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Bibliografias temáticas / Machine de turing

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Literatura científica selecionada sobre o tema "Machine de turing"

Autor: Grafiati

Publicado: 5 de outubro de 2024

Última modificação: 19 de julho de 2025

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Artigos de revistas sobre o assunto "Machine de turing"

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Daylight, Edgar Graham. "Refining Mark Burgin’s Case against the Church–Turing Thesis." Philosophies 9, no. 4 (2024): 122. http://dx.doi.org/10.3390/philosophies9040122.

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The outputs of a Turing machine are not revealed for inputs on which the machine fails to halt. Why is an observer not allowed to see the generated output symbols as the machine operates? Building on the pioneering work of Mark Burgin, we introduce an extension of the Turing machine model with a visible output tape. As a subtle refinement to Burgin’s theory, we stipulate that the outputted symbols cannot be overwritten: at step i, the content of the output tape is a prefix of the content at step j, where i<j. Our Refined Burgin Machines (RBMs) compute more functions than Turing machines, but fewer than Burgin’s simple inductive Turing machines. We argue that RBMs more closely align with both human and electronic computers than Turing machines do. Consequently, RBMs challenge the dominance of Turing machines in computer science and beyond.

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Vitanyi, Paul. "Turing machine." Scholarpedia 4, no. 3 (2009): 6240. http://dx.doi.org/10.4249/scholarpedia.6240.

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Kurgaev, A. F., and S. N. Grygoryev. "The universal turing machine interpreter." Reports of the National Academy of Sciences of Ukraine, no. 10 (November 16, 2016): 28–34. http://dx.doi.org/10.15407/dopovidi2016.10.028.

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Beggs, Edwin, José Félix Costa, Bruno Loff, and John V. Tucker. "Computational complexity with experiments as oracles." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2098 (2008): 2777–801. http://dx.doi.org/10.1098/rspa.2008.0085.

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We discuss combining physical experiments with machine computations and introduce a form of analogue–digital (AD) Turing machine. We examine in detail a case study where an experimental procedure based on Newtonian kinematics is combined with a class of Turing machines. Three forms of AD machine are studied, in which physical parameters can be set exactly and approximately. Using non-uniform complexity theory, and some probability, we prove theorems that show that these machines can compute more than classical Turing machines.

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ROBINSON, RAPHAEL M. "MINSKY'S SMALL UNIVERSAL TURING MACHINE." International Journal of Mathematics 02, no. 05 (1991): 551–62. http://dx.doi.org/10.1142/s0129167x91000302.

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Marvin L. Minsky constructed a 4-symbol 7-state universal Turing machine in 1962. It was first announced in a postscript to [2] and is also described in [3, Sec. 14.8]. This paper contains everything that is needed for an understanding of his machine, including a complete description of its operation. Minsky's machine remains one of the minimal known universal Turing machines. That is, there is no known such machine which decreases one parameter without increasing the other. However, Rogozhin [6], [7] has constructed seven universal machines with the following parameters: [Formula: see text] His 4-symbol 7-state machine is somewhat different from Minsky's, but all of his machines use a construction similar to that used by Minsky. The following corrections should be noted: First machine, for q 6 00Lq 1 read q 6 00Lq 7; second machine, for q 4 11Rq 4 read q 4 11Rq 10; last machine, for q 2 b 2 bLq 2 read [Formula: see text]. A generalized Turing machine with 4 symbols and 7 states, closely related to Minsky's, was constructed and used in [5].

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Thiagarajan, P. S. "The Turing machine." Resonance 2, no. 7 (1997): 3–4. http://dx.doi.org/10.1007/bf02838584.

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ITO, AKIRA, KATSUSHI INOUE, ITSUO TAKANAMI, and YUE WANG. "THE EFFECT OF INKDOTS FOR TWO-DIMENSIONAL AUTOMATA." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 05 (1995): 777–96. http://dx.doi.org/10.1142/s0218001495000328.

