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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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Interpreting dense retrieval as mixture of topics

arXiv

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arXiv 2021年

作者： Zhan, Jingtao Mao, Jiaxin Liu, Yiqun Guo, Jiafeng Zhang, Min Ma, Shaoping Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success. Therefore, in this work, we conduct an interpretation study of recently proposed DR models. Specifically, we first discretize the embeddings output by the document and query encoders. Based on the discrete representations, we analyze the attribution of input tokens. Both qualitative and quantitative experiments are carried out on public test collections. Results suggest that DR models pay attention to different aspects of input and extract various high-level topic representations. Therefore, we can regard the representations learned by DR models as a mixture of high-level topics. Copyright © 2021, The Authors. All rights reserved.

关键词： Mixtures

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Learning discrete representations via constrained clustering for effective and efficient dense retrieval

arXiv

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arXiv 2021年

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space. Therefore, we present RepCONC, a novel retrieval model that learns discrete Representations via CONstrained Clustering. RepCONC jointly trains dual-encoders and the Product Quantization (PQ) method to learn discrete document representations and enables fast approximate NNS with compact indexes. It models quantization as a constrained clustering process, which requires the document embeddings to be uniformly clustered around the quantization centroids and supports end-to-end optimization of the quantization method and dual-encoders. We theoretically demonstrate the importance of the uniform clustering constraint in RepCONC and derive an efficient approximate solution for constrained clustering by reducing it to an instance of the optimal transport problem. Besides constrained clustering, RepCONC further adopts a vector-based inverted file system (IVF) to support highly efficient vector search on CPUs. Extensive experiments on two popular ad-hoc retrieval benchmarks show that RepCONC achieves better ranking effectiveness than competitive vector quantization baselines under different compression ratio settings. It also substantially outperforms a wide range of existing retrieval models in terms of retrieval effectiveness, memory efficiency, and time efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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An information-theoretic approach to hypergraph psychometrics

arXiv

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arXiv 2022年

作者： Marinazzo, Daniele Van Roozendaal, Jan Rosas, Fernando E. Stella, Massimo Comolatti, Renzo Colenbier, Nigel Stramaglia, Sebastiano Rosseel, Yves Department of Data Analysis Ghent University Belgium Data Science Institute Imperial College London London United Kingdom Centre for Psychedelic Research Imperial College London London United Kingdom Centre for Complexity Science Imperial College London London United Kingdom Department of Informatics University of Sussex Brighton United Kingdom CogNosco Lab Dept. of Computer Science University of Exeter United Kingdom Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano Italy IRCCS San Camillo Hospital Via Alberoni 70 Venice30126 Italy Physics Department Universitá degli Studi di Bari Aldo Moro INFN Sezione di Bari Italy

Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to assess the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, focusing the modelling solely on pairwise relationships can neglect potentially critical information shared by groups of three or more variables in the form of higher-order interdependencies. To overcome this important limitation, here we propose an information-theoretic framework based on hypergraphs as psychometric models. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer representation of the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-art, re-analyzed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, extending the psychometric toolbox and opening promising avenues for future investigation. © 2022, CC BY.

关键词： Information theory

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Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection

arXiv

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arXiv 2022年

作者： Xu, Shaoqing Li, Fang Song, Ziying Fang, Jin Wang, Sifen Yang, Zhi-Xin The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China School of Mechanical Engineering Beijing Institute of Technology China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China School of Transportation Science and Engineering Beihang University Beijing100083 China Inceptio United States

LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps impedes accurate re-projection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To well handle this limitation, we propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, we employ 2D/3D semantic segmentation methods to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further reprojected into the 3D point clouds with calibration parameters. To handle the misalignment between the 2D and 3D parsing results, we propose an Adaptive Attention-based Fusion (AAF) module to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a Deep Feature Fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets state-of-the-art results on the nuScenes testing benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Quantifying high-order interdependencies on individual patterns via the local O-information: Theory and applications to music analysis

arXiv

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arXiv 2021年

作者： Scagliarini, Tomas Marinazzo, Daniele Guo, Yike Stramaglia, Sebastiano Rosas, Fernando E. Dipartimento Interateneo di Fisica Universitá degli Studi Aldo Moro Bari Italy INFN Italy Department of Data Analysis Ghent University Belgium Department of Computer Science Hong Kong Baptist University Hong Kong Data Science Institute Imperial College London United Kingdom Universitá degli Studi Aldo Moro Italy Centre for Psychedelic Research Department of Brain Science Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom

High-order, beyond-pairwise interdependencies are at the core of biological, economic, and social complex systems, and their adequate analysis is paramount to understand, engineer, and control such systems. This paper presents a framework to measure high-order interdependence that disentangles their effect on each individual pattern exhibited by a multivariate system. The approach is centred on the local O-information, a new measure that assesses the balance between synergistic and redundant interdependencies at each pattern. To illustrate the potential of this framework, we present a detailed analysis of music scores from J.S. Bach, which reveals how high-order interdependence is deeply connected with highly non-trivial aspects of the musical discourse. Our results place the local O-information as a promising tool of wide applicability, which opens new perspectives for analysing high-order relationships in the patterns exhibited by complex systems. © 2021, CC BY.

