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arXiv 2024年

作者： Bian, Junyi Zhai, Weiqi Huang, Xiaodi Zheng, Jiaxuan Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia

Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling. However, such methods are inherently task-specific, demonstrate poor generalizability, and often require dedicated model for each dataset. To leverage the versatile capabilities of recently remarkable large language models (LLMs), several endeavors have explored generative approaches to entity extraction. Yet, these approaches often fall short of the effectiveness of previouly sequence labeling approaches. In this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and datasets. By combining the LLM’s understanding of instructions with sequence labeling techniques, we use mix of datasets to train a model capable of extracting various types of entities. Given that the backbone LLMs lacks specialized medical knowledge, we also integrate external entity knowledge bases and employ instruction tuning to compel the model to densely recognize carefully curated entities. Our model VANER, trained with a small partition of parameters, significantly outperforms previous LLMs-based models and, for the first time, as a model based on LLM, surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Pan-omics-based characterization and prediction of highly multidrugadapted strains from an outbreak fungal species complex

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The Innovation 2024年第5期5卷 101-109,100页

作者： Xin Fan Lei Chen Min Chen Na Zhang Hong Chang Mingjie He Zhenghao Shen Lanyue Zhang Hao Ding Yuyan Xie Yemei Huang Weixin Ke Meng Xiao Xuelei Zang Heping Xu Wenxia Fang Shaojie Li Cunwei Cao Yingchun Xu Shiguang Shan Wenjuan Wu Changbin Chen Xinying Xue Linqi Wang Department of Infectious Diseases and Clinical Microbiology Beijing Institute of Respiratory Medicine and Beijing Chao-Yang HospitalCapital Medical UniversityBeijing 100020China Beijing Research Center for Respiratory Infectious Diseases Beijing 100020China State Key Laboratory of Mycology Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Department of Dermatology Shanghai Key Laboratory of Molecular Medical MycologyChangzheng HospitalShanghai 200003China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Department of Respiratory and Critical Care Emergency and Critical Care Medical CenterBeijing Shijitan HospitalCapital Medical UniversityBeijing 100038China Department of Laboratory Medicine State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical SciencesBeijing 100730China Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases Beijing 100730China Department of Clinical Laboratory First Affiliated Hospital of Xiamen UniversityXiamen 361003China Institute of Biological Science and Technology Guangxi Academy of SciencesNanning 530007China Department of Dermatology The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021China Guangxi Key Laboratory of Mycosis Prevention and Treatment Nanning 530021China Department of Laboratory Medicine Shanghai East HospitalTongji University School of MedicineShanghai 200120China The Unit of Pathogenic Fungal Infection&Host Immunity Shanghai Institute of Immunity and InfectionChinese Academy of SciencesShanghai 200031China Nanjing Advanced Academy of Life and Health Nanjing 211135China Department of Respiratory and Critical Care Shandong Second Medical UniversityWeifang 261035China

Strains from the Cryptococcus gattii species complex(CGSC)have caused the Pacific Northwest cryptococcosis outbreak,the largest cluster of lifethreatening fungal infections in otherwise healthy human hosts known to *** this study,we utilized a pan-phenome-based method to assess the fitness outcomes of CGSC strains under 31 stress conditions,providing a comprehensive overview of 2,821 phenotype-strain associations within this pathogenic *** clustering analysis revealed a strong correlation between distinct types of stress phenotypes in a subset of CGSC strains,suggesting that shared determinants coordinate their adaptations to various ***,a specific group of strains,including the outbreak isolates,exhibited a remarkable ability to adapt to all three of the most commonly used antifungal drugs for treating cryptococcosis(amphotericin B,5-fluorocytosine,and fluconazole).By integrating pan-genomic and pan-transcriptomic analyses,we identified previously unrecognized genes that play crucial roles in conferring multidrug resistance in an outbreak strain with high multidrug *** these genes,we identified biomarkers that enable the accurate prediction of highly multidrug-adapted CGSC strains,achieving maximum accuracy and area under the curve(AUC)of 0.79 and 0.86,respectively,using machine learning ***,we developed a pan-omic approach to identify cryptococcal multidrug resistance determinants and predict highly multidrug-adapted CGSC strains that may pose significant clinical concern.

