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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Su Zhu Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University China

ISBN: (纸本)9781509041183

This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.

关键词： Spoken language understanding encoder-decoder focus-mechanism robustness spoken language understanding Word Packaging Short-Term Memory Robustness Speech recognition

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Concept transfer learning for adaptive language understanding

arXiv

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arXiv 2017年

作者： Zhu, Su Yu, Kai Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Semantic transfer is an important problem of the language understanding (LU), which is about how the recognition pattern of a semantic concept benefits other associated concepts. In this paper, we propose a new semantic representation based on combinatory concepts. Semantic slot is represented as a composition of different atomic concepts in different semantic dimensions. Specifically, we propose the concept transfer learning methods for extending combinatory concepts in LU. The concept transfer learning makes use of the common ground of combinatory concepts shown in the literal description. Our methods are applied to two adaptive LU problems: semantic slot refinement and domain adaptation, and respectively evaluated on two benchmark LU datasets: ATIS and DSTC 2&3. The experiment results show that the concept transfer learning is very efficient for semantic slot refinement and domain adaptation in the LU. Copyright © 2017, The Authors. All rights reserved.

关键词： Semantics

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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18th International Conference on human-computer interaction, HCI International 2016

作者： Deniz, Gencay Durdu, Pınar Onay Department of Computer Engineering Graduate School of Natural and Applied Sciences Kocaeli University IzmitKocaeli Turkey Human Computer Interaction Research Laboratory Department of Computer Engineering Kocaeli University IzmitKocaeli Turkey

ISBN: (数字)9783319395166

ISBN: (纸本)9783319395159

Brain computer Interface (BCI) is an emerging research area which has been studied over thirty years extensively in the fields of clinical neurophysiology and neuroscience however it is now recognized as an interdisciplinary field involving neurobiology, psychology, engineering, mathematics and computer science. HCI community has begun to deal with BCI and substantial publications have been made in HCI related journals and conferences in recent years. In the scope of this research we conducted a systematic mapping study with the articles published in proceedings of HCI International Conferences to reveal the trends, general aims of the articles, which signal recording modality were used, for what kind of practical applications were developed, which feature types and classification algorithms were used and what are the nationalities and institutes of contributing authors in the scope of HCI International conference. The aim was to give insight to the researchers who were studying or would like to study in the area of BCI. According to the results of the mapping, research type of BCI articles were mainly in the category of emerging applications not related to communication and control. General aim of the articles was determined as practical applications of BCI technologies first and then development or improvement of methodologies for signal processing issues. Visual P300 was the mostly applied BCI paradigms and SSVEP and motor imagery followed next. Most of the articles had reported EEG as a signal acquisition method, ICA or bandpass filter as a preprocessing or filtering method, ERP as a feature type and LDA and LR as a classification algorithm method. © Springer International Publishing Switzerland 2016.

关键词： Brain computer interface

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Multi-view LSTM Language Model with Word-synchronized Auxiliary Feature for LVCSR

Multi-view LSTM Language Model with Word-synchronized Auxili...

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第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

作者： Yue Wu Tianxing He Zhehuai Chen Yanmin Qian Kai Yu Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong UniversityShanghaiChina

ISBN: (纸本)9783319690049

Recently long short-term memory language model(LSTMLM)has received tremendous interests from both language and speech communities,due to its superiorty on modelling long-term ***,integrating auxiliary information,such as context feature,into the LSTM LM has shown improved performance in perplexity(PPL).However,improper feed of auxiliary information won't give consistent gain on word error rate(WER)in a large vocabulary continuous speech recognition(LVCSR)*** solve this problem,a multi-view LSTM LM architecture combining a tagging model is proposed in this *** an on-line unidirectional LSTM-RNN is built as a tagging model,which can generate word-synchronized auxiliary *** the auxiliary feature from the tagging model is combined with the word sequence to train a multi-view unidirectional LSTM *** training modes for the tagging model and language model are explored and *** new architecture is evaluated on PTB,Fisher English and SMS Chinese data sets,and the results show that not only LM PPL promotion is observed,but also the improvements can be well transferred to WER reduction in ASR-rescore task.

