Training of continuous density hidden Markov models (CDHMMs) is usually time-consuming and tedious due to the large number of model parameters involved. Recently we proposed a new derivative of CDHMM, the sub-space distribution clustering hidden Markov model (SD-CHMM) which tie CDHMMs at the ner level of subspace distributions, resulting in many fewer model parameters. An SDCHMM training algori...

The impact on recall of reinstatement or change of the environment existing at the time of learning is reviewed. While strong and stable effects have been observed for recall, effects on recognition rarely occur because the word itself is a powerful cue (the outshining hypothesis). A classification of context effects is presented and the conditions where such effects are likely to be maximized ...

Context-dependent interpretations are a termination proof method developed by Hofbauer in 2001. They extend the interpretations into F-algebras by introducing an additional parameter to the interpretation functions. The additional parameter is changed by the context of the evaluated subterm, thus giving rise to the name “context-dependent interpretations”. They were designed to give good upper ...

Context-dependent metabolic networks Mariano Beguerisse-Dı́az1,2∗, Gabriel Bosque3,†, Diego Oyarzún, Jesús Picó, Mauricio Barahona1,‡ 1 Department of Mathematics, Imperial College London, London, SW7 2AZ, U.K. 2 Mathematical Institute, University of Oxford, Oxford, OX2 6GG, U.K. 3 Institut Universitari d’Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camı́ de Vera s/n, ...

Abstra t. A hara ter-based en oding method is presented for naturallanguage texts and geneti data. Exa t string mat hing from the en oded text is faster than from the original text, with medium and longer patterns. A ompression ratio of about 50% is a hieved as a by-produ t. The method en odes hara ters with variable-length odewords of 2-bit base symbols. An advan ed variant is ontext-dependent...

Computer speech recognition gains more and more attention these days with its implementation in nearly everyday life. But the ultimate goal is still out of reach. The automatic recognition (ASR) systems can very precisely work on small domain. However the bigger the domain is the worse is the performance of the ASR system. The aim of many researchers is to diminish this problem on various level...

Cells process extra-cellular signals with multiple layers of complex biological networks. Due to the stochastic nature of the networks, the signals become significantly noisy within the cells and in addition, due to the nonlinear nature of the networks, the signals become distorted, shifted, and (de-)amplified. Such nonlinear signal processing can lead to non-trivial cellular phenotypes such as...

Word embedding has been widely studied and proven helpful in solving many natural language processing tasks. However, the ambiguity of natural language is always a problem on learning high quality word embeddings. A possible solution is sense embedding which trains embedding for each sense of words instead of each word. Some recent work on sense embedding uses context clustering methods to dete...

The pattern of generalization following motor learning can provide a probe on the neural mechanisms underlying learning. For example, the breadth of generalization to untrained regions of space after visuomotor adaptation to targets in a restricted region of space has been attributed to the directional tuning properties of neurons in the motor system. Building on this idea, the effect of differ...

This article presents the concept of context dependent classiication. In context dependent classiication of elementary objects, an elementary object is represented by a node in a graph. A node can be identiied to belong to a certain node class, if one of the characterizing contexts for that class can be embedded in the graph properly. Thus, the classiication of a node does not only depend on pr...