Bibliografie tematiche / Computation

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Letteratura scientifica selezionata sul tema "Computation"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Computation"

Brock, Oliver. "Intelligence as Computation." IOP Conference Series: Materials Science and Engineering 1321, no. 1 (December 1, 2024): 012001. https://doi.org/10.1088/1757-899x/1321/1/012001.

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Abstract This paper proposes a specific conceptualization of intelligence as computation. This conceptualization is intended to provide a unified view for all disciplines of intelligence research. Already, it unifies several conceptualizations currently under investigation, including physical, neural, embodied, morphological, and mechanical intelligences. To achieve this, the proposed conceptualization explains the differences among existing views by different computational paradigms, such as digital, analog, mechanical, or morphological computation. Viewing intelligence as a composition of computations from different paradigms, the challenges posed by previous conceptualizations are resolved. Intelligence is hypothesized as a multi-paradigmatic computation relying on specific computational principles. These principles distinguish intelligence from other, non-intelligent computations. The proposed conceptualization implies a multi-disciplinary research agenda that is intended to lead to unified science of intelligence.

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Olakanmi, Oladayo Olufemi, and Adedamola Dada. "An Efficient Privacy-preserving Approach for Secure Verifiable Outsourced Computing on Untrusted Platforms." International Journal of Cloud Applications and Computing 9, no. 2 (April 2019): 79–98. http://dx.doi.org/10.4018/ijcac.2019040105.

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In outsourcing computation models, weak devices (clients) increasingly rely on remote servers (workers) for data storage and computations. However, most of these servers are hackable or untrustworthy, which makes their computation questionable. Therefore, there is need for clients to validate the correctness of the results of their outsourced computations and ensure that servers learn nothing about their clients other than the outputs of their computation. In this work, an efficient privacy preservation validation approach is developed which allows clients to store and outsource their computations to servers in a semi-honest model such that servers' computational results could be validated by clients without re-computing the computation. This article employs a morphism approach for the client to efficiently perform the proof of correctness of its outsourced computation without re-computing the whole computation. A traceable pseudonym is employed by clients to enforce anonymity.

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Fresco, Nir. "Long-arm functional individuation of computation." Synthese 199, no. 5-6 (November 1, 2021): 13993–4016. http://dx.doi.org/10.1007/s11229-021-03407-x.

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AbstractA single physical process may often be described equally well as computing several different mathematical functions—none of which is explanatorily privileged. How, then, should the computational identity of a physical system be determined? Some computational mechanists hold that computation is individuated only by either narrow physical or functional properties. Even if some individuative role is attributed to environmental factors, it is rather limited. The computational semanticist holds that computation is individuated, at least in part, by semantic properties. She claims that the mechanistic account lacks the resources to individuate the computations performed by some systems, thereby leaving interesting cases of computational indeterminacy unaddressed. This article examines some of these views, and claims that more cases of computational indeterminacy can be addressed, if the system-environment interaction plays a greater role in individuating computations. A new, long-arm functional strategy for individuating computation is advanced.

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Larsen, Brett W., and Shaul Druckmann. "Towards a more general understanding of the algorithmic utility of recurrent connections." PLOS Computational Biology 18, no. 6 (June 21, 2022): e1010227. http://dx.doi.org/10.1371/journal.pcbi.1010227.

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Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation” algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.

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MACLENNAN, BRUCE J. "EMBODIED COMPUTATION: APPLYING THE PHYSICS OF COMPUTATION TO ARTIFICIAL MORPHOGENESIS." Parallel Processing Letters 22, no. 03 (July 8, 2012): 1240013. http://dx.doi.org/10.1142/s0129626412400130.

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We discuss the problem of assembling complex physical systems that are structured from the nanoscale up through the macroscale, and argue that embryological morphogenesis provides a good model of how this can be accomplished. Morphogenesis (whether natural or artificial) is an example of embodied computation, which exploits physical processes for computational ends, or performs computations for their physical effects. Examples of embodied computation in natural morphogenesis can be found at many levels, from allosteric proteins, which perform simple embodied computations, up through cells, which act to create tissues with specific patterns, compositions, and forms. We outline a notation for describing morphogenetic programs and illustrate its use with two examples: simple diffusion and the assembly of a simple spine with attachment points for legs. While much research remains to be done — at the simulation level before we attempt physical implementations — our results to date show how we may implement the fundamental processes of morphogenesis as a practical application of embodied computation at the nano- and microscale.

