Speech Recorder and Translator using Google Cloud Speech-to-Text and Translation

The most popular video website YouTube has about 2 billion users worldwide who speak and understand different languages. Subtitles are essential for the users to get the message from the video. However, not all video owners provide subtitles for their videos. It causes the potential audiences to have difficulties in understanding the video content. Thus, this study proposed a speech recorder and translator to solve this problem. The general concept of this study was to combine Automatic Speech Recognition (ASR) and translation technologies to recognize the video content and translate it into other languages. This paper compared and discussed three different ASR technologies. They are Google Cloud Speech-to-Text, Limecraft Transcriber, and VoxSigma. Finally, the proposed system used Google Cloud Speech-to-Text because it supports more languages than Limecraft Transcriber and VoxSigma. Besides, it was more flexible to use with Google Cloud Translation. This paper also consisted of a questionnaire about the crucial features of the speech recorder and translator. There was a total of 19 university students participated in the questionnaire. Most of the respondents stated that high translation accuracy is vital for the proposed system. This paper also discussed a related work of speech recorder and translator. It was a study that compared speech recognition between ordinary voice and speech impaired voice. It used a mobile application to record acoustic voice input. Compared to the existing mobile App, this project proposed a web application. It was a different and new study, especially in terms of development and user experience. Finally, this project developed the proposed system successfully. The results showed that Google Cloud Speech-to-Text and Translation were reliable to use in video translation. However, it could not recognize the speech when the background music was too loud. Besides, it had a problem of direct translation, which was challenging. Thus, future research may need a custom trained model. In conclusion, the proposed system in this project was to contribute a new idea of a web application to solve the language barrier on the video watching platform

Acute Encephalopathy Associated with Influenza A Infection in Adults

We report acute encephalopathy associated with influenza A infection in 3 adults. We detected high cerebrospinal fluid (CSF) and plasma concentrations of CXCL8/IL-8 and CCL2/MCP-1 (CSF/plasma ratios >3), and interleukin-6, CXCL10/IP-10, but no evidence of viral neuroinvasion. Patients recovered without sequelae. Hyperactivated cytokine response may play a role in pathogenesis

BESII Detector Simulation

A Monte Carlo program based on Geant3 has been developed for BESII detector simulation. The organization of the program is outlined, and the digitization procedure for simulating the response of various sub-detectors is described. Comparisons with data show that the performance of the program is generally satisfactory.Comment: 17 pages, 14 figures, uses elsart.cls, to be submitted to NIM

Human Metapneumovirus Detection in Patients with Severe Acute Respiratory Syndrome

We used a combination approach of conventional virus isolation and molecular techniques to detect human metapneumovirus (HMPV) in patients with severe acute respiratory syndrome (SARS). Of the 48 study patients, 25 (52.1%) were infected with HMPV; 6 of these 25 patients were also infected with coronavirus, and another 5 patients (10.4%) were infected with coronavirus alone. Using this combination approach, we found that human laryngeal carcinoma (HEp-2) cells were superior to rhesus monkey kidney (LLC-MK2) cells commonly used in previous studies for isolation of HMPV. These widely available HEp-2 cells should be included in conjunction with a molecular method for cell culture followup to detect HMPV, particularly in patients with SARS

Stochastic programming approaches to stochastic scheduling

Practical scheduling problems typically require decisions without full information about the outcomes of those decisions. Yields, resource availability, performance, demand, costs, and revenues may all vary. Incorporating these quantities into stochastic scheduling models often produces diffculties in analysis that may be addressed in a variety of ways. In this paper, we present results based on stochastic programming approaches to the hierarchy of decisions in typical stochastic scheduling situations. Our unifying framework allows us to treat all aspects of a decision in a similar framework. We show how views from different levels enable approximations that can overcome nonconvexities and duality gaps that appear in deterministic formulations. In particular, we show that the stochastic program structure leads to a vanishing Lagrangian duality gap in stochastic integer programs as the number of scenarios increases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44935/1/10898_2004_Article_BF00121682.pd

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Peer reviewe

Chemical analysis of etching residues in metal gate stack for CMOS process

Studies in Surface Science and Catalysis159365-368SSCT

Client-server synchronization and buffering for variable rate multimedia retrievals

We consider the use of large buffers and feedback as a mechanism to maintain loosely coupled synchronization between a multimedia server and a client. The multimedia stream is modeled as a fluid flow through rate controlled valves and buffers with multiple thresholds. These thresholds are used to control the rates upstream. The quality of service for the multimedia connection is characterized in terms of the jitter in the received media stream due to buffer underflow and overflow. This quality of service is used to exercise rate and admission control in the presence of congestion. The feedback mechanism is, implemented in GRAMS, an adaptive multimedia client-server system. Experimental statistics are gathered for the purpose of traffic engineering. We employ a fluid flow and first passage time analysis to understand the traffic process through the pipelines and the buffers and to estimate the amount of signaling required by the feedback mechanism