Search Results (8)

Life without computers is unimaginable. However, computers remain vulnerable to cybercrimes, a USD 6 trillion industry that the world has come to accept as a “necessary evil”. Third-party permissions resulting in an attack surface (AS) and in-computer storage that computers mandate are key design elements that hackers exploit, formerly by remote malware installation and later by stealing personal data using authentication faking techniques. In legacy computers, the AS cannot be completely eliminated, nor can a connected device retain data offline, rendering fool-proof cybersecurity impossible. Although the architects of legacy computers made perfectly reasonable engineering trade-offs for their world, our world is very different. Zero vulnerability computing (ZVC) challenges the impossible with in-computer offline storage (ICOS) and Supra OS (SOS), to deliver comprehensive protection against vulnerabilities. The feasibility of ZVC is demonstrated in a tiny permanently computer-mounted hardware wallet, providing the first evidence of the complete obliteration of the AS. Malware cannot infect the ZVC device on account of lacking an AS, nor can personal data be hacked as they mostly remain offline, except for sporadic processing. Further research should explore whether ZVC can fully secure computers in more complex real-world scenarios and open a new epoch in the evolution of computers and the Internet. Full article

Virus-like particles (VLPs) are a versatile, safe, and highly immunogenic vaccine platform. Recently, there are developmental vaccines targeting SARS-CoV-2, the causative agent of COVID-19. The COVID-19 pandemic affected humanity worldwide, bringing out incomputable human and financial losses. The race for better, more efficacious vaccines is happening almost simultaneously as the virus increasingly produces variants of concern (VOCs). The VOCs Alpha, Beta, Gamma, and Delta share common mutations mainly in the spike receptor-binding domain (RBD), demonstrating convergent evolution, associated with increased transmissibility and immune evasion. Thus, the identification and understanding of these mutations is crucial for the production of new, optimized vaccines. The use of a very flexible vaccine platform in COVID-19 vaccine development is an important feature that cannot be ignored. Incorporating the spike protein and its variations into VLP vaccines is a desirable strategy as the morphology and size of VLPs allows for better presentation of several different antigens. Furthermore, VLPs elicit robust humoral and cellular immune responses, which are safe, and have been studied not only against SARS-CoV-2 but against other coronaviruses as well. Here, we describe the recent advances and improvements in vaccine development using VLP technology. Full article

We discuss mathematical and physical arguments contrasting continuous and discrete, limitless discretization as arbitrary granularity. In this regard, we focus on Incomputable (lacking an algorithm that computes in finite time) Real Numbers (IRNs). We consider how, for measurements, the usual approach to dealing with IRNs is to approximate to avoid the need for more detailed, unrealistic surveys. In this regard, we contrast effective computation and emergent computation. Furthermore, we consider the alternative option of taking into account the properties of the decimal part of IRNs, such as the occurrence, distribution, combinations, quasi-periodicities, and other contextual properties, e.g., topological. For instance, in correspondence with chaotic behaviors, quasi-periodic solutions, quasi-systems, uniqueness, and singularities, non-computability represents and corresponds to theoretically incomplete properties of the processes of complexity, such as emergence and quantum-like properties. We elaborate upon cases of equivalences and symmetries, characterizing complexity and infiniteness as corresponding to the usage of multiple non-equivalent models that are constructively and theoretically incomplete due to the non-exhaustive nature of the multiplicity of complexity. Finally, we detail alternative computational approaches, such as hypercomputation, natural computing, quantum computing, and analog and hybrid computing. The reality of IRNs is considered to represent the theoretical incompleteness of complex phenomena taking place through collapse from equivalences and symmetries. A world of precise finite values, even if approximated, is assumed to have dynamics that are zippable in analytical formulae and to be computable and symbolically representable in the way it functions. A world of arbitrary precise infinite values with dynamics that are non-zippable in analytical formulae, non-computable, and, for instance, sub-symbolically representable, is assumed to be almost compatible with the coherence of emergence. The real world is assumed to be a continuous combination of the two—functioning and emergent—where the second dominates and is the norm, and the first is the locus of primarily epistemic extracts. Research on IRNs should focus on properties representing and corresponding to those that are detectable in real, even if extreme, phenomena, such as emergence and quantum phenomena. Full article

Kolmogorov complexity is the length of the ultimately compressed version of a file (i.e., anything which can be put in a computer). Formally, it is the length of a shortest program from which the file can be reconstructed. We discuss the incomputability of Kolmogorov complexity, which formal loopholes this leaves us with, recent approaches to compute or approximate Kolmogorov complexity, which approaches are problematic, and which approaches are viable. Full article

The inaccessibility to the experimenter agent of the complete quantum state is well-known. However, decisive answers are still missing for the following question: What underpins and governs the physics of agent inaccessibility? Specifically, how does nature prevent the agent from accessing, predicting, and controlling, individual quantum measurement outcomes? The orthodox interpretation of quantum mechanics employs the metaphysical assumption of indeterminism—‘intrinsic randomness’—as an axiomatic, in-principle limit on agent–quantum access. By contrast, ontological and deterministic interpretations of quantum mechanics typically adopt an operational, in-practice limit on agent access and knowledge—‘effective ignorance’. The present work considers a third option—‘objective ignorance’: an in-principle limit for ontological quantum mechanics based upon self-referential dynamics, including undecidable dynamics and dynamical chaos, employing uncomputability as a formal limit. Given a typical quantum random sequence, no formal proof is available for the truth of quantum indeterminism, whereas a formal proof for the uncomputability of the quantum random sequence—as a fundamental limit on agent access ensuring objective unpredictability—is a plausible option. This forms the basis of the present proposal for an agent-inaccessibility principle in quantum mechanics. Full article

