Gotowe bibliografie tematyczne / Machine de turing

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Machine de turing”

Autor: Grafiati
Data publikacji: 5 października 2024
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "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, bu
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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] H
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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 sp
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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 s
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Rozprawy doktorskie na temat "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 b
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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 occ
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Masum, Hassan Carleton University Dissertation Mathematics. "An exploration of turing machine based complexity." Ottawa, 1995.

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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 to
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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 cel
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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,
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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
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Książki na temat "Machine de turing"

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

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

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

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

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

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Części książek na temat "Machine de turing"

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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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Streszczenia konferencji na temat "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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Raporty organizacyjne na temat "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 app
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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 lite
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