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Recently, related to the open problem of whether deterministic and nondeterministic space (especially lower-level) complexity classes are separated, the inkdot Turing machine was introduced. An inkdot machine is a conventional Turing machine capable of dropping an inkdot on a given input tape for a landmark, but not to pick it up nor further erase it. In this paper, we introduce a finite state version of the inkdot machine as a weak recognizer of the properties of digital pictures, rather than a Turing machine supplied with a one-dimensional working tape. We first investigate the sufficient spaces of three-way Turing machines to simulate two-dimensional inkdot finite automaton, as preliminary results. Next, we investigate the basic properties of two-dimensional inkdot automaton, i.e. the hierarchy based on the number of inkdots and the relationship of two-dimensional inkdot automata to other conventional two-dimensional automata. Finally, we investigate the recognizability of connected pictures of two-dimensional inkdot finite machines.

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CALUDE, CRISTIAN S., and LUDWIG STAIGER. "A note on accelerated Turing machines." Mathematical Structures in Computer Science 20, no. 6 (2010): 1011–17. http://dx.doi.org/10.1017/s0960129510000344.

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In this paper we prove that any Turing machine that uses only a finite computational space for every input cannot solve an uncomputable problem even when it runs in accelerated mode. We also propose two ways to define the language accepted by an accelerated Turing machine. Accordingly, the classes of languages accepted by accelerated Turing machines are the closure under Boolean operations of the sets Σ1 and Σ2.

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Macura, Wiktor K. "n-Skip Turing Machines." Complex Systems 15, no. 3 (2005): 237–44. http://dx.doi.org/10.25088/complexsystems.15.3.237.

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A Turing Machine's head is limited to moving one cell in either direction on the tape for a given iteration. We investigate a form of Turing Machine where the head is allowed to move n cells in either direction. We find that such Turing Machines, named n-Skip Turing Machines, are capable of exhibiting complex behavior for simple initial conditions with two states and two colors.

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De Brito, Vasco Boavida, José Félix Costa, and Diogo Poças. "The Power of Machines That Control Experiments." International Journal of Foundations of Computer Science 33, no. 02 (2022): 91–118. http://dx.doi.org/10.1142/s0129054122500010.

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We consider the experimenter (e.g. the experimental physicist) as a Turing machine — the digital component — and the experiment of measurement — the analog component — as an oracle to the Turing machine. The algorithm running in the machine abstracts the experimental method of measurement (encoding the recursive structure of experimental actions) chosen by the experimenter. In this paper we prove that the central analogue-digital complexity classes [Formula: see text], [Formula: see text] and [Formula: see text] can be characterized in terms of protocols to perform measurements controlled by standard Turing machines.

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Teses / dissertações sobre o assunto "Machine de turing"

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Chen, Yin Fu. "SIMTM turing machine simulator." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1229.

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Toister, Yanai. "Photography from the Turin Shroud to the Turing Machine." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14911.

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Photography has always been a migratory system of representation. Today, it is integrated into numerous systems, in a profusion of specialties, sub-disciplines and disciplines. Within many of these domains, the exponentially growing powers of information processing enable the manufacturing of images that are seemingly photographic, yet partly (or fully) synthetic. How do we define these images? Are traditional disciplinary accounts relevant? Photography’s cultural value is most often measured in terms of its products, the various kinds of pictures that it generates. Instead, photography can be interrogated by studying the dynamic relationships between its components: the electromagnetic, optical, mechanical, chemical and recently mathematical elements and procedures that combine as a process that produces images. This dissertation utilizes two metaphors for defining photography: the Turin Shroud and the Universal Turing Machine. The former is presented as a set of propositions that facilitate new understandings about the history and theory of photography. The latter is introduced as a conceptual model that expands the theory and philosophy of photography into new realms, most notably those of new media and media philosophy. In support of this novel exposition, and to more adequately portray the trajectory of photography’s reincarnation as a form of computation, various terms from Vilém Flusser’s philosophy are reinterpreted and further developed. Through these it is suggested that photography is a family of programs wherein both ‘analogue’ and ‘digital’ characteristics always coexist. These are not to be seen as mutually exclusive qualities but as complementary discursive modalities. Further, because photography has always had mathematical qualities and potentialities, the recent technological turmoil does not designate the ‘end’ of the medium, but rather its coming of age. Importantly, now that the medium of photography has become media, photographic images should no longer be understood as bearers of ontological qualities but only as epistemic containers.

