></math> (black curve) are plotted as a function of the fraction <span class="html-italic">m</span> (<math display="inline"><semantics> <mrow> <mi>m</mi> <mo>∈</mo> <mo>{</mo> <mn>0</mn> <mo>,</mo> <mn>0.05</mn> <mo>,</mo> <mn>0.1</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>0.95</mn> <mo>,</mo> <mn>1</mn> <mo>}</mo> </mrow> </semantics></math>). The first ordinal distance characterizes linear and nonlinear behaviors globally, the second one quantifies just the nonlinear component, the third one measures linear temporal correlations solely, and, finally, the last ordinal distance is estimated to set a baseline reference. Results obtained are qualitatively analogous for the different orders <span class="html-italic">D</span>.</p>
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<p>Mean and standard deviation (as error bar) of the PE estimations with <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>∈</mo> <mo>{</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>5</mn> <mo>,</mo> <mn>6</mn> <mo>}</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> from the same ensemble of numerical realizations of the mixed model detailed in <a href="#entropy-26-00432-f001" class="html-fig">Figure 1</a>. The behavior followed by the PE as a function of the fraction <span class="html-italic">m</span> is opposed to that observed for <math display="inline"><semantics> <mrow> <mi>PJSD</mi> <mo>(</mo> <msub> <mi>P</mi> <mi>X</mi> </msub> <mo>,</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>S</mi> <mi>h</mi> </mrow> </msub> </msub> <mo>)</mo> </mrow> </semantics></math>. Thus, both measures provide the same qualitative information about the mixed model.</p>
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<p>Discrimination of the two resting states, EO and EC, by implementing <math display="inline"><semantics> <mrow> <mi>PJSD</mi> <mo>(</mo> <msub> <mi>P</mi> <mi>X</mi> </msub> <mo>,</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>S</mi> <mi>h</mi> </mrow> </msub> </msub> <mo>)</mo> </mrow> </semantics></math> as discriminative statistic. Results obtained for the raw EEG records when an order <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math> is chosen for estimating the ordinal quantifier are depicted. Differences in average between EO and EC resting conditions and <span class="html-italic">p</span>-values (in logarithmic base 10 scale), calculated by using the Wilcoxon rank sum test, as a function of the channel and the lag <math display="inline"><semantics> <mi>τ</mi> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>∈</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>50</mn> <mo>}</mo> </mrow> </semantics></math>) are displayed in (<b>a</b>,<b>b</b>), respectively. Topographic maps of the <span class="html-italic">p</span>-values (in logarithmic base 10 scale) for two specific lags, <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>24</mn> </mrow> </semantics></math>, are illustrated in (<b>c</b>,<b>d</b>), respectively.</p>
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<p>The same as in <a href="#entropy-26-00432-f003" class="html-fig">Figure 3</a> but using <math display="inline"><semantics> <mrow> <mi>PJSD</mi> <mo>(</mo> <msub> <mi>P</mi> <mi>X</mi> </msub> <mo>,</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>F</mi> <mi>T</mi> </mrow> </msub> </msub> <mo>)</mo> </mrow> </semantics></math> as the discriminative statistic. In this case, the topographic map of the <span class="html-italic">p</span>-values (in logarithmic base 10 scale) for <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> is illustrated in (<b>d</b>).</p>
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<p>The same as in <a href="#entropy-26-00432-f003" class="html-fig">Figure 3</a> but using <math display="inline"><semantics> <mrow> <mi>PJSD</mi> <mo>(</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>S</mi> <mi>h</mi> </mrow> </msub> </msub> <mo>,</mo> <msub> <mi>P</mi> <msub> <mi>X</mi> <mrow> <mi>F</mi> <mi>T</mi> </mrow> </msub> </msub> <mo>)</mo> </mrow> </semantics></math> as a discriminative statistic. In this case, the topographic map of the <span class="html-italic">p</span>-values (in logarithmic base 10 scale) for <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math> is illustrated in (<b>d</b>).</p>
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<p>The same as in <a href="#entropy-26-00432-f003" class="html-fig">Figure 3</a> but using PE as discriminative statistic.</p>
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19 pages, 1256 KiB  
Article
AM-MSFF: A Pest Recognition Network Based on Attention Mechanism and Multi-Scale Feature Fusion
by Meng Zhang, Wenzhong Yang, Danny Chen, Chenghao Fu and Fuyuan Wei
Entropy 2024, 26(5), 431; https://doi.org/10.3390/e26050431 - 20 May 2024
Cited by 1 | Viewed by 1145
Abstract
Traditional methods for pest recognition have certain limitations in addressing the challenges posed by diverse pest species, varying sizes, diverse morphologies, and complex field backgrounds, resulting in a lower recognition accuracy. To overcome these limitations, this paper proposes a novel pest recognition method [...] Read more.
Traditional methods for pest recognition have certain limitations in addressing the challenges posed by diverse pest species, varying sizes, diverse morphologies, and complex field backgrounds, resulting in a lower recognition accuracy. To overcome these limitations, this paper proposes a novel pest recognition method based on attention mechanism and multi-scale feature fusion (AM-MSFF). By combining the advantages of attention mechanism and multi-scale feature fusion, this method significantly improves the accuracy of pest recognition. Firstly, we introduce the relation-aware global attention (RGA) module to adaptively adjust the feature weights of each position, thereby focusing more on the regions relevant to pests and reducing the background interference. Then, we propose the multi-scale feature fusion (MSFF) module to fuse feature maps from different scales, which better captures the subtle differences and the overall shape features in pest images. Moreover, we introduce generalized-mean pooling (GeMP) to more accurately extract feature information from pest images and better distinguish different pest categories. In terms of the loss function, this study proposes an improved focal loss (FL), known as balanced focal loss (BFL), as a replacement for cross-entropy loss. This improvement aims to address the common issue of class imbalance in pest datasets, thereby enhancing the recognition accuracy of pest identification models. To evaluate the performance of the AM-MSFF model, we conduct experiments on two publicly available pest datasets (IP102 and D0). Extensive experiments demonstrate that our proposed AM-MSFF outperforms most state-of-the-art methods. On the IP102 dataset, the accuracy reaches 72.64%, while on the D0 dataset, it reaches 99.05%. Full article
(This article belongs to the Section Entropy and Biology)
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<p>The structure of AM-MSFF.</p>
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<p>The structure of relation-aware global attention.</p>
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<p>The structure of spatial relation-aware global attention.</p>
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<p>The structure of channel relation-aware global attention.</p>
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<p>The structure of multi-scale feature fusion.</p>
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<p>The example images from the IP102 dataset include various morphologies of insects, such as eggs, larvae, pupae, and adults.</p>
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<p>Example of Cletus punctiger (Dallas) in the D0 dataset.</p>
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<p>Visualization of Grad-CAMs produced by ResNet-50 and AM-MSFF.</p>
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12 pages, 1987 KiB  
Review
Information Theory, Living Systems, and Communication Engineering
by Dragana Bajić
Entropy 2024, 26(5), 430; https://doi.org/10.3390/e26050430 - 18 May 2024
Cited by 2 | Viewed by 1860
Abstract
Mainstream research on information theory within the field of living systems involves the application of analytical tools to understand a broad range of life processes. This paper is dedicated to an opposite problem: it explores the information theory and communication engineering methods that [...] Read more.
Mainstream research on information theory within the field of living systems involves the application of analytical tools to understand a broad range of life processes. This paper is dedicated to an opposite problem: it explores the information theory and communication engineering methods that have counterparts in the data transmission process by way of DNA structures and neural fibers. Considering the requirements of modern multimedia, transmission methods chosen by nature may be different, suboptimal, or even far from optimal. However, nature is known for rational resource usage, so its methods have a significant advantage: they are proven to be sustainable. Perhaps understanding the engineering aspects of methods of nature can inspire a design of alternative green, stable, and low-cost transmission. Full article
(This article belongs to the Section Entropy Reviews)
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<p>Pulses of nerve action potential (NAP)—information is transferred using digital frequency modulation.</p>
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<p>Codons—triplets of nucleotides. Here, 1, 2, 3, 4, or 6 codons can be mapped into the same amino acid. The codons UAA, UAG and UGA are not amino acids, they mark the end of transcription process, while amino acid Met sometimes mark the beginning.</p>
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<p>Non-destructive compression. (<b>a</b>) Two overlapping genes with differently synchronized codons; (<b>b</b>) overlapping the contents of dictionary and input buffer in the Ziv–Lempel 77 procedure.</p>
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<p>(<b>a</b>) Non-synchronized stream of digital symbols; (<b>b</b>) the same text with included blanks, capital letters, and punctuation marks to differentiate (synchronize) words and sentences by denoting their beginnings.</p>
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<p>(<b>a</b>) Transcription start site (TSS) and markers TATAAT and TTGACA at the offset of approximately 10 and 35 positions from TSS; (<b>b</b>) probability that the nucleotide is at its expected position.</p>
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8 pages, 944 KiB  
Article
Heat Bath in a Quantum Circuit
by Jukka P. Pekola and Bayan Karimi
Entropy 2024, 26(5), 429; https://doi.org/10.3390/e26050429 - 17 May 2024
Cited by 3 | Viewed by 1477
Abstract
We discuss the concept and realization of a heat bath in solid state quantum systems. We demonstrate that, unlike a true resistor, a finite one-dimensional Josephson junction array or analogously a transmission line with non-vanishing frequency spacing, commonly considered as a reservoir of [...] Read more.
We discuss the concept and realization of a heat bath in solid state quantum systems. We demonstrate that, unlike a true resistor, a finite one-dimensional Josephson junction array or analogously a transmission line with non-vanishing frequency spacing, commonly considered as a reservoir of a quantum circuit, does not strictly qualify as a Caldeira–Leggett type dissipative environment. We then consider a set of quantum two-level systems as a bath, which can be realized as a collection of qubits. We show that only a dense and wide distribution of energies of the two-level systems can secure long Poincare recurrence times characteristic of a proper heat bath. An alternative for this bath is a collection of harmonic oscillators, for instance, in the form of superconducting resonators. Full article
(This article belongs to the Special Issue Advances in Quantum Thermodynamics)
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<p>Basic properties of a one-dimensional Josephson junction array. (<b>a</b>) An array with <span class="html-italic">N</span> junctions, terminated by impedance <math display="inline"><semantics> <msub> <mi>Z</mi> <mi>L</mi> </msub> </semantics></math>. The current is <span class="html-italic">I</span>, and the voltage is <span class="html-italic">V</span>. Junctions can be replaced by superconducting interference devices (SQUIDs) acting as tunable junctions. (<b>b</b>) An equivalent circuit for a uniform array with junctions linearized as inductors <span class="html-italic">L</span>. The junction capacitance is <span class="html-italic">C</span>, and the stray “ground” capacitance of each island is <math display="inline"><semantics> <msub> <mi>C</mi> <mi>g</mi> </msub> </semantics></math>. (<b>c</b>) The dispersion relation for modes in the array for two cases, <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (black line, <math display="inline"><semantics> <mrow> <mi>L</mi> <msub> <mi>C</mi> <mi>g</mi> </msub> </mrow> </semantics></math>) and <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>100</mn> <msub> <mi>C</mi> <mi>g</mi> </msub> </mrow> </semantics></math> (green line, <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>L</mi> <msub> <mi>C</mi> <mi>g</mi> </msub> </mrow> </semantics></math>), for an array with <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>3000</mn> </mrow> </semantics></math>. Here, we assume an open ended array (<math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mi>L</mi> </msub> <mo>=</mo> <mo>∞</mo> </mrow> </semantics></math>). The (angular) frequencies are scaled by the plasma frequency <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>/</mo> <msqrt> <mrow> <mi>L</mi> <mi>C</mi> </mrow> </msqrt> </mrow> </semantics></math> of each junction. (<b>d</b>) The modulus of the impedance of the <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>L</mi> <msub> <mi>C</mi> <mi>g</mi> </msub> </mrow> </semantics></math> array as a function of the frequency and (<b>e</b>) a zoom out of it for lower frequencies (red line), together with that of the linear <math display="inline"><semantics> <mrow> <mi>L</mi> <msub> <mi>C</mi> <mi>g</mi> </msub> </mrow> </semantics></math> array as well (blue line). (<b>f</b>) At frequencies <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>≫</mo> <msub> <mi>ω</mi> <mi>p</mi> </msub> </mrow> </semantics></math>, the <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>L</mi> <msub> <mi>C</mi> <mi>g</mi> </msub> </mrow> </semantics></math> array behaves as a capacitor with effective capacitance <math display="inline"><semantics> <msqrt> <mrow> <mi>C</mi> <msub> <mi>C</mi> <mi>g</mi> </msub> </mrow> </msqrt> </semantics></math>.</p>
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<p>A qubit coupled to a linear Josephson junction array or a transmission line. (<b>a</b>) A schematic presentation of the circuit. (<b>b</b>) Time-dependent population <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>e</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> of the qubit after initialization to the excited state. The transmission line is assumed to be initially in the ground state. The coupling parameter between the qubit and the line is <math display="inline"><semantics> <mrow> <mi>g</mi> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics></math>. We have chosen <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>ω</mi> <mo>=</mo> <mn>0.01</mn> <mspace width="0.166667em"/> <mo>Ω</mo> </mrow> </semantics></math>, typically corresponding to either <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math>–<math display="inline"><semantics> <msup> <mn>10</mn> <mn>5</mn> </msup> </semantics></math> junctions or a 1 m long transmission line, close to that in Ref. [<a href="#B28-entropy-26-00429" class="html-bibr">28</a>]. The value of the impedance <math display="inline"><semantics> <msub> <mi>Z</mi> <mi>L</mi> </msub> </semantics></math> has almost no effect on <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>e</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>c</b>) Initially the qubit decays exponentially, until at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>2</mn> <mi>π</mi> <mo>/</mo> <mo>Δ</mo> <mi>ω</mi> </mrow> </semantics></math> the first revival sets abruptly in. The solid line is an exponential fit in this range. The dashed line, also following closely the numerical result, is given by the analytic expression with a decay rate <math display="inline"><semantics> <mrow> <mo>Γ</mo> <mo>=</mo> <mn>2</mn> <mi>π</mi> <mfrac> <msup> <mi>g</mi> <mn>2</mn> </msup> <msup> <mo>ℏ</mo> <mn>2</mn> </msup> </mfrac> <mfrac> <mo>Ω</mo> <mrow> <mo>Δ</mo> <msup> <mi>ω</mi> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </semantics></math>, corresponding to a continuum approximation of frequencies. (<b>d1</b>–<b>d3</b>) Populations of the states in the multimode resonator at three time instants indicated by arrows in (<b>b</b>).</p>
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<p>A qubit coupled to a reservoir of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>7</mn> </msup> </mrow> </semantics></math> two-level systems in (<b>a</b>). The central qubit is coupled to each TLS via coupling constants <math display="inline"><semantics> <msub> <mi>γ</mi> <mi>i</mi> </msub> </semantics></math> that have a uniform distribution between 0 and its maximum level, corresponding to the overall relaxation rate <math display="inline"><semantics> <mrow> <msub> <mo>Γ</mo> <mn>0</mn> </msub> <mo>=</mo> <mn>0.03</mn> </mrow> </semantics></math>. The dark blue line corresponds to the evolution of <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>e</mi> </msub> <mo>≡</mo> <msup> <mrow> <mo>|</mo> <msub> <mi mathvariant="script">C</mi> <mn>0</mn> </msub> <mo>|</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> in the environment of TLSs with uniform distribution of energies in the range <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>ω</mi> <mi>i</mi> </msub> <mo>&lt;</mo> <mn>2</mn> <mo>Ω</mo> </mrow> </semantics></math> leading to nearly exponential decay. The oscillatory qubit populations of the other curves correspond to uniform environments with <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>i</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>−</mo> <mi>r</mi> <mo>)</mo> </mrow> <mo>Ω</mo> </mrow> </semantics></math> for all <span class="html-italic">i</span>, with <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>0.25</mn> </mrow> </semantics></math> for grey and red lines, respectively. These dynamics follow that given by Equation (<a href="#FD9-entropy-26-00429" class="html-disp-formula">9</a>) quantitatively. (<b>b</b>,<b>c</b>) show the population in a similar distributed bath of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>3000</mn> </mrow> </semantics></math> TLSs, respectively, over a time period of <math display="inline"><semantics> <mrow> <mo>Ω</mo> <mi>t</mi> <mo>=</mo> <mn>3</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> </mrow> </semantics></math>. The horizontal lines are the analytical long time predictions given in the text.</p>
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15 pages, 2610 KiB  
Article
A Novel Fault Diagnosis Method of High-Speed Train Based on Few-Shot Learning
by Yunpu Wu, Jianhua Chen, Xia Lei and Weidong Jin
Entropy 2024, 26(5), 428; https://doi.org/10.3390/e26050428 - 16 May 2024
Viewed by 1317
Abstract
Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated [...] Read more.
Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated fault data for model training. However, in practice, the availability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited information. To address this issue, this paper proposes a novel few-shot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation injection and meta-confidence learning to improve detection accuracy. Experimental results demonstrate the superior performance of the proposed method, which introduces perturbations, compared to existing methods. The impact of perturbation effects and class numbers on fault detection is analyzed, confirming the effectiveness of our learning strategy. Full article
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<p>Comparison of images and vibration signals in different channels. (a) RGB channels of the image and their 2D FFT; (b) Multiple channels of vibration sensors and their FFT.</p>
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<p>The effect of perturbation on different signals. The blue solid line represents the original signal, the red dashed line represents the signal after perturbation, and the orange solid line represents the injected perturbation.</p>
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<p>Overll Framework. Sensor-wise perturbations are randomly added to the entire data within each episode to enhance the model’s generalization capability in the face of data uncertainty. The last residual block of the residual network is randomly dropped to capture model uncertainty, representing a form of model perturbation. Meta-learning is employed to adaptively adjust the distance metric based on input data, aiming to enhance the transductive inference performance amid these perturbations.</p>
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<p>Multibody dynamics simulation model of the high-speed train.</p>
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<p>Monitoring signals in the dataset.</p>
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<p>Average detection accuracy of different methods under various settings.</p>
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<p>Visualization of the energy distribution of sensor-wise perturbations across different channels. The heatmap illustrates log-scaled energy values of injected perturbations.</p>
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18 pages, 3382 KiB  
Article
Fault Diagnosis Method for Space Fluid Loop Systems Based on Improved Evidence Theory
by Yue Liu, Zhenxiang Li, Lu Zhang and Hongyong Fu
Entropy 2024, 26(5), 427; https://doi.org/10.3390/e26050427 - 16 May 2024
Cited by 1 | Viewed by 1066
Abstract
Addressing the challenges posed by the complexity of the structure and the multitude of sensor types installed in space application fluid loop systems, this paper proposes a fault diagnosis method based on an improved D-S evidence theory. The method first employs the Gaussian [...] Read more.
Addressing the challenges posed by the complexity of the structure and the multitude of sensor types installed in space application fluid loop systems, this paper proposes a fault diagnosis method based on an improved D-S evidence theory. The method first employs the Gaussian affiliation function to convert the information acquired by sensors into BPA functions. Subsequently, it utilizes a pignistic probability transformation to convert the multiple subset focal elements into single subset focal elements. Finally, it comprehensively evaluates the credibility and uncertainty factors between evidences, introducing Bray–Curtis dissimilarity and belief entropy to achieve the fusion of conflicting evidence. The proposed method is initially validated on the classic Iris dataset, demonstrating its reliability. Furthermore, when applied to fault diagnosis in space application fluid circuit loop pumps, the results indicate that the method can effectively fuse multiple sensors and accurately identify faults. Full article
(This article belongs to the Section Multidisciplinary Applications)
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<p>Flowchart of the proposed method.</p>
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<p>Gaussian models for four attributes.</p>
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<p>Matching degree between testing sample and Gaussian models.</p>
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<p>System configuration and sensor positioning.</p>
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<p>Gaussian models for four attributes.</p>
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<p>Matching degree between the testing sample and gaussian models.</p>
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<p>Matching degree between the testing sample and gaussian models.</p>
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20 pages, 1657 KiB  
Article
Exploring Simplicity Bias in 1D Dynamical Systems
by Kamal Dingle, Mohammad Alaskandarani, Boumediene Hamzi and Ard A. Louis
Entropy 2024, 26(5), 426; https://doi.org/10.3390/e26050426 - 16 May 2024
Cited by 2 | Viewed by 1587
Abstract
Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input–output maps. This phenomenon is known as simplicity bias. By viewing the parameters of dynamical systems as inputs, and the [...] Read more.
Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input–output maps. This phenomenon is known as simplicity bias. By viewing the parameters of dynamical systems as inputs, and the resulting (digitised) trajectories as outputs, we study simplicity bias in the logistic map, Gauss map, sine map, Bernoulli map, and tent map. We find that the logistic map, Gauss map, and sine map all exhibit simplicity bias upon sampling of map initial values and parameter values, but the Bernoulli map and tent map do not. The simplicity bias upper bound on the output pattern probability is used to make a priori predictions regarding the probability of output patterns. In some cases, the predictions are surprisingly accurate, given that almost no details of the underlying dynamical systems are assumed. More generally, we argue that studying probability–complexity relationships may be a useful tool when studying patterns in dynamical systems. Full article
(This article belongs to the Section Complexity)
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<p>An example of a real-valued (orange) and digitised (blue) trajectory of the logistic map, with <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>3.8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. The discretisation is defined by writing 1 if <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>k</mi> </msub> <mo>≥</mo> <mn>0.5</mn> </mrow> </semantics></math> and 0 otherwise, resulting in the pattern <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> </mrow> </semantics></math> 0101011011111011010110111, which has a length of <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> </mrow> </semantics></math> 25 bits.</p>
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<p>A bifurcation diagram for the logistic map. In (<b>a</b>), we see the diagram for parameters <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> (0, 4.0]; and in (<b>b</b>), we see the diagram for values <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> (2.9, 4.0]. The value 0.5 has been highlighted in red, to indicate the cut-off threshold used to digitise trajectories by a value of 0 if the output is below the threshold, and a value of 1 if it is greater than or equal to the threshold.</p>
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<p>Simplicity bias in the digitised logistic map from random samples with <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>∈</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mi>μ</mi> </semantics></math> sampled in different intervals. Each blue data-point corresponds to a different binary digitised trajectory <span class="html-italic">x</span> of 25 bits in length. The black line is the upper-bound prediction of Equation (<a href="#FD3-entropy-26-00426" class="html-disp-formula">3</a>). (<b>a</b>) Clear simplicity bias for <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> (0.0, 4.0] with <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </semantics></math> closely following the upper bound, except for low frequency and high complexity outputs which suffer from increased sampling noise; (<b>b</b>) simplicity bias is still present for <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> [3.0, 4.0]; (<b>c</b>) the distribution of <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </semantics></math> becomes more flat (less biased) and simplicity bias is much less clear when <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> [3.57, 4.0] due to constraining the sampling to <math display="inline"><semantics> <mi>μ</mi> </semantics></math>-regions more likely to show chaos; (<b>d</b>) the distribution of <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </semantics></math> is roughly uniform when using <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math>, with almost no bias, and hence no possibility of simplicity bias.</p>
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<p>The distribution <math display="inline"><semantics> <mrow> <mi>P</mi> <mo stretchy="false">(</mo> <mover accent="true"> <mi>K</mi> <mo stretchy="false">˜</mo> </mover> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>r</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> of output complexity values, with <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>0</mn> </msub> <mo>∈</mo> <mrow> <mo>(</mo> <mn>0.0</mn> <mo>,</mo> <mn>1.0</mn> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mi>μ</mi> </semantics></math> sampled from different intervals. (<b>a</b>) A roughly uniform complexity distribution for <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> (0.0, 4.0], with some bias towards lower complexities (mean is 3.4 bits); (<b>b</b>) close to uniform distribution of complexities for <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> [3.0, 4.0], mean is 10.3 bits; (<b>c</b>) the distribution leans toward higher complexities when <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> </mrow> </semantics></math> [3.57, 4.0], mean is 14.1 bits; (<b>d</b>) the distribution is biased to higher complexity values when <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math> (mean is 16.4 bits); (<b>e</b>) for comparison, purely random binary strings of 25 bits in length were generated (mean is 16.2 bits). The distributions of complexity values in (<b>d</b>,<b>e</b>) are very similar, but (<b>a</b>–<b>c</b>) show distinct differences. Calculating and comparing <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>(</mo> <mi>K</mi> <mo>)</mo> </mrow> </semantics></math> is an efficient way of checking how simplicity-biased a map is.</p>
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<p>Simplicity bias in (<b>a</b>) the logistic map with <math display="inline"><semantics> <mi>μ</mi> </semantics></math> sampled in [0.0, 3.5699], which is the non-chaotic period doubling regime (upper bound fitted slope is −0.17); (<b>b</b>) the Gauss map (upper bound fitted slope is −0.13); and (<b>c</b>) the sine map (upper bound fitted slope is −0.17).</p>
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<p>Simplicity bias with different number of iterations. (<b>a</b>) With <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> iterations, there is some simplicity bias but it is not pronounced; (<b>b</b>) with <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> iterations, the simplicity bias is very clear; with (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> iterations there is still clear simplicity bias, but a long ‘tail’ begins to emerge, illustrating low-frequency patterns; (<b>d</b>) with <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> iterations, there is still some simplicity bias but the ‘tail’ has become more dominant and the simplicity bias is less clear.</p>
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<p>Simplicity bias in the logistic map, which is the same as in <a href="#entropy-26-00426-f003" class="html-fig">Figure 3</a>, but with semi-transparent data points. (<b>a</b>) sampling <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> <mo>[</mo> <mn>0.0</mn> <mo>,</mo> <mn>4.0</mn> <mo>]</mo> </mrow> </semantics></math>; (<b>b</b>) sampling <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> <mo>[</mo> <mn>3.0</mn> <mo>,</mo> <mn>4.0</mn> <mo>]</mo> </mrow> </semantics></math>; (<b>c</b>) sampling <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>∈</mo> <mo>[</mo> <mn>3.57</mn> <mo>,</mo> <mn>4.0</mn> <mo>]</mo> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>4.0</mn> </mrow> </semantics></math>.</p>
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<p>Simplicity bias in (<b>a</b>) the logistic, (<b>b</b>) Gauss map, and (<b>c</b>) sine map, the same as in <a href="#entropy-26-00426-f005" class="html-fig">Figure 5</a>, but with semi-transparent data points.</p>
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10 pages, 290 KiB  
Article
Memory Corrections to Markovian Langevin Dynamics
by Mateusz Wiśniewski, Jerzy Łuczka and Jakub Spiechowicz
Entropy 2024, 26(5), 425; https://doi.org/10.3390/e26050425 - 16 May 2024
Cited by 5 | Viewed by 1162
Abstract
Analysis of non-Markovian systems and memory-induced phenomena poses an everlasting challenge in the realm of physics. As a paradigmatic example, we consider a classical Brownian particle of mass M subjected to an external force and exposed to correlated thermal fluctuations. We show that [...] Read more.
Analysis of non-Markovian systems and memory-induced phenomena poses an everlasting challenge in the realm of physics. As a paradigmatic example, we consider a classical Brownian particle of mass M subjected to an external force and exposed to correlated thermal fluctuations. We show that the recently developed approach to this system, in which its non-Markovian dynamics given by the Generalized Langevin Equation is approximated by its memoryless counterpart but with the effective particle mass M<M, can be derived within the Markovian embedding technique. Using this method, we calculate the first- and the second-order memory correction to Markovian dynamics of the Brownian particle for the memory kernel represented as the Prony series. The second one lowers the effective mass of the system further and improves the precision of the approximation. Our work opens the door for the derivation of higher-order memory corrections to Markovian Langevin dynamics. Full article
(This article belongs to the Collection Foundations of Statistical Mechanics)
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<p>Average velocity <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>v</mi> <mo>〉</mo> </mrow> </semantics></math> of the Brownian particle as a function of the memory time <math display="inline"><semantics> <mi>τ</mi> </semantics></math> for the original GLE and the approximate equation with first- and second-order correction. The memoryless limit <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>→</mo> <mn>0</mn> </mrow> </semantics></math> is also depicted for reference.</p>
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50 pages, 652 KiB  
Article
Non-Negative Decomposition of Multivariate Information: From Minimum to Blackwell-Specific Information
by Tobias Mages, Elli Anastasiadi and Christian Rohner
Entropy 2024, 26(5), 424; https://doi.org/10.3390/e26050424 - 15 May 2024
Cited by 3 | Viewed by 1715
Abstract
Partial information decompositions (PIDs) aim to categorize how a set of source variables provides information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying [...] Read more.