关键词： LSTM language model speech recognition multi-view aux-iliary feature tagging model

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Unknown input observer based on discrete-time nonlinear descriptor model for understanding sitting control

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IFAC-PapersOnLine 2017年第1期50卷 820-825页

作者： Blandeau M. Guerra T.M. Estrada-Manzo V. Pudlo P. Gabrielli F. Laboratory of Industrial and Human Automation control Mechanical engineering and Computer Science (LAMIH UMR CNRS 8201) Université de Valenciennes et du Hainaut-Cambrésis Valenciennes France Department of Electrical and Electronics Engineering Sonora Institute of Technology 5 de febrero 818 Sur CP Obregon Sonora 85000 Mexico

The present paper studies sitting control for persons with complete thoracic spinal cord injury by means of automatic control. The methodology begins by modeling sitting control, after a discretization of the nonlinear descriptor model is computed, then a Takagi-Sugeno model is obtained for further manipulations. The approach uses unknown input observers to estimate states and internal variable of the system. The design conditions are expressed as linear matrix inequalities, whose solvability depends on convex optimization techniques. Simulations are conducted to show the effectiveness of the proposed observer. © 2017

关键词： Discrete-Time systems Inverted pendulum Nonlinear descriptor systems Sitting movement Spinal Cord Injury Takagi-Sugeno models Unknown input observer

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Towards automatic semantic segmentation in volumetric ultrasound 1

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20th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Yang, Xin Yu, Lequan Li, Shengli Wang, Xu Wang, Na Qin, Jing Ni, Dong Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Department of Ultrasound Affliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical University Shenzhen China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China School of Nursing Centre for Smart Health The Hong Kong Polytechnic University Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9783319661827

ISBN: (纸本)9783319661810

3D ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. However, lacking of efficient tools to decompose the volumetric data greatly limits its widespread. In this paper, we are looking at the problem of volumetric segmentation in ultrasound to promote the volume-based, precise maternal and fetal health monitoring. Our contribution is threefold. First, we propose the first and fully automatic framework for the simultaneous segmentation of multiple objects, including fetus, gestational sac and placenta, in ultrasound volumes, which remains as a rarely-studied but great challenge. Second, based on our customized 3D Fully Convolutional Network, we propose to inject a Recurrent Neural Network (RNN) to flexibly explore 3D semantic knowledge from a novel, sequential perspective, and therefore significantly refine the local segmentation result which is initially corrupted by the ubiquitous boundary uncertainty in ultrasound volumes. Third, considering sequence hierarchy, we introduce a hierarchical deep supervision mechanism to effectively boost the information flow within RNN and further improve the semantic segmentation results. Extensively validated on our in-house large datasets, our approach achieves superior performance and presents to be promising in boosting the interpretation of prenatal ultrasound volumes. Our framework is general and can be easily extended to other volumetric ultrasound segmentation tasks. © 2017, Springer International Publishing AG.

关键词： Ultrasonics

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Sex differences in viral entry protein expression and host transcript responses to SARS-CoV-2

Research Square

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Research Square 2020年 rs3rs-100914页

作者： Sun, Mengying Shankar, Rama Ko, Meehyun Chang, Christopher Daniel Yeh, Shan-Ju Li, Shilong Liu, Ke Zhou, Guoli Xing, Jing VanVelsen, Austin VanVelsen, Tyler Paithankar, Shreya Feng, Benjamin Y. Young, Krista Strug, Michael Turco, Lauren Wang, Zichen Schadt, Eric Chen, Rong Li, Xiaohong Oskotsky, Tomiko Sirota, Marina Glicksberg, Benjamin S. Nadkarni, Girish N. Moeser, Adam J. Li, Li Kim, Seungtaek Zhou, Jiayu Chen, Bin Department of Pediatrics and Human Development Michigan State University Grand RapidsMI United States Department of Computer Science and Engineering Michigan State University East LansingMI United States Department of Pharmacology and Toxicology Michigan State University Grand RapidsMI United States College of Osteopathic Medicine Michigan State University East LansingMI United States Biomedical Research Informatics Core Clinical & Translational Sciences Institute Michigan State University East LansingMI United States Van Andel Research Institute Grand RapidsMI United States Sema4 StamfordCT United States Department of Genetics and Genomic Sciences The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine Mount SinaiNY United States Department of Obstetrics and Gynecology Spectrum Health Grand RapidsMI United States Department of Obstetrics Gynecology and Reproductive Biology Michigan State University Grand RapidsMI United States Emergency Medicine Residency Spectrum Health Grand RapidsMI United States Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Zoonotic Virus Laboratory Institut Pasteur Korea Seongnam Korea Republic of Large Animal Clinical Sciences and Department of Physiology Michigan State University East LansingMI United States Department of Pediatrics and Bakar Computational Health Sciences Institute University of California San FranciscoCA United States Department of Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States The Hasso Plattner Institute of Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New YorkNY United States The Charles Bronfman Institute of Personalized Medicine Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY United States