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Capuni, Ilir. "A fault-tolerant Turing machine." Thesis, Boston University, 2013. https://hdl.handle.net/2144/13608.

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Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>The Turing machine is the most studied universal model of computation. This thesis studies the question if there is a Turing machine that can compute reliably even when violations of its transition function occur independently of each other with some small probability. In this thesis, we prove the existence of a Turing machine that - with a polynomial overhead - can simulate any other Turing machine, even when it is subject to faults of the above type, thereby answering the question that was open for 25 years.

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Masum, Hassan Carleton University Dissertation Mathematics. "An exploration of turing machine based complexity." Ottawa, 1995.

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5

Müller, Markus. "Quantum Kolmogorov complexity and the quantum turing machine." [S.l.] : [s.n.], 2007. http://opus.kobv.de/tuberlin/volltexte/2007/1655.

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Kalyanasundaram, Subrahmanyam. "Turing machine algorithms and studies in quasi-randomness." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42808.

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Randomness is an invaluable resource in theoretical computer science. However, pure random bits are hard to obtain. Quasi-randomness is a tool that has been widely used in eliminating/reducing the randomness from randomized algorithms. In this thesis, we study some aspects of quasi-randomness in graphs. Specifically, we provide an algorithm and a lower bound for two different kinds of regularity lemmas. Our algorithm for FK-regularity is derived using a spectral characterization of quasi-randomness. We also use a similar spectral connection to also answer an open question about quasi-random tournaments. We then provide a "Wowzer" type lower bound (for the number of parts required) for the strong regularity lemma. Finally, we study the derandomization of complexity classes using Turing machine simulations. 1. Connections between quasi-randomness and graph spectra. Quasi-random (or pseudo-random) objects are deterministic objects that behave almost like truly random objects. These objects have been widely studied in various settings (graphs, hypergraphs, directed graphs, set systems, etc.). In many cases, quasi-randomness is very closely related to the spectral properties of the combinatorial object that is under study. In this thesis, we discover the spectral characterizations of quasi-randomness in two different cases to solve open problems. A Deterministic Algorithm for Frieze-Kannan Regularity: The Frieze-Kannan regularity lemma asserts that any given graph of large enough size can be partitioned into a number of parts such that, across parts, the graph is quasi-random. . It was unknown if there was a deterministic algorithm that could produce a parition satisfying the conditions of the Frieze-Kannan regularity lemma in deterministic sub-cubic time. In this thesis, we answer this question by designing an O(n[superscript]w) time algorithm for constructing such a partition, where w is the exponent of fast matrix multiplication. Even Cycles and Quasi-Random Tournaments: Chung and Graham in had provided several equivalent characterizations of quasi-randomness in tournaments. One of them is about the number of "even" cycles where even is defined in the following sense. A cycle is said to be even, if when walking along it, an even number of edges point in the wrong direction. Chung and Graham showed that if close to half of the 4-cycles in a tournament T are even, then T is quasi-random. They asked if the same statement is true if instead of 4-cycles, we consider k-cycles, for an even integer k. We resolve this open question by showing that for every fixed even integer k geq 4, if close to half of the k-cycles in a tournament T are even, then T must be quasi-random. 2. A Wowzer type lower bound for the strong regularity lemma. The regularity lemma of Szemeredi asserts that one can partition every graph into a bounded number of quasi-random bipartite graphs. Alon, Fischer, Krivelevich and Szegedy obtained a variant of the regularity lemma that allows one to have an arbitrary control on this measure of quasi-randomness. However, their proof only guaranteed to produce a partition where the number of parts is given by the Wowzer function, which is the iterated version of the Tower function. We show here that a bound of this type is unavoidable by constructing a graph H, with the property that even if one wants a very mild control on the quasi-randomness of a regular partition, then any such partition of H must have a number of parts given by a Wowzer-type function. 3. How fast can we deterministically simulate nondeterminism? We study an approach towards derandomizing complexity classes using Turing machine simulations. We look at the problem of deterministically counting the exact number of accepting computation paths of a given nondeterministic Turing machine. We provide a deterministic algorithm, which runs in time roughly O(sqrt(S)), where S is the size of the configuration graph. The best of the previously known methods required time linear in S. Our result implies a simulation of probabilistic time classes like PP, BPP and BQP in the same running time. This is an improvement over the currently best known simulation by van Melkebeek and Santhanam.