Partial information decompositions (PIDs) aim to categorize how a set of source variables provides information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying decomposition measure is still an open research question at an arbitrary number of discrete random variables. This work proposes a solution with a non-negative PID that satisfies an inclusion–exclusion relation for any f-information measure. The decomposition is constructed from a pointwise perspective of the target variable to take advantage of the equivalence between the Blackwell and zonogon order in this setting. Zonogons are the Neyman–Pearson region for an indicator variable of each target state, and f-information is the expected value of quantifying its boundary. We prove that the proposed decomposition satisfies the desired axioms and guarantees non-negative partial information results. Moreover, we demonstrate how the obtained decomposition can be transformed between different decomposition lattices and that it directly provides a non-negative decomposition of Rényi-information at a transformed inclusion–exclusion relation. Finally, we highlight that the decomposition behaves differently depending on the information measure used and how it can be used for tracing partial information flows through Markov chains. Full article
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<p>Partial information decomposition representations at two variables <math display="inline"><semantics> <mrow> <mi mathvariant="bold">V</mi> <mo>=</mo> <mo>{</mo> <msub> <mi>V</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> <mo>}</mo> </mrow> </semantics></math>. (<b>a</b>) Desired set-theoretic analogy: Visualization of the desired intuition for multivariate information as a Venn diagram. (<b>b</b>) Representation as redundancy lattice, where the redundancy measure <math display="inline"><semantics> <msub> <mi>I</mi> <mo>∩</mo> </msub> </semantics></math> quantifies the information that is contained in all of its provided variables (inside their intersection). The ordering represents the expected subset relation of redundancy. (<b>c</b>) Representation as synergy lattice, where the loss measure <math display="inline"><semantics> <msub> <mi>I</mi> <mo>∪</mo> </msub> </semantics></math> quantifies the information that is contained in neither of its provided variables (outside their union). (<b>d</b>) Information flow visualization: When having two partial information decompositions with respect to the same target variable, we can study how the partial information of one decomposition propagates into the next. We refer to this as information flow analysis of a Markov chain such as <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>→</mo> <mrow> <mo>(</mo> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>A</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>→</mo> <mrow> <mo>(</mo> <msub> <mi>B</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>B</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>An example zonogon (blue) for a binary input channel <math display="inline"><semantics> <mi>κ</mi> </semantics></math> from <math display="inline"><semantics> <mrow> <mi mathvariant="script">T</mi> <mo>=</mo> <mo>{</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>}</mo> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mi mathvariant="script">S</mi> <mo>=</mo> <mo>{</mo> <msub> <mi>s</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>s</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>s</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>s</mi> <mn>4</mn> </msub> <mo>}</mo> </mrow> </semantics></math>. The zonogon is the Neyman–Pearson region, and its perimeter corresponds to the vectors <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>v</mi> <mo>→</mo> </mover> <msub> <mi>s</mi> <mi>i</mi> </msub> </msub> <mo>∈</mo> <mi>κ</mi> </mrow> </semantics></math> sorted by an increasing/decreasing slope for the lower/upper half, which results from the likelihood ratio test. The zonogon, thus, represents the achievable (TPR,FPR)-pairs for predicting <span class="html-italic">T</span> while knowing <span class="html-italic">S</span>.</p>
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<p>Visualizations for Example 1 where <math display="inline"><semantics> <mrow> <mo>|</mo> <mi mathvariant="script">T</mi> <mo>|</mo> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>. (<b>a</b>) A randomized decision strategy for predictions based on <math display="inline"><semantics> <mrow> <mi>T</mi> <mover> <mo>→</mo> <mi>κ</mi> </mover> <mi>S</mi> </mrow> </semantics></math> can be represented by a <math display="inline"><semantics> <mrow> <mo>|</mo> <mi mathvariant="script">S</mi> <mo>|</mo> <mo>×</mo> <mn>2</mn> </mrow> </semantics></math> stochastic matrix <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. The first column of this decision matrix provides the weights for summing the columns of channel <math display="inline"><semantics> <mi>κ</mi> </semantics></math> to determine the resulting prediction performance (TPR, FPR). Any decision strategy corresponds to a point in the zonogon. (<b>b</b>) All presented ordering relations in <a href="#sec2dot1-entropy-26-00424" class="html-sec">Section 2.1</a> are equivalent at binary targets and correspond to the subset relation of the visualized zonogons. The variable <math display="inline"><semantics> <msub> <mi>S</mi> <mn>3</mn> </msub> </semantics></math> is less informative than both <math display="inline"><semantics> <msub> <mi>S</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>S</mi> <mn>2</mn> </msub> </semantics></math> with respect to <span class="html-italic">T</span>, and the variables <math display="inline"><semantics> <msub> <mi>S</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>S</mi> <mn>2</mn> </msub> </semantics></math> are incomparable. The shown channel in (a) is the Blackwell join of <math display="inline"><semantics> <msub> <mi>κ</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>κ</mi> <mn>2</mn> </msub> </semantics></math> in (<b>b</b>).</p>
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<p>For the visualization, we abbreviated the notation by indicating the contained visible variable as the index of the source, for example <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold">S</mi> <mn>12</mn> </msub> <mo>=</mo> <mrow> <mo>{</mo> <msub> <mi>V</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> <mo>}</mo> </mrow> </mrow> </semantics></math> to represent their joint distribution: (<b>a</b>) A redundancy/gain lattice <math display="inline"><semantics> <mrow> <mo>(</mo> <mi mathvariant="script">A</mi> <mrow> <mo>(</mo> <mrow> <mo>{</mo> <msub> <mi>V</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>3</mn> </msub> <mo>}</mo> </mrow> <mo>)</mo> </mrow> <mo>,</mo> <mspace width="4pt"/> <mo>≼</mo> <mo>)</mo> </mrow> </semantics></math> based on the ordering of Equation (<a href="#FD9-entropy-26-00424" class="html-disp-formula">9</a>) quantifies information present in all sources. The redundancy of all sources within an atom increases while moving up on the redundancy lattice. (<b>b</b>) A synergy/loss lattice <math display="inline"><semantics> <mrow> <mo>(</mo> <mi mathvariant="script">A</mi> <mrow> <mo>(</mo> <mrow> <mo>{</mo> <msub> <mi>V</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>3</mn> </msub> <mo>}</mo> </mrow> <mo>)</mo> </mrow> <mo>,</mo> <mspace width="4pt"/> <mo>⪯</mo> <mo>)</mo> </mrow> </semantics></math> based on the ordering of Equation (<a href="#FD10-entropy-26-00424" class="html-disp-formula">10</a>) quantifies information present in neither source. On the synergy lattice, the information that is obtained from neither source of an atom increases while moving up.</p>
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<p>Example of the unexpected behavior of <math display="inline"><semantics> <msubsup> <mi>I</mi> <mo>∩</mo> <mo movablelimits="true" form="prefix">min</mo> </msubsup> </semantics></math>: the dashed isoline indicates the pairs <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> for which channel <math display="inline"><semantics> <mrow> <mi>κ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>T</mi> <mo>→</mo> <msub> <mi>V</mi> <mi>i</mi> </msub> </mrow> </semantics></math> results in pointwise information <math display="inline"><semantics> <mrow> <mo>∀</mo> <mi>t</mi> <mo>∈</mo> <mi mathvariant="script">T</mi> <mo>:</mo> <mi>I</mi> <mo>(</mo> <msub> <mi>V</mi> <mi>i</mi> </msub> <mo>,</mo> <mi>T</mi> <mo>=</mo> <mi>t</mi> <mo>)</mo> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math> for a uniform binary target variable. Even though observing the output of both indicated example channels (blue/green) provides significantly different abilities for predicting the target variable state, the measure <math display="inline"><semantics> <msubsup> <mi>I</mi> <mo>∩</mo> <mo movablelimits="true" form="prefix">min</mo> </msubsup> </semantics></math> indicates full redundancy.</p>
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<p>This example visualizes the computation of <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math>-information by indicating its results on the representation of zonogons of an indicator variable. (<b>a</b>) For the pointwise information of <math display="inline"><semantics> <msub> <mi>t</mi> <mn>1</mn> </msub> </semantics></math>, both vectors of the zonogon perimeter are quantified to the sum 0.292653. (<b>b</b>) For the pointwise information of <math display="inline"><semantics> <msub> <mi>t</mi> <mn>2</mn> </msub> </semantics></math>, both vectors of the zonogon perimeter are quantified to the sum of <math display="inline"><semantics> <mrow> <mn>0.130068</mn> </mrow> </semantics></math>. The final <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math>-information is their expected value <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <msup> <mi>χ</mi> <mn>2</mn> </msup> </msub> <mrow> <mo>(</mo> <mi>S</mi> <mo>;</mo> <mi>T</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.4</mn> <mo>·</mo> <mrow> <mn>0.292653</mn> </mrow> <mo>+</mo> <mn>0.6</mn> <mo>·</mo> <mrow> <mn>0.130068</mn> </mrow> <mo>=</mo> <mrow> <mn>0.195102</mn> </mrow> </mrow> </semantics></math>.</p>
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<p>Visualization of the functions <math display="inline"><semantics> <mo>Ψ</mo> </semantics></math> and <math display="inline"><semantics> <mo>Ξ</mo> </semantics></math>: The application of function <math display="inline"><semantics> <mo>Ψ</mo> </semantics></math> is equal to reversing the redundancy order, and the application of function <math display="inline"><semantics> <mo>Ξ</mo> </semantics></math> is equal to swapping the ordering relation used between the redundancy and synergy lattice.</p>
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<p>Visualization of lattice duality and Lemma 5. We abbreviate the notation of sources within this figure by listing the contained visible variables as source index (<math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold">S</mi> <mn>12</mn> </msub> <mo>=</mo> <mrow> <mo>{</mo> <msub> <mi>V</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>V</mi> <mn>2</mn> </msub> <mo>}</mo> </mrow> </mrow> </semantics></math>). (i) All bottom elements are mapped to each other and quantified to zero. (ii) To identify the dual for <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mo>{</mo> <msub> <mi mathvariant="bold">S</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="bold">S</mi> <mn>12</mn> </msub> <mo>}</mo> </mrow> </semantics></math> from the redundancy lattice, we first apply the transformation <math display="inline"><semantics> <mrow> <mo>Ψ</mo> <mrow> <mo>(</mo> <mi>α</mi> <mo>)</mo> </mrow> <mo>≃</mo> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">S</mi> <mn>13</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="bold">S</mi> <mn>23</mn> </msub> <mo>}</mo> </mrow> </mrow> </semantics></math> and, then, <math display="inline"><semantics> <mrow> <mo>Ξ</mo> <mrow> <mo>(</mo> <mo>Ψ</mo> <mrow> <mo>(</mo> <mi>α</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>≅</mo> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">S</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="bold">S</mi> <mn>2</mn> </msub> <mo>}</mo> </mrow> </mrow> </semantics></math>. (iii) Ignoring the bottom elements, the down-set of <math display="inline"><semantics> <mi>α</mi> </semantics></math> on the redundancy lattice corresponds to the up-set of <math display="inline"><semantics> <mrow> <mo>Ξ</mo> <mo>(</mo> <mo>Ψ</mo> <mo>(</mo> <mi>α</mi> <mo>)</mo> <mo>)</mo> </mrow> </semantics></math> on the synergy lattice for duality (gray areas). (iv) Lemma 5 states that, on the synergy lattice, exactly those atoms that are not in the up-set of <math display="inline"><semantics> <mrow> <mo>Ξ</mo> <mo>(</mo> <mo>Ψ</mo> <mrow> <mo>(</mo> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">S</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="bold">S</mi> <mn>12</mn> </msub> <mo>}</mo> </mrow> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </semantics></math> must be in the down-set of either <math display="inline"><semantics> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">S</mi> <mn>3</mn> </msub> <mo>}</mo> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <mo>{</mo> <msub> <mi mathvariant="bold">S</mi> <mn>12</mn> </msub> <mo>}</mo> </mrow> </semantics></math>.</p>
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<p>Comparison of different <span class="html-italic">f</span>-information measures normalized to the <span class="html-italic">f</span>-entropy of the target variable. All distributions are shown in <a href="#app6-entropy-26-00424" class="html-app">Appendix F</a> and correspond to the examples of [<a href="#B13-entropy-26-00424" class="html-bibr">13</a>,<a href="#B20-entropy-26-00424" class="html-bibr">20</a>]. The example name abbreviations are listed below in <a href="#entropy-26-00424-t0A1" class="html-table">Table A1</a>. The measures behave mostly similarly since the decompositions follow an identical structure. However, it can be seen that total variation attributes more information to being redundant than other measures and appears to behave differently in the generic example since it does not attribute any partial information to the first variable or their synergy.</p>
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<p>Visualization of the maximal (Euclidean) height <math display="inline"><semantics> <msup> <mi>h</mi> <mo>*</mo> </msup> </semantics></math> at point <math display="inline"><semantics> <msup> <mi>P</mi> <mo>*</mo> </msup> </semantics></math> that a zonogon (blue) reaches above the diagonal.</p>
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<p>Analysis of the Markov chain information flow (Equation (<a href="#app8-entropy-26-00424" class="html-app">Appendix H</a>)). Visualized results for the information measures: KL, TV, and <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math>. The remaining results (<math display="inline"><semantics> <msup> <mi mathvariant="normal">H</mi> <mn>2</mn> </msup> </semantics></math>-, LC-, and JS-information) can be found in <a href="#entropy-26-00424-f0A3" class="html-fig">Figure A3</a>.</p>
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<p>Intuition for the definition of Equation (<a href="#FD61-entropy-26-00424" class="html-disp-formula">A8</a>). We can divide the set <math display="inline"><semantics> <mrow> <mi mathvariant="script">P</mi> <mo>(</mo> <msub> <mi mathvariant="bold">A</mi> <mrow> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> </semantics></math> into <math display="inline"><semantics> <mrow> <mi mathvariant="script">P</mi> <mo>(</mo> <msub> <mi mathvariant="bold">A</mi> <mi>n</mi> </msub> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>{</mo> <mi mathvariant="bold">B</mi> <mo>∪</mo> <mrow> <mo>{</mo> <mi>q</mi> <mo>}</mo> </mrow> <mspace width="3.33333pt"/> <mo>:</mo> <mspace width="3.33333pt"/> <mi mathvariant="bold">B</mi> <mo>∈</mo> <mi mathvariant="script">P</mi> <mrow> <mo>(</mo> <msub> <mi mathvariant="bold">A</mi> <mi>n</mi> </msub> <mo>)</mo> </mrow> <mo>}</mo> </mrow> </semantics></math>. The definition of function <math display="inline"><semantics> <msub> <mi>G</mi> <mrow> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </semantics></math> mirrors <math display="inline"><semantics> <msub> <mi>G</mi> <mi>n</mi> </msub> </semantics></math> if <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mi>e</mi> </msub> <mrow> <mo>(</mo> <mi mathvariant="bold">O</mi> <mo>∪</mo> <mrow> <mo>{</mo> <mi>q</mi> <mo>}</mo> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>{</mo> <mi>q</mi> <mo>}</mo> </mrow> </mrow> </semantics></math> (blue) and otherwise breaks its mapping (orange).</p>
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<p>Visualization of the zonogons from the generic example of [<a href="#B20-entropy-26-00424" class="html-bibr">20</a>] in state <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>. The target variable <span class="html-italic">T</span> has two states. Therefore, the zonogons of its second state are symmetric (second column of Equation (<a href="#FD6-entropy-26-00424" class="html-disp-formula">6</a>)) and have identical heights.</p>
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<p>Analysis of the Markov chain information flow (Equation (A47)). Visualized results for the information measures: <math display="inline"><semantics> <msup> <mi mathvariant="normal">H</mi> <mn>2</mn> </msup> </semantics></math>, LC, and JS. The remaining results (KL, TV, and <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math>) can be found in <a href="#entropy-26-00424-f011" class="html-fig">Figure 11</a>.</p>
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18 pages, 957 KiB  
Article
Landauer Bound in the Context of Minimal Physical Principles: Meaning, Experimental Verification, Controversies and Perspectives
by Edward Bormashenko
Entropy 2024, 26(5), 423; https://doi.org/10.3390/e26050423 - 15 May 2024
Cited by 3 | Viewed by 2370
Abstract
The physical roots, interpretation, controversies, and precise meaning of the Landauer principle are surveyed. The Landauer principle is a physical principle defining the lower theoretical limit of energy consumption necessary for computation. It states that an irreversible change in information stored in a [...] Read more.
The physical roots, interpretation, controversies, and precise meaning of the Landauer principle are surveyed. The Landauer principle is a physical principle defining the lower theoretical limit of energy consumption necessary for computation. It states that an irreversible change in information stored in a computer, such as merging two computational paths, dissipates a minimum amount of heat kBTln2 per a bit of information to its surroundings. The Landauer principle is discussed in the context of fundamental physical limiting principles, such as the Abbe diffraction limit, the Margolus–Levitin limit, and the Bekenstein limit. Synthesis of the Landauer bound with the Abbe, Margolus–Levitin, and Bekenstein limits yields the minimal time of computation, which scales as τmin~hkBT. Decreasing the temperature of a thermal bath will decrease the energy consumption of a single computation, but in parallel, it will slow the computation. The Landauer principle bridges John Archibald Wheeler’s “it from bit” paradigm and thermodynamics. Experimental verifications of the Landauer principle are surveyed. The interrelation between thermodynamic and logical irreversibility is addressed. Generalization of the Landauer principle to quantum and non-equilibrium systems is addressed. The Landauer principle represents the powerful heuristic principle bridging physics, information theory, and computer engineering. Full article
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<p>Particle <span class="html-italic">M</span> placed in the twin-well potential is depicted. The position of the particle in the double-well potential will determine the state of the single bit. If the particle is found on the left-hand side of the potential, then we say that the bit is in the “zero” state. If it is found on the right-hand side of the well, then we define that the bit is in the “one” state. The picture is taken from the Bormashenko Ed. “Generalization of the Landauer Principle for Computing Devices Based on Many-Valued Logic” [<a href="#B35-entropy-26-00423" class="html-bibr">35</a>].</p>
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<p>A twin-well system containing particle <span class="html-italic">M</span> illuminated with monochromatic light <math display="inline"><semantics> <mi>ν</mi> </semantics></math> is depicted. The system is in thermal equilibrium with the surrounding <span class="html-italic">T</span>.</p>
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<p>A scheme of the Leo Szilárd minimal engine is depicted. (<b>a</b>) A particle in a box is shown; (<b>b</b>) the partition defines the location of the particle; (<b>c</b>) the particle pushes the piston and the engine performs work; (<b>d</b>) one bit of information is erased.</p>
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16 pages, 299 KiB  
Article
Model Selection for Exponential Power Mixture Regression Models
by Yunlu Jiang, Jiangchuan Liu, Hang Zou and Xiaowen Huang
Entropy 2024, 26(5), 422; https://doi.org/10.3390/e26050422 - 15 May 2024
Viewed by 1298
Abstract
Finite mixture of linear regression (FMLR) models are among the most exemplary statistical tools to deal with various heterogeneous data. In this paper, we introduce a new procedure to simultaneously determine the number of components and perform variable selection for the different regressions [...] Read more.
