Towards Structured Deep Neural Network for Automatic Speech Recognition

In this paper we propose the Structured Deep Neural Network (Structured DNN) as a structured and deep learning algorithm, learning to find the best structured object (such as a label sequence) given a structured input (such as a vector sequence) by globally considering the mapping relationships between the structure rather than item by item. When automatic speech recognition is viewed as a special case of such a structured learning problem, where we have the acoustic vector sequence as the input and the phoneme label sequence as the output, it becomes possible to comprehensively learned utterance by utterance as a whole, rather than frame by frame. Structured Support Vector Machine (structured SVM) was proposed to perform ASR with structured learning previously, but limited by the linear nature of SVM. Here we propose structured DNN to use nonlinear transformations in multi-layers as a structured and deep learning algorithm. It was shown to beat structured SVM in preliminary experiments on TIMIT

Influence of Mesogenic Properties of Cruciform-Shaped Liquid Crystals by Incorporating Side-Arms with a Laterally-Substituted-Fluorine

[[abstract]]Fluoro substitution in thermotropic liquid crystals provides a general way of modifying the properties of a parent system. Transition temperatures, mesophase types and other physical properties can be affected by fluoro substitution, so that frequently the behaviors of the parent compound can be manipulated and improved in a predictable manner. This paper discusses the effects of a fluoro substitution in each side-arm of 1,2,4,5-tetrakis((4-(alkoxy)phenyl)ethynyl)benzenes on the resulting mesomorphic properties characterized by optical polarizing microscopy and differential scanning calorimetry. Without any fluoro-substituted side-arms, longer chain-length leads to a wider nematic temperature range on cooling. Incorporation of a fluoro substitution in each side-arm induces the formation of a lamellar suprastructure, lowers transition temperatures and results in a wider mesophase temperature range on cooling.[[sponsorship]]國科會[[booktype]]電子

The role of macroeconomic policy in export-led growth

노트 : - This PDF is a selection from an out-of-print volume from the National Bureau of Economic Research -Volume Title: Financial Deregulation and Integration in East Asia, NBER-EASE Volume

Critical Neurons: optimized visual recognition in a neuronal network

In the past few decades, there have been intense debates whether the brain operates at a critical state. To verify the criticality hypothesis in the neuronal networks is challenging and the accumulating experimental and theoretical results remain controversial at this point. Here we simulate how visual information of a nature image is processed by the finite Kinouchi-Copelli neuronal network, extracting the trends of the mutual information (how sensible the neuronal network is), the dynamical range (how sensitive the network responds to external stimuli) and the statistical fluctuations (how criticality is defined in conventional statistical physics). It is rather remarkable that the optimized state for visual recognition, although close to, does not coincide with the critical state where the statistical fluctuations reach the maximum. Different images and/or network sizes of course lead to differences in details but the trend of the information optimization remains the same. Our findings pave the first step to investigate how the information processing is optimized in different neuronal networks and suggest that the criticality hypothesis may not be necessary to explain why a neuronal network can process information smartly.Comment: 11 pages, 4 figures in the main text and 3 figures in the Supplementary Informatio