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Rendell, P. "Turing machine universality of the game of life." Thesis, University of the West of England, Bristol, 2014. http://eprints.uwe.ac.uk/22323/.

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This project proves universal computation in the Game of Life cellular automaton by using a Turing machine construction. Existing proofs of universality in the Game of Life rely on a counter machine. These machines require complex encoding and decoding of the input and output and the proof of universality for these machines by the Church Turing thesis is that they can perform the equivalent of a Turing machine. A proof based directly on a Turing machine is much more accessible. The computational power available today allows powerful algorithms such as HashLife to calculate the evolution of cellular automata patterns sufficiently fast that an efficient universal Turing machine can be demonstrated in a conveniently short period of time. Such a universal Turing machine is presented here. It is a direct simulation of a Turing machine and the input and output are easily interpreted. In order to achieve full universal behaviour an infinite storage media is required. The storage media used to represent the Turing machine tape is a pair of stacks. One stack representing the Turing tape to the left of the read/write head and one for the Turing tape to the right. Collision based construction techniques have been used to add stack cells to the ends of the stacks continuously. The continuous construction of the stacks is equivalent to the formatting of blank media. This project demonstrates that large areas of a cellular automata can be formatted in real time to perform complex functions.

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Shah, Huma. "Deception-detection and machine intelligence in practical Turing tests." Thesis, University of Reading, 2010. http://centaur.reading.ac.uk/24768/.

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Deception-detection is the crux of Turing’s experiment to examine machine thinking conveyed through a capacity to respond with sustained and satisfactory answers to unrestricted questions put by a human interrogator. However, in 60 years to the month since the publication of Computing Machinery and Intelligence little agreement exists for a canonical format for Turing’s textual game of imitation, deception and machine intelligence. This research raises from the trapped mine of philosophical claims, counter-claims and rebuttals Turing’s own distinct five minutes question-answer imitation game, which he envisioned practicalised in two different ways: a) A two-participant, interrogator-witness viva voce, b) A three-participant, comparison of a machine with a human both questioned simultaneously by a human interrogator. Using Loebner’s 18th Prize for Artificial Intelligence contest, and Colby et al.’s 1972 transcript analysis paradigm, this research practicalised Turing’s imitation game with over 400 human participants and 13 machines across three original experiments. Results show that, at the current state of technology, a deception rate of 8.33% was achieved by machines in 60 human-machine simultaneous comparison tests. Results also show more than 1 in 3 Reviewers succumbed to hidden interlocutor misidentification after reading transcripts from experiment 2. Deception-detection is essential to uncover the increasing number of malfeasant programmes, such as CyberLover, developed to steal identity and financially defraud users in chatrooms across the Internet. Practicalising Turing’s two tests can assist in understanding natural dialogue and mitigate the risk from cybercrime.

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Tantau, Till. "On structural similarities of finite automata and turing machine enumerability classes." [S.l.] : [s.n.], 2003. http://edocs.tu-berlin.de/diss/2003/tantau_till.pdf.

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Girardot, Johan. "Toward higher-order and many-symbol infinite time Turing machines." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX028.

