Results for 'random strings'

1000+ found
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  1.  19
    What is a Random String?Cristian Calude - 1995 - Vienna Circle Institute Yearbook 3:101-113.
    Suppose that persons A and B give us a sequence of 32 bits each, saying that they were obtained from independent coin flips. If A gives the stringu = 01001110100111101001101001110101and B gives the stringv = 00000000000000000000000000000000,then we would tend to believe A and would not believe B: the string u seems to be random, but the string v does not. Further on, if we change the value of a bit in a “random” string, then the result is still (...)
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  2.  20
    Connected discourse and random strings: Effects of number of inputs on recognition and recall.Roy Lachman & D. James Dooling - 1968 - Journal of Experimental Psychology 77 (4):517.
  3.  13
    What can be efficiently reduced to the Kolmogorov-random strings?Eric Allender, Harry Buhrman & Michal Koucký - 2006 - Annals of Pure and Applied Logic 138 (1):2-19.
    We investigate the question of whether one can characterize complexity classes in terms of efficient reducibility to the set of Kolmogorov-random strings . This question arises because and , and no larger complexity classes are known to be reducible to in this way. We show that this question cannot be posed without explicitly dealing with issues raised by the choice of universal machine in the definition of Kolmogorov complexity. What follows is a list of some of our main (...)
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  4.  25
    On the computational power of random strings.Adam R. Day - 2009 - Annals of Pure and Applied Logic 160 (2):214-228.
    There are two fundamental computably enumerable sets associated with any Kolmogorov complexity measure. These are the set of non-random strings and the overgraph. This paper investigates the computational power of these sets. It follows work done by Kummer, Muchnik and Positselsky, and Allender and co-authors. Muchnik and Positselsky asked whether there exists an optimal monotone machine whose overgraph is not tt-complete. This paper answers this question in the negative by proving that the overgraph of any optimal monotone machine, (...)
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  5.  8
    On the Topological Size of Sets of Random Strings.M. Zimand - 1986 - Mathematical Logic Quarterly 32 (6):81-88.
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  6.  23
    On the Topological Size of Sets of Random Strings.M. Zimand - 1986 - Zeitschrift fur mathematische Logik und Grundlagen der Mathematik 32 (6):81-88.
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  7.  11
    Infinite strings and their large scale properties.Bakh Khoussainov & Toru Takisaka - 2022 - Journal of Symbolic Logic 87 (2):585-625.
    The aim of this paper is to shed light on our understanding of large scale properties of infinite strings. We say that one string $\alpha $ has weaker large scale geometry than that of $\beta $ if there is color preserving bi-Lipschitz map from $\alpha $ into $\beta $ with small distortion. This definition allows us to define a partially ordered set of large scale geometries on the classes of all infinite strings. This partial order compares large scale (...)
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  8.  53
    Algorithmic randomness in empirical data.James W. McAllister - 2003 - Studies in History and Philosophy of Science Part A 34 (3):633-646.
    According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers (...)
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  9.  50
    Algorithmic randomness in empirical data.James W. McAllister - 2003 - Studies in History and Philosophy of Science Part A 34 (3):633-646.
    According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers (...)
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  10.  82
    Randomness and computability: Open questions.Joseph S. Miller & André Nies - 2006 - Bulletin of Symbolic Logic 12 (3):390-410.
    It is time for a new paper about open questions in the currently very active area of randomness and computability. Ambos-Spies and Kučera presented such a paper in 1999 [1]. All the question in it have been solved, except for one: is KL-randomness different from Martin-Löf randomness? This question is discussed in Section 6.Not all the questions are necessarily hard—some simply have not been tried seriously. When we think a question is a major one, and therefore likely to be hard, (...)
