196,055 research outputs found

    The Dirac equation, the concept of quanta, and the description of interactions in quantum electrodynamics

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    In this article the Dirac equation is used as a guideline to the historical emergence of the concept of quanta, associated with the quantum field. In Pascual Jordan’s approach, electrons as quanta result from the quantization of a classical field described by the Dirac equation. With this quantization procedure – also used for the electromagnetic field – the concept of quanta becomes a central piece in quantum electrodynamics. This does not seem to avoid the apparent impossibility of using the concept of quanta when interacting fields are considered together as a closed system. In this article it is defended that the type of analysis that leads to so drastic conclusions is avoidable if we look beyond the mathematical structure of the theory and take into account the physical ideas inscribed in this mathematical structure. In this case we see that in quantum electrodynamics we are not considering a closed system of interacting fields, what we have is a description of the interactions between distinct fields. In this situation the concept of quanta is central, the Fock space being the natural mathematical structure that permits maintaining the concept of quanta when considering the interaction between the fields

    Survey of Machine Learning Techniques for Malware Analysis

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    Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies for keeping pace with the speed of development of novel malware. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis. We systematize surveyed papers according to their objectives (i.e., the expected output, what the analysis aims to), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of problems concerning the datasets used in considered works, and finally introduce the novel concept of malware analysis economics, regarding the study of existing tradeoffs among key metrics, such as analysis accuracy and economical costs

    Complexity over Uncertainty in Generalized Representational\ud Information Theory (GRIT): A Structure-Sensitive General\ud Theory of Information

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    What is information? Although researchers have used the construct of information liberally to refer to pertinent forms of domain-specific knowledge, relatively few have attempted to generalize and standardize the construct. Shannon and Weaver(1949)offered the best known attempt at a quantitative generalization in terms of the number of discriminable symbols required to communicate the state of an uncertain event. This idea, although useful, does not capture the role that structural context and complexity play in the process of understanding an event as being informative. In what follows, we discuss the limitations and futility of any generalization (and particularly, Shannon’s) that is not based on the way that agents extract patterns from their environment. More specifically, we shall argue that agent concept acquisition, and not the communication of\ud states of uncertainty, lie at the heart of generalized information, and that the best way of characterizing information is via the relative gain or loss in concept complexity that is experienced when a set of known entities (regardless of their nature or domain of origin) changes. We show that Representational Information Theory perfectly captures this crucial aspect of information and conclude with the first generalization of Representational Information Theory (RIT) to continuous domains

    What Can We Learn Privately?

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    Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.Comment: 35 pages, 2 figure

    Neural Mechanisms for Information Compression by Multiple Alignment, Unification and Search

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    This article describes how an abstract framework for perception and cognition may be realised in terms of neural mechanisms and neural processing. This framework — called information compression by multiple alignment, unification and search (ICMAUS) — has been developed in previous research as a generalized model of any system for processing information, either natural or artificial. It has a range of applications including the analysis and production of natural language, unsupervised inductive learning, recognition of objects and patterns, probabilistic reasoning, and others. The proposals in this article may be seen as an extension and development of Hebb’s (1949) concept of a ‘cell assembly’. The article describes how the concept of ‘pattern’ in the ICMAUS framework may be mapped onto a version of the cell assembly concept and the way in which neural mechanisms may achieve the effect of ‘multiple alignment’ in the ICMAUS framework. By contrast with the Hebbian concept of a cell assembly, it is proposed here that any one neuron can belong in one assembly and only one assembly. A key feature of present proposals, which is not part of the Hebbian concept, is that any cell assembly may contain ‘references’ or ‘codes’ that serve to identify one or more other cell assemblies. This mechanism allows information to be stored in a compressed form, it provides a robust mechanism by which assemblies may be connected to form hierarchies and other kinds of structure, it means that assemblies can express abstract concepts, and it provides solutions to some of the other problems associated with cell assemblies. Drawing on insights derived from the ICMAUS framework, the article also describes how learning may be achieved with neural mechanisms. This concept of learning is significantly different from the Hebbian concept and appears to provide a better account of what we know about human learning

    Non-locality of the phenomenon of consciousness according to Roger Penrose

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    Roger Penrose is known for his proposals, in collaboration with Stuart Hameroff, for quantum action in the brain. These proposals, which are still recent, have a prior, less known basis, which will be studied in the following work. First, the paper situates the framework from which a mathematical physicist like Penrose proposes to speak about consciousness. Then it shows how he understands the possible relationships between computation and consciousness and what criticism from other authors he endorses, to conclude by explaining how he understands this relationship between consciousness and computation. Then, it focuses on the concept of non-locality so essential to his understanding of consciousness. With some examples, such as impossible objects or aperiodic tiling, the study addresses the concept of non-locality as Penrose understands it, and then shows how far he intends to arrive with that concept of non-locality. At all times the approach will be more philosophical than physical

    On the evolution of hyperlinking

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    Across time, the hyperlink object has supported different applications and studies. This is one perspective on the evolution of the hyperlinking concept, its context and related behaviors. Through a spectrum of hyperlinking applications and practices, the article contrasts the status quo with its related, broader, conceptual roots; it also bridges to some theorized and prototyped hyperlink variations, namely "stigmergic hyperlinks", to make the case that the ubiquitousness of some objects and certain usage patterns can obfuscate opportunities to (re)think them. In trying to contribute an answer to "what has the common hyperlink (such an apparently simple object) done to society, and what has society done to it?", the article identifies situations that have become so embedded in the daily routine, that it is now hard to think of hyperlinking alternatives.info:eu-repo/semantics/publishedVersio

    An analysis of the benefits of signal injection for low-speed sensorless control of induction motors

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    We analyze why low-speed sensorless control of the IM is intrinsically difficult, and what is gained by signal injection. The explanation relies on the control-theoretic concept of observability applied to a general model of the saturated IM. We show that the IM is not observable when the stator speed is zero in the absence of signal injection, but that observability is restored thanks to signal injection and magnetic saturation. The analysis also reveals that existing sensorless algorithms based on signal injection may perform poorly for some IMs under particular operating conditions. The approach is illustrated by simulations and experimental data
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