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Ce travail traite des machines de Turing infinies (ITTM) telles que développées par Hamkins et Lewis au début des années 2000. Plus particulièrement il s'intéresse à leur généralisation. Un aspect notable de ces machines infinies est que, en comparaison du modèle classique de Turing, celles-ci sont presque aussi simple. Une ITTM a la même structure qu'une machine de Turing à trois rubans. Elle fait des calculs en temps ordinaux et à n'importe quelle étape, l'instantané de la machine permet de calculer, comme dans le modèle classique, l'instantané à l'étape suivant. Le seule différence est aux étapes limites: la tête de lecture est remise au début, la machine est dans un certain état spécial et la valeur de chaque cellule est la limite supérieure de l'historique de ses valeurs précédentes. Si le choix pour la tête de lecture et pour l'état limite sont d'une façon logique, le choix de la valeur limite pour les cellules peut apparaître arbitraire. Pourquoi pas une liminf ? Ou encore quelque chose de plus compliqué ? Finalement, la justification de cette règle limite est une corroboration : avec cette règle, Hamkins et Lewis ont montré que le modèle de machines qu'ils ont développé est robuste, puissant et se comporte bien.L'objectif est de proposer des règles limites différentes de la règle limsup produisant donc des généralisations de ce modèle de machines.%La plupart de preuves portant sur les ITTMs utilise une « machine universelle », c'est-à-dire une machine qui simule en parallèle toutes les autres ITTMs. Une une telle machine est en fait simple à définir ; mais cette simplicité est fortuite : il pourrait y avoir de nombreuses difficultés qui sont évitées grâce à plusieurs propriétés implicite des ITTMs. Nous avons ainsi mis en lumière un ensemble de quatre propriétés, satisfaites par la règle de limsup. Elles nous permettent de définir un concept plus général de machine simulationnelles : des modèles de machines dont la règle limite satisfait ces propriétés et pour lesquelles on prouve qu'il existe une machine universelle. Le premier résultat de ce travail est un théorème qui établit, pour ces modèles de machines auxquelles deux contraintes sont rajoutées, une égalité entre les temps de calcul et les ordinaux qui peuvent être écrits.Le second résultat principal se base sur ce premier résultat. Un corollaire immédiat de la première partie est le suivant : il n'existe que deux modèles de machines simulationnelles (et non pathologiques), à savoir les ITTM avec la règle limsup et leur symétrique avec la règle liminf. Ainsi, pour produire des machines infinies d'ordre supérieur, il faut construire des machines à n symboles. C'est le second résultat : nous avons construit un modèle de machines simulationnelles à 3 symboles, strictement plus puissant que celui des ITTMs et pour lequel nous parvenons à établir les principaux résultats mettant en relation les ITTMs avec la théorie des ensembles<br>This thesis studies infinite time Turing machines (ITTM) as developed by Hamkins and Lewis at the beginning of the years 2000. In particular, it aims at providing new generalizations of this model of infinite computation, or the tools and the results to develop those.A notable aspect of this model of infinite computation is that it is simple enough when compared to the usual finite model of Turing machines: an ITTM has the same structure as a three tapes Turing machine, it computes through the ordinals and at any successor stage, the next snapshot of the machine is a function of its machine code and the actual snapshot, as done in the classical setting. The only difference being that, at limit, tape heads are back on their first cells, the state is set to some distinguished limit state and the value of any cell is set to the limit superior of its previous values. While the choices for the heads and the states at limit stages may appear somewhat canonical, the principal justification for the rule of the limsup is actually a corroboration: with this rule, Hamkins and Lewis showed how this produces a robust, powerful and well-behaved model of infinite computation.So this work was focused on devising limit rules that would yield more powerful but equally well-behaved models of generalized infinite Turing machines.Most of the proofs done on ITTMs use a universal machine: an ITTM which simulates in parallel all other ITTMs. It happens to be straightforward to define such an universal ITTM.But its definition is only fortuitously straightforward. This construction rests on strong but implicit properties of the limsup rule. Hence, we exhibit a set of four properties satisfied by the limsup rule that allow us to define the more general concept of simulational machine: a model of infinite machines whose machines compute with a limit rule that satisfy this set of four properties, for which we prove that there exists a universal machine. The first main result is that the machines in this class of infinite machines satisfy (with two other constraints) an important equality satisfied by the usual ITTM, relating the time of computations and the ordinals that are writable.The second main result builds on the previous result. An immediate corollary is the following: there exists only two 2-symbol simulational and "well-behaved" model of ITTM; namely the limsup ITTM and the liminf ITTM. So, to produce higher-order machines, we need to consider n-symbols machine. And this is the second result: we construct a 3-symbol ITTM, strictly more powerful that the previous one and for which we establish the same set-theoretic results that were established for it

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Mais fontes

Livros sobre o assunto "Machine de turing"

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Arnold, Schönhage. Fast algorithms: A multitape Turing machine implementation. B.I. Wissenschaftsverlag, 1994.