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  11.  15
    Jeffrey Shallit and Ming-Wei Wang. Automatic complexity of strings. Journal of Automata, Languages and Combinatorics, vol. 6 , pp. 537–554. - Cristian S. Calude, Kai Salomaa and Tania K. Roblot. Finite-state complexity and randomness. Theoretical Computer Science, vol. 412 , no. 41, pp. 5668–5677. - Cristian S. Calude, Kai Salomaa and Tania K. Roblot. State-size hierarchy for finite-state complexity. International Journal of Foundations of Computer Science, vol. 23 , no. 1, pp. 37–50. [REVIEW]Mia Minnes - 2012 - Bulletin of Symbolic Logic 18 (4):579-580.
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  12.  45
    Randomness and Halting Probabilities.VeróNica Becher, Santiago Figueira, Serge Grigorieff & Joseph S. Miller - 2006 - Journal of Symbolic Logic 71 (4):1411 - 1430.
    We consider the question of randomness of the probability ΩU[X] that an optimal Turing machine U halts and outputs a string in a fixed set X. The main results are as follows: ΩU[X] is random whenever X is $\Sigma _{n}^{0}$-complete or $\Pi _{n}^{0}$-complete for some n ≥ 2. However, for n ≥ 2, ΩU[X] is not n-random when X is $\Sigma _{n}^{0}$ or $\Pi _{n}^{0}$ Nevertheless, there exists $\Delta _{n+1}^{0}$ sets such that ΩU[X] is n-random. There are (...)
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  13.  50
    Algorithmic randomness and measures of complexity.George Barmpalias - forthcoming - Association for Symbolic Logic: The Bulletin of Symbolic Logic.
    We survey recent advances on the interface between computability theory and algorithmic randomness, with special attention on measures of relative complexity. We focus on (weak) reducibilities that measure (a) the initial segment complexity of reals and (b) the power of reals to compress strings, when they are used as oracles. The results are put into context and several connections are made with various central issues in modern algorithmic randomness and computability.
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  14.  20
    Algorithmic Randomness and Measures of Complexity.George Barmpalias - 2013 - Bulletin of Symbolic Logic 19 (3):318-350.
    We survey recent advances on the interface between computability theory and algorithmic randomness, with special attention on measures of relative complexity. We focus on reducibilities that measure the initial segment complexity of reals and the power of reals to compress strings, when they are used as oracles. The results are put into context and several connections are made with various central issues in modern algorithmic randomness and computability.
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  15.  26
    Recursive events in random sequences.George Davie - 2001 - Archive for Mathematical Logic 40 (8):629-638.
    Let ω be a Kolmogorov–Chaitin random sequence with ω1: n denoting the first n digits of ω. Let P be a recursive predicate defined on all finite binary strings such that the Lebesgue measure of the set {ω|∃nP(ω1: n )} is a computable real α. Roughly, P holds with computable probability for a random infinite sequence. Then there is an algorithm which on input indices for any such P and α finds an n such that P holds (...)
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  16.  25
    The complexity of random ordered structures.Joel H. Spencer & Katherine St John - 2008 - Annals of Pure and Applied Logic 152 (1):174-179.
    We show that for random bit strings, Up, with probability, image, the first order quantifier depth D) needed to distinguish non-isomorphic structures is Θ, with high probability. Further, we show that, with high probability, for random ordered graphs, G≤,p with edge probability image, D)=Θ, contrasting with the results for random graphs, Gp, given by Kim et al. [J.H. Kim, O. Pikhurko, J. Spencer, O. Verbitsky, How complex are random graphs in first order logic? Random (...)
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  17.  11
    The complexity of random ordered structures.Joel Spencer & Katherine St John - 2008 - Annals of Pure and Applied Logic 152 (1-3):174-179.
    We show that for random bit strings, Up, with probability, image, the first order quantifier depth D) needed to distinguish non-isomorphic structures is Θ, with high probability. Further, we show that, with high probability, for random ordered graphs, G≤,p with edge probability image, D)=Θ, contrasting with the results for random graphs, Gp, given by Kim et al. [J.H. Kim, O. Pikhurko, J. Spencer, O. Verbitsky, How complex are random graphs in first order logic? Random (...)
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  18.  10
    Every computably enumerable random real is provably computably enumerable random.Cristian Calude & Nicholas Hay - 2009 - Logic Journal of the IGPL 17 (4):351-374.