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Aho, A. V. "Computation and Computational Thinking." Computer Journal 55, no. 7 (June 29, 2012): 832–35. http://dx.doi.org/10.1093/comjnl/bxs074.

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Smolensky, Paul. "Symbolic functions from neural computation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1971 (July 28, 2012): 3543–69. http://dx.doi.org/10.1098/rsta.2011.0334.

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Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and formulated computational systems in which meaningful concepts are encoded by symbols which are the objects of computation. Cognition has been carved into parts, each a function defined over such symbols. This paper reports on a research program aimed at computing these symbolic functions without computing over the symbols. Symbols are encoded as patterns of numerical activation over multiple abstract neurons, each neuron simultaneously contributing to the encoding of multiple symbols. Computation is carried out over the numerical activation values of such neurons, which individually have no conceptual meaning. This is massively parallel numerical computation operating within a continuous computational medium. The paper presents an axiomatic framework for such a computational account of cognition, including a number of formal results. Within the framework, a class of recursive symbolic functions can be computed. Formal languages defined by symbolic rewrite rules can also be specified, the subsymbolic computations producing symbolic outputs that simultaneously display central properties of both facets of human language: universal symbolic grammatical competence and statistical, imperfect performance.

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Raussendorf, Robert. "Cohomological framework for contextual quantum computations." quantum Information and Computation 19, no. 13&14 (November 2019): 1141–70. http://dx.doi.org/10.26421/qic19.13-14-4.

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We describe a cohomological framework for measurement-based quantum computation in which symmetry plays a central role. Therein, the essential information about the computation is contained in either of two topological invariants, namely two cohomology groups. One of them applies only to deterministic quantum computations, and the other to general probabilistic ones. Those invariants characterize the computational output, and at the same time witness quantumness in the form of contextuality. In result, they give rise to fundamental algebraic structures underlying quantum computation.

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Mussabayev, Rustam. "Optimizing Euclidean Distance Computation." Mathematics 12, no. 23 (November 29, 2024): 3787. https://doi.org/10.3390/math12233787.

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This paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical operation that plays a critical role in a wide range of algorithms, particularly in machine learning and data analysis. The Euclidean distance, being a computational bottleneck in large-scale optimization problems, requires efficient computation techniques to improve the performance of various distance-dependent algorithms. To address this, several optimization strategies can be employed to accelerate distance computations. From spatial data structures and approximate nearest neighbor algorithms to dimensionality reduction, vectorization, and parallel computing, various approaches exist to accelerate Euclidean distance computation in different contexts. Such approaches are particularly important for speeding up key machine learning algorithms like K-means and K-nearest neighbors (KNNs). By understanding the trade-offs and assessing the effectiveness, complexity, and scalability of various optimization techniques, our findings help practitioners choose the most appropriate methods for improving Euclidean distance computations in specific contexts. These optimizations enable scalable and efficient processing for modern data-driven tasks, directly leading to reduced energy consumption and a minimized environmental impact.

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Burgin, Mark, Eugene Eberbach, and Rao Mikkilineni. "Processing Information in the Clouds." Proceedings 47, no. 1 (May 7, 2020): 25. http://dx.doi.org/10.3390/proceedings2020047025.

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Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.

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Tesi sul tema "Computation"

Cattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.

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This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems.

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Berzowska, Joanna Maria 1972. "Computational expressionism : a study of drawing with computation." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/61101.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February 1999. Includes bibliographical references (leaves 68-73). This thesis presents computational expressionism, an exploration of drawing using a computer that redefines the concepts of line and composition for the digital medium. It examines the artistic process involved in computational drawing, addressing the issues of skill, algorithmic style, authorship, re-appropriation, interactivity, dynamism, and the creative/evaluative process. The computational line augments the traditional concept of line making as a direct deposit or a scratching on a surface. Digital representation is based on computation; appearance is procedurally determined. The computational line embodies not only an algorithmic construction, but also dynamic and interactive behavior. A computer allows us to construct drawing instruments that take advantage of the dynamism, interactivity, behavioral elements and other features of a programming environment. Drawing becomes a two-fold process, at two distinct levels of interaction with the computer. The artist has to program the appearance and behavior of lines and subsequently draw with these lines by dragging a mouse or gesturing with some other input device. The compositions incorporate the beauty of computation with the creative impetus of the hand, whose apparent mistakes, hesitations and inspirations form a complex and critical component of visual expression. by Joanna Maria Berzowska. S.M.