Finite mixture of linear regression (FMLR) models are among the most exemplary statistical tools to deal with various heterogeneous data. In this paper, we introduce a new procedure to simultaneously determine the number of components and perform variable selection for the different regressions for FMLR models via an exponential power error distribution, which includes normal distributions and Laplace distributions as special cases. Under some regularity conditions, the consistency of order selection and the consistency of variable selection are established, and the asymptotic normality for the estimators of non-zero parameters is investigated. In addition, an efficient modified expectation-maximization (EM) algorithm and a majorization-maximization (MM) algorithm are proposed to implement the proposed optimization problem. Furthermore, we use the numerical simulations to demonstrate the finite sample performance of the proposed methodology. Finally, we apply the proposed approach to analyze a baseball salary data set. Results indicate that our proposed method obtains a smaller BIC value than the existing method. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
13 pages, 371 KiB  
Article
Optimal Decoding Order and Power Allocation for Sum Throughput Maximization in Downlink NOMA Systems
by Zhuo Han, Wanming Hao, Zhiqing Tang and Shouyi Yang
Entropy 2024, 26(5), 421; https://doi.org/10.3390/e26050421 - 15 May 2024
Viewed by 1247
Abstract
In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under [...] Read more.
In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels. Full article
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<p>Method to solve the optimization problem. Each decoding order has an optimal power allocation sub-range, and the optimal decoding order is the one that produces the maximum sum throughput.</p>
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<p>The outage probability of two users under a given decoding order and fixed power allocation.</p>
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<p>All the power allocation ranges for two-user NOMA.</p>
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<p>Outage probability scatter diagram of two users under different power allocation ranges.</p>
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<p>Optimal decoding order for sum throughput maximization problem at different total power levels.</p>
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28 pages, 509 KiB  
Review
The SU(3)C × SU(3)L × U(1)X (331) Model: Addressing the Fermion Families Problem within Horizontal Anomalies Cancellation
by Claudio Corianò and Dario Melle
Entropy 2024, 26(5), 420; https://doi.org/10.3390/e26050420 - 14 May 2024
Cited by 1 | Viewed by 1213
Abstract
One of the most important and unanswered problems in particle physics is the origin of the three generations of quarks and leptons. The Standard Model does not provide any hint regarding its sequential charge assignments, which remain a fundamental mystery of Nature. One [...] Read more.
One of the most important and unanswered problems in particle physics is the origin of the three generations of quarks and leptons. The Standard Model does not provide any hint regarding its sequential charge assignments, which remain a fundamental mystery of Nature. One possible solution of the puzzle is to look for charge assignments, in a given gauge theory, that are inter-generational, by employing the cancellation of the gravitational and gauge anomalies horizontally. The 331 model, based on an SU(3)C×SU(3)L×U(1)X does this in an economical way and defines a possible extension of the Standard Model, where the number of families has necessarily to be three. We review the model in Pisano, Pleitez, and Frampton’s formulation, which predicts the existence of bileptons. Another characteristics of the model is to unify the SU(3)C×SU(2)L×U(1)X into the 331 symmetry at a scale that is in the TeV range. Expressions of the scalar mass eigenstates and of the renormalization group equations of the model are also presented. Full article
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<p>Behavior of the matching condition between the Standard Model and 331 model, which makes it clear how the Landau pole at the TeV scale emerges from the matching with the Standard Model. (<b>a</b>) Running of the coupling <math display="inline"><semantics> <msub> <mi>g</mi> <mn>2</mn> </msub> </semantics></math> in the Standard Model. (<b>b</b>) Running of the coupling <math display="inline"><semantics> <msub> <mi>g</mi> <mi>y</mi> </msub> </semantics></math> in the Standard Model. (<b>c</b>) Evolution of the matching condition of the coupling <math display="inline"><semantics> <msub> <mi>g</mi> <mn>1</mn> </msub> </semantics></math> of the 331 model with the Standard Model coupling constants. (<b>d</b>) Running of the coupling <math display="inline"><semantics> <msub> <mi>α</mi> <mn>1</mn> </msub> </semantics></math> in the minimal 331 model.</p>
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32 pages, 3331 KiB  
Article
Capacity Analysis of Hybrid Satellite–Terrestrial Systems with Selection Relaying
by Predrag Ivaniš, Jovan Milojković, Vesna Blagojević and Srđan Brkić
Entropy 2024, 26(5), 419; https://doi.org/10.3390/e26050419 - 13 May 2024
Cited by 4 | Viewed by 1467
Abstract
A hybrid satellite–terrestrial relay network is a simple and flexible solution that can be used to improve the performance of land mobile satellite systems, where the communication links between satellite and mobile terrestrial users can be unstable due to the multipath effect, obstacles, [...] Read more.
A hybrid satellite–terrestrial relay network is a simple and flexible solution that can be used to improve the performance of land mobile satellite systems, where the communication links between satellite and mobile terrestrial users can be unstable due to the multipath effect, obstacles, as well as the additional atmospheric losses. Motivated by these facts, in this paper, we analyze a system where the satellite–terrestrial links undergo shadowed Rice fading, and, following this, terrestrial relay applies the selection relaying protocol and forwards the information to the final destination using the communication link subjected to Nakagami-m fading. For the considered relaying protocol, we derive the exact closed-form expressions for the outage probability, outage capacity, and ergodic capacity, presented in polynomial–exponential form for the integer-valued fading parameters. The presented numerical results illustrate the usefulness of the selection relaying for various propagation scenarios and system geometry parameters. The obtained analytical results are corroborated by an independent simulation method, based on the originally developed fading simulator. Full article
(This article belongs to the Special Issue Information Theory and Coding for Wireless Communications II)
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<p>MRC combiner and time slots.</p>
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<p>The system geometry.</p>
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<p>The simulator of the Nakagami-<span class="html-italic">m</span> and shadowed Rice channel gains with desired first- and second-order statistics.</p>
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<p>The discrete waveforms of the instantaneous SNRs at the S-R link, γ<sub>2</sub>(<span class="html-italic">n</span>), at the S-D link, γ<sub>1</sub>(<span class="html-italic">n</span>), γ<sub>3</sub>(<span class="html-italic">n</span>), and at the output of the MRC receiver at the destination, γ(<span class="html-italic">n</span>).</p>
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<p>PDF of the signal–noise at the MRC output for various thresholds at the relay.</p>
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<p>ACF of the channel gain, average shadowing, <span class="html-italic">f<sub>Dm</sub></span> = 100 Hz, <span class="html-italic">f<sub>Ds</sub></span> = 1 Hz, and <span class="html-italic">f<sub>Ds</sub></span> = 15 Hz.</p>
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<p>Instantaneous capacity of the system with relaying <span class="html-italic">C</span>(<span class="html-italic">t</span>) and the referent system <span class="html-italic">C</span><sub>ref</sub>(<span class="html-italic">t</span>).</p>
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<p>Outage probability vs. normalized SNR threshold for various protocols.</p>
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<p>Outage probability vs. normalized SNR threshold for various propagation scenarios.</p>
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<p>Outage capacity vs. outage probability for various propagation scenarios.</p>
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<p>Outage capacity for heavy shadowing in the S-D channel and various outage probabilities.</p>
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<p>Outage capacity vs. average SNR; various propagation conditions, <span class="html-italic">P<sub>out</sub></span> = 0.01.</p>
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<p>Ergodic capacity vs. average SNR; various propagation conditions.</p>
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<p>Ergodic capacity vs. average SNR; the impact of the system geometry.</p>
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49 pages, 468 KiB  
Article
In Our Mind’s Eye: Thinkable and Unthinkable, and Classical and Quantum in Fundamental Physics, with Schrödinger’s Cat Experiment
by Arkady Plotnitsky
Entropy 2024, 26(5), 418; https://doi.org/10.3390/e26050418 - 13 May 2024
Cited by 2 | Viewed by 1651
Abstract
This article reconsiders E. Schrödinger’s cat paradox experiment from a new perspective, grounded in the interpretation of quantum mechanics that belongs to the class of interpretations designated as “reality without realism” (RWR) interpretations. These interpretations assume that the reality ultimately responsible for quantum [...] Read more.
This article reconsiders E. Schrödinger’s cat paradox experiment from a new perspective, grounded in the interpretation of quantum mechanics that belongs to the class of interpretations designated as “reality without realism” (RWR) interpretations. These interpretations assume that the reality ultimately responsible for quantum phenomena is beyond conception, an assumption designated as the Heisenberg postulate. Accordingly, in these interpretations, quantum physics is understood in terms of the relationships between what is thinkable and what is unthinkable, with, physical, classical, and quantum, corresponding to thinkable and unthinkable, respectively. The role of classical physics becomes unavoidable in quantum physics, the circumstance designated as the Bohr postulate, which restores to classical physics its position as part of fundamental physics, a position commonly reserved for quantum physics and relativity. This view of quantum physics and relativity is maintained by this article as well but is argued to be sufficient for understanding fundamental physics. Establishing this role of classical physics is a distinctive contribution of the article, which allows it to reconsider Schrödinger’s cat experiment, but has a broader significance for understanding fundamental physics. RWR interpretations have not been previously applied to the cat experiment, including by N. Bohr, whose interpretation, in its ultimate form (he changed it a few times), was an RWR interpretation. The interpretation adopted in this article follows Bohr’s interpretation, based on the Heisenberg and Bohr postulates, but it adds the Dirac postulate, stating that the concept of a quantum object only applies at the time of observation and not independently. Full article
18 pages, 734 KiB  
Article
Aging Intensity for Step-Stress Accelerated Life Testing Experiments
by Francesco Buono and Maria Kateri
Entropy 2024, 26(5), 417; https://doi.org/10.3390/e26050417 - 13 May 2024
Viewed by 1346
Abstract
The aging intensity (AI), defined as the ratio of the instantaneous hazard rate and a baseline hazard rate, is a useful tool for the describing reliability properties of a random variable corresponding to a lifetime. In this work, the concept of AI is [...] Read more.
The aging intensity (AI), defined as the ratio of the instantaneous hazard rate and a baseline hazard rate, is a useful tool for the describing reliability properties of a random variable corresponding to a lifetime. In this work, the concept of AI is introduced in step-stress accelerated life testing (SSALT) experiments, providing new insights to the model and enabling the further clarification of the differences between the two commonly employed cumulative exposure (CE) and tampered failure rate (TFR) models. New AI-based estimators for the parameters of a SSALT model are proposed and compared to the MLEs in terms of examples and a simulation study. Full article
(This article belongs to the Special Issue Information-Theoretic Criteria for Statistical Model Selection)
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<p>Aging intensity function on the second stress level for the Weibull and exponential distributions for the TFR and CE SSALT models, for <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> (<b>upper</b>), <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> (<b>lower</b>), <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>θ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math> (<b>left</b>), and <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>θ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> (<b>right</b>). The dashed lines represent the AI of a CSALT model at level <math display="inline"><semantics> <msub> <mi>x</mi> <mn>1</mn> </msub> </semantics></math> (truncated at <math display="inline"><semantics> <mi>τ</mi> </semantics></math>, it is also the AI on the first level of a SSALT model, i.e., the constant 1 for the exponential distribution and <math display="inline"><semantics> <mi>δ</mi> </semantics></math> for the Weibull distribution.</p>
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<p>True and estimated CDF, PDF, and AI function in red and blue, respectively, for two simulated samples of the considered SSALT exponential model.</p>
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<p>True and estimated PDF and AI function in red and blue, respectively, based on the kernel method with data reflection, for the two simulated samples of Example 1.</p>
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<p>Histograms with the estimates of <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>θ</mi> <mn>2</mn> </msub> </semantics></math> based on AI and MLE (<math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>θ</mi> <mo>ˇ</mo> </mover> <mi>i</mi> </msub> <mo>=</mo> <msubsup> <mi>θ</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>A</mi> <mi>I</mi> <mo>)</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mi>i</mi> </msub> <mo>=</mo> <msubsup> <mi>θ</mi> <mi>i</mi> <mrow> <mo>(</mo> <mi>M</mi> <mi>L</mi> <mi>E</mi> <mo>)</mo> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </semantics></math>).</p>
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<p>True and estimated AI function in red and blue, respectively, for two simulated samples of the considered SSALT exponential model with three stress levels.</p>
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<p>True CDF (red), estimated CDFs based on MLE (black), and AI (blue) with points corresponding to empirical CDF (red dots) for two simulated samples from Example 3.</p>
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<p>Histogram of the differences <math display="inline"><semantics> <mrow> <mi>K</mi> <mi>L</mi> <mrow> <mo>(</mo> <mi>g</mi> <mo>,</mo> <mover accent="true"> <mi>g</mi> <mo stretchy="false">^</mo> </mover> <mo>)</mo> </mrow> <mo>−</mo> <mi>K</mi> <mi>L</mi> <mrow> <mo>(</mo> <mi>g</mi> <mo>,</mo> <mover accent="true"> <mi>g</mi> <mo>ˇ</mo> </mover> <mo>)</mo> </mrow> </mrow> </semantics></math> based on the simulation study described in Example 2.</p>
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<p>True CDF (red), estimated CDFs based on MLE (black), and AI (blue) with points corresponding to empirical CDF (red dots) for sample with outliers (<b>left</b>) and random selected sample (<b>right</b>) from Example 2.</p>
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20 pages, 6707 KiB  
Article
Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards
by Tengteng Zhang and Hongwei Mo
Entropy 2024, 26(5), 416; https://doi.org/10.3390/e26050416 - 12 May 2024
Viewed by 1777
Abstract
In unstructured environments, robots need to deal with a wide variety of objects with diverse shapes, and often, the instances of these objects are unknown. Traditional methods rely on training with large-scale labeled data, but in environments with continuous and high-dimensional state spaces, [...] Read more.
In unstructured environments, robots need to deal with a wide variety of objects with diverse shapes, and often, the instances of these objects are unknown. Traditional methods rely on training with large-scale labeled data, but in environments with continuous and high-dimensional state spaces, the data become sparse, leading to weak generalization ability of the trained models when transferred to real-world applications. To address this challenge, we present an innovative maximum entropy Deep Q-Network (ME-DQN), which leverages an attention mechanism. The framework solves complex and sparse reward tasks through probabilistic reasoning while eliminating the trouble of adjusting hyper-parameters. This approach aims to merge the robust feature extraction capabilities of Fully Convolutional Networks (FCNs) with the efficient feature selection of the attention mechanism across diverse task scenarios. By integrating an advantage function with the reasoning and decision-making of deep reinforcement learning, ME-DQN propels the frontier of robotic grasping and expands the boundaries of intelligent perception and grasping decision-making in unstructured environments. Our simulations demonstrate a remarkable grasping success rate of 91.6%, while maintaining excellent generalization performance in the real world. Full article
Show Figures

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Figure 1
<p>Priority sampling and storage based on SumTree structure.</p>
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<p>The policy framework of robot dexterous grasping.</p>
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<p>Convolutional attention mechanism block.</p>
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<p>The schematic diagram of channel attention module and spatial attention module.</p>
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<p>The schematic diagram of maximum entropy DQN network.</p>
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<p>The reward value curve for grasping actions.</p>
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<p>The schematic diagram of ME-DQN network.</p>
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<p>The same structure.</p>
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<p>The different structure.</p>
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<p>The training results for a multi-object with different structures based on different backbones.</p>
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<p>The comparison of training for novel unknown objects with benchmarks in simulation.</p>
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<p>The evaluation of mean action efficiency.</p>
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<p>The grasping experiments of multiple unknown objects in real world.</p>
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26 pages, 1014 KiB  
Article
Quantum Synchronization and Entanglement of Dissipative Qubits Coupled to a Resonator
by Alexei D. Chepelianskii and Dima L. Shepelyansky
Entropy 2024, 26(5), 415; https://doi.org/10.3390/e26050415 - 11 May 2024
Cited by 2 | Viewed by 1519
Abstract
In a dissipative regime, we study the properties of several qubits coupled to a driven resonator in the framework of a Jaynes–Cummings model. The time evolution and the steady state of the system are numerically analyzed within the Lindblad master equation, with up [...] Read more.