    We prove that every computably enumerable random real is provable in Peano Arithmetic to be c.e. random. A major step in the proof is to show that the theorem stating that “a real is c.e. and random iff it is the halting probability of a universal prefix-free Turing machine” can be proven in PA. Our proof, which is simpler than the standard one, can also be used for the original theorem. Our positive result can be contrasted with (...)
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  19.  4
    Einstein's dice and Schrödinger's cat: how two great minds battled quantum randomness to create a unified theory of physics.Paul Halpern - 2015 - New York: Basic Books, a member of the Perseus Group.
    When the fuzzy indeterminacy of quantum mechanics overthrew the orderly world of Isaac Newton, Albert Einstein and Erwin Schrödinger were at the forefront of the revolution. Neither man was ever satisfied with the standard interpretation of quantum mechanics, however, and both rebelled against what they considered the most preposterous aspect of quantum mechanics: its randomness. Einstein famously quipped that God does not play dice with the universe, and Schrödinger constructed his famous fable of a cat that was neither alive nor (...)
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  20.  7
    Fearing the Black Body. The Racial Origins of Fat Phobia.Sabrina Strings - 2019 - New York University Press.
    Winner, 2020 Body and Embodiment Best Publication Award, given by the American Sociological Association Honorable Mention, 2020 Sociology of Sex and Gender Distinguished Book Award, given by the American Sociological Association How the female body has been racialized for over two hundred years There is an obesity epidemic in this country and poor Black women are particularly stigmatized as “diseased” and a burden on the public health care system. This is only the most recent incarnation of the fear of fat (...)
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  21.  12
    Towards a stable definition of algorithmic randomness.Hector Zenil - unknown
    Although information content is invariant up to an additive constant, the range of possible additive constants applicable to programming languages is so large that in practice it plays a major role in the actual evaluation of K(s), the Kolmogorov complexity of a string s. We present a summary of the approach we've developed to overcome the problem by calculating its algorithmic probability and evaluating the algorithmic complexity via the coding theorem, thereby providing a stable framework for Kolmogorov complexity even for (...)
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  22. Charged vortices: An explicit solution, its properties and relevance as.A. Cosmic String - 1988 - Scientia 52:233.
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  23.  26
    Henry VIII's illuminated 'great bible'.Tatiana C. String - 1996 - Journal of the Warburg and Courtauld Institutes 59 (1):315-324.
  24. Peter Kirschenmann.Concepts Of Randomness - 1973 - In Mario Bunge (ed.), Exact philosophy; problems, tools, and goals. Boston,: D. Reidel. pp. 129.
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  25.  14
    Reference Explained Away: Anaphoric Reference and Indirect.Robert Bb Random - 2005 - In J. C. Beall & B. Armour-Garb (eds.), Deflationary Truth. Open Court. pp. 258.
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  26.  41
    Immunity and Hyperimmunity for Sets of Minimal Indices.Frank Stephan & Jason Teutsch - 2008 - Notre Dame Journal of Formal Logic 49 (2):107-125.
    We extend Meyer's 1972 investigation of sets of minimal indices. Blum showed that minimal index sets are immune, and we show that they are also immune against high levels of the arithmetic hierarchy. We give optimal immunity results for sets of minimal indices with respect to the arithmetic hierarchy, and we illustrate with an intuitive example that immunity is not simply a refinement of arithmetic complexity. Of particular note here are the fact that there are three minimal index sets located (...)
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  27.  12
    Fandom as Methodology: A Sourcebook for Artists and Writers.Catherine Grant & Kate Random Love (eds.) - 2019 - London: MIT Press.
    An illustrated exploration of fandom that combines academic essays with artist pages and experimental texts. Fandom as Methodology examines fandom as a set of practices for approaching and writing about art. The collection includes experimental texts, autobiography, fiction, and new academic perspectives on fandom in and as art. Key to the idea of “fandom as methodology” is a focus on the potential for fandom in art to create oppositional spaces, communities, and practices, particularly from queer perspectives, but also through transnational, (...)
