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arXiv 2022年

作者： Pan, Xiaoqin Lin, Xuan Cao, Dongsheng Zeng, Xiangxiang Yu, Philip S. He, Lifang Nussinov, Ruth Cheng, Feixiong School of Computer Science and Engineering Hunan University China School of Computer Science Xiangtan University China Xiangya School of Pharmaceutical Sciences Central South University China Department of Computer Science University of Illinois at Chicago United States Department of Computer Science and Engineering Lehigh University United States Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick FrederickMD21702 United States Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv69978 Israel Genomic Medicine Institute Lerner Research Institute Cleveland Clinic ClevelandOH44195 United States Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University ClevelandOH44195 United States Case Comprehensive Cancer Center Case Western Reserve University School of Medicine ClevelandOH44106 United States

Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. Finally, we present applications of drug repurposing to fight the COVID-19 pandemic, and outline its future challenges. © 2022, CC BY-NC-SA.

关键词： Coronavirus

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Instance Shadow Detection

Instance Shadow Detection

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Tianyu Wang Xiaowei Hu Qiong Wang Pheng-Ann Heng Chi-Wing Fu Department of Computer Science and Engineering The Chinese University of Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, we design LISA, named after Light-guided Instance Shadow-object Association, an end-to-end framework to automatically predict the shadow and object instances, together with the shadow-object associations and light direction. Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results. In our evaluations, we formulate a new metric named the shadow-object average precision to measure the performance of our results. Further, we conducted various experiments and demonstrate our method's applicability on light direction estimation and photo editing.

关键词： Head Feature extraction Proposals Image segmentation Image color analysis Training computer vision

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Temporal memory relation network for workflow recognition from surgical video

arXiv

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arXiv 2021年

作者： Jin, Yueming Long, Yonghao Chen, Cheng Zhao, Zixu Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering T Stone Robotics Institute Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automatic surgical workflow recognition is a key component for developing context-aware computer-assisted systems in the operating theatre. Previous works either jointly modeled the spatial features with short fixed-range temporal information, or separately learned visual and long temporal cues. In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features. We establish a long-range memory bank to serve as a memory cell storing the rich supportive information. Through our designed temporal variation layer, the supportive cues are further enhanced by multi-scale temporal-only convolutions. To effectively incorporate the two types of cues without disturbing the joint learning of spatio-temporal features, we introduce a non-local bank operator to attentively relate the past to the present. In this regard, our TMRNet enables the current feature to view the long-range temporal dependency, as well as tolerate complex temporal extents. We have extensively validated our approach on two benchmark surgical video datasets, M2CAI challenge dataset and Cholec80 dataset. Experimental results demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin (e.g., 67.0% v.s. 78.9% Jaccard on Cholec80 dataset). Copyright © 2021, The Authors. All rights reserved.

关键词： Surgery

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Ferroelectrets:Heterogenous polymer electrets with high piezoelectric sensitivity for transducers

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Journal of Advanced Dielectrics 2023年第4期13卷 3-26页

作者： Xunlin Qiu Peng Fang Axel Mellinger Ruy Alberto Pisani Altafim Werner Wirges Gunnar Gidion Dmitry Rychkov Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237P.R.China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055P.R.China Department of Physics Central Michigan University 223 Dow Science ComplexMount PleasantMI 48859USA Computer Systems Department Informatics Center Federal University of ParáıbaJoão Pessoa-PBBrazil Department of Physics and Astronomy University of Potsdam PotsdamBrandenburgGermany Laboratory for Electrical Instrumentation Department of Microsystems Engineering-IMTEK University of FreiburgGeorges-Köhler-Allee 106Freiburg 79110Germany Technology and Study Center Weissenburg Deggendorf Institute of Technology WeissenburgBavariaGermany

Nowadays,the demand for advanced functional materials in transducer technology is growing *** materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)and vice *** are interesting from both fundamental and application points of ***(also called piezoelectrets)are a relatively young group of piezo-,pyro-and ferroelectric *** exhibit ferroic behavior phenomenologically undistinguishable from that of traditional ferroelectrics,although the materials per se are essentially non-polar space-charge electrets with artificial macroscopic dipoles(i.e.,internally charged cavities).A lot of work has been done on ferroelectrets and their applications up to *** this paper,we review and discuss mostly the work done at University of Potsdam on the research and development of *** will,however,also mention important results from other teams,and prospect the challenges and future progress trend of the field of ferroelectret research.

关键词： Ferroelectret piezoelectret space-charge electret transducer advanced functional materials

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A mobile visual analytics approach for instant trend analysis in mobile contexts 5

A mobile visual analytics approach for instant trend analysi...

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5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019

作者： Burkhardt, Dirk Nazemi, Kawa Kuijper, Arjan Ginters, Egils Human-Computer Interaction and Visual Analytics Research Group Darmstadt University of Applied Sciences Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Riga Technical University Riga Latvia

ISBN: (纸本)9788885741423

The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design. © 5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019. All rights reserved.

关键词： Decision support systems

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Multi-tasking dialogue comprehension with discourse parsing

arXiv

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arXiv 2021年

作者： He, Yuchen Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Multi-party dialogue machine reading comprehension (MRC) raises an even more challenging understanding goal on dialogue with more than two involved speakers, compared with the traditional plain passage style MRC. To accurately perform the question-answering (QA) task according to such multi-party dialogue, models have to handle fundamentally different discourse relationships from common non-dialogue plain text, where discourse relations are supposed to connect two far apart utterances in a linguistics-motivated way. To further explore the role of such unusual discourse structure on the correlated QA task in terms of MRC, we propose the first multi-task model for jointly performing QA and discourse parsing (DP) on the multi-party dialogue MRC task. Our proposed model is evaluated on the latest benchmark Molweni, whose results indicate that training with complementary tasks indeed benefits not only QA task, but also DP task itself. We further find that the joint model is distinctly stronger when handling longer dialogues which again verifies the necessity of DP in the related MRC. Copyright © 2021, The Authors. All rights reserved.