In a dissipative regime, we study the properties of several qubits coupled to a driven resonator in the framework of a Jaynes–Cummings model. The time evolution and the steady state of the system are numerically analyzed within the Lindblad master equation, with up to several million components. Two semi-analytical approaches, at weak and strong (semiclassical) dissipations, are developed to describe the steady state of this system and determine its validity by comparing it with the Lindblad equation results. We show that the synchronization of several qubits with the driving phase can be obtained due to their coupling to the resonator. We establish the existence of two different qubit synchronization regimes: In the first one, the semiclassical approach describes well the dynamics of qubits and, thus, their quantum features and entanglement are suppressed by dissipation and the synchronization is essentially classical. In the second one, the entangled steady state of a pair of qubits remains synchronized in the presence of dissipation and decoherence, corresponding to the regime non-existent in classical synchronization. Full article
(This article belongs to the Section Quantum Information)
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Figure 1

Figure 1
<p>Distribution of the oscillator occupation number, <span class="html-italic">n</span>, as a function of the detuning from the cavity resonance <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math> and <span class="html-italic">z</span>-axis spin projection. The color scale shows the amplitude <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>ρ</mi> <mo>(</mo> <mi>n</mi> <mo>,</mo> <mi>s</mi> <mo>;</mo> <mi>n</mi> <mo>,</mo> <mi>s</mi> <mo>)</mo> <mo>|</mo> </mrow> </semantics></math> where <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mo>↑</mo> <mo>,</mo> <mo>↓</mo> </mrow> </semantics></math> is the spin up/down state. We remind readers that <math display="inline"><semantics> <mi>λ</mi> </semantics></math> is the strength of the spin cavity interaction; we use it as the energy scale to facilitate the comparison between direct time integration and RWA, which depends only on the difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mo>=</mo> <mi mathvariant="sans-serif">Ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> between the Zeeman splitting <math display="inline"><semantics> <mi mathvariant="sans-serif">Ω</mi> </semantics></math> and the cavity frequency <math display="inline"><semantics> <msub> <mi>ω</mi> <mn>0</mn> </msub> </semantics></math>, which is set to <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math> in this figure. Here, the driving strength is set to <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math>, cavity dissipation is <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.3</mn> <mi>λ</mi> </mrow> </semantics></math>, and qubit is dissipationless <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>. The top rows in the figures are obtained by direct time integration for <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, while the bottom rows present the results from RWA. For the Lindblad time integration, numerical data show the density matrices after <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math> excitation periods, starting from the system’s ground state for <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (we use the same <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>p</mi> </msub> </semantics></math> for the results presentations from other cases of the time-dependent Lindblad equation). The oscillator phase spaces in the simulations were truncated to the first 100 oscillator levels (usually used for other cases).</p>
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<p>Mean spin projections <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </msub> <mo>〉</mo> </mrow> </semantics></math> and oscillator quantum number <math display="inline"><semantics> <mrow> <mrow> <mo>〈</mo> <mi>n</mi> <mo>〉</mo> </mrow> <mo>=</mo> <mrow> <mo>〈</mo> <msup> <mi>a</mi> <mo>+</mo> </msup> <mi>a</mi> <mo>〉</mo> </mrow> </mrow> </semantics></math> as functions of the detuning <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.3</mn> <mi>λ</mi> </mrow> </semantics></math> (the same values as in <a href="#entropy-26-00415-f001" class="html-fig">Figure 1</a>). Different traces correspond to quantum dynamics to increase the RWA parameter <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>30</mn> <mo>,</mo> <mn>50</mn> <mo>,</mo> <mn>100</mn> </mrow> </semantics></math> (for case 50, we show a green curve on a shorter range since—outside of it all—color curves overlap, making them hardly distinguishable). The agreement with RWA improves as <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math> increases but worsens a bit for the largest value. This behavior is explained as a transient effect in <a href="#entropy-26-00415-f0A1" class="html-fig">Figure A1</a> of <a href="#app1-entropy-26-00415" class="html-app">Appendix A</a>, where we show that relaxation to the steady state is not complete, even after <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>4</mn> </msup> </mrow> </semantics></math> microwave periods. Indeed, since the qubit is not dissipative in this simulation (<math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>), the relaxation time scale can be much longer than <math display="inline"><semantics> <msup> <mi>γ</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>.</p>
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<p>Comparison between the RWA simulation and the summation of the rate equation series for <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0.3</mn> <mi>λ</mi> </mrow> </semantics></math>. The trace (R) corresponds to the summation of the series from the direct rate expansion in Equation (<a href="#FD19-entropy-26-00415" class="html-disp-formula">19</a>), while (R)*, which exhibits a larger radius of convergence, corresponds to Equation (30). The series (R) qualitatively reproduces the position of the multiphoton resonances, but with excessive amplitude, and it fails to converge. The series (R)* reproduces multiphoton resonance accurately but still fails to converge close to the cavity resonance <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> <mo>/</mo> <mi>λ</mi> <mo>∼</mo> <mn>1</mn> </mrow> </semantics></math> (further studies are needed to know if divergence occurs on energy scale <math display="inline"><semantics> <mi>λ</mi> </semantics></math> or <math display="inline"><semantics> <mi>γ</mi> </semantics></math> around the resonance).</p>
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<p>Comparison between RWA and rate equation series for weak damping <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0.005</mn> <mi>λ</mi> </mrow> </semantics></math>. As expected in this regime, the rate equation series (R), from Equation (<a href="#FD19-entropy-26-00415" class="html-disp-formula">19</a>), converges. Excitation was reduced to avoid overheating at resonance with <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>0.1</mn> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>0.2</mn> <mi>λ</mi> </mrow> </semantics></math>. As in the previous figures <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>.</p>
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<p>Spin projections of the two spins as functions of the detuning <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math> for excitation strength <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>5</mn> <mi>λ</mi> </mrow> </semantics></math>. Dashed lines show the spin projection predicted by the semiclassical functional Equation (<a href="#FD38-entropy-26-00415" class="html-disp-formula">38</a>), and dotted lines show the RWA steady state. The qubit–cavity detunings are as follows: <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi mathvariant="sans-serif">Ω</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>2</mn> </msub> <mo>=</mo> <msub> <mi mathvariant="sans-serif">Ω</mi> <mn>2</mn> </msub> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mi>λ</mi> </mrow> </semantics></math>. The dissipation is fixed to <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>, and the relatively large values of the dissipation rates ensure good agreement with the semiclassical predictions.</p>
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<p>This figure compares the spin polarization <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>〉</mo> </mrow> </semantics></math> for one qubit coupled to a cavity for increasing dissipation rates. The polarization is shown as a function of <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>. Smooth thin curves show the RWA steady state while dashed curves show the semiclassical theory. For the lowest dissipation, <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mi>γ</mi> <mo>=</mo> <mn>0.3</mn> <mi>λ</mi> </mrow> </semantics></math>, the semiclassical theory predicts a reversal of <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>〉</mo> </mrow> </semantics></math> in the range of <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>/</mo> <mi>λ</mi> <mo>∈</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>, which is not present in RWA, which gives the exact quantum result. But the agreement is good outside of this range. Adding some dissipation to the spin <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mi>γ</mi> <mo>=</mo> <mn>0.3</mn> <mi>λ</mi> </mrow> </semantics></math> is enough to observe the polarization reversal in RWA. The agreement becomes very good for <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mi>γ</mi> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>. As expected, at a higher friction, the system behaves in a more semiclassical way.</p>
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<p>RWA calculation of the total spin of the qubit pair as a function of <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math> for two values of the qubit–cavity detuning <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>=</mo> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math>. The top curve corresponds to <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>1</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>2</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> with only a weak deviation from the equilibrium total spin triplet <math display="inline"><semantics> <mrow> <mrow> <mo>〈</mo> <msup> <mi>S</mi> <mn>2</mn> </msup> <mo>〉</mo> </mrow> <mo>=</mo> <mi>S</mi> <mrow> <mo>(</mo> <mi>S</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>. When the two Zeeman splittings are antisymmetric with respect to the cavity <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <msub> <mo>Δ</mo> <mn>2</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>, a significantly stronger reduction of <math display="inline"><semantics> <mrow> <mo>〈</mo> <msup> <mi>S</mi> <mn>2</mn> </msup> <mo>〉</mo> </mrow> </semantics></math> is observed. Dissipative rates are set to <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0.2</mn> <mi>λ</mi> </mrow> </semantics></math> and excitation is <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>1.5</mn> <mi>λ</mi> </mrow> </semantics></math>. As previously 100 oscillator levels are used in the simulation.</p>
Full article ">Figure 8
<p>When dissipative rates are reduced compared to <a href="#entropy-26-00415-f007" class="html-fig">Figure 7</a>, the reduction of <math display="inline"><semantics> <mrow> <mo>〈</mo> <msup> <mi>S</mi> <mn>2</mn> </msup> <mo>〉</mo> </mrow> </semantics></math> for antisymmetric qubit–cavity detunings <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <msub> <mo>Δ</mo> <mn>2</mn> </msub> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math> becomes stronger and the singlet state of the qubit pair becomes the most probable state (75% singlet probability at the minimum of <math display="inline"><semantics> <mrow> <mo>〈</mo> <msup> <mi>S</mi> <mn>2</mn> </msup> <mo>〉</mo> </mrow> </semantics></math>). Here, the driving strength is set to <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, the dissipative rates are <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.3</mn> <mi>λ</mi> </mrow> </semantics></math> with a weak qubit dissipation <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mi>γ</mi> </mrow> </semantics></math> (for these parameters, at resonance, <math display="inline"><semantics> <mrow> <mrow> <mo>〈</mo> <mi>n</mi> <mo>〉</mo> </mrow> <mo>≃</mo> <mn>4</mn> <msup> <mi>F</mi> <mn>2</mn> </msup> <mo>/</mo> <msup> <mi>γ</mi> <mn>2</mn> </msup> <mo>≃</mo> <mn>3</mn> </mrow> </semantics></math>). To confirm that the singlet formation is not an artifact of the RWA (black curve), we performed direct integration of the time-dependent Lindblad dynamics up to the total simulation time <math display="inline"><semantics> <mrow> <mn>3</mn> <msubsup> <mi>γ</mi> <mi>s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> for increasing RWA parameter <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math> (color curves with symbols). The singlet formation is robust to non-RWA effects with the minimum <math display="inline"><semantics> <mrow> <mo>〈</mo> <msup> <mi>S</mi> <mn>2</mn> </msup> <mo>〉</mo> </mrow> </semantics></math> remaining unchanged as <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math> is varied by an order of magnitude. Only weak non-RWA effects are visible as a small shift of the minimum from <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and an asymmetric <math display="inline"><semantics> <mrow> <mo>〈</mo> <msup> <mi>S</mi> <mn>2</mn> </msup> <mo>〉</mo> </mrow> </semantics></math> dependence since non-RWA effects break the symmetry between two anti-symmetrically detuned qubits.</p>
Full article ">Figure 9
<p>Bell inequality violation and negativity of the steady-state RWA qubit pair with the reduced density matrix (trace done over the cavity) for the parameters of <a href="#entropy-26-00415-f008" class="html-fig">Figure 8</a>. Since the qubit pair is in a mixture of singlet and triplet states, the polarization choice for the Bell inequality has to be adjusted to observe a Bell inequality violation (see <a href="#app1-entropy-26-00415" class="html-app">Appendix A</a> and <a href="#entropy-26-00415-f0A2" class="html-fig">Figure A2</a>). Maximal negativity for two qubits is <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math> [<a href="#B50-entropy-26-00415" class="html-bibr">50</a>] and, thus, this steady state shows a high degree of stationary entanglement despite the dissipative decoherence of both qubits and cavities.</p>
Full article ">Figure 10
<p>We test here if our two semi-analytic approaches can reproduce singlet formation for antisymmetric detuning presented in <a href="#entropy-26-00415-f008" class="html-fig">Figure 8</a> and <a href="#entropy-26-00415-f009" class="html-fig">Figure 9</a>. The left panel shows the RWA spin projection <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mrow> <mn>1</mn> <mi>x</mi> </mrow> </msub> <mo>〉</mo> </mrow> </semantics></math> for the first qubit compared with the rate equation series and the variational approximation, and the right panel shows the mean total spin <math display="inline"><semantics> <mrow> <mo>〈</mo> <msup> <mi>S</mi> <mn>2</mn> </msup> <mo>〉</mo> </mrow> </semantics></math>. While both approaches reproduce some qualitative features, they both fail to describe the singlet formation at <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>RWA calculation of total spin projection <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>〉</mo> </mrow> </semantics></math> of an increasing number of qubits coupled to one cavity (full curves) as a function of cavity-excitation detuning and a comparison to the semiclassical theory (dashed curves). The qubit cavity detunings are set to <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>1</mn> </msub> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>2</mn> </msub> <mo>=</mo> <mn>1.5</mn> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>3</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>4</mn> </msub> <mo>=</mo> <mn>2.5</mn> <mi>λ</mi> </mrow> </semantics></math> (only the first qubits are kept when the number of qubits is smaller than four). Dissipative rates are <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0.2</mn> <mi>λ</mi> </mrow> </semantics></math> and excitation is <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mn>0.77</mn> <mi>λ</mi> </mrow> </semantics></math>. Even if relaxation rates are all small, the semiclassical theory still captures many properties of <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>〉</mo> </mrow> </semantics></math>, reproducing the increase of <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>〉</mo> </mrow> </semantics></math> with the number of qubits, which corresponds to the synchronization of qubit rotation by the external drive. In both RWA and semiclassical data, synchronization occurs at resonance <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math> but perhaps less expected at a higher frequency detuned from both cavities and qubits (<math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> <mo>/</mo> <mi>λ</mi> </mrow> </semantics></math>, increasing from 2 to 3 with the number of qubits). Interestingly, the accuracy of the semiclassical approximation seems to improve with the number of qubits, which may be due to its mean-field character.</p>
Full article ">Figure 12
<p>The RWA calculation of the total spin projection <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>〉</mo> </mrow> </semantics></math> from <a href="#entropy-26-00415-f011" class="html-fig">Figure 11</a> is compared to the result of the summation of the rate equation series (R)* (dotted curves). The rate equation series yield results that are nearly exact away from resonance, but they suffer from instabilities near <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math>. It seems that the convergence range decreases with the number of qubits with various multi-qubit resonances making the series unstable even at <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5</mn> <mi>λ</mi> </mrow> </semantics></math> for four qubits.</p>
Full article ">Figure A1
<p>Mean oscillator quantum number <math display="inline"><semantics> <mrow> <mrow> <mo>〈</mo> <mi>n</mi> <mo>〉</mo> </mrow> <mo>=</mo> <mrow> <mo>〈</mo> <msup> <mi>a</mi> <mo>+</mo> </msup> <mi>a</mi> <mo>〉</mo> </mrow> </mrow> </semantics></math> as a function of the number of driving field oscillation periods <math display="inline"><semantics> <mrow> <mi>ω</mi> <mi>t</mi> <mo>/</mo> <mo>(</mo> <mn>2</mn> <mi>π</mi> <mo>)</mo> </mrow> </semantics></math> obtained by integrating the time-dependent Lindblad equation for the data in <a href="#entropy-26-00415-f002" class="html-fig">Figure 2</a> (we remind readers that <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>2</mn> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.03</mn> <mi>λ</mi> </mrow> </semantics></math>). The different curves correspond to different values of <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>ω</mi> <mo>=</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math>. The left panel shows <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> and the right one 100. Relaxation is slower for positive values of <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>ω</mi> </mrow> </semantics></math> near resonance compared to negative <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>ω</mi> </mrow> </semantics></math>. The comparison with RWA allowed us to notice the incomplete relaxation in the simulations for <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, which is easy to miss because it occurs in a narrow range of <math display="inline"><semantics> <mrow> <mi>δ</mi> <mi>ω</mi> </mrow> </semantics></math>.</p>
Full article ">Figure A2