关键词： multidrug adapted prediction

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MAC-AC: A Novel Distributed MAC Protocol for Accessing Channel in Vehicular Ad Hoc Networks

MAC-AC: A Novel Distributed MAC Protocol for Accessing Chann...

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IEEE Conference on Vehicular Technology (VTC)

作者： Baozhu Li Fen Hou Changyue Zhang Shaohua Li Shujuan Ji Shanzhi Chen School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan China Faculty of Science and Technology University of Macau Macau China Automotive Research Institute China National Heavy Duty Truck Group Co. Ltd Jinan China Shandong Key Laboratory of Wisdom Mine Information Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Lab of Wireless Mobile Communication Beijing China China Academy of Telecommunication Technology Beijing China

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

As a promising paradigm, VANET has been attracting more and more attention from the industry and academia recently. However, due to rapid movement of vehicles and highly dynamic topology, designing efficient MAC protocol for VANET is still challenging. In this paper, we propose MAC-AC, a novel TDMA-based distributed MAC protocol designed specifically for a vehicular ad hoc network. In MAC-AC, when multiple vehicles compete for the same time slot within their two-hop communication range, one of contending vehicle can obtain this time slot by a simple method, which increases the success probability of vehicles accessing channel. Analysis results are presented to demonstrate the efficiency of MAC-AC and compare it to ADHOC MAC, an existing MAC protocol based on TDMA.

关键词： Vehicular and wireless technologies Time division multiple access Network topology Vehicular ad hoc networks Media Access Protocol Topology Vehicle dynamics

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Estimation of Speech Features Using a Wearable Inertial Sensor

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Journal of Voice 2024年

作者： Du, Zuyu Xu, Yaodan Yu, Xinsheng Wang, Sen Xu, Lin School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Science Shanghai China Shanghai Ruiwei Digital Technology Shanghai China Shanghai Frontiers Science Center of Human-centered Artificial Intelligence Shanghai China MoE Key Lab of Intelligent Perception and Human-Machine Collaboration (ShanghaiTech University) Shanghai China

Speech features have been investigated as novel digital biomarkers for many psychiatric and neurocognitive diseases. Microphones are the most used devices for speech recording but inevitably suffering from several disadvantages such as privacy leakage and environmental noises, limiting their clinical applications particularly for long-term ambulatory monitoring. The aim of the present study is therefore to explore the feasibility of extracting speech features from the acceleration recorded on the sternum. Ten healthy subjects volunteered in our study. Two speech tasks, that is, repeating one sentence 20 times and reading 20 different sentences, were performed by each subject, with each task repeated eight times under different speech rate and loudness. Voice signals and speech-caused chest vibrations were simultaneously recorded by a microphone and an accelerometer placed on the sternum. Forty-two acoustic features and six time-related prosodic features were extracted from both signals using a standard toolbox, and then compared by a linear fit and correlation analysis. Good agreement between the acceleration features and microphone features is observed in all six time-related prosodic features for both tasks, but only in 19 and 17 acoustic features for task 1 and 2, respectively, with most of them loudness- or pitch-related. Our results suggest the sternum acceleration to track time-related speech prosody, loudness, and pitch very well, demonstrating the feasibility of deriving digital biomarkers from the acceleration signal for diseases strongly related to time-related prosodic and loudness features. © 2024 The Voice Foundation

关键词： Correlation coefficient Digital biomarker Inertial sensor Microphone Speech features

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Fast stochastic ordinal embedding with variance reduction and adaptive step size

arXiv

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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Overview of the Tenth Dialog System Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio Speech and Language processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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MRI-guided robot intervention—current state-of-the-art and new challenges