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2

Rolf, Herken, ed. The Universal Turing machine: A half-centurysurvey. Oxford University Press, 1988.

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Rolf, Herken, ed. The Universal Turing machine: A half-century survey. Oxford University Press, 1988.

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4

Rolf, Herken, ed. The universal turing machine: A half-century survey. Oxford University Press, 1988.

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5

Rendell, Paul. Turing Machine Universality of the Game of Life. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19842-2.

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Herken, Rolf, ed. The Universal Turing Machine A Half-Century Survey. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-6597-3.

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Herken, Rolf. The Universal Turing Machine A Half-Century Survey. Springer Vienna, 1995.

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8

Rolf, Herken, ed. The universal Turing machine: A half-century survey. 2nd ed. Springer-Verlag, 1995.

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9

Rolf, Herken, ed. The universal Turing machine: A half-century survey. Springer-Verlag, 1994.

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10

DuPuis, Christopher. An animated Turning [sic] machine simulator in Forms/3. Oregon State University, Dept. of Computer Science, 1997.

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Capítulos de livros sobre o assunto "Machine de turing"

1

Maruoka, Akira. "Turing Machine." In Concise Guide to Computation Theory. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-535-4_6.

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Weik, Martin H. "Turing machine." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_20210.

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Kurgalin, Sergei, and Sergei Borzunov. "Turing Machine." In Texts in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92645-2_10.

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Kurgalin, Sergei, and Sergei Borzunov. "Turing Machine." In Texts in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42221-9_10.

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Robič, Borut. "The Turing Machine." In The Foundations of Computability Theory. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44808-3_6.

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Robič, Borut. "The Turing Machine." In The Foundations of Computability Theory. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62421-0_6.

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Weik, Martin H. "universal Turing machine." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_20474.

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Morazán, Marco T. "Turing Machine Composition." In Texts in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43973-5_16.

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Morazán, Marco T. "Turing Machine Extensions." In Texts in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43973-5_17.

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Johnson, Michael L. "Turing, More, Analogies." In Mind, Language, Machine. Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-19404-9_14.

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Trabalhos de conferências sobre o assunto "Machine de turing"

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Büyükbiçakci, Erdal, Selcuk Bulat, C. Senthilkumar, A. Rajesh, Malatthi Sivasundaram, and Rama Raju Svsv Palla. "Optimizing Electricity Theft Detection using Neural Turing Machines and Machine Learning." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10797213.

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Azogagh, Sofiane, Victor Delfour, and Marc-Olivier Killijian. "Oblivious Turing Machine." In 2024 19th European Dependable Computing Conference (EDCC). IEEE, 2024. http://dx.doi.org/10.1109/edcc61798.2024.00017.

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Grosz, Barbara, Edward A. Feigenbaum, Marvin Minsky, Judea Pearl, and Raj Reddy. "Human and Machine Intelligence." In ACM Turing Centenary Celebration. ACM Press, 2012. http://dx.doi.org/10.1145/2322176.2322180.

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Mallik, Abhishek, and Anavi Khetarpal. "Turing Machine based Syllable Splitter." In 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT). IEEE, 2021. http://dx.doi.org/10.1109/ccict53244.2021.00028.

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Luo, Yue, and Jia-li Feng. "Turing Machine on Attribute Theory." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.9.

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Christy, D. K. Sheena, A. John Kaspar, and D. G. Thomas. "On reversible fuzzy turing machine." In 2ND INTERNATIONAL CONFERENCE ON MATHEMATICAL TECHNIQUES AND APPLICATIONS: ICMTA2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0109238.