<p>Violation of the Bell inequality for the RWA steady state for antisymmetric cavity–qubit detunings <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>1</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <msub> <mo>Δ</mo> <mn>2</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> (see also <a href="#entropy-26-00415-f009" class="html-fig">Figure 9</a>). Different curves correspond to increasing driving fields, expressed as the mean cavity occupation number at resonance <math display="inline"><semantics> <mrow> <mrow> <mo>〈</mo> <mi>n</mi> <mo>〉</mo> </mrow> <mo>=</mo> <mn>4</mn> <msup> <mi>F</mi> <mn>2</mn> </msup> <mo>/</mo> <msup> <mi>γ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>, which is shown in the legend. Dissipative rates are <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>0.025</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>/</mo> <mi>γ</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>. Maximal violation of Bell inequality is observed for <math display="inline"><semantics> <mrow> <mo>〈</mo> <mi>n</mi> <mo>〉</mo> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> (at resonance). Further increase of the driving field leads to a reduction of the Bell inequality violation, highlighting the delicate quantum nature of the singlet state. To determine the violation of Bell inequalities, a maximum was taken over randomly chosen sets of four spin projection directions for the correlation measurement.</p>
Full article ">Figure A3
<p>Synchronization between a driven cavity and a qubit in the system’s steady state for <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi mathvariant="sans-serif">Ω</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>−</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>)</mo> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>0.3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>=</mo> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mi>λ</mi> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math> and RWA parameter <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>. The qubit is non-dissipative <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and data are obtained by integrating Lindblad dynamics. Due to the moderate value of the RWA parameter, vibrations around the mean RWA values of the spin projections are clearly visible in the left panel. The right-hand panel shows the synchronization between the angle <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mo form="prefix">arg</mo> <mo>〈</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>+</mo> <mi>i</mi> <msub> <mi>S</mi> <mi>y</mi> </msub> <mo>〉</mo> </mrow> </semantics></math>, which represents the in-plane spin projection of the qubit, and the phase of the cavity driving field.</p>
Full article ">Figure A4
<p>RWA calculation of the total spin projection <math display="inline"><semantics> <mrow> <mo>〈</mo> <msub> <mi>S</mi> <mi>x</mi> </msub> <mo>〉</mo> </mrow> </semantics></math> as a function of the excitation frequency-cavity detuning for an increasing number of qubits coupled to the cavity (full lines). The cavity–qubit interaction is weak <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mi>γ</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>. The qubit–cavity detunings are set to <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> <mi>γ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>2</mn> </msub> <mo>=</mo> <mn>15</mn> <mi>γ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>3</mn> </msub> <mo>=</mo> <mn>12.5</mn> <mi>γ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mo>Δ</mo> <mn>4</mn> </msub> <mo>=</mo> <mn>17.5</mn> <mi>γ</mi> </mrow> </semantics></math> (only the first qubits are kept when the number of qubits is smaller than four). The qubit dissipation rate is <math display="inline"><semantics> <mrow> <msub> <mi>γ</mi> <mi>s</mi> </msub> <mo>=</mo> <mi>γ</mi> </mrow> </semantics></math> and excitation is <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>/</mo> <mi>γ</mi> <mo>=</mo> <mn>2.2</mn> </mrow> </semantics></math>. In this weak interaction regime, the semiclassical calculation (shown as dashed lines for 2 and 4 spins) coincides almost exactly with RWA.</p>
Full article ">Figure A5
<p>Minimum of the semiclassical functional (<math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>=</mo> <msqrt> <mi>S</mi> </msqrt> </mrow> </semantics></math>) rescaled by the dissipation rate <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <msub> <mi>γ</mi> <mi>s</mi> </msub> </mrow> </semantics></math> for the semiclassical data from <a href="#entropy-26-00415-f011" class="html-fig">Figure 11</a>. Good agreement between quantum and semiclassical values for the mean spin projection is observed when <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>/</mo> <mi>γ</mi> <mo>≤</mo> <mn>1</mn> </mrow> </semantics></math> while strong deviations are observed at peaks of <math display="inline"><semantics> <mrow> <mi>ϵ</mi> <mo>/</mo> <mi>γ</mi> </mrow> </semantics></math>.</p>
Full article ">
16 pages, 3749 KiB  
Article
Quantum Tunneling and Complex Dynamics in the Suris’s Integrable Map
by Yasutaka Hanada and Akira Shudo
Entropy 2024, 26(5), 414; https://doi.org/10.3390/e26050414 - 11 May 2024
Viewed by 1640
Abstract
Quantum tunneling in a two-dimensional integrable map is studied. The orbits of the map are all confined to the curves specified by the one-dimensional Hamiltonian. It is found that the behavior of tunneling splitting for the integrable map and the associated Hamiltonian system [...] Read more.
Quantum tunneling in a two-dimensional integrable map is studied. The orbits of the map are all confined to the curves specified by the one-dimensional Hamiltonian. It is found that the behavior of tunneling splitting for the integrable map and the associated Hamiltonian system is qualitatively the same, with only a slight difference in magnitude. However, the tunneling tails of the wave functions, obtained by superposing the eigenfunctions that form the doublet, exhibit significant differences. To explore the origin of the difference, we observe the classical dynamics in the complex plane and find that the existence of branch points appearing in the potential function of the integrable map could play the role of yielding non-trivial behavior in the tunneling tail. The result highlights the subtlety of quantum tunneling, which cannot be captured in nature only by the dynamics in the real plane. Full article
(This article belongs to the Special Issue Tunneling in Complex Systems)
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Figure 1

Figure 1
<p>(<b>a</b>) Plot of the functions <math display="inline"><semantics> <mrow> <msubsup> <mi>V</mi> <mi>λ</mi> <mo>′</mo> </msubsup> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (upper) and <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>λ</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (lower) for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>2</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>. Phase space portrait for (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math> and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>2</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math>. In (<b>b</b>,<b>c</b>), dots and solid curves represent the orbits for <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>λ</mi> </msub> </mrow> </semantics></math> and the associated contour curve <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>λ</mi> </msub> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> </mrow> </semantics></math>, respectively. Black dashed and black dash-dotted curves represent the separatrix starting from (<span class="html-italic">q</span>, <span class="html-italic">p</span>) = (0, 0) and (0, ±1/2), respectively.</p>
Full article ">Figure 2
<p>(<b>a</b>) Eigenstates <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (cyan) and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">Ψ</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (black) with <math display="inline"><semantics> <mrow> <mo>ℏ</mo> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>200</mn> <mi>π</mi> </mrow> </semantics></math> for (<b>i</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, (<b>ii</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, (<b>iii</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>150</mn> </mrow> </semantics></math>, and (<b>iv</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>199</mn> </mrow> </semantics></math>. (<b>b</b>) Husimi representation of <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> in normal scale for (<b>i</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, (<b>ii</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, (<b>iii</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>150</mn> </mrow> </semantics></math>, and (<b>iv</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>199</mn> </mrow> </semantics></math>. The intensity of Husim representations is indicated by a yellow-red color scheme shown in the right panel. The black dotted curve shows the contour curve with <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>λ</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> </mrow> <msub> <mi>E</mi> <mi>n</mi> </msub> </mrow> </semantics></math>. The black broken curve shows the separatrix starting from <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>(<b>a</b>) Plots of the tunneling splittings <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Δ</mi> <msub> <mi mathvariant="script">E</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (solid) and <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Δ</mi> <msub> <mi>E</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (dashed) as a function of (<b>i</b>) <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mo>ℏ</mo> </mrow> </semantics></math> and (<b>ii</b>) <math display="inline"><semantics> <mrow> <mo>−</mo> <mi>λ</mi> </mrow> </semantics></math>. (<b>b</b>) Plots of the ratio <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Δ</mi> <msub> <mi mathvariant="script">E</mi> <mn>0</mn> </msub> <mo>/</mo> <mi mathvariant="normal">Δ</mi> <msub> <mi>E</mi> <mn>0</mn> </msub> </mrow> </semantics></math> as a function (<b>i</b>) <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mo>ℏ</mo> </mrow> </semantics></math> and (<b>ii</b>) <math display="inline"><semantics> <mrow> <mo>−</mo> <mi>λ</mi> </mrow> </semantics></math>. (<b>c</b>) Plot of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>−</mo> <mo>|</mo> <mo>〈</mo> <msub> <mi>ψ</mi> <mn>0</mn> </msub> <mo>|</mo> <msub> <mi mathvariant="normal">Ψ</mi> <mn>0</mn> </msub> <mo>〉</mo> <mo>|</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mrow> <mo>−</mo> <mi>λ</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 4
<p>(<b>a</b>) Wave functions <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mi>L</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (solid curves) and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">Ψ</mi> <mi>L</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (dashed curves) for different values of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mo>ℏ</mo> </mrow> </semantics></math>. The “★” mark is put to indicate the deviation point <math display="inline"><semantics> <msubsup> <mi>q</mi> <mo>−</mo> <mo>★</mo> </msubsup> </semantics></math>. (<b>b</b>) Husimi representation of (<b>i</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">Ψ</mi> <mi>L</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and (<b>ii</b>) <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mi>L</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mo>ℏ</mo> <mo>=</mo> <mn>200</mn> <mi>π</mi> </mrow> </semantics></math> in the <math display="inline"><semantics> <msub> <mo form="prefix">log</mo> <mn>10</mn> </msub> </semantics></math> scale. The dashed gray curve represents the separatrix. Closed cyan curves show the contour curve associated with the ground state energy level <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.63905702</mn> </mrow> </semantics></math>. Black and white “×” symbols indicate the position of the turning points for <math display="inline"><semantics> <mrow> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>; <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mo>±</mo> <mn>0.450847</mn> <mo>,</mo> <mo>±</mo> <mn>0.012215</mn> <mo>)</mo> </mrow> </semantics></math>, respectively. (<b>i</b>) The cyan curve connected to the two turning points shows the instanton curve projected onto the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>,</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>p</mi> <mo>)</mo> </mrow> </semantics></math>-plane.</p>
Full article ">Figure 5
<p>The dots represent the deviation point <math display="inline"><semantics> <msubsup> <mi>q</mi> <mo>−</mo> <mo>★</mo> </msubsup> </semantics></math> vs. <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mo>ℏ</mo> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math> on (<b>a</b>) the double logarithmic scale and (<b>b</b>) the normal scale. The dashed line is obtained by applying linear regression to the plot of (<b>a</b>) whose slope is obtained as <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>0.97418081</mn> </mrow> </semantics></math>. The dotted-dashed line with slope <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> in (<b>a</b>) is shown just for reference. The black horizontal line in (<b>b</b>) represents <math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mo>−</mo> <mo>★</mo> </msubsup> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> as a guide.</p>
Full article ">Figure 6
<p>Plot of <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi>U</mi> <mo stretchy="false">^</mo> </mover> <mi>λ</mi> <mi>k</mi> </msubsup> <mrow> <mo>|</mo> <msub> <mi mathvariant="normal">Ψ</mi> <mi>L</mi> </msub> <mo>〉</mo> </mrow> </mrow> </semantics></math> (<b>a</b>) in the <span class="html-italic">q</span>-representation and (<b>b</b>) in the Husimi representation. The dashed gray curve in (<b>b</b>) represents the separatrix starting from <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>The solution curves obtained under the continuous Hamiltonian flow <math display="inline"><semantics> <msub> <mi>F</mi> <mi>λ</mi> </msub> </semantics></math>. They are confined in the equi-energy surface given by the condition <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>λ</mi> </msub> <mo>=</mo> <msub> <mi>E</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.63905702</mn> </mrow> </semantics></math>. The curves are projected onto (<b>a</b>) the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>,</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>p</mi> <mo>,</mo> <mi>Im</mi> <mspace width="0.166667em"/> <mi>p</mi> <mo>)</mo> </mrow> </semantics></math>-space and (<b>b</b>) the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>,</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>p</mi> <mo>)</mo> </mrow> </semantics></math>-plane. In (<b>a</b>,<b>b</b>) the black closed curve and the black semicircular curve represent the equi-energy curves in the real plane and the instanton curve, respectively. The symbol “×” stands for the turning point of <math display="inline"><semantics> <mrow> <mi>Re</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>a</b>) The cyan line tending to infinity shows a solution curve of <math display="inline"><semantics> <msub> <mi>H</mi> <mi>λ</mi> </msub> </semantics></math> starting from the point <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>,</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math>, marked by the cyan point on the instanton. (<b>b</b>) The cyan dots show the orbits of <math display="inline"><semantics> <msub> <mi>f</mi> <mi>λ</mi> </msub> </semantics></math> starting from the point <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>,</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> on the instanton.</p>
Full article ">Figure 8
<p>Flow of Equation (13) in the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Im</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>,</mo> <mi>Im</mi> <mspace width="0.166667em"/> <mi>p</mi> <mo>)</mo> </mrow> </semantics></math>-plane for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math>. Vertical dashed lines represent <math display="inline"><semantics> <mrow> <mi>Im</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>=</mo> <msup> <mi>q</mi> <mo>⋄</mo> </msup> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>The function <math display="inline"><semantics> <mrow> <msubsup> <mi>V</mi> <mi>λ</mi> <mo>′</mo> </msubsup> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> in the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Re</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>,</mo> <mi>Im</mi> <mspace width="0.166667em"/> <mi>q</mi> <mo>)</mo> </mrow> </semantics></math>-plane with <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics></math>. The argument of <math display="inline"><semantics> <mrow> <msubsup> <mi>V</mi> <mi>λ</mi> <mo>′</mo> </msubsup> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and the absolute value <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msubsup> <mi>V</mi> <mi>λ</mi> <mo>′</mo> </msubsup> <mrow> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>|</mo> </mrow> </mrow> </semantics></math> are distinguished by color and brightness, respectively. The blue diamond “⋄” symbol represents the branch point of <math display="inline"><semantics> <mrow> <msup> <mi>V</mi> <mo>′</mo> </msup> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. The white line starting from the branch point represents the branch cut. The black curve shows a contour curve <math display="inline"><semantics> <mrow> <mi>Im</mi> <mspace width="0.166667em"/> <msubsup> <mi>V</mi> <mi>λ</mi> <mo>′</mo> </msubsup> <mrow> <mo>(</mo> <mi>q</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>(<b>a</b>) Plot of the wave functions <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>U</mi> <mo stretchy="false">^</mo> </mover> <mi>λ</mi> </msub> <mrow> <mo>|</mo> <msub> <mi mathvariant="normal">Ψ</mi> <mi>L</mi> </msub> <mo>〉</mo> </mrow> </mrow> </semantics></math> (red curve) and <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi mathvariant="normal">Ψ</mi> <mi>L</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math> (black dashed curve) for (<b>i</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>0.3</mn> </mrow> </semantics></math>, (<b>ii</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics></math>, (<b>iii</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>0.7</mn> </mrow> </semantics></math>, and (<b>iv</b>) <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mo>−</mo> <mn>0.9</mn> </mrow> </semantics></math>. (<b>b</b>) Husimi representation of <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>U</mi> <mo stretchy="false">^</mo> </mover> <mi>λ</mi> </msub> <mrow> <mo>|</mo> <msub> <mi mathvariant="normal">Ψ</mi> <mi>L</mi> </msub> <mo>〉</mo> </mrow> </mrow> </semantics></math>. The cyan dotted line represents a line <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> <mo>)</mo> </mrow> </semantics></math> for a guide. The gray solid curve and the gray dashed curve represent a contour curve of <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mi>λ</mi> </msub> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> </mrow> <msub> <mi>E</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and the separatrix starting from <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>q</mi> <mo>,</mo> <mi>p</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math>, respectively.</p>
Full article ">
17 pages, 3408 KiB  
Article
Efficient Quantum Private Comparison Based on GHZ States
by Min Hou, Yue Wu and Shibin Zhang
Entropy 2024, 26(5), 413; https://doi.org/10.3390/e26050413 - 10 May 2024
Cited by 7 | Viewed by 1659
Abstract
Quantum private comparison (QPC) is a fundamental cryptographic protocol that allows two parties to compare the equality of their private inputs without revealing any information about those inputs to each other. In recent years, QPC protocols utilizing various quantum resources have been proposed. [...] Read more.