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Med-X 2023年第1期1卷 1-41页

作者： Huang, Shaoping Lou, Chuqian Zhou, Ying He, Zhao Jin, Xuejun Feng, Yuan Gao, Anzhu Yang, Guang-Zhong Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT) Shanghai Jiao Tong University Shanghai China Materials and Technology Center of Robotics Empa Dübendorf Switzerland State Key Lab of Metal Matrix Composites and School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics and Department of Automation Shanghai Jiao Tong University Shanghai China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Magnetic Resonance Imaging (MRI) is now a widely used modality for providing multimodal, high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing radiation. In addition to its diagnostic potential, its future theranostic value lies in its ability to provide MRI-guided robot intervention with combined structural and functional mapping, as well as integrated instrument localization, target recognition, and in situ, in vivo monitoring of the therapeutic efficacy. Areas of current applications include neurosurgery, breast biopsy, cardiovascular intervention, prostate biopsy and radiotherapy. Emerging applications in targeted drug delivery and MRI-guided chemoembolization are also being pursued. Whilst promising progress has been made in recent years, there are still significant basic science research and engineering challenges. This paper provides a comprehensive review of the current state-of-the-art in MRI-guided robot intervention and allied technologies in actuation, sensing, new materials, interventional instruments, and interactive/real-time MRI. Potential future research directions and new clinical developments are also discussed.

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous information. All rights reserved.

关键词： Semantics

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V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results

arXiv

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arXiv 2024年

作者： Wang, Jiaqi Zang, Yuhang Zhang, Pan Chu, Tao Cao, Yuhang Sun, Zeyi Liu, Ziyu Dong, Xiaoyi Wu, Tong Lin, Dahua Chen, Zeming Wang, Zhi Meng, Lingchen Yao, Wenhao Yang, Jianwei Wu, Sihong Chen, Zhineng Wu, Zuxuan Jiang, Yu-Gang Wu, Peixi Chai, Bosong Nie, Xuan Yan, Longquan Wang, Zeyu Zhou, Qifan Wang, Boning Huang, Jiaqi Xu, Zunnan Li, Xiu Yuan, Kehong Zu, Yanyan Hao, Jiayao Gao, Qiong Jiao, Licheng Shanghai AI Laboratory China Chinese University of Hong Kong Hong Kong Shanghai Jiao Tong University China Wuhan University China Shenzhen International Graduate School Tsinghua University China Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Microsoft Research Redmond United States University of Science and Technology of China China Zhejiang University China Northwestern Polytechnical University China Northwest University China Tsinghua Shenzhen International Graduate School Tsinghua University China Intelligent Perception and Image Understanding Lab Xidian University China

Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown or unseen objects. The challenges necessitate the development of public benchmarks and challenges to advance the field of object detection. Inspired by the success of previous COCO [15] and LVIS [11] Challenges, we organize the V3Det Challenge 2024 in conjunction with the 4th Open World Vision Workshop: Visual Perception via Learning in an Open World (VPLOW) at CVPR 2024, Seattle, US. This challenge aims to push the boundaries of object detection research and encourage innovation in this field. The V3Det Challenge 2024 consists of two tracks: 1) Vast Vocabulary Object Detection: This track focuses on detecting objects from a large set of 13204 categories, testing the detection algorithm's ability to recognize and locate diverse objects. 2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects. In the following sections, we will provide a comprehensive summary and analysis of the solutions submitted by participants. By analyzing the methods and solutions presented, we aim to inspire future research directions in vast vocabulary and open-vocabulary object detection, driving progress in this field. Challenge homepage: https://***/challenge. © 2024, CC BY-NC-SA.

关键词： Object detection

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Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning

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BMC bioinformatics 2019年第1期20卷 616页

作者： Haicang Zhang Qi Zhang Fusong Ju Jianwei Zhu Yujuan Gao Ziwei Xie Minghua Deng Shiwei Sun Wei-Mou Zheng Dongbo Bu Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. University of Chinese Academy of Sciences Beijing China. Center for Quantitative Biology School of Mathematical Sciences Center for Statistical Sciences Peking University Beijing China. College of Life Science and Technology Huazhong University of Science and Technology Wuhan China. Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. dwsun@***. Institute of Theoretical Physics Chinese Academy of Sciences Beijing China. zheng@***. Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. dbu@***. University of Chinese Academy of Sciences Beijing China. dbu@***.

Following publication of the original article [1], the author explained that there are several errors in the original article.

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