Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201

MATEX: A Distributed Framework for Transient Simulation of Power Distribution Networks

We proposed MATEX, a distributed framework for transient simulation of power distribution networks (PDNs). MATEX utilizes matrix exponential kernel with Krylov subspace approximations to solve differential equations of linear circuit. First, the whole simulation task is divided into subtasks based on decompositions of current sources, in order to reduce the computational overheads. Then these subtasks are distributed to different computing nodes and processed in parallel. Within each node, after the matrix factorization at the beginning of simulation, the adaptive time stepping solver is performed without extra matrix re-factorizations. MATEX overcomes the stiff-ness hinder of previous matrix exponential-based circuit simulator by rational Krylov subspace method, which leads to larger step sizes with smaller dimensions of Krylov subspace bases and highly accelerates the whole computation. MATEX outperforms both traditional fixed and adaptive time stepping methods, e.g., achieving around 13X over the trapezoidal framework with fixed time step for the IBM power grid benchmarks.Comment: ACM/IEEE DAC 2014. arXiv admin note: substantial text overlap with arXiv:1505.0669

Nanographite/polyaniline composite films as the counter electrodes for dye-sensitized solar cells

100學年度研究獎補助論文[[abstract]]Nanographite/polyaniline (NG/PANI) composite films were developed and characterized, and the performances of the dye-sensitized solar cells (DSSCs) employing these composite films as the counter electrode (CE) were evaluated in this study. The nanographite/aniline (NG/ANI) particle was firstly synthesized by a reflux method and served as the monomer for the electro-polymerization of the NG/PANI composite films. The surface modification of NG by ANI was confirmed by EDX mapping, TEM image, zeta-potential, and UV-Vis absorption measurements. The electro-polymerized NG/PANI composite films were characterized by Raman spectroscopy, XPS, and conducting-AFM, which verified the successful incorporation of NGs in the PANI films. The electro-catalytic activity of the NG/PANI composite film was evaluated using the positive-feedback mode of scanning electrochemical microscopy (SECM), by which a comparable heterogeneous rate constant (ks0) for the ferrocene/ferrocenium (Fc/Fc+) redox pair was obtained and compared with that of a sputtered Pt. The DSSC employing the NG/PANI (20 mC cm−2) CE exhibited a higher short-circuit current density (JSC) but lower fill factor (FF), and gave a comparable power-conversion efficiency (η) of 7.07%, as compared to that of a DSSC containing a sputtered Pt CE (η = 7.19%).[[incitationindex]]SCI[[booktype]]紙

Effects of Curriculum Design on Students’ Creative Potential Developing — A Case Study on Students in the Department of Business Management

The rapid advance of science technology and civilization has resulted in people’s activities being more complicated as various new problems are likely to occur at any time. Problem-solving abilities therefore become a basic competence to survive in modern societies. In the problem-solving process, the development of creativity is required to break through dilemmas. School education aims to cultivate students’ decision-making and problem-solving competence. Nonetheless, the educational approaches and contents in Taiwan stress too much on mastery learning, and ignore the development of curiosity and creative potential. Aiming at the students in the department of business management in national universities in Taiwan, total 300 copies of questionnaires are distributed, and 187 valid copies are retrieved, with the retrieval rate 62%, in which each retrieved copy is regarded as a valid sample. The research findings show that Curriculum Design presents partially positive effects on Fluency, Flexibility, Originality, and Elaboration in Creative Potential Developing and Background Variables reveal significant moderating effects on the correlations between Curriculum Design and Creative Potential Developing

The Improvement of Reliability of High-k/Metal Gate pMOSFET Device with Various PMA Conditions

The oxygen and nitrogen were shown to diffuse through the TiN layer in the high-k/metal gate devices during PMA. Both the oxygen and nitrogen annealing will reduce the gate leakage current without increasing oxide thickness. The threshold voltages of the devices changed with various PMA conditions. The reliability of the devices, especially for the oxygen annealed devices, was improved after PMA treatments

BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature

BACKGROUND: To automatically process large quantities of biological literature for knowledge discovery and information curation, text mining tools are becoming essential. Abbreviation recognition is related to NER and can be considered as a pair recognition task of a terminology and its corresponding abbreviation from free text. The successful identification of abbreviation and its corresponding definition is not only a prerequisite to index terms of text databases to produce articles of related interests, but also a building block to improve existing gene mention tagging and gene normalization tools. RESULTS: Our approach to abbreviation recognition (AR) is based on machine-learning, which exploits a novel set of rich features to learn rules from training data. Tested on the AB3P corpus, our system demonstrated a F-score of 89.90% with 95.86% precision at 84.64% recall, higher than the result achieved by the existing best AR performance system. We also annotated a new corpus of 1200 PubMed abstracts which was derived from BioCreative II gene normalization corpus. On our annotated corpus, our system achieved a F-score of 86.20% with 93.52% precision at 79.95% recall, which also outperforms all tested systems. CONCLUSION: By applying our system to extract all short form-long form pairs from all available PubMed abstracts, we have constructed BIOADI. Mining BIOADI reveals many interesting trends of bio-medical research. Besides, we also provide an off-line AR software in the download section on http://bioagent.iis.sinica.edu.tw/BIOADI/