Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM

We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder. During the beam search process, we combine the CTC predictions, the attention-based decoder predictions and a separately trained LSTM language model. We achieve a 5-10\% error reduction compared to prior systems on spontaneous Japanese and Chinese speech, and our end-to-end model beats out traditional hybrid ASR systems.Comment: Accepted for INTERSPEECH 201

Fate control and well-being in Chinese rural people living with HIV: Mediation effect of resilience

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Call Audio Quality Determination and Root Cause Analysis Using Machine Learning

Accurate assessment and categorization of real-world audio quality in a call, e.g., a call over VoLTE/VoNR, is essential to provide a satisfactory call experience. However, current techniques to determine call quality do not accurately categorize the audio quality. Also, there are no techniques to determine the root cause of poor audio quality or to identify potential solutions. This disclosure describes the use of machine learning clustering techniques to cluster audio metrics and using the obtained clusters to generate a root cause table. Further, a classifier is trained to determine whether an ongoing call has unsatisfactory audio quality. The quality can be categorized and labeled, e.g., good, mildly choppy, severely choppy, and no audio. If the audio quality is unsatisfactory, the likely root cause is identified using the root cause table to identify and apply solutions while the call is in progress. The described techniques are a closed loop technique to identify solutions to audio quality problems in an audio call

Development of compress air transportation (CAT) for alternative energy utilization

Compress Air Transportation (CAT) is a new concept of transportation that utilizes air as the source of energy. Work on alternative power system for vehicle is becoming very important for the future due to combination of high prices on fuel, emission factor and also the source of the current energy will eventually run out. Several advantages for utilizing air as the source of energy compared to other alternative energy sources makes it become the subject for this project. In this project, a simple transportation utilizing air as the source of energy has been developed. The main component of the engine of the CAT is a device called the "Air Impact Wrench" which can be purchased from local stores. The CAT was tested and analyzed for further studies on expanding this new technology

An Analysis of Mode III Doubly Periodic Crack-Tip Field of Orthotropic Composite Materials

The mechanical behavior near the doubly periodic crack tips for orthotropic composite materials plate subjected to antiplane shear loading is studied. This is done by complex function theory and conformal mapping of the Jacobi elliptic function with the help of boundary conditions. The analytical solution of the crack-tips stress intensity factor and the expression of stress fields are obtained. Numerical examples are given to analyze the impact of the different transverse spacing, longitudinal spacing, and the ratio of cracks periods on stress intensity factors. The results show that the crack-tip field increases with reducing either the transverse spacing or the longitudinal spacing. At the same time, the crack-tip field increases with the decrease of the ratio of cracks periods. This shows that the distribution form makes an important effect on the crack-tip field, but the crack density parameter is not the only cause

Prevalence of insomnia symptoms and their associated factors in patients treated in outpatient clinics of four general hospitals in Guangzhou, China

Background: Data on the prevalence of insomnia symptoms in medical outpatient clinics in China are lacking. This study examined the prevalence of insomnia symptoms and their socio-demographic correlates in patients treated at medical outpatient clinics affiliated with four general hospitals in Guangzhou, a large metropolis in southern China. Method: A total of 4399 patients were consecutively invited to participate in the study. Data on insomnia and its socio-demographic correlates were collected with standardized questionnaires. Results: The prevalence of any type of insomnia symptoms was 22.1% (95% confidence interval (CI): 20.9–23.3%); the prevalence of difficulty initiating sleep was 14.3%, difficulty maintaining sleep was 16.2%, and early morning awakening was 12.4%. Only 17.5% of the patients suffering from insomnia received sleeping pills. Multiple logistic regression analysis revealed that male gender, education level, rural residence, and being unemployed or retired were negatively associated with insomnia symptoms, while lacking health insurance, older age and more severe depressive symptoms were positively associated with insomnia symptoms. Conclusions: Insomnia symptoms are common in patients attending medical outpatient clinics in Guangzhou. Increasing awareness of sleep hygiene measures, regular screening and psychosocial and pharmacological interventions for insomnia are needed in China. Trial registration: ChiCTR-INR-16008066. Registered 8 March 2016