Epidemiological studies suggest that men exhibit a higher mortality rate to COVID-19 than women, yet the underlying biology is largely unknown. Here, we seek to delineate sex differences in the gene expression of viral entry proteins ACE2 and TMPRSS2, and host transcriptional responses to SARS-CoV-2 through large-scale analysis of genomic and clinical data. We first compiled 220,000 human gene expression profiles from three databases and completed the meta-information through machine learning and manual annotation. Large scale analysis of these profiles indicated that male samples show higher expression levels of ACE2 and TMPRSS2 than female samples, especially in the older group (>60 years) and in the kidney. Subsequent analysis of 6,031 COVID-19 patients at Mount Sinai Health System revealed that men have significantly higher creatinine levels, an indicator of impaired kidney function. Further analysis of 782 COVID-19 patient gene expression profiles taken from upper airway and blood suggested men and women present distinct expression changes. Computational deconvolution analysis of these profiles revealed male COVID-19 patients have enriched kidney-specific mesangial cells in blood compared to healthy patients. Together, this study suggests biological differences in the kidney between sexes may contribute to sex disparity in COVID-19. © 2020, CC BY.

关键词： COVID-19

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Social and moral psychology of COVID-19 across 69 countries

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Scientific data 2023年第1期10卷 272页

作者： Flavio Azevedo Tomislav Pavlović Gabriel G Rêgo F Ceren Ay Biljana Gjoneska Tom W Etienne Robert M Ross Philipp Schönegger Julián C Riaño-Moreno Aleksandra Cichocka Valerio Capraro Luca Cian Chiara Longoni Ho Fai Chan Jay J Van Bavel Hallgeir Sjåstad John B Nezlek Mark Alfano Michele J Gelfand Michèle D Birtel Aleksandra Cislak Patricia L Lockwood Koen Abts Elena Agadullina John Jamir Benzon Aruta Sahba Nomvula Besharati Alexander Bor Becky L Choma Charles David Crabtree William A Cunningham Koustav De Waqas Ejaz Christian T Elbaek Andrej Findor Daniel Flichtentrei Renata Franc June Gruber Estrella Gualda Yusaku Horiuchi Toan Luu Duc Huynh Agustin Ibanez Mostak Ahamed Imran Jacob Israelashvili Katarzyna Jasko Jaroslaw Kantorowicz Elena Kantorowicz-Reznichenko André Krouwel Michael Laakasuo Claus Lamm Caroline Leygue Ming-Jen Lin Mohammad Sabbir Mansoor Antoine Marie Lewend Mayiwar Honorata Mazepus Cillian McHugh John Paul Minda Panagiotis Mitkidis Andreas Olsson Tobias Otterbring Dominic J Packer Anat Perry Michael Bang Petersen Arathy Puthillam Tobias Rothmund Hernando Santamaría-García Petra C Schmid Drozdstoy Stoyanov Shruti Tewari Bojan Todosijević Manos Tsakiris Hans H Tung Radu G Umbres Edmunds Vanags Madalina Vlasceanu Andrew Vonasch Meltem Yucel Yucheng Zhang Mohcine Abad Eli Adler Narin Akrawi Hamza Alaoui Mdarhri Hanane Amara David M Amodio Benedict G Antazo Matthew Apps Mouhamadou Hady Ba Sergio Barbosa Brock Bastian Anton Berg Maria P Bernal-Zárate Michael Bernstein Michał Białek Ennio Bilancini Natalia Bogatyreva Leonardo Boncinelli Jonathan E Booth Sylvie Borau Ondrej Buchel C Daryl Cameron Chrissie F Carvalho Tatiana Celadin Chiara Cerami Hom Nath Chalise Xiaojun Cheng Kate Cockcroft Jane Conway Mateo Andres Córdoba-Delgado Chiara Crespi Marie Crouzevialle Jo Cutler Marzena Cypryańska Justyna Dabrowska Michael A Daniels Victoria H Davis Pamala N Dayley Sylvain Delouvée Ognjan Denkovski Guillaume Dezecache Nathan A Dhaliwal Alelie B Diato Roberto Di Paolo Marianna Drosinou Uwe Dulleck Jānis Ekmanis Department of Psychology University of Cambridge Cambridge