Quantum private comparison (QPC) is a fundamental cryptographic protocol that allows two parties to compare the equality of their private inputs without revealing any information about those inputs to each other. In recent years, QPC protocols utilizing various quantum resources have been proposed. However, these QPC protocols have lower utilization of quantum resources and qubit efficiency. To address this issue, we propose an efficient QPC protocol based on GHZ states, which leverages the unique properties of GHZ states and rotation operations to achieve secure and efficient private comparison. The secret information is encoded in the rotation angles of rotation operations performed on the received quantum sequence transmitted along the circular mode. This results in the multiplexing of quantum resources and enhances the utilization of quantum resources. Our protocol does not require quantum key distribution (QKD) for sharing a secret key to ensure the security of the inputs, resulting in no consumption of quantum resources for key sharing. One GHZ state can be compared to three bits of classical information in each comparison, leading to qubit efficiency reaching 100%. Compared with the existing QPC protocol, our protocol does not require quantum resources for sharing a secret key. It also demonstrates enhanced performance in qubit efficiency and the utilization of quantum resources. Full article
(This article belongs to the Special Issue Quantum Computation, Communication and Cryptography)
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Figure 1
<p>The diagram of the QPC protocol.</p>
Full article ">Figure 2
<p>The quantum circuit of two GHZ states <math display="inline"><semantics> <mrow> <mfenced close="" open="|"> <mrow> <msub> <mi>φ</mi> <mn>1</mn> </msub> </mrow> </mfenced> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mfenced close="" open="|"> <mrow> <msub> <mi>φ</mi> <mn>6</mn> </msub> <mo>〉</mo> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>The measurement outcome in <a href="#entropy-26-00413-f002" class="html-fig">Figure 2</a>.</p>
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<p>The quantum circuit corresponding to the concrete example.</p>
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<p>The measurement outcome in <a href="#entropy-26-00413-f004" class="html-fig">Figure 4</a>.</p>
Full article ">Figure 6
<p>The relationship between the number of decoy photons and the probability that Eve will deceive the eavesdropping detection.</p>
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15 pages, 349 KiB  
Article
Finite-Temperature Correlation Functions Obtained from Combined Real- and Imaginary-Time Propagation of Variational Thawed Gaussian Wavepackets
by Jens Aage Poulsen and Gunnar Nyman
Entropy 2024, 26(5), 412; https://doi.org/10.3390/e26050412 - 10 May 2024
Cited by 1 | Viewed by 1042
Abstract
We apply the so-called variational Gaussian wavepacket approximation (VGA) for conducting both real- and imaginary-time dynamics to calculate thermal correlation functions. By considering strongly anharmonic systems, such as a quartic potential and a double-well potential at high and low temperatures, it is shown [...] Read more.
We apply the so-called variational Gaussian wavepacket approximation (VGA) for conducting both real- and imaginary-time dynamics to calculate thermal correlation functions. By considering strongly anharmonic systems, such as a quartic potential and a double-well potential at high and low temperatures, it is shown that this method is partially able to account for tunneling. This is contrary to other popular many-body methods, such as ring polymer molecular dynamics and the classical Wigner method, which fail in this respect. It is a historical peculiarity that no one has considered the VGA method for representing both the Boltzmann operator and the real-time propagation. This method should be well suited for molecular systems containing many atoms. Full article
(This article belongs to the Special Issue Tunneling in Complex Systems)
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Figure 1
<p>The position correlation function <math display="inline"><semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> as calculated for a quartic (<span class="html-italic">Q</span>) and a double-well potential (<math display="inline"><semantics> <mrow> <mi>D</mi> <mi>W</mi> </mrow> </semantics></math>) for <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mo stretchy="false">{</mo> <mn>1</mn> <mo>,</mo> <mn>8</mn> <mo stretchy="false">}</mo> </mrow> </semantics></math>.</p>
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<p>Shape of the Gaussian Wigner function when tunneling. Initial parameters of Wigner function are <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.237</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>0</mn> </msub> <mo>=</mo> <mi>θ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mi>λ</mi> <mo>=</mo> <mn>0.864</mn> </mrow> </semantics></math>.</p>
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<p>VGA and exact results for <math display="inline"><semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> for slightly different double-well potentials.</p>
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<p>Domains of (i) two or more negative eigenvalues/maximum one negative eigenvalue and (ii) tunneling/not tunneling for the VGA method.</p>
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<p>Dynamics for the low-temperature double-well problem. <math display="inline"><semantics> <mrow> <mi>G</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> calculated by both coupled VGA functions (one original and one spawned) and a single VGA function.</p>
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14 pages, 323 KiB  
Article
Does Quantum Mechanics Require “Conspiracy”?
by Ovidiu Cristinel Stoica
Entropy 2024, 26(5), 411; https://doi.org/10.3390/e26050411 - 9 May 2024
Cited by 8 | Viewed by 1383
Abstract
Quantum states containing records of incompatible outcomes of quantum measurements are valid states in the tensor-product Hilbert space. Since they contain false records, they conflict with the Born rule and with our observations. I show that excluding them requires a fine-tuning to an [...] Read more.
Quantum states containing records of incompatible outcomes of quantum measurements are valid states in the tensor-product Hilbert space. Since they contain false records, they conflict with the Born rule and with our observations. I show that excluding them requires a fine-tuning to an extremely restricted subspace of the Hilbert space that seems “conspiratorial”, in the sense that (1) it seems to depend on future events that involve records (including measurement settings) and on the dynamical law (normally thought to be independent of the initial conditions), and (2) it violates Statistical Independence, even when it is valid in the context of Bell’s theorem. To solve the puzzle, I build a model in which, by changing the dynamical law, the same initial conditions can lead to different histories in which the validity of records is relative to the new dynamical law. This relative validity of the records may restore causality, but the initial conditions still must depend, at least partially, on the dynamical law. While violations of Statistical Independence are often seen as non-scientific, they turn out to be needed to ensure the validity of records and our own memories and, by this, of science itself. A Past Hypothesis is needed to ensure the existence of records and turns out to require violations of Statistical Independence. It is not excluded that its explanation, still unknown, ensures such violations in the way needed by local interpretations of quantum mechanics. I suggest that an as-yet unknown law or superselection rule may restrict the full tensor-product Hilbert space to the very special subspace required by the validity of records and the Past Hypothesis. Full article
(This article belongs to the Section Quantum Information)
21 pages, 331 KiB  
Article
Geometric Algebra Jordan–Wigner Transformation for Quantum Simulation
by Grégoire Veyrac and Zeno Toffano
Entropy 2024, 26(5), 410; https://doi.org/10.3390/e26050410 - 8 May 2024
Cited by 2 | Viewed by 2069
Abstract
Quantum simulation qubit models of electronic Hamiltonians rely on specific transformations in order to take into account the fermionic permutation properties of electrons. These transformations (principally the Jordan–Wigner transformation (JWT) and the Bravyi–Kitaev transformation) correspond in a quantum circuit to the introduction of [...] Read more.
Quantum simulation qubit models of electronic Hamiltonians rely on specific transformations in order to take into account the fermionic permutation properties of electrons. These transformations (principally the Jordan–Wigner transformation (JWT) and the Bravyi–Kitaev transformation) correspond in a quantum circuit to the introduction of a supplementary circuit level. In order to include the fermionic properties in a more straightforward way in quantum computations, we propose to use methods issued from Geometric Algebra (GA), which, due to its commutation properties, are well adapted for fermionic systems. First, we apply the Witt basis method in GA to reformulate the JWT in this framework and use this formulation to express various quantum gates. We then rewrite the general one and two-electron Hamiltonian and use it for building a quantum simulation circuit for the Hydrogen molecule. Finally, the quantum Ising Hamiltonian, widely used in quantum simulation, is reformulated in this framework. Full article
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<p>Representation of a bivector.</p>
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<p>Quantum circuit for exponential of the number operator.</p>
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<p>Quantum circuit for exponential of the Coulomb operator.</p>
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<p>Quantum circuit for exponential of the Excitation operator assuming <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Quantum circuit for exponential of the excitation-number operator.</p>
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<p>Quantum circuit for the quantum anisotropic Ising model simulation given in (<a href="#FD117-entropy-26-00410" class="html-disp-formula">117</a>).</p>
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17 pages, 6146 KiB  
Article
Entropy-Aided Meshing-Order Modulation Analysis for Wind Turbine Planetary Gear Weak Fault Detection under Variable Rotational Speed
by Shaodan Zhi, Hengshan Wu, Haikuo Shen, Tianyang Wang and Hongfei Fu
Entropy 2024, 26(5), 409; https://doi.org/10.3390/e26050409 - 8 May 2024
Cited by 7 | Viewed by 1501
Abstract
As one of the most vital energy conversation systems, the safe operation of wind turbines is very important; however, weak fault and time-varying speed may challenge the conventional monitoring strategies. Thus, an entropy-aided meshing-order modulation method is proposed for detecting the optimal frequency [...] Read more.
As one of the most vital energy conversation systems, the safe operation of wind turbines is very important; however, weak fault and time-varying speed may challenge the conventional monitoring strategies. Thus, an entropy-aided meshing-order modulation method is proposed for detecting the optimal frequency band, which contains the weak fault-related information. Specifically, the variable rotational frequency trend is first identified and extracted based on the time–frequency representation of the raw signal by constructing a novel scaling-basis local reassigning chirplet transform (SLRCT). A new entropy-aided meshing-order modulation (EMOM) indicator is then constructed to locate the most sensitive modulation frequency area according to the extracted fine speed trend with the help of order tracking technique. Finally, the raw vibration signal is bandpass filtered via the corresponding optimal frequency band with the highest EMOM indicator. The order components resulting from the weak fault can be highlighted to accomplish weak fault detection. The effectiveness of the proposed EMOM analysis-based method has been tested using the experimental data of three different gear fault types of different fault levels from a planetary test rig. Full article
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<p>Lenze ESV222N02YXB Fault diagnosis test rig.</p>
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<p>Sun gear failure modes (<b>left</b>: missing tooth; <b>middle</b>: tooth break; <b>right</b>: tooth crack).</p>
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<p>Raw signal waveform with gear missing tooth fault.</p>
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<p>(<b>a</b>) TFR, and (<b>b</b>) instantaneous rotational frequency with sun gear missing tooth fault.</p>
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<p>Kurtogram (<b>a</b>), and EMOMgram (<b>b</b>) of the raw signal with sun gear missing tooth fault.</p>
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<p>Envelope spectrum of filtered result with frequency band determined by Kurtogram with sun gear missing tooth fault.</p>
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<p>Envelope spectrum of filtered result with frequency band determined by EMOMgram with sun gear missing tooth fault.</p>
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<p>Raw signal waveform with gear break tooth fault.</p>
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<p>(<b>a</b>) TFR, and (<b>b</b>) instantaneous rotational frequency with sun gear tooth break fault.</p>
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<p>Kurtogram (<b>a</b>), and EMOMgram (<b>b</b>) of the raw signal with sun gear tooth break fault.</p>
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<p>Envelope spectrum of filtered result with frequency band determined by Kurtogram with sun gear tooth break fault.</p>
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<p>Envelope spectrum of filtered result with frequency band determined by EMOMgram with sun gear tooth break fault.</p>
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<p>Raw signal waveform with gear tooth crack fault.</p>
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<p>(<b>a</b>) TFR, and (<b>b</b>) instantaneous rotational frequency with sun gear tooth root crack fault.</p>
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<p>Kurtogram (<b>a</b>), and EMOMgram (<b>b</b>) of the raw signal with gear tooth crack fault.</p>
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<p>Envelope spectrum of filtered result with frequency band determined by Kurtogram with gear tooth crack fault.</p>
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<p>Envelope spectrum of filtered result with frequency band determined by EMOMgram with gear tooth crack fault.</p>
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25 pages, 14468 KiB  
Article
Investigation of Thermo-Hydraulics in a Lid-Driven Square Cavity with a Heated Hemispherical Obstacle at the Bottom
by Farhan Lafta Rashid, Abbas Fadhil Khalaf, Arman Ameen and Mudhar A. Al-Obaidi
Entropy 2024, 26(5), 408; https://doi.org/10.3390/e26050408 - 8 May 2024
Viewed by 1229
Abstract
Lid-driven cavity (LDC) flow is a significant area of study in fluid mechanics due to its common occurrence in engineering challenges. However, using numerical simulations (ANSYS Fluent) to accurately predict fluid flow and mixed convective heat transfer features, incorporating both a moving top [...] Read more.