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Miller, Jacob K. "Disentanglement Puzzles and Computation." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500630352520138.

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Bogan, Nathaniel Rockwood. "Economic allocation of computation time with computation markets." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/32603.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994. Includes bibliographical references (leaves 88-91). by Nathaniel Rockwood Bogan. M.Eng.

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Giannakopoulos, Dimitrios. "Quantum computation." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA365665.

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Brekne, Tønnes. "Encrypted Computation." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-27.

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The ability to construct software, call it a functional ciphertext, which can be remotely executed in encrypted form as an entirely self-contained unit, has the potential for some interesting applications. One such application is the construction of autonomous mobile agents capable of entering into certain types of legally binding contracts on behalf of the sender. At a premium in such circumstances is the ability to protect secret cryptographic keys or other secret information, which typically is necessary for legally binding contracts. Also important is the ability to do powerful computations, that are more than just one-off secure function evaluations.The problem of constructing computation systems that achieve this, has been attempted by many to little or no avail. This thesis presents three similar cryptographic systems that take a step closer to making such encrypted software a reality.First is demonstrated how one can construct mappings from finite automata, that through iteration can do computations. A stateless storage construction, called a Turing platform, is defined and it is shown that such a platform, in conjunction with a functional representation of a finite automaton, can perform Turing universal computation. The univariate, multivariate, and parametric ciphers for the encryption of multivariate mappings are presented and cryptanalyzed. Cryptanalysis of these ciphers shows that they must be used very carefully, in order to resist cryptanalysis. Entirely new to cryptography is the ability to remotely and securely re-encrypt functional ciphertexts made with either univariate or multivariate encryption.Lastly it is shown how the ciphers presented can be applied to the automaton representations in the form of mappings, to do general encrypted computation. Note: many of the novel constructions in this thesis are covered by a patent application.

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Barenco, Adriano. "Quantum computation." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360152.

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Gourlay, Iain. "Quantum computation." Thesis, Heriot-Watt University, 2000. http://hdl.handle.net/10399/568.

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Li, Fulu 1970. "Community computation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/63016.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 171-186). In this thesis we lay the foundations for a distributed, community-based computing environment to tap the resources of a community to better perform some tasks, either computationally hard or economically prohibitive, or physically inconvenient, that one individual is unable to accomplish efficiently. We introduce community coding, where information systems meet social networks, to tackle some of the challenges in this new paradigm of community computation. We design algorithms, protocols and build system prototypes to demonstrate the power of community computation to better deal with reliability, scalability and security issues, which are the main challenges in many emerging community-computing environments, in several application scenarios such as community storage, community sensing and community security. For example, we develop a community storage system that is based upon a distributed P2P (peer-to-peer) storage paradigm, where we take an array of small, periodically accessible, individual computers/peer nodes and create a secure, reliable and large distributed storage system. The goal is for each one of them to act as if they have immediate access to a pool of information that is larger than they could hold themselves, and into which they can contribute new stuff in a both open and secure manner. Such a contributory and self-scaling community storage system is particularly useful where reliable infrastructure is not readily available in that such a system facilitates easy ad-hoc construction and easy portability. In another application scenario, we develop a novel framework of community sensing with a group of image sensors. The goal is to present a set of novel tools in which software, rather than humans, examines the collection of images sensed by a group of image sensors to determine what is happening in the field of view. We also present several design principles in the aspects of community security. In one application example, we present community-based email spain detection approach to deal with email spams more efficiently. by Fulu Li. Ph.D.

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Pratt, Gill. "Pulse computation." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/14260.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1990. Includes bibliographical references (leaves 134-135). by Gill Andrews Pratt. Ph.D.

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Libri sul tema "Computation"

Kostitsyna, Irina, and Pekka Orponen, eds. Unconventional Computation and Natural Computation. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87993-8.

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Mauri, Giancarlo, Alberto Dennunzio, Luca Manzoni, and Antonio E. Porreca, eds. Unconventional Computation and Natural Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39074-6.

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Amos, Martyn, and ANNE CONDON, eds. Unconventional Computation and Natural Computation. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41312-9.

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Patitz, Matthew J., and Mike Stannett, eds. Unconventional Computation and Natural Computation. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58187-3.

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Ibarra, Oscar H., Lila Kari, and Steffen Kopecki, eds. Unconventional Computation and Natural Computation. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08123-6.

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Du, Zhenyu, ed. Intelligence Computation and Evolutionary Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31656-2.