England. fa441@cam.ac.uk. Institute of Communication Science Friedrich-Schiller University Jena Jena Germany. fa441@cam.ac.uk. Institute of Social Sciences Ivo Pilar Zagreb Croatia. Social and Cognitive Neuroscience Laboratory Mackenzie Presbyterian University São Paulo Brazil. Department of Economics Norwegian School of Economics Bergen Norway. Telenor Research Oslo Norway. Macedonian Academy of Sciences and Arts Skopje Republic of North Macedonia. Kieskompas - Election Compass Amsterdam Netherlands. Department of Political Science & Annenberg School for Communication University of Pennsylvania Philadelphia PA USA. Department of Psychology Macquarie University Sydney NSW Australia. Department of Philosophy University of St Andrews St Andrews Scotland. School of Economics and Finance University of St Andrews St Andrews Scotland. Medicine Faculty Cooperative University of Colombia Villavicencio Colombia. Department of Bioethics El Bosque University Bogotá Colombia. School of Psychology University of Kent Canterbury England. Department of Economics Middlesex University London London England. Darden School of Business University of Virginia Charlottesville VA USA. Questrom School of Business Boston University Boston MA USA. School of Economics and Finance Queensland University of Technology Brisbane QLD Australia. Center for Behavioural Economics Society and Technology Queensland University of Technology Brisbane QLD Australia. Department of Psychology and Neural Science New York University New York NY USA. Department of Strategy and Management Norwegian School of Economics Bergen Norway. SWPS University of Social Sciences and Humanities Warsaw Poland. Department of Psychological Sciences College of William and Mary Williamsburg VA USA. Department of Philosophy Macquarie University Sydney NSW Australia. Stanford Graduate School of Business Stanford University Stanford CA USA.

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

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Understanding the current learning techniques of wayfinding: A case study at Malaysian association for the Blind (MAB) 4

Understanding the current learning techniques of wayfinding:...

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4th International Conference on User science and Engineering, i-USEr 2016

作者： Hamid, Nazatul Naquiah Ahba Abd Adnan, Wan Adilah Wan Razak, Fariza Hanis Abdul Edwards, Alistair D.N. Shah Alam Selangor Malaysia Human Computer Interaction Research Group Department of Computer Science University of York YorkYO10 5GH United Kingdom

ISBN: (纸本)9781509026319

In preparing blind people to navigate in the real world, they need to be equipped with the knowledge and skills in mobility and orientation. This study aimed to investigate the current learning techniques of way finding for blind people in Malaysia. The study took place at Malaysian Association for the Blind (MAB) where observation and interview were carried out where the objective is first, to explore the existing learning techniques of way finding conducted at MAB and second, to identify the problems faced by the mobility and orientation (M&O) instructor in teaching way finding. The study revealed that the main problem faced by the instructor was lack of appropriate tools used to describe the environment to blind students. This led to the difficulties in developing the cognitive maps of the described environment for some blind students at MAB. However, positive responses given by the blind students after using an audio-tactile map which helped them to alleviate the problems they were facing. © 2016 IEEE.

关键词： Students

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