The paper introduces the notion of “metacomputable” processes as those which are the product of computable processes. This notion seems interesting in the instance when metacomputable processes may not be computable themselves, but are produced by computable ones. The notion of computability used here relies on Turing computability. When we talk about something being non-computable, this can be viewed as computation that incorporates Turing’s oracle, maybe a true randomizer (perhaps a quantum one). The notions of “processes” is used broadly, so that it also covers “objects” under the functional description; for the sake of this paper an object is seen as computable if processes that fully describe relevant aspects of its functioning are computable. The paper also introduces a distinction between phenomenal content and the epistemic subject which holds that content. The distinction provides an application of the notion of the metacomputable. In accordance with the functional definition of computable objects, sketched out above, it is possible to think of objects, such as brains, as being computable. If we take the functionality of brains relevant for consideration to be their supposed ability to generate first-person consciousness, and if they were computable in this regard, it would mean that brains, as generators of consciousness, could be described, straightforwardly, by Turing-computable mathematical functions. If there were other, maybe artificial, generators of first-person consciousness, then we could hope to design those as Turing-computable machines as well. However, thinking of such generators of consciousness as computable does not preclude the stream of consciousness being non-computable. This is the main point of this article—computable processes, including functionally described machines, may be able to generate incomputable products. Those processes, while not computable, are metacomputable—by regulative definition introduced in this article. Another example of a metacomputable process that is not also computable would be a true randomizer, if we were able to build one. Presumably, it would be built according to a computable design, e.g., by a machine designed using AutoCAD, that could be programmed into an industrial robot. Yet, its product—a perfect randomizer—would be incomputable. The last point I need to make belongs to ontology in the theory of computability. The claim that computable objects, or processes, may produce incomputable ones does not commit us to what I call computational monism—the idea that non-computable processes may, strictly speaking, be transformed into computable ones. Metacomputable objects, or processes, may originate from computable systems (systems will be understood here as complex, structured objects or processes) that have non-computable admixtures. Such processes are computable as long as those non-computable admixtures are latent, or otherwise irrelevant for a given functionality, and they are non-computable if the admixtures become active and relevant. Ontology, in which computational processes, or objects, can produce non-computable processes, or objects, iff the former ones have non-computable components, may be termed computational dualism. Such objects or processes may be computable despite containing non-computable elements, in particular if there is an on and off switch of those non-computable processes, and it is off. One kind of such a switch is provided, in biology, by latent genes that become active only in specific environmental situations, or at a given age. Both ontologies, informational dualism and informational monism, are compatible with some non-computable processes being metacomputable. Full article

Conscious and unconscious brain mechanisms, including cognition, emotions and language are considered in this review. The fundamental mechanisms of cognition include interactions between bottom-up and top-down signals. The modeling of these interactions since the 1960s is briefly reviewed, analyzing the ubiquitous difficulty: incomputable combinatorial complexity (CC). Fundamental reasons for CC are related to the Gödel’s difficulties of logic, a most fundamental mathematical result of the 20th century. Many scientists still “believed” in logic because, as the review discusses, logic is related to consciousness; non-logical processes in the brain are unconscious. CC difficulty is overcome in the brain by processes “from vague-unconscious to crisp-conscious” (representations, plans, models, concepts). These processes are modeled by dynamic logic, evolving from vague and unconscious representations toward crisp and conscious thoughts. We discuss experimental proofs and relate dynamic logic to simulators of the perceptual symbol system. “From vague to crisp” explains interactions between cognition and language. Language is mostly conscious, whereas cognition is only rarely so; this clarifies much about the mind that might seem mysterious. All of the above involve emotions of a special kind, aesthetic emotions related to knowledge and to cognitive dissonances. Cognition-language-emotional mechanisms operate throughout the hierarchy of the mind and create all higher mental abilities. The review discusses cognitive functions of the beautiful, sublime, music. Full article

Information content and compression are tightly related concepts that can be addressed through both classical and algorithmic information theories, on the basis of Shannon entropy and Kolmogorov complexity, respectively. The definition of several entities in Kolmogorov’s framework relies upon ideas from classical information theory, and these two approaches share many common traits. In this work, we expand the relations between these two frameworks by introducing algorithmic cross-complexity and relative complexity, counterparts of the cross-entropy and relative entropy (or Kullback-Leibler divergence) found in Shannon’s framework. We define the cross-complexity of an object x with respect to another object y as the amount of computational resources needed to specify x in terms of y, and the complexity of x related to y as the compression power which is lost when adopting such a description for x, compared to the shortest representation of x. Properties of analogous quantities in classical information theory hold for these new concepts. As these notions are incomputable, a suitable approximation based upon data compression is derived to enable the application to real data, yielding a divergence measure applicable to any pair of strings. Example applications are outlined, involving authorship attribution and satellite image classification, as well as a comparison to similar established techniques. Full article