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Self- And Pseudo-self-supervised prediction of speaker and key-utterance for multi-party dialogue reading comprehension

arXiv

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arXiv 2021年

作者： Li, Yiyang Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Multi-party dialogue machine reading comprehension (MRC) brings tremendous challenge since it involves multiple speakers at one dialogue, resulting in intricate speaker information flows and noisy dialogue contexts. To alleviate such difficulties, previous models focus on how to incorporate these information using complex graph-based modules and additional manually labeled data, which is usually rare in real scenarios. In this paper, we design two labour-free self- and pseudo-self-supervised prediction tasks on speaker and key-utterance to implicitly model the speaker information flows, and capture salient clues in a long dialogue. Experimental results on two benchmark datasets have justified the effectiveness of our method over competitive baselines and current state-of-the-art models1 Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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SJTU at MRP 2019: A transition-based multi-task parser for cross-framework meaning representation parsing 23

SJTU at MRP 2019: A transition-based multi-task parser for c...

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2019 Shared Task on Cross-Framework Meaning Representation Parsing, MRP 2019 at the 23rd Conference for Computational Language Learning, CoNLL 2019

作者： Bai, Hongxiao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

ISBN: (纸本)9781950737604

This paper describes the system of our team SJTU for our participation in the CoNLL 2019 Shared Task: Cross-Framework Meaning Representation Parsing. The goal of the task is to advance data-driven parsing into graph-structured representations of sentence meaning. This task includes five meaning representation frameworks: DM, PSD, EDS, UCCA, and AMR. These frameworks have different properties and structures. To tackle all the frameworks in one model, it is needed to find out the commonality of them. In our work, we define a set of the transition actions to once-for-all tackle all the frameworks and train a transition-based model to parse the meaning representation. The adopted multi-task model also can allow learning for one framework to benefit the others. In the final official evaluation of the shared task, our system achieves 42% F1 unified MRP metric score. © 2019 Association for Computational Linguistics

关键词： Syntactics

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A Lightweight Flexible Semi-Cylindrical Valve for Seamless Integration in Soft Robots Based on the Giant Electrorheological Fluid

SSRN

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SSRN 2022年

作者： Huang, Tiantian Xu, Denan Zhang, Hang Bai, Ou Aravelli, Aparna Zhou, Xiaoxiao Han, Bin School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430070 China Human Cyber-Physical Systems Laboratory The Department of Electrical and Computer Engineering Florida International University Florida33174 United States Applied Research Center Florida International University Florida33174 United States Guangdong Intelligent Robotics Institute Dongguan523808 China

Complete compliance of soft robots is a trending research topic. One significant aspect is the control element. In the current work, a flexible electro-rheological (ER) valve development is chosen due to the advantage of its dynamic power density, convenient electronic control mode and no mechanical moving parts. These properties enable the ER valve to be miniaturized and easily integrated into a fluid-driven soft robot. However, the integration effect of the existing flexible ER valve configuration in soft robots is not ideal. In this study, a novel concentric semi-cylindrical flexible giant ER (GER) valve with a dual flowing direction is proposed for a soft robot to enable a larger deformation bending motion with multiple motion modes. The resulting valve has an overall volume of 50 mm 3 , a mass of 0.88 g, and a maximum controlling pressure of 84 kPa with a pressure change rate of 13.9, which is suitable for most soft robot applications. The pressure difference calculation method for the concentric semi-cylindrical GER valve is discussed. The effects of size, voltages, and flow rates on the valve’s static and dynamic performances, as well as the characteristics of the two pressure regulation modes, are investigated through simulations and experiments. The seamless integration and flexibility of the proposed valve are demonstrated by controlling a typical network actuator with six motion modes. Finally, a soft robot composed of two actuators is fabricated and realized the grasping of multiple everyday objects using different motion modes. © 2022, The Authors. All rights reserved.

关键词： Nanofluidics

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Graph-free multi-hop reading comprehension: A select-to-guide strategy

arXiv

引用

arXiv 2021年

作者： Wu, Bohong Zhang, Zhuosheng Zhao, Hai The Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Multi-hop reading comprehension (MHRC) requires not only to predict the correct answer span in the given passage, but also to provide a chain of supporting evidences for reasoning interpretability. It is natural to model such a process into graph structure by understanding multi-hop reasoning as jumping over entity nodes, which has made graph modelling dominant on this task. Recently, there have been dissenting voices about whether graph modelling is indispensable due to the inconvenience of the graph building, however existing state-of-the-art graph-free attempts suffer from huge performance gap compared to graph-based ones. This work presents a novel graph-free alternative which firstly outperform all graph models on MHRC. In detail, we exploit a select-to-guide (S2G) strategy to accurately retrieve evidence paragraphs in a coarse-to-fine manner, incorporated with two novel attention mechanisms, which surprisingly shows conforming to the nature of multi-hop reasoning. Our graph-free model achieves significant and consistent performance gain over strong baselines and the current new state-of-the-art on the MHRC benchmark, HotpotQA, among all the published works. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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