关键词： Music

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Low-rank reduced biquaternion tensor ring decomposition and tensor completion

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Applied Mathematics and Computation 2025年 504卷

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence at Sun Yat-sen University the Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025 Elsevier Inc.

关键词： Numerical methods

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Reauthorise PEPFAR to prevent death, orphanhood, and suffering for millions of children

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Lancet (London, England) 2023年第10404期402卷 769-770页

作者： Lucie Cluver Jeffrey W Imai-Eaton Lorraine Sherr Mary Mahy Seth Flaxman Department of Social Policy and Intervention University of Oxford Oxford OX1 2ER UK Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa. Electronic address: lucie.cluver@spi.ox.ac.uk. Center for Communicable Disease Dynamics Department of Epidemiology Harvard T H Chan School of Public Health Harvard University Boston MA USA MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London UK. Institute for Global Health Faculty of Population Health Sciences University College London London UK. Data for Impact UNAIDS Geneva Switzerland. Department of Computer Science University of Oxford Oxford OX1 2ER UK.

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Spin-Phonon Interactions Induced Anomalous Thermal Conductivity in Nickel (Ii) Oxide

SSRN

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SSRN 2023年

作者： Sun, Qiyang Hou, Songrui Wei, Bin Su, Yaokun Ortiz, Victor Sun, Bo Lin, Jiao Y.Y. Smith, Hillary Danilkin, Sergey Abernathy, Douglas L. Wilson, Richard Li, Chen Mechanical Engineering University of California Riverside RiversideCA92521 United States Materials Science and Engineering University of California Riverside RiversideCA92521 United States Henan Key Laboratory of Materials on Deep-Earth Engineering School of Materials Science and Engineering Henan Polytechnic University Jiaozuo454000 China Tsinghua-Berkeley Shenzhen Institute Guangdong Provincial Key Laboratory of Thermal Management Engineering and Materials TSIGS Tsinghua University Shenzhen518055 China Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Physics and Astronomy Swarthmore College SwarthmorePA19081 United States Australian Centre for Neutron Scattering Australian Nuclear Science and Technology Organization Lucas HeightsNSW2234 Australia Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37830 United States

Nickel (II) oxide is a prominent candidate for spintronic and spin-caloritronic applications operating at room temperature. Although there are extensive studies on nickel oxide, the roles of magnon- and spin-phonon interactions on thermal transport are not well understood. In the present work, the relationship between spin-phonon interactions and thermal transport is investigated by performing inelastic neutron scattering, time-domain thermoreflectance thermal conductivity measurements, and atomistic thermal transport calculations. Inelastic neutron scattering measurements of the magnon lifetime imply that magnon thermal conductivity is trivial, and so heat is conducted only by phonons. Time-domain thermoreflectance measurements of the thermal conductivity vs. temperature follow T-1.47 in the antiferromagnetic phase. This temperature dependence cannot be explained by phonon-isotope and phonon-defect scattering or phonon softening. Instead, we attribute this to magnon-phonon scattering and spin-induced dynamic symmetry breaking. The spin-phonon interactions are saturated in the paramagnetic phase and lead to a weaker temperature dependence of T-1.00 at 550-700 K. These results reveal the importance of spin-phonon interactions on lattice thermal transport, shedding light on the engineering of functional antiferromagnetic spintronic and spin-caloritronic materials through these interactions. © 2023, The Authors. All rights reserved.

关键词： Nickel oxide

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IoT-enabled social relationships meet artificial social intelligence

arXiv

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arXiv 2021年

作者： Dhelim, Sahraoui Ning, Huansheng Farha, Fadi Chen, Liming Atzori, Luigi Daneshmand, Mahmoud The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing China The School of Computing Ulster University NewtownabbeyBT37 0QB United Kingdom The Department of Electrical and Electronic Engineering University of Cagliari piazza d'Armi Cagliari09123 Italy The Department of Business Intelligence and Analytics The Department of Computer Science Stevens Institute of Technology Hoboken United States

With the recent advances of the Internet of Things, and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This paper discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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