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  28. Introduction: Fandom as methodology.Catherine Grant & Kate Random Love - 2019 - In Catherine Grant & Kate Random Love (eds.), Fandom as Methodology: A Sourcebook for Artists and Writers. London: MIT Press.
     
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  29.  19
    Commentary on Risto Naatanen (1990). The role of attention in auditory information processing as revealed by event-related potentials and other brain measures of cognitive fenctiono BBS 13s201-2888. [REVIEW]A. Ryan, R. D. Ryder, L. Schiebinger, P. Singer & Random House - 1991 - Behavioral and Brain Sciences 14:4.
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  30.  25
    Bounded Immunity and Btt‐Reductions.Stephen Fenner & Marcus Schaefer - 1999 - Mathematical Logic Quarterly 45 (1):3-21.
    We define and study a new notion called k-immunity that lies between immunity and hyperimmunity in strength. Our interest in k-immunity is justified by the result that θ does not k-tt reduce to a k-immune set, which improves a previous result by Kobzev [7]. We apply the result to show that Φ′ does not btt-reduce to MIN, the set of minimal programs. Other applications include the set of Kolmogorov random strings, and retraceable and regressive sets. We also give (...)
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  31.  45
    An incomplete set of shortest descriptions.Frank Stephan & Jason Teutsch - 2012 - Journal of Symbolic Logic 77 (1):291-307.
    The truth-table degree of the set of shortest programs remains an outstanding problem in recursion theory. We examine two related sets, the set of shortest descriptions and the set of domain-random strings, and show that the truth-table degrees of these sets depend on the underlying acceptable numbering. We achieve some additional properties for the truth-table incomplete versions of these sets, namely retraceability and approximability. We give priority-free constructions of bounded truth-table chains and bounded truth-table antichains inside the truth-table (...)
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  32.  27
    Are binary codings universal?Cristian Calude & Cezar Câmpeanu - 1996 - Complexity 1 (5):47-50.
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  33.  55
    The K -Degrees, Low for K Degrees,and Weakly Low for K Sets.Joseph S. Miller - 2009 - Notre Dame Journal of Formal Logic 50 (4):381-391.
    We call A weakly low for K if there is a c such that $K^A(\sigma)\geq K(\sigma)-c$ for infinitely many σ; in other words, there are infinitely many strings that A does not help compress. We prove that A is weakly low for K if and only if Chaitin's Ω is A-random. This has consequences in the K-degrees and the low for K (i.e., low for random) degrees. Furthermore, we prove that the initial segment prefix-free complexity of 2- (...) reals is infinitely often maximal. This had previously been proved for plain Kolmogorov complexity. (shrink)
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  34. Bayesians Commit the Gambler's Fallacy.Kevin Dorst - manuscript
    The gambler’s fallacy is the tendency to expect random processes to switch more often than they actually do—for example, to think that after a string of tails, a heads is more likely. It’s often taken to be evidence for irrationality. It isn’t. Rather, it’s to be expected from a group of Bayesians who begin with causal uncertainty, and then observe unbiased data from an (in fact) statistically independent process. Although they converge toward the truth, they do so in an (...)
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  35.  8
    Statistically Induced Chunking Recall: A Memory‐Based Approach to Statistical Learning.Erin S. Isbilen, Stewart M. McCauley, Evan Kidd & Morten H. Christiansen - 2020 - Cognitive Science 44 (7):e12848.
    The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short‐term memory is fundamentally shaped by long‐term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to (...)
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  36.  41
    Knowledge applied to new domains: The unconscious succeeds where the conscious fails.Ryan B. Scott & Zoltan Dienes - 2010 - Consciousness and Cognition 19 (1):391-398.
    A common view holds that consciousness is needed for knowledge acquired in one domain to be applied in a novel domain. We present evidence for the opposite; where the transfer of knowledge is achieved only in the absence of conscious awareness. Knowledge of artificial grammars was examined where training and testing occurred in different vocabularies or modalities. In all conditions grammaticality judgments attributed to random selection showed above-chance accuracy , while those attributed to conscious decisions did not. Participants also (...)