Lid-driven cavity (LDC) flow is a significant area of study in fluid mechanics due to its common occurrence in engineering challenges. However, using numerical simulations (ANSYS Fluent) to accurately predict fluid flow and mixed convective heat transfer features, incorporating both a moving top wall and a heated hemispherical obstruction at the bottom, has not yet been attempted. This study aims to numerically demonstrate forced convection in a lid-driven square cavity (LDSC) with a moving top wall and a heated hemispherical obstacle at the bottom. The cavity is filled with a Newtonian fluid and subjected to a specific set of velocities (5, 10, 15, and 20 m/s) at the moving wall. The finite volume method is used to solve the governing equations using the Boussinesq approximation and the parallel flow assumption. The impact of various cavity geometries, as well as the influence of the moving top wall on fluid flow and heat transfer within the cavity, are evaluated. The results of this study indicate that the movement of the wall significantly disrupts the flow field inside the cavity, promoting excellent mixing between the flow field below the moving wall and within the cavity. The static pressure exhibits fluctuations, with the highest value observed at the top of the cavity of 1 m width (adjacent to the moving wall) and the lowest at 0.6 m. Furthermore, dynamic pressure experiences a linear increase until reaching its peak at 0.7 m, followed by a steady decrease toward the moving wall. The velocity of the internal surface fluctuates unpredictably along its length while other parameters remain relatively stable. Full article
(This article belongs to the Special Issue Modern Trends in Multi-Phase Flow and Heat Transfer)
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<p>Dimensions and coordinates used in this study.</p>
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<p>Mesh generation layout.</p>
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<p>Comparison of velocity distributions between the results of this study and Golkarfard et al. [<a href="#B14-entropy-26-00408" class="html-bibr">14</a>] for a top moving wall velocity of 5 m/s.</p>
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<p>Contours of pressure distribution for different moving wall velocities at a power density of 100 W/m<sup>3</sup>.</p>
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<p>Contours of pressure distribution for different moving wall velocities at a power density of 200 W/m<sup>3</sup>.</p>
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<p>Static pressure distribution versus position at a power density of 100 W/m<sup>3</sup> for different moving wall velocities.</p>
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<p>Static pressure distribution versus position at a power density of 200 W/m<sup>3</sup> for different moving wall velocities.</p>
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<p>Dynamic pressure distribution versus position at a power density of 100 W/m<sup>3</sup> for different moving wall velocities.</p>
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<p>Dynamic pressure distribution versus position at a power density of 200 W/m<sup>3</sup> for different moving wall velocities.</p>
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<p>Pressure distribution versus position at a power density of 100 W/m<sup>3</sup> for different moving wall velocities.</p>
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<p>Pressure distribution versus position at a power density of 200 W/m<sup>3</sup> for different moving wall velocities.</p>
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<p>Contours of velocity distribution for different moving wall velocities at a power density of 100 W/m<sup>3</sup>.</p>
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<p>Contours of velocity distribution for different moving wall velocities at a power density of 200 W/m<sup>3</sup>.</p>
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<p>Velocity distribution versus position for different moving wall velocities at a power density of 100 W/m<sup>3</sup>.</p>
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<p>Velocity distribution versus position for different moving wall velocities at a power density of 200 W/m<sup>3</sup>.</p>
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<p>Airflow velocity distribution versus position for different moving wall velocities at a power density of 100 W/m<sup>3</sup>.</p>
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<p>Airflow velocity distribution versus position for different moving wall velocities at a power density of 200 W/m<sup>3</sup>.</p>
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<p>Contours of temperature distribution at a power density of 100 W/m<sup>3</sup> for different moving wall velocities.</p>
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<p>Contours of temperature distribution at a power density of 200 W/m<sup>3</sup> for different moving wall velocities.</p>
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<p>Static temperature distribution versus position for different moving wall velocities at a power density of 100 W/m<sup>3</sup>.</p>
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<p>Static temperature distribution versus position for different moving wall velocities at a power density of 200 W/m<sup>3</sup>.</p>
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<p>Temperature distribution versus position for different moving wall velocities at a power density of 100 W/m<sup>3</sup>.</p>
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<p>Temperature distribution versus position for different moving wall velocities at a power density of 200 W/m<sup>3</sup>.</p>
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<p>Stream function distribution versus position for different moving wall velocities at a power density of 100 W/m<sup>3</sup>.</p>
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<p>Stream function distribution versus position for different moving wall velocities at a power density of 200 W/m<sup>3</sup>.</p>
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<p>Comparison of velocity distribution versus point number for different moving wall velocities and power densities.</p>
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<p>Comparison of pressure distribution versus point number for different moving wall velocities and power densities.</p>
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<p>Comparison of temperature distribution versus point number for different moving wall velocities at different power densities.</p>
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18 pages, 579 KiB  
Article
Minimizing Computation and Communication Costs of Two-Sided Secure Distributed Matrix Multiplication under Arbitrary Collusion Pattern
by Jin Li, Nan Liu and Wei Kang
Entropy 2024, 26(5), 407; https://doi.org/10.3390/e26050407 - 8 May 2024
Viewed by 1155
Abstract
This paper studies the problem of minimizing the total cost, including computation cost and communication cost, in the system of two-sided secure distributed matrix multiplication (SDMM) under an arbitrary collusion pattern. In order to perform SDMM, the two input matrices are split into [...] Read more.
This paper studies the problem of minimizing the total cost, including computation cost and communication cost, in the system of two-sided secure distributed matrix multiplication (SDMM) under an arbitrary collusion pattern. In order to perform SDMM, the two input matrices are split into some blocks, blocks of random matrices are appended to protect the security of the two input matrices, and encoded copies of the blocks are distributed to all computing nodes for matrix multiplication calculation. Our aim is to minimize the total cost, overall matrix splitting factors, number of appended random matrices, and distribution vector, while satisfying the security constraint of the two input matrices, the decodability constraint of the desired result of the multiplication, the storage capacity of the computing nodes, and the delay constraint. First, a strategy of appending zeros to the input matrices is proposed to overcome the divisibility problem of matrix splitting. Next, the optimization problem is divided into two subproblems with the aid of alternating optimization (AO), where a feasible solution can be obtained. In addition, some necessary conditions for the problem to be feasible are provided. Simulation results demonstrate the superiority of our proposed scheme compared to the scheme without appending zeros and the scheme with no alternating optimization. Full article
(This article belongs to the Special Issue Information-Theoretic Cryptography and Security)
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<p>Two-sided secure distributed matrix multiplication.</p>
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<p>Total cost versus <span class="html-italic">T</span> when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>11</mn> </mrow> </semantics></math>.</p>
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<p>Total cost versus <span class="html-italic">D</span> when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>11</mn> </mrow> </semantics></math>.</p>
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<p>Total cost versus <span class="html-italic">S</span> when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>11</mn> </mrow> </semantics></math>.</p>
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<p>Total cost versus <span class="html-italic">T</span> when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Total cost versus <span class="html-italic">D</span> when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Total cost versus <span class="html-italic">S</span> when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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22 pages, 391 KiB  
Article
Relativistic Roots of κ-Entropy
by Giorgio Kaniadakis
Entropy 2024, 26(5), 406; https://doi.org/10.3390/e26050406 - 7 May 2024
Cited by 3 | Viewed by 1533
Abstract
The axiomatic structure of the κ-statistcal theory is proven. In addition to the first three standard Khinchin–Shannon axioms of continuity, maximality, and expansibility, two further axioms are identified, namely the self-duality axiom and the scaling axiom. It is shown that both the [...] Read more.
The axiomatic structure of the κ-statistcal theory is proven. In addition to the first three standard Khinchin–Shannon axioms of continuity, maximality, and expansibility, two further axioms are identified, namely the self-duality axiom and the scaling axiom. It is shown that both the κ-entropy and its special limiting case, the classical Boltzmann–Gibbs–Shannon entropy, follow unambiguously from the above new set of five axioms. It has been emphasized that the statistical theory that can be built from κ-entropy has a validity that goes beyond physics and can be used to treat physical, natural, or artificial complex systems. The physical origin of the self-duality and scaling axioms has been investigated and traced back to the first principles of relativistic physics, i.e., the Galileo relativity principle and the Einstein principle of the constancy of the speed of light. It has been shown that the κ-formalism, which emerges from the κ-entropy, can treat both simple (few-body) and complex (statistical) systems in a unified way. Relativistic statistical mechanics based on κ-entropy is shown that preserves the main features of classical statistical mechanics (kinetic theory, molecular chaos hypothesis, maximum entropy principle, thermodynamic stability, H-theorem, and Lesche stability). The answers that the κ-statistical theory gives to the more-than-a-century-old open problems of relativistic physics, such as how thermodynamic quantities like temperature and entropy vary with the speed of the reference frame, have been emphasized. Full article
9 pages, 2877 KiB  
Article
Chaos Synchronization of Integrated Five-Section Semiconductor Lasers
by Yuanyuan Guo, Yao Du, Hua Gao, Min Tan, Tong Zhao, Zhiwei Jia, Pengfa Chang and Longsheng Wang
Entropy 2024, 26(5), 405; https://doi.org/10.3390/e26050405 - 6 May 2024
Viewed by 1570
Abstract
We proposed and verified a scheme of chaos synchronization for integrated five-section semiconductor lasers with matching parameters. The simulation results demonstrated that the integrated five-section semiconductor laser could generate a chaotic signal within a large parameter range of the driving currents of five [...] Read more.
We proposed and verified a scheme of chaos synchronization for integrated five-section semiconductor lasers with matching parameters. The simulation results demonstrated that the integrated five-section semiconductor laser could generate a chaotic signal within a large parameter range of the driving currents of five sections. Subsequently, chaos synchronization between two integrated five-section semiconductor lasers with matched parameters was realized by using a common noise signal as a driver. Moreover, it was found that the synchronization was sensitive to the current mismatch in all five sections, indicating that the driving currents of the five sections could be used as keys of chaotic optical communication. Therefore, this synchronization scheme provides a candidate to increase the dimension of key space and enhances the security of the system. Full article
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<p>Schematic diagram of chaos synchronization integrated five-section semiconductor lasers. SLD, superluminescent light diode; OI, optical isolator; LPF, low-pass filter; OC, optical coupler; VOA, variable optical attenuator; MSSL, multi-section semiconductor laser; PD, photodetector; m, original message; m’, recovered message.</p>
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<p>When <span class="html-italic">I</span><sub>P2</sub> = <span class="html-italic">I</span><sub>A</sub> = 0 mA, <span class="html-italic">I</span><sub>DFB2</sub> = 60 mA and <span class="html-italic">I</span><sub>DFB2</sub> = 66 mA, the temporal waveform (first column), optical spectra (second column) and R-F spectrum (third column) of IFSSL under different <span class="html-italic">I</span><sub>P1</sub>. (<b>a1</b>–<b>a3</b>) <span class="html-italic">I</span><sub>P1</sub> = 9 mA; (<b>b1</b>–<b>b3</b>) <span class="html-italic">I</span><sub>P1</sub> = 12 mA; (<b>c1</b>–<b>c3</b>) <span class="html-italic">I</span><sub>P1</sub> = 15 mA; (<b>d1</b>–<b>d3</b>) <span class="html-italic">I</span><sub>P1</sub> = 17 mA.</p>
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<p>Diagram of dynamic state of the IFSSL in the parameter space of <span class="html-italic">I</span><sub>P1</sub> and <span class="html-italic">I</span><sub>DFB2</sub>, where <span class="html-italic">I</span><sub>DFB1</sub> = 60 mA, <span class="html-italic">I</span><sub>P2</sub> = 0 mA, <span class="html-italic">I</span><sub>A</sub> = 0 mA and the different colors denote different states.</p>
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<p>Diagram of dynamic state of the IFSSL under different <span class="html-italic">I</span><sub>P2</sub> and <span class="html-italic">I</span><sub>A</sub>, When <span class="html-italic">I</span><sub>DFB1</sub> = 60 mA, <span class="html-italic">I</span><sub>P1</sub> = 26 mA and <span class="html-italic">I</span><sub>DFB2</sub> are (<b>a</b>) 71 mA, (<b>b</b>) 77 mA, (<b>c</b>) 86 mA and (<b>d</b>) 93 mA. The different colors correspond to different states.</p>
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<p>Chaos synchronization of IFSSLA and IFSSLB. (<b>a</b>) Temporal waveforms of response IFSSLA and IFSSLB; (<b>b</b>) correlation plots of response IFSSLA and IFSSLB; (<b>c</b>) temporal waveforms of driving signal SLD and response IFSSLA; (<b>d</b>) correlation plots of driving signal SLD and response IFSSLA.</p>
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<p>The effects of the injection power on synchronization quality under different conditions (<b>a</b>) <span class="html-italic">I</span><sub>DFB1</sub>, (<b>b</b>) <span class="html-italic">I</span><sub>DFB2</sub> and (<b>c</b>) <span class="html-italic">I</span><sub>A</sub> where: <span class="html-italic">I</span><sub>P1</sub> = 26 mA, <span class="html-italic">I</span><sub>P2</sub> = 15 mA, (<b>a</b>) <span class="html-italic">I</span><sub>DFB2</sub> = 77 mA, <span class="html-italic">I</span><sub>A</sub> = 15 mA; (<b>b</b>) <span class="html-italic">I</span><sub>DFB1</sub> = 60 mA, <span class="html-italic">I</span><sub>A</sub> = 15 mA; (<b>c</b>) <span class="html-italic">I</span><sub>DFB1</sub> = 60 mA, <span class="html-italic">I</span><sub>DFB2</sub> = 77 mA.</p>
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<p>Effects of the current mismatch of the five sections on the synchronization coefficient of IFSSLA and IFSSLB.</p>
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32 pages, 2235 KiB  
Article
Importance of Characteristic Features and Their Form for Data Exploration
by Urszula Stańczyk, Beata Zielosko and Grzegorz Baron
Entropy 2024, 26(5), 404; https://doi.org/10.3390/e26050404 - 6 May 2024
Viewed by 1575
Abstract
The nature of the input features is one of the key factors indicating what kind of tools, methods, or approaches can be used in a knowledge discovery process. Depending on the characteristics of the available attributes, some techniques could lead to unsatisfactory performance [...] Read more.
The nature of the input features is one of the key factors indicating what kind of tools, methods, or approaches can be used in a knowledge discovery process. Depending on the characteristics of the available attributes, some techniques could lead to unsatisfactory performance or even may not proceed at all without additional preprocessing steps. The types of variables and their domains affect performance. Any changes to their form can influence it as well, or even enable some learners. On the other hand, the relevance of features for a task constitutes another element with a noticeable impact on data exploration. The importance of attributes can be estimated through the application of mechanisms belonging to the feature selection and reduction area, such as rankings. In the described research framework, the data form was conditioned on relevance by the proposed procedure of gradual discretisation controlled by a ranking of attributes. Supervised and unsupervised discretisation methods were employed to the datasets from the stylometric domain and the task of binary authorship attribution. For the selected classifiers, extensive tests were performed and they indicated many cases of enhanced prediction for partially discretised datasets. Full article
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<p>Performance [%] for the Naive Bayes classifier observed in the discretisation of the female writer dataset while following the Relief ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the Naive Bayes classifier observed in the discretisation of the male writer dataset while following the Relief ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the Naive Bayes classifier observed in the discretisation of the female writer dataset while following the OneR ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the Naive Bayes classifier observed in the discretisation of the male writer dataset while following the OneR ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the J48 classifier observed in the discretisation of the female writer dataset while following the Relief ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the J48 classifier observed in the discretisation of the male writer dataset while following the Relief ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the J48 classifier observed in the discretisation of the female writer dataset while following the OneR ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the J48 classifier observed in the discretisation of the male writer dataset while following the OneR ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the k-NN classifier observed in the discretisation of the female writer dataset while following the Relief ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the k-NN classifier observed in the discretisation of the male writer dataset while following the Relief ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the k-NN classifier observed in the discretisation of the female writer dataset while following the OneR ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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<p>Performance [%] for the k-NN classifier observed in the discretisation of the male writer dataset while following the OneR ranking. For unsupervised equal-frequency (duf) and equal-width (duw) binning, the categories reflect the number of constructed bins, and for supervised approaches, the method is given. The series specify the number of discretised attributes.</p>
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21 pages, 5009 KiB  
Article
A Novel Classification Method: Neighborhood-Based Positive Unlabeled Learning Using Decision Tree (NPULUD)
by Bita Ghasemkhani, Kadriye Filiz Balbal, Kokten Ulas Birant and Derya Birant
Entropy 2024, 26(5), 403; https://doi.org/10.3390/e26050403 - 4 May 2024
Cited by 4 | Viewed by 2376
Abstract
In a standard binary supervised classification task, the existence of both negative and positive samples in the training dataset are required to construct a classification model. However, this condition is not met in certain applications where only one class of samples is obtainable. [...] Read more.
In a standard binary supervised classification task, the existence of both negative and positive samples in the training dataset are required to construct a classification model. However, this condition is not met in certain applications where only one class of samples is obtainable. To overcome this problem, a different classification method, which learns from positive and unlabeled (PU) data, must be incorporated. In this study, a novel method is presented: neighborhood-based positive unlabeled learning using decision tree (NPULUD). First, NPULUD uses the nearest neighborhood approach for the PU strategy and then employs a decision tree algorithm for the classification task by utilizing the entropy measure. Entropy played a pivotal role in assessing the level of uncertainty in the training dataset, as a decision tree was developed with the purpose of classification. Through experiments, we validated our method over 24 real-world datasets. The proposed method attained an average accuracy of 87.24%, while the traditional supervised learning approach obtained an average accuracy of 83.99% on the datasets. Additionally, it is also demonstrated that our method obtained a statistically notable enhancement (7.74%), with respect to state-of-the-art peers, on average. Full article
(This article belongs to the Special Issue Entropy in Real-World Datasets and Its Impact on Machine Learning II)
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Figure 1

Figure 1
<p>A general overview of the presented method.</p>
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<p>An illustration of the proposed method by an example.</p>
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<p>A comparison of the supervised learning and NPULUD method in precision.</p>
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<p>A comparison of the supervised learning and NPULUD method in recall.</p>
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<p>A comparison of the supervised learning and NPULUD method in F-measure.</p>
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