GREY STATISTICS METHOD OF TECHNOLOGY SELECTION FOR ADVANCED PUBLIC TRANSPORTATION SYSTEMS

Taiwan is involved in intelligent transportation systems planning, and is now selecting its prior focus areas for investment and development. The high social and economic impact associated with which intelligent transportation systems technology are chosen explains the efforts of various electronics and transportation corporations for developing intelligent transportation systems technology to expand their business opportunities. However, there has been no detailed research conducted with regard to selecting technology for advanced public transportation systems in Taiwan. Thus, the present paper demonstrates a grey statistics method integrated with a scenario method for solving the problem of selecting advanced public transportation systems technology for Taiwan. A comprehensive questionnaire survey was conducted to demonstrate the effectiveness of the grey statistics method. The proposed approach indicated that contactless smart card technology is the appropriate technology for Taiwan to develop in the near future. The significance of our research results implies that the grey statistics method is an effective method for selecting advanced public transportation systems technologies. We feel our information will be beneficial to the private sector for developing an appropriate intelligent transportation systems technology strategy. Document type: Articl

AV2Wav: Diffusion-Based Re-synthesis from Continuous Self-supervised Features for Audio-Visual Speech Enhancement

Speech enhancement systems are typically trained using pairs of clean and noisy speech. In audio-visual speech enhancement (AVSE), there is not as much ground-truth clean data available; most audio-visual datasets are collected in real-world environments with background noise and reverberation, hampering the development of AVSE. In this work, we introduce AV2Wav, a resynthesis-based audio-visual speech enhancement approach that can generate clean speech despite the challenges of real-world training data. We obtain a subset of nearly clean speech from an audio-visual corpus using a neural quality estimator, and then train a diffusion model on this subset to generate waveforms conditioned on continuous speech representations from AV-HuBERT with noise-robust training. We use continuous rather than discrete representations to retain prosody and speaker information. With this vocoding task alone, the model can perform speech enhancement better than a masking-based baseline. We further fine-tune the diffusion model on clean/noisy utterance pairs to improve the performance. Our approach outperforms a masking-based baseline in terms of both automatic metrics and a human listening test and is close in quality to the target speech in the listening test. Audio samples can be found at https://home.ttic.edu/~jcchou/demo/avse/avse_demo.html.Comment: Submitted to ICASSP 202

iPDA: integrated protein disorder analyzer

This article presents a web server iPDA, which aims at identifying the disordered regions of a query protein. Automatic prediction of disordered regions from protein sequences is an important problem in the study of structural biology. The proposed classifier DisPSSMP2 is different from several existing disorder predictors by its employment of position-specific scoring matrices with respect to physicochemical properties (PSSMP), where the physicochemical properties adopted here especially take the disorder propensity of amino acids into account. The web server iPDA integrates DisPSSMP2 with several other sequence predictors in order to investigate the functional role of the detected disordered region. The predicted information includes sequence conservation, secondary structure, sequence complexity and hydrophobic clusters. According to the proportion of the secondary structure elements predicted, iPDA dynamically adjusts the cutting threshold of determining protein disorder. Furthermore, a pattern mining package for detecting sequence conservation is embedded in iPDA for discovering potential binding regions of the query protein, which is really helpful to uncovering the relationship between protein function and its primary sequence. The web service is available at http://biominer.bime.ntu.edu.tw/ipda and mirrored at http://biominer.cse.yzu.edu.tw/ipda

Few-Shot Spoken Language Understanding via Joint Speech-Text Models

Recent work on speech representation models jointly pre-trained with text has demonstrated the potential of improving speech representations by encoding speech and text in a shared space. In this paper, we leverage such shared representations to address the persistent challenge of limited data availability in spoken language understanding tasks. By employing a pre-trained speech-text model, we find that models fine-tuned on text can be effectively transferred to speech testing data. With as little as 1 hour of labeled speech data, our proposed approach achieves comparable performance on spoken language understanding tasks (specifically, sentiment analysis and named entity recognition) when compared to previous methods using speech-only pre-trained models fine-tuned on 10 times more data. Beyond the proof-of-concept study, we also analyze the latent representations. We find that the bottom layers of speech-text models are largely task-agnostic and align speech and text representations into a shared space, while the top layers are more task-specific

THE EFFECT OF TWO DIFFERENT WEIGHTED BADMINTON RACKETS ABOUT VELOCITY AND TORQUE WHEN OUTSTANDING BADMINTON PLAYERS WAS PERFORMING SMASH MOVEMENT

The purpose of this research is to study and to analyze the relationship of the velocity and torque between two different weighted badminton rackets while the elite players was performing the smash movement .All the data of this study is filmed by digital video (60Hz/s) and is analyzed on the space of 20 by sagittal plane and horizontal axis movement of the participator .In order to derive \rom the primary parameters of smash motion, including velocity, movement of inertia, angular acceleration, torque. The data are digitized and filtered in APAS (the Ariel Performance Analyze System). As a result, by T-Test, the T-values are up to the observable level ,and the level a is 0.05

Apoptosis signal-regulating kinase 1 mediates denbinobin-induced apoptosis in human lung adenocarcinoma cells

In the present study, we explore the role of apoptosis signal-regulating kinase 1 (ASK1) in denbinobin-induced apoptosis in human lung adenocarcinoma (A549) cells. Denbinobin-induced cell apoptosis was attenuated by an ASK1 dominant-negative mutant (ASK1DN), two antioxidants (N-acetyl-L-cysteine (NAC) and glutathione (GSH)), a c-Jun N-terminal kinase (JNK) inhibitor (SP600125), and an activator protein-1 (AP-1) inhibitor (curcumin). Treatment of A549 cells with denbinobin caused increases in ASK1 activity and reactive oxygen species (ROS) production, and these effects were inhibited by NAC and GSH. Stimulation of A549 cells with denbinobin caused JNK activation; this effect was markedly inhibited by NAC, GSH, and ASK1DN. Denbinobin induced c-Jun phosphorylation, the formation of an AP-1-specific DNA-protein complex, and Bim expression. Bim knockdown using a bim short interfering RNA strategy also reduced denbinobin-induced A549 cell apoptosis. The denbinobin-mediated increases in c-Jun phosphorylation and Bim expression were inhibited by NAC, GSH, SP600125, ASK1DN, JNK1DN, and JNK2DN. These results suggest that denbinobin might activate ASK1 through ROS production to cause JNK/AP-1 activation, which in turn induces Bim expression, and ultimately results in A549 cell apoptosis