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  37.  36
    Textual Appropriation in Engineering Master’s Theses: A Preliminary Study.Edward J. Eckel - 2011 - Science and Engineering Ethics 17 (3):469-483.
    In the thesis literature review, an engineering graduate student is expected to place original research in the context of previous work by other researchers. However, for some students, particularly those for whom English is a second language, the literature review may be a mixture of original writing and verbatim source text appropriated without quotations. Such problematic use of source material leaves students vulnerable to an accusation of plagiarism, which carries severe consequences. Is such textual appropriation common in engineering master’s writing? (...)
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  38.  41
    The importance of chance and interactivity in creativity.David Kirsh - 2014 - Pragmatics and Cognition 22 (1):5-26.
    Individual creativity is standardly treated as an ‘internalist’ process occurring solely in the head. An alternative, more interactionist view is presented here, where working with objects, media and other external things is seen as a fundamental component of creative thought. The value of chance interaction and chance cueing — practices widely used in the creative arts — is explored briefly in an account of the creative method of choreographer Wayne McGregor and then more narrowly in an experimental study that compared (...)
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  39.  38
    Measuring strategic control in artificial grammar learning.Elisabeth Norman, Mark C. Price & Emma Jones - 2011 - Consciousness and Cognition 20 (4):1920-1929.
    In response to concerns with existing procedures for measuring strategic control over implicit knowledge in artificial grammar learning , we introduce a more stringent measurement procedure. After two separate training blocks which each consisted of letter strings derived from a different grammar, participants either judged the grammaticality of novel letter strings with respect to only one of these two grammars , or had the target grammar varying randomly from trial to trial which required a higher degree of conscious (...)
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  40.  27
    The Matter-Gravity Entanglement Hypothesis.Bernard S. Kay - 2018 - Foundations of Physics 48 (5):542-557.
    I outline some of my work and results on my matter-gravity entanglement hypothesis, according to which the entropy of a closed quantum gravitational system is equal to the system’s matter-gravity entanglement entropy. The main arguments presented are: that this hypothesis is capable of resolving what I call the second-law puzzle, i.e. the puzzle as to how the entropy increase of a closed system can be reconciled with the asssumption of unitary time-evolution; that the black hole information loss puzzle may be (...)
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  41. Quantum computation and pseudotelepathic games.Jeffrey Bub - 2008 - Philosophy of Science 75 (4):458-472.
    A quantum algorithm succeeds not because the superposition principle allows ‘the computation of all values of a function at once’ via ‘quantum parallelism’, but rather because the structure of a quantum state space allows new sorts of correlations associated with entanglement, with new possibilities for information‐processing transformations between correlations, that are not possible in a classical state space. I illustrate this with an elementary example of a problem for which a quantum algorithm is more efficient than any classical algorithm. I (...)
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  42.  38
    Is Classical Reality Completely Deterministic?B. P. Kosyakov - 2008 - Foundations of Physics 38 (1):76-88.
    We interpret the concept of determinism for a classical system as the requirement that the solution to the Cauchy problem for the equations of motion governing this system be unique. This requirement is generally believed to hold for all autonomous classical systems. Our analysis of classical electrodynamics in a world with one temporal and one spatial dimension provides counterexamples of this belief. Given the initial conditions of a particular type, the Cauchy problem may have an infinite set of solutions. Therefore, (...)
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  43. The Poetry of Jeroen Mettes.Samuel Vriezen & Steve Pearce - 2012 - Continent 2 (1):22-28.
    continent. 2.1 (2012): 22–28. Jeroen Mettes burst onto the Dutch poetry scene twice. First, in 2005, when he became a strong presence on the nascent Dutch poetry blogosphere overnight as he embarked on his critical project Dichtersalfabet (Poet’s Alphabet). And again in 2011, when to great critical acclaim (and some bafflement) his complete writings were published – almost five years after his far too early death. 2005 was the year in which Dutch poetry blogging exploded. That year saw the foundation (...)
