33,011 research outputs found
A Continuous-Discontinuous Second-Order Transition in the Satisfiability of Random Horn-SAT Formulas
We compute the probability of satisfiability of a class of random Horn-SAT
formulae, motivated by a connection with the nonemptiness problem of finite
tree automata. In particular, when the maximum clause length is 3, this model
displays a curve in its parameter space along which the probability of
satisfiability is discontinuous, ending in a second-order phase transition
where it becomes continuous. This is the first case in which a phase transition
of this type has been rigorously established for a random constraint
satisfaction problem
Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality
The mutual information between stimulus and spike-train response is commonly
used to monitor neural coding efficiency, but neuronal computation broadly
conceived requires more refined and targeted information measures of
input-output joint processes. A first step towards that larger goal is to
develop information measures for individual output processes, including
information generation (entropy rate), stored information (statistical
complexity), predictable information (excess entropy), and active information
accumulation (bound information rate). We calculate these for spike trains
generated by a variety of noise-driven integrate-and-fire neurons as a function
of time resolution and for alternating renewal processes. We show that their
time-resolution dependence reveals coarse-grained structural properties of
interspike interval statistics; e.g., -entropy rates that diverge less
quickly than the firing rate indicate interspike interval correlations. We also
find evidence that the excess entropy and regularized statistical complexity of
different types of integrate-and-fire neurons are universal in the
continuous-time limit in the sense that they do not depend on mechanism
details. This suggests a surprising simplicity in the spike trains generated by
these model neurons. Interestingly, neurons with gamma-distributed ISIs and
neurons whose spike trains are alternating renewal processes do not fall into
the same universality class. These results lead to two conclusions. First, the
dependence of information measures on time resolution reveals mechanistic
details about spike train generation. Second, information measures can be used
as model selection tools for analyzing spike train processes.Comment: 20 pages, 6 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/trdctim.ht
The use of data-mining for the automatic formation of tactics
This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques
Fast algorithms for computing isogenies between ordinary elliptic curves in small characteristic
The problem of computing an explicit isogeny between two given elliptic
curves over F_q, originally motivated by point counting, has recently awaken
new interest in the cryptology community thanks to the works of Teske and
Rostovstev & Stolbunov.
While the large characteristic case is well understood, only suboptimal
algorithms are known in small characteristic; they are due to Couveignes,
Lercier, Lercier & Joux and Lercier & Sirvent. In this paper we discuss the
differences between them and run some comparative experiments. We also present
the first complete implementation of Couveignes' second algorithm and present
improvements that make it the algorithm having the best asymptotic complexity
in the degree of the isogeny.Comment: 21 pages, 6 figures, 1 table. Submitted to J. Number Theor
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