This paper may be regarded as a new numerical method for the analysis of triangular thin plates using the natural area coordinates. Previous studies on the solution of triangular plates with different boundary conditions are mostly based on the Rayleigh-Ritz principle which is performed in the Cartesian coordinates. Consequently, manipulation of the geometry and numerical calculation of the int...

In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new model, Embeddings for Query Expansion (CEQE), that utilizes query-focused contextualized vectors. study behavior contextual representations generated...

We develop a language model using probabilistic context-free grammars (PCFGs) that is \pseudo context-sensitive" in that the probability that a non-terminal N expands using a rule r depends on N's parent. We derive the equations for estimating the necessary probabilities using a variant of the inside-outside algorithm. We give experimental results showing that, beginning with a high-performance...

In this article, we construct a random number generator using a one dimensional, nonuniform 4-Neighborhood Cellular Automata (4NCA). A probabilistic analysis of CA rules has been done to select the appropriate rules for 4NCA random number generator (RNG). A comparison is made between the pseudo random patterns generated by using the proposed CA RNGs with those obtained using a recent Cellular P...

We develop a language model using probabilistic context-free grammars (PCFGs) that is “pseudo context-sensitive” in that the probability that a nonterminal N expands using a rule T depends on N’s parent. We give the equations for estimating the necessary probabilities using a variant of the inside-outside algorithm. We give experimental results showing that, beginning with a high-performance PC...

Zero-resource speech processing involves the automatic analysis of a collection of speech data in a completely unsupervised fashion without the benefit of any transcriptions or annotations of the data. In this thesis, we describe a zero-resource framework that automatically discovers important words, phrases and topical themes present in an audio corpus. This system employs a segmental dynamic ...

This report documents the program and the outcomes of Dagstuhl Seminar 15181 “Challenges and Trends in Probabilistic Programming”. Probabilistic programming is at the heart of machine learning for describing distribution functions; Bayesian inference is pivotal in their analysis. Probabilistic programs are used in security for describing both cryptographic constructions (such as randomised encr...

There is given an overview on the results related to the generalization of the decision theory to non probabilistic uncertainty based on the characterization of the families of operations involved in generalized mixtures. It turns out that this is based on a previous result on the characterization of the pair of continuous t-norm T and t-conorm S such that the former is conditionally distributi...

In prior work [9] we developed an approach to formally representing behavior-based multi-robot programs, and the uncertain environments in which they operate, as process networks. We automatically extract a set of probabilistic equations governing program execution in that environment using a static analysis module called VIPARS, and solve these using a Dynamic Bayesian Network (DBN) to establi...