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McQuillan, Ian, and Shinnosuke Seki, eds. Unconventional Computation and Natural Computation. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19311-9.

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Stepney, Susan, and Sergey Verlan, eds. Unconventional Computation and Natural Computation. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92435-9.

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Durand-Lose, Jérôme, and Nataša Jonoska, eds. Unconventional Computation and Natural Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32894-7.

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Calude, Cristian S., and Michael J. Dinneen, eds. Unconventional Computation and Natural Computation. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21819-9.

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Capitoli di libri sul tema "Computation"

Katzenbeisser, Stefan. "Computational Complexity and Efficient Computation." In Recent Advances in RSA Cryptography, 13–24. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1431-2_2.

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Colombo, Matteo. "(Mis)computation in Computational Psychiatry." In Neural Mechanisms, 427–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54092-0_18.

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Segal, Lynn. "Computation." In The Dream of Reality, 73–95. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0115-8_6.

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Lloyd, J. W. "Computation." In Logic for Learning, 183–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-08406-9_5.

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Troffaes, Matthias C. M., and Robert Hable. "Computation." In Introduction to Imprecise Probabilities, 329–37. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118763117.ch16.

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Jack Copeland, B. "Computation." In The Blackwell Guide to the Philosophy of Computing and Information, 1–17. Oxford, UK: Blackwell Publishing Ltd, 2008. http://dx.doi.org/10.1002/9780470757017.ch1.

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Ramnath, Rudrapatna V. "Computation." In SpringerBriefs in Applied Sciences and Technology, 9–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25749-0_2.

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Yiannoudes, Socrates. "Computation." In Architecture in Digital Culture, 89–133. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003241287-4.

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glossaire, Dir du. "Computation." In Angles morts du numérique ubiquitaire, 74. Nanterre: Presses universitaires de Paris Nanterre, 2023. http://dx.doi.org/10.4000/11tvn.

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van Buuren, Stef, and Iris Eekhout. "Computation." In Child Development with the D-score, 37–51. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003216315-5.

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Atti di convegni sul tema "Computation"

Zhu, Jinbao, Lanping Li, Xiaohu Tang, and Ping Deng. "Private Multiple Linear Computation: A Flexible Communication-Computation Tradeoff." In 2024 IEEE International Symposium on Information Theory (ISIT), 939–44. IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619123.

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Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 11th annual conference companion. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570416.

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Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 2007 GECCO conference companion. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274112.

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Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 12th annual conference comp. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830914.

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Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 2008 GECCO conference companion. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1388969.1389062.

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Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2002127.

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Maley, Corey. "Analog Computation in Computational Cognitive Neuroscience." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1178-0.

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Dhamodaran, M., and R. Dhanasekaran. "Efficient capacitance computation for computational electromagnetics." In 2014 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2014. http://dx.doi.org/10.1109/iccsp.2014.6950133.

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Bakar, Rohani binti Abu, and Junzo Watada. "Computational cluster validation in DNA-based computation." In 2009 IEEE International Symposium on Intelligent Signal Processing - (WISP 2009). IEEE, 2009. http://dx.doi.org/10.1109/wisp.2009.5286561.

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Salmani, Mahsa, and Timothy N. Davidson. "Multiple access computational offloading with computation constraints." In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2017. http://dx.doi.org/10.1109/spawc.2017.8227713.

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Rapporti di organizzazioni sul tema "Computation"

Golub, Gene H. Computational Equipment for the Development of Numerical Algorithms Computation. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada226702.

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Chandy, K. M. Parallel Computation. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada284831.

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Oldehoeft, Rodney R. Parallel Functional Computation. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada214627.

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Austin, Robert H. Computation by Bacteria. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada535062.

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Lawrence, Jim. Polytope volume computation. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4123.

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Traub, Joseph F. Continuous Quantum Computation. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada465614.

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Yepez, Jeffrey. New World Vistas: New Models of Computation Lattice Based Quantum Computation. Fort Belvoir, VA: Defense Technical Information Center, July 1996. http://dx.doi.org/10.21236/ada421712.

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Grefenstette, John. Topics in Evolutionary Computation. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada398950.

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Crawford, D. Computation 2013 Annual Report. Office of Scientific and Technical Information (OSTI), December 2013. http://dx.doi.org/10.2172/1129142.

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Cristoforides, Andreas, and Aaaron Miller. Linear optical quantum computation. Web of Open Science, July 2020. http://dx.doi.org/10.37686/qrl.v1i2.58.

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