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23rd International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2020

作者： Liu, Qiuyue Zhou, Zhen Liu, Feng Fang, Xiangming Yu, Yizhou Wang, Yizhou Center for Data Science Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China Deepwise AI Lab Beijing China Department of Radiology Wuxi Peoples Hospital Nanjing Medical University Wuxi China The University of Hong Kong Pokfulam Hong Kong Center on Frontiers of Computing Studies Peking University Beijing China Computer Science Department Peking University Beijing China

ISBN: (纸本)9783030597245

Pulmonary computerized tomography (CT) images with small slice thickness (thin) is very helpful in clinical practice due to its high resolution for precise diagnosis. However, there are still a lot of CT images with large slice thickness (thick) because of the benefits of storage-saving and short taking time. Therefore, it is necessary to build a pipeline to leverage advantages from both thin and thick slices. In this paper, we try to generate thin slices from the thick ones, in order to obtain high quality images with a low storage requirement. Our method is implemented in an encoder-decoder manner with a proposed progressive up-sampling module to exploit enough information for reconstruction. To further lower the difficulty of the task, a multi-stream architecture is established to separately learn the inner- and outer-lung regions. During training, a contrast-aware loss and feature matching loss are designed to capture the appearance of lung markings and reduce the influence of noise. To verify the performance of the proposed method, a total of 880 pairs of CT images with both thin and thick slices are collected. Ablation study demonstrates the effectiveness of each component of our method and higher performance is obtained compared with previous work. Furthermore, three radiologists are required to detect pulmonary nodules in raw thick slices and the generated thin slices independently, the improvement in both sensitivity and precision shows the potential value of the proposed method in clinical applications. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Hamiltonian simulation with random inputs

arXiv

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arXiv 2021年

作者： Zhao, Qi Zhou, You Shaw, Alexander F. Li, Tongyang Childs, Andrew M. Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States Nanyang Quantum Hub School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States

The algorithmic error of digital quantum simulations is usually explored in terms of the spectral norm distance between the actual and ideal evolution operators. In practice, this worst-case error analysis may be unnecessarily pessimistic. To address this, we develop a theory of average-case performance of Hamiltonian simulation with random initial states. We relate the average-case error to the Frobenius norm of the multiplicative error and give upper bounds for the product formula (PF) and truncated Taylor series methods. As applications, we estimate average-case error for digital Hamiltonian simulation of general lattice Hamiltonians and k-local Hamiltonians. In particular, for the nearest-neighbor Heisenberg chain with n spins, the error is quadratically reduced from O(n) in the worst case to O(√n) on average for both the PF method and the Taylor series method. Numerical evidence suggests that this theory accurately characterizes the average error for concrete models. We also apply our results to error analysis in the simulation of quantum scrambling. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamiltonians

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Swarm Foraging Review: Closing the Gap Between Proof and Practice

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Current Robotics Reports 2020年第4期1卷 215-225页

作者： Lu, Qi Fricke, G. Matthew Ericksen, John C. Moses, Melanie E. Department of Computer Science University of Texas San Antonio San Antonio USA Department of Computer Science University of New Mexico Albuquerque USA Center for Advanced Research Computing University of New Mexico Albuquerque USA Honeywell Federal Manufacturing & Technologies Albuquerque USA Santa Fe Institute Santa Fe USA

We review recent research on swarm robot foraging and contextualize it with foundational work. Recent work can be divided into two complementary camps: self-organizing algorithms that provide practical gains and analytical research focus on theoretical proofs. Encouragingly, the convergence between theory and practice is evident in analytical work on the scaling of transportation networks and in behavioral grammars that give formal insight into emergent properties of foraging. Augmented reality has enabled virtual pheromones to be used with hardware, blurring the line between physical and simulation experiments. In this review we highlight bio-inspired and self-organizing approaches to swarm foraging and contrast them with approaches that can provide theoretical proofs, but which abstract away important features from foraging in real-world environments.

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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

arXiv

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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The collaborative strategy of multiple USVs with deep reinforcement learning method 18

The collaborative strategy of multiple USVs with deep reinfo...

引用

18th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2019

作者： Wu, Xing Zhong, Mingyu Feng, Guofei Xie, Shaorong Guo, Yike School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai China Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9781643680125

The unmanned surface vehicle (USV) has been widely used to accomplish tasks that cannot be completed by ships with human drivers on certain sea areas. It is not only necessary but essential to obtain a robust strategy in order to ensure multiple USVs accomplish collaborative tasks successfully and efficiently. To meet the challenge, a deep reinforcement learning method is proposed, which is combined with an improved A star algorithm. A statistically promising collaborative strategy is achieved by the proposed method under the guidance from the unmanned aerial vehicles (UAVs). After the collaborative strategy is generated, the improved A star algorithm is used to navigate the USVs. To verify the proposed algorithm, several tasks are tested on a simulation platform. Experimental results demonstrate that the proposed method outperforms state-of-the-art reinforcement learning methods such as DQN and DeepSarsa. © 2019 The authors and IOS Press. All rights reserved.

关键词： Reinforcement learning

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Active interactions between animals and technology: biohybrid approaches for animal behaviour research

引用

Animal Behaviour 2025年 224卷

作者： Papadopoulou, M. Ball, M. Bartashevich, P. Burns, A.L.J. Chiara, V. Clark, M.A. Costelloe