2024
<p>This episode features one of our most anticipated guests: M. Beatrice Fazi.</p>\n<p>M. Beatrice Fazi is a philosopher working in philosophy of computation, philosophy of technology and media philosophy. In this episode we mostly cover some key definitions relating to computation and its onto-epistemology grounded in Fazi’s landmark book, <em>Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics</em> published in 2018. But our discussion doesn&#8217;t end in 2018.</p>\n<p>Now more than ever, Fazi&#8217;s work on computation holds unbelievable importance with wide-ranging implications. Philosophy is becoming a major foil to technocapital and technopolitics, forcing us to seriously (re)consider fundamental questions about technology and correlated fundamentals of knowledge and being.</p>\n<p>Ever wondered what computation actually is? According to Fazi, it exists and unfolds not only as a function, but also as a creative modality forming its own conditions for existence. This episode dives deep into the concept of computation as an autonomous form of thought and creation, that is nevertheless contingent, i.e. not independent from the material conditions of the world.</p>\n<p>We move further into Fazi&#8217;s more recent work in ontology: the triangulation of abstraction, representation and thought. This pushes us into massive questions—what does computation mean for the future of thought? How should we conceptualize the relationship between humans and technology? And why should we rethink the idea of technology as merely an extension of ourselves?</p>\n
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Anne Condon, Martyn Amos · Lecture notes in computer science · 2016
Cited by 4Open access
Mike Stannett, Matthew J. Patitz, Stannett, Mike · Lecture notes in computer science · 2017
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AJ McIntosh · eCite Digital Repository (University of Tasmania) · 2002
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Yanhong A. Liu · 1998
Incremental computation takes advantage of repeated computations on inputs that differ slightly from one another, computing each output efficiently by exploiting the previous output. This paper gives an overview of a general and systematic approach to incrementalization. Given a program f and an operation \\Phi, the approach yields an incremental program that computes f(x \\Phi y) efficiently by using the result of f(x), the intermediate results of f(x), and auxiliary information about f(x) that can be inexpensively maintained. Since every non-trivial computation proceeds by iteration or recursion, the approach can be used for achieving efficient computation in general, by computing each iteration incrementally using an appropriate incremental program. This method has been applied to problems in interactive systems, optimizing compilers, transformational programming, etc. The design and implementation of a prototype system, CACHET, for deriving incremental programs is also described. Ke...
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Juan C. Agudelo, et al. · 2006
We present an original model of paraconsistent Turing machines (PTMs), a generalization of the classical Turing machines model of computation using a paraconsistent logic. Next, we briefly describe the standard models of quantum computation: quantum Turing machines and quantum circuits, and revise quantum algorithms to solve the so-called Deutsch’s problem and Deutsch-Jozsa problem. Then, we show the potentialities of the PTMs model of computation simulating the presented quantum algorithms via paraconsistent algorithms. This way, we show that PTMs can resolve some problems in exponentially less time than any classical deterministic Turing machine. Finally, We show that it is not possible to simulate all characteristics (in particular entangled states) of quantum computation by the particular model of PTMs here presented, therefore we open the possibility of constructing a new model of PTMs by which it is feasible to simulate such states.
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Daniela Genova, Jarkko Kari · Lecture notes in computer science · 2023
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Cho, Da-Jung, Kim, Jongmin · Lecture notes in computer science · 2024
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Joachim Niehren · Publications of the UdS (Saarland University) · 1996
We investigate functional computation as a special form of concurrent computation. As formal basis, we use a uniformly confluent core of the pi-calculus, which is also contained in models of higher-order concurrent constraint programming. We embed the call-by-need and the call-by-value lambda-calculus into the pi-calculus. We prove that call-by-need complexity is dominated by call-by-value complexity. In contrast to the recently proposed call-by-need lambda-calculus, our concurrent call-by-need model incorporates mutual recursion and can be extended to cyclic data structures by means of constraints.
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Lecture notes in computer science · 2026
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Florian-Lennert Lau · 2024
Abstract This chapter introduces the fundamental knowledge of how to perform and understand computation at the nanoscale. We first analyze how state can be represented at the nanoscale. Based on that, we formally define rules of state transformation, which are also called programs . Finding suitable programs for networks of possibly millions of devices can be extremely challenging. Then we introduce the very basics of complexity theory that are necessary to represent the prevalent resource constraint at the nanoscale. Complexity classes can be used to assess the difficulty of given operations/programs by putting them in relation to similar methods. Next, we analyze a number of nanoscale applications with regard to the necessary basic operations. Then we analyze a number of computational models regarding their potential use and feasibility at the nanoscale. Finally, we analyze computations based on self-assembling particle systems in considerable depth and summarize the chapter.
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The MIT Press eBooks · 2024
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Duddell, J · ResearchSPAce (Bath Spa University) · 1999
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Directory of Open Access Journals (EBSCO) · 2014
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John W.B. Hershey · Digital Signal Processing · 1991
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2016
This chapter introduces the basic concept of MapReduce and the details of implementing Hadoop MapReduce. Hadoop MapReduce is easily understood by engineers who are familiar with distributed computation or high performance computing. Hadoop MapReduce currently runs on Yet Another Resource Negotiator (YARN), which is a resource manager developed by the Hadoop project. The chapter covers the MapReduce architecture, which describes the shuffle and sort mechanism that is a core system of MapReduce. Shuffle is the most resource consuming process in the MapReduce application. It is necessary to understand the basic knowledge of writing Java programs and compiling them, in order to develop a MapReduce application. The chapter presents that how to write a map task, reduce task class, and job class to submit an application on the Hadoop cluster. Finally, it examines some of the advanced MapReduce features such as distributed cache, pre-defined counters and Job history server.
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Dir. du glossaire · Presses universitaires de Paris Nanterre eBooks · 2023
La computation peut se définir comme le traitement de données numérisées, à l’aide de calculs algorithmiques, c’est-à-dire de recettes procédurales itératives automatisées. Elle donne lieu à des modes de pensée et de gestion des réalités induits par les puissances des calculs algorithmiques.
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P. C. Br, Yulia Timofeeva, Q. Caudron et al. · 2016
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Neurons are characterised by a morphological structure unique amongst biological cells, the core of which is the dendritic tree. The vast number of dendritic geometries, combined with heterogeneous properties of the cell membrane, continue to challenge scientists in predicting neuronal input-output relationships, even in the case of sub-threshold dendritic currents. The Green’s function obtained for a given dendritic geometry provides this functional relationship for passive or quasi-active dendrites and can be constructed by a sum-over-trips approach based on a path in-tegral formalism. In this paper, we introduce a number of efficient algorithms for realisation of the sum-over-trips framework and investigate the convergence of these algorithms on different dendritic geometries. We demonstrate that the convergence of the trip sampling methods strongly depends on dendritic morphology as well as the biophysical properties of the cell membrane. For real morphologies, the number of trips to guarantee a small convergence error might become very large and strongly
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F. Dufour, G. Pijaudier-cabot, M. Choinska et al. · 2014
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D. B. Henderson · 2023
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