777,398 research outputs found
Quantum Hamiltonian Complexity
Constraint satisfaction problems are a central pillar of modern computational
complexity theory. This survey provides an introduction to the rapidly growing
field of Quantum Hamiltonian Complexity, which includes the study of quantum
constraint satisfaction problems. Over the past decade and a half, this field
has witnessed fundamental breakthroughs, ranging from the establishment of a
"Quantum Cook-Levin Theorem" to deep insights into the structure of 1D
low-temperature quantum systems via so-called area laws. Our aim here is to
provide a computer science-oriented introduction to the subject in order to
help bridge the language barrier between computer scientists and physicists in
the field. As such, we include the following in this survey: (1) The
motivations and history of the field, (2) a glossary of condensed matter
physics terms explained in computer-science friendly language, (3) overviews of
central ideas from condensed matter physics, such as indistinguishable
particles, mean field theory, tensor networks, and area laws, and (4) brief
expositions of selected computer science-based results in the area. For
example, as part of the latter, we provide a novel information theoretic
presentation of Bravyi's polynomial time algorithm for Quantum 2-SAT.Comment: v4: published version, 127 pages, introduction expanded to include
brief introduction to quantum information, brief list of some recent
developments added, minor changes throughou
A simple reconstruction of GPSG
Like most linguistic theories, the theory of generalized phrase structure grammar (GPSG) has described language axiomatically, that is, as a set of universal and language-specific constraints on the well-formedness of linguistic elements of some sort. The coverage and detailed analysis of English grammar in the ambitious recent volume by Gazdar, Klein, Pullum, and Sag entitled Generalized Phrase Structure Grammar are impressive, in part because of the complexity of the axiomatic system developed by the authors. In this paper. We examine the possibility that simpler descriptions of the same theory can be achieved through a slightly different, albeit still axiomatic, method. Rather than characterize the well-formed trees directly, we progress in two stages by procedurally characterizing the well-formedness axioms themselves, which in turn characterize the trees.Engineering and Applied Science
What Syntactic Structures block Dependencies in RNN Language Models?
Recurrent Neural Networks (RNNs) trained on a language modeling task have
been shown to acquire a number of non-local grammatical dependencies with some
success. Here, we provide new evidence that RNN language models are sensitive
to hierarchical syntactic structure by investigating the filler--gap dependency
and constraints on it, known as syntactic islands. Previous work is
inconclusive about whether RNNs learn to attenuate their expectations for gaps
in island constructions in particular or in any sufficiently complex syntactic
environment. This paper gives new evidence for the former by providing control
studies that have been lacking so far. We demonstrate that two state-of-the-art
RNN models are are able to maintain the filler--gap dependency through
unbounded sentential embeddings and are also sensitive to the hierarchical
relationship between the filler and the gap. Next, we demonstrate that the
models are able to maintain possessive pronoun gender expectations through
island constructions---this control case rules out the possibility that island
constructions block all information flow in these networks. We also evaluate
three untested islands constraints: coordination islands, left branch islands,
and sentential subject islands. Models are able to learn left branch islands
and learn coordination islands gradiently, but fail to learn sentential subject
islands. Through these controls and new tests, we provide evidence that model
behavior is due to finer-grained expectations than gross syntactic complexity,
but also that the models are conspicuously un-humanlike in some of their
performance characteristics.Comment: To Appear at the 41st Annual Meeting of the Cognitive Science
Society, Montreal, Canada, July 201
Exploring the movement dynamics of deception
Both the science and the everyday practice of detecting a lie rest on the same assumption: hidden cognitive states that the liar would like to remain hidden nevertheless influence observable behavior. This assumption has good evidence. The insights of professional interrogators, anecdotal evidence, and body language textbooks have all built up a sizeable catalog of non-verbal cues that have been claimed to distinguish deceptive and truthful behavior. Typically, these cues are discrete, individual behaviors—a hand touching a mouth, the rise of a brow—that distinguish lies from truths solely in terms of their frequency or duration. Research to date has failed to establish any of these non-verbal cues as a reliable marker of deception. Here we argue that perhaps this is because simple tallies of behavior can miss out on the rich but subtle organization of behavior as it unfolds over time. Research in cognitive science from a dynamical systems perspective has shown that behavior is structured across multiple timescales, with more or less regularity and structure. Using tools that are sensitive to these dynamics, we analyzed body motion data from an experiment that put participants in a realistic situation of choosing, or not, to lie to an experimenter. Our analyses indicate that when being deceptive, continuous fluctuations of movement in the upper face, and somewhat in the arms, are characterized by dynamical properties of less stability, but greater complexity. For the upper face, these distinctions are present despite no apparent differences in the overall amount of movement between deception and truth. We suggest that these unique dynamical signatures of motion are indicative of both the cognitive demands inherent to deception and the need to respond adaptively in a social context
A Stuady on the Application of Flipped Classroom in ESP Teaching: Taking English for Science and Technology as an Example
English for Specific Purposes (ESP) aims to cultivate students’ ability to use language in a professional field, thus, it has a strong application value. However, due to the abstractness of its vocabulary, complexity and diversity of its sentence structure and the obscurity of its contents, students are intimidated and unable to achieve the intended teaching goals. Based on modern information technology, flipped classroom teaching models provide direction for ESP curriculum reforms such as English for science and technology. ESP courses, based on flipped classrooms, integrate the time inside and outside the classroom through rich teaching design before, during, and after class, thus achieving the goal of improving teaching effects of ESP courses
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Complexity, Parsing, and Factorization of Tree-Local Multi-Component Tree-Adjoining Grammar
Tree-Local Multi-Component Tree-Adjoining Grammar (TL-MCTAG) is an appealing formalism for natural language representation because it arguably allows the encapsulation of the appropriate domain of locality within its elementary structures. Its multicomponent structure allows modeling of lexical items that may ultimately have elements far apart in a sentence, such as quantifiers and Wh-words. When used as the base formalism for a synchronous grammar, its flexibility allows it to express both the close relationships and the divergent structure necessary to capture the links between the syntax and semantics of a single language or the syntax of two different languages. Its limited expressivity provides constraints on movement and, we posit, may have generated additional popularity based on a misconception about its parsing complexity. Although TL-MCTAG was shown to be equivalent in expressivity to TAG when it was first introduced (Weir 1988), the complexity of TL-MCTAG is still not well-understood. This paper offers a thorough examination of the problem of TL-MCTAG recognition, showing that even highly restricted forms of TL-MCTAG are NP-complete to recognize. However, in spite of the provable difficulty of the recognition problem, we offer several algorithms that can substantially improve processing efficiency. First, we present a parsing algorithm that improves on the baseline parsing method and runs in polynomial time when both the fan-out and rank of the input grammar are bounded. Second, we offer an optimal, efficient algorithm for factorizing a grammar to produce a strongly-equivalent TL-MCTAG grammar with the rank of the grammar minimized.Engineering and Applied Science
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