115 research outputs found
The Unsupervised Acquisition of a Lexicon from Continuous Speech
We present an unsupervised learning algorithm that acquires a
natural-language lexicon from raw speech. The algorithm is based on the optimal
encoding of symbol sequences in an MDL framework, and uses a hierarchical
representation of language that overcomes many of the problems that have
stymied previous grammar-induction procedures. The forward mapping from symbol
sequences to the speech stream is modeled using features based on articulatory
gestures. We present results on the acquisition of lexicons and language models
from raw speech, text, and phonetic transcripts, and demonstrate that our
algorithm compares very favorably to other reported results with respect to
segmentation performance and statistical efficiency.Comment: 27 page technical repor
How to Handle Assumptions in Synthesis
The increased interest in reactive synthesis over the last decade has led to
many improved solutions but also to many new questions. In this paper, we
discuss the question of how to deal with assumptions on environment behavior.
We present four goals that we think should be met and review several different
possibilities that have been proposed. We argue that each of them falls short
in at least one aspect.Comment: In Proceedings SYNT 2014, arXiv:1407.493
Bypass transition and spot nucleation in boundary layers
The spatio-temporal aspects of the transition to turbulence are considered in
the case of a boundary layer flow developing above a flat plate exposed to
free-stream turbulence. Combining results on the receptivity to free-stream
turbulence with the nonlinear concept of a transition threshold, a physically
motivated model suggests a spatial distribution of spot nucleation events. To
describe the evolution of turbulent spots a probabilistic cellular automaton is
introduced, with all parameters directly fitted from numerical simulations of
the boundary layer. The nucleation rates are then combined with the cellular
automaton model, yielding excellent quantitative agreement with the statistical
characteristics for different free-stream turbulence levels. We thus show how
the recent theoretical progress on transitional wall-bounded flows can be
extended to the much wider class of spatially developing boundary-layer flows
Design for a Darwinian Brain: Part 1. Philosophy and Neuroscience
Physical symbol systems are needed for open-ended cognition. A good way to
understand physical symbol systems is by comparison of thought to chemistry.
Both have systematicity, productivity and compositionality. The state of the
art in cognitive architectures for open-ended cognition is critically assessed.
I conclude that a cognitive architecture that evolves symbol structures in the
brain is a promising candidate to explain open-ended cognition. Part 2 of the
paper presents such a cognitive architecture.Comment: Darwinian Neurodynamics. Submitted as a two part paper to Living
Machines 2013 Natural History Museum, Londo
Lazy Probabilistic Model Checking without Determinisation
The bottleneck in the quantitative analysis of Markov chains and Markov
decision processes against specifications given in LTL or as some form of
nondeterministic B\"uchi automata is the inclusion of a determinisation step of
the automaton under consideration. In this paper, we show that full
determinisation can be avoided: subset and breakpoint constructions suffice. We
have implemented our approach---both explicit and symbolic versions---in a
prototype tool. Our experiments show that our prototype can compete with mature
tools like PRISM.Comment: 38 pages. Updated version for introducing the following changes: -
general improvement on paper presentation; - extension of the approach to
avoid full determinisation; - added proofs for such an extension; - added
case studies; - updated old case studies to reflect the added extensio
Synthesizing Systems with Optimal Average-Case Behavior for Ratio Objectives
We show how to automatically construct a system that satisfies a given
logical specification and has an optimal average behavior with respect to a
specification with ratio costs.
When synthesizing a system from a logical specification, it is often the case
that several different systems satisfy the specification. In this case, it is
usually not easy for the user to state formally which system she prefers. Prior
work proposed to rank the correct systems by adding a quantitative aspect to
the specification. A desired preference relation can be expressed with (i) a
quantitative language, which is a function assigning a value to every possible
behavior of a system, and (ii) an environment model defining the desired
optimization criteria of the system, e.g., worst-case or average-case optimal.
In this paper, we show how to synthesize a system that is optimal for (i) a
quantitative language given by an automaton with a ratio cost function, and
(ii) an environment model given by a labeled Markov decision process. The
objective of the system is to minimize the expected (ratio) costs. The solution
is based on a reduction to Markov Decision Processes with ratio cost functions
which do not require that the costs in the denominator are strictly positive.
We find an optimal strategy for these using a fractional linear program.Comment: In Proceedings iWIGP 2011, arXiv:1102.374
An Introduction to Quantum Computing for Non-Physicists
Richard Feynman's observation that quantum mechanical effects could not be
simulated efficiently on a computer led to speculation that computation in
general could be done more efficiently if it used quantum effects. This
speculation appeared justified when Peter Shor described a polynomial time
quantum algorithm for factoring integers.
In quantum systems, the computational space increases exponentially with the
size of the system which enables exponential parallelism. This parallelism
could lead to exponentially faster quantum algorithms than possible
classically. The catch is that accessing the results, which requires
measurement, proves tricky and requires new non-traditional programming
techniques.
The aim of this paper is to guide computer scientists and other
non-physicists through the conceptual and notational barriers that separate
quantum computing from conventional computing. We introduce basic principles of
quantum mechanics to explain where the power of quantum computers comes from
and why it is difficult to harness. We describe quantum cryptography,
teleportation, and dense coding. Various approaches to harnessing the power of
quantum parallelism are explained, including Shor's algorithm, Grover's
algorithm, and Hogg's algorithms. We conclude with a discussion of quantum
error correction.Comment: 45 pages. To appear in ACM Computing Surveys. LATEX file. Exposition
improved throughout thanks to reviewers' comment
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