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This article extends the concept of compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a…

Probability · Mathematics 2010-11-10 Holger Rauhut , Karin Schnass , Pierre Vandergheynst

The paper concerns the probabilistic evaluation of plans in the presence of unmeasured variables, each plan consisting of several concurrent or sequential actions. We establish a graphical criterion for recognizing when the effects of a…

Artificial Intelligence · Computer Science 2013-02-21 Judea Pearl , James M. Robins

Collatz Conjecture sequences increase and decrease in seemingly random fashion. By identifying and analyzing the forms of numbers, we discover that Collatz sequences are governed by very specific, well-defined rules, which we call cascades.

General Mathematics · Mathematics 2022-09-14 H. Nelson Crooks , Chigozie Nwoke

Consider a finite set of sources, each producing i.i.d. observations that follow a unique probability distribution on a finite alphabet. We study the problem of matching a finite set of observed sequences to the set of sources under the…

Information Theory · Computer Science 2014-12-09 Jayakrishnan Unnikrishnan

Compressed sensing (CS) with prior information concerns the problem of reconstructing a sparse signal with the aid of a similar signal which is known beforehand. We consider a new approach to integrate the prior information into CS via…

Information Theory · Computer Science 2017-05-23 Xu Zhang , Wei Cui , Yulong Liu

The Poisson process is the most elementary continuous-time stochastic process that models a stream of repeating events. It is uniquely characterised by a single parameter called the rate. Instead of a single value for this rate, we here…

Probability · Mathematics 2019-06-05 Alexander Erreygers , Jasper De Bock

The problem of compressing a real-valued sparse source using compressive sensing techniques is studied. The rate distortion optimality of a coding scheme in which compressively sensed signals are quantized and then reconstructed is…

Information Theory · Computer Science 2010-11-09 Rajiv Soundararajan , Sriram Vishwanath

Sampling is often a necessary evil to reduce the processing and storage costs of distributed tracing. In this work, we describe a scalable and adaptive sampling approach that can preserve events of interest better than the widely used…

Data Structures and Algorithms · Computer Science 2021-07-19 Otmar Ertl

Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. We describe a system which performs unrestricted word sense disambiguation (on all content words in free text) by combining…

cmp-lg · Computer Science 2007-05-23 Yorick Wilks , Mark Stevenson

The automatic summarization of multimedia sources is an important task that facilitates the understanding of an individual by condensing the source while maintaining relevant information. In this paper we focus on audio summarization based…

Computation and Language · Computer Science 2020-04-03 Carlos-Emiliano González-Gallardo , Romain Deveaud , Eric SanJuan , Juan-Manuel Torres-Moreno

Provenance, or information about the sources, derivation, custody or history of data, has been studied recently in a number of contexts, including databases, scientific workflows and the Semantic Web. Many provenance mechanisms have been…

Logic in Computer Science · Computer Science 2010-06-09 James Cheney

We consider a finite mixture model with varying mixing probabilities. Linear regression models are assumed for observed variables with coefficients depending on the mixture component the observed subject belongs to. A modification of the…

Probability · Mathematics 2016-01-07 Daryna Liubashenko , Rostyslav Maiboroda

Correlated sources are present in communication systems where protocols ensure that there is some predetermined information for sources to transmit. Here, two correlated sources across a channel with eavesdroppers are investigated, and…

Information Theory · Computer Science 2014-11-06 R. Balmahoon , H. Vinck , L. Cheng

Compressed sensing is a technique for recovering an unknown sparse signal from a small number of linear measurements. When the measurement matrix is random, the number of measurements required for perfect recovery exhibits a phase…

Optimization and Control · Mathematics 2016-12-30 Mateo Díaz , Mauricio Junca , Felipe Rincón , Mauricio Velasco

In this work, we explore the novel idea of employing dependency parsing information in the context of few-shot learning, the task of learning the meaning of a rare word based on a limited amount of context sentences. Firstly, we use…

Computation and Language · Computer Science 2022-05-13 Stefania Preda , Guy Emerson

Observations on the past provide some hints about what will happen in the future, and this can be quantified using information theory. The ``predictive information'' defined in this way has connections to measures of complexity that have…

Statistical Mechanics · Physics 2007-05-23 William Bialek , Naftali Tishby

We call $i$ a fixed point of a given sequence if the value of that sequence at the $i$-th position coincides with $i$. Here, we enumerate fixed points in the class of restricted growth sequences. The counting process is conducted by…

Combinatorics · Mathematics 2021-06-25 Toufik Mansour , Reza Rastegar

The problem of performing similarity queries on compressed data is considered. We focus on the quadratic similarity measure, and study the fundamental tradeoff between compression rate, sequence length, and reliability of queries performed…

Information Theory · Computer Science 2016-11-18 Amir Ingber , Thomas Courtade , Tsachy Weissman

What is the minimum number of guesses needed on average to correctly guess a realization of a random variable? The answer to this question led to the introduction of the notion of a quantity called guesswork by Massey in 1994, which can be…

Quantum Physics · Physics 2021-12-28 Eric P. Hanson , Vishal Katariya , Nilanjana Datta , Mark M. Wilde

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data…

Machine Learning · Statistics 2017-12-25 Thibault Laugel , Marie-Jeanne Lesot , Christophe Marsala , Xavier Renard , Marcin Detyniecki
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