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Jordan, Michael I. "Machine learning." In TURC 2018: ACM Turing Celebration Conference - China. ACM, 2018. http://dx.doi.org/10.1145/3210713.3210718.

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Kundra, Rohan, Harshul Singhal, and Nitin Nitin. "Turing Machine for i-Head Hydra." In 2010 12th International Conference on Computer Modelling and Simulation. IEEE, 2010. http://dx.doi.org/10.1109/uksim.2010.11.

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Torres-Aviles, Rodrigo. "Reversibility in Turing machine topological models." In 2022 41st International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2022. http://dx.doi.org/10.1109/sccc57464.2022.10000314.

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Li, Wanwan. "Simulating Turing Machine in Augmented Reality." In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00362.

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Relatórios de organizações sobre o assunto "Machine de turing"

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Garrard, K. P., L. W. Taylor, B. F. Knight, and R. J. Fornaro. Diamond turning machine controller implementation. Office of Scientific and Technical Information (OSTI), 1988. http://dx.doi.org/10.2172/476633.

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Estler, W. Tyler, and Edward B. Magrab. Validation metrology of the large optics diamond turning machine. National Bureau of Standards, 1985. http://dx.doi.org/10.6028/nbs.ir.85-3182.

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Hayes, S. Displacement driven balancing of a diamond turning machine flycutter. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1810662.

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Baird, E. D., R. R. Donaldson, and S. R. Patterson. The laser interferometer system for the large optics diamond turning machine. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/9646.

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Fessel, Kimberly. Machine Learning in Python. Instats Inc., 2024. http://dx.doi.org/10.61700/s74zy0ivgwioe1764.

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This intensive, hands-on workshop offers a deep dive into machine learning with Python, designed for PhD students, professors, and researchers across various fields. Participants will master practical skills in data cleaning, exploratory data analysis, and building powerful machine learning models, including neural networks, to elevate their research. With real-world coding exercises and expert guidance, this workshop will equip you with the tools to turn data into actionable insights.

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Paredes, Juan Roberto, María Clara Ramos, Marina Robles, and Emma Näslund-Hadley. Energy Savings, Efficient Use, and Alternative Technologies. Inter-American Development Bank, 2015. http://dx.doi.org/10.18235/0006241.

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How much energy do you think is needed to light and mobilize all the machines and devices operating on the planet? Have you ever thought that by turning on a light in your house or school you are impacting the environment and emitting gases into the atmosphere?

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Siantar, M. Seismic Bracing Development for the Moore 3 Diamond Turning Machine at Lawrence Livermore National Laboratory. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2370174.

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Lewin, Alex, Karla Diaz-Ordaz, Chris Bonell, James Hargreaves, and Edoardo Masset. Machine learning for impact evaluation in CEDIL-funded studies: an ex ante lesson learning paper. Centre for Excellence and Development Impact and Learning (CEDIL), 2023. http://dx.doi.org/10.51744/llp3.

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The Centre of Excellence for Development Impact and Learning (CEDIL) has recently funded several studies that use machine learning methods to enhance the inferences made from impact evaluations. These studies focus on assessing the impact of complex development interventions, which can be expected to have impacts in different domains, possibly over an extended period of time. These studiestherefore involve study participants being followed up at multiple time-points after the intervention, and typically collect large numbers of variables at each follow-up. The hope is that machine learning approaches can uncover new insights into the variation in responses to interventions, and possible causal mechanisms, which in turn can highlight potential areas that policy and planning can focus on. This paper describes these studies using machine learning methods, gives an overview of the common aims and methodological approaches used in impact evaluations, and highlights some lessons and important caveats.

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Hoffman, Wyatt. AI and the Future of Cyber Competition. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/2020ca007.

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As states turn to AI to gain an edge in cyber competition, it will change the cat-and-mouse game between cyber attackers and defenders. Embracing machine learning systems for cyber defense could drive more aggressive and destabilizing engagements between states. Wyatt Hoffman writes that cyber competition already has the ingredients needed for escalation to real-world violence, even if these ingredients have yet to come together in the right conditions.

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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Banco de España, 2023. http://dx.doi.org/10.53479/29594.

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Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area.

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