     
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  44.  36
    Degrees of Monotone Complexity.William C. Calhoun - 2006 - Journal of Symbolic Logic 71 (4):1327 - 1341.
    Levin and Schnorr (independently) introduced the monotone complexity, Km(α), of a binary string α. We use monotone complexity to define the relative complexity (or relative randomness) of reals. We define a partial ordering ≤Km on 2ω by α ≤Km β iff there is a constant c such that Km(α ↾ n) ≤ Km(β ↾ n) + c for all n. The monotone degree of α is the set of all β such that α ≤Km β and β ≤Km α. We (...)
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  45. Code {poems}.Ishac Bertran - 2012 - Continent 2 (2):148-151.
    continent. 2.2 (2012): 148–151 When things get complex, as they may indeed be getting, the distinction between tools and the things that can be made with them begins to dissolve. The medium is not only also a message, it is an essential counter-valence to our own impulses towards the creation of meaning, beauty and knowledge. The tools we think we are using also use us: They push us around, make us think new things, do new things, even be new things. (...)
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  46.  31
    The Problem: First Pass.Branden Fitelson & Daniel Osherson - unknown
    Intuitively, it seems that S 1 is “more random” or “less regular” than S 2. In other words, it seems more plausible (in some sense) that S 1 (as opposed to S 2) was generated by a random process ( e.g. , by tossing a fair coin eight times, and recording an H for a heads outcome and a T for a tails outcome). We will use the notation x σ 1 ą σ 2y to express the claim (...)
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  47.  9
    Working Memory and Its Mediating Role on the Relationship of Math Anxiety and Math Performance: A Meta-Analysis.Jonatan Finell, Ellen Sammallahti, Johan Korhonen, Hanna Eklöf & Bert Jonsson - 2022 - Frontiers in Psychology 12.
    It is well established that math anxiety has a negative relationship with math performance. A few theories have provided explanations for this relationship. One of them, the Attentional Control Theory, suggests that anxiety can negatively impact the attentional control system and increase one's attention to threat-related stimuli. Within the ACT framework, the math anxiety —working memory relationship is argued to be critical for math performance. The present meta-analyses provides insights into the mechanisms of the MA—MP relation and the mediating role (...)
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  48.  30
    Kolmogorov complexity and symmetric relational structures.W. L. Fouché & P. H. Potgieter - 1998 - Journal of Symbolic Logic 63 (3):1083-1094.
    We study partitions of Fraïssé limits of classes of finite relational structures where the partitions are encoded by infinite binary strings which are random in the sense of Kolmogorov-Chaitin.
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  49.  12
    Physical theories in the context of multiverse.Ivan A. Karpenko - 2018 - Epistemology and Philosophy of Science 55 (2):139-152.
    The article analyzes the problem of physical theory nature and its criteria in the context of several concepts of modern physics. Such physical concepts allow multiple possible universes (the last usually happens to be a random consequence of the theory). Since the study requires several universe models, which basic principles (physical laws) can vary, the two theories have become the objects of analysis: the first, which includes the concept of eternal inflation, the second – the string cosmology (the string (...)
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  50.  14
    Evaluating the Effects of Metalinguistic and Working Memory Training on Reading Fluency in Chinese and English: A Randomized Controlled Trial.Tik-Sze Carrey Siu, Catherine McBride, Chi-Shing Tse, Xiuhong Tong & Urs Maurer - 2018 - Frontiers in Psychology 9.
    Children traditionally learn to read Chinese characters by rote, and thus stretching children’s memory span could possibly improve their reading in Chinese. Nevertheless, 85% of Chinese characters are semantic-phonetic compounds that contain probabilistic information about meaning and pronunciation. Hence, enhancing children’s metalinguistic skills might also facilitate reading in Chinese. In the present study we tested whether training children’s metalinguistic skills or training their working-memory capacity in eight weeks would produce reading gains, and whether these gains would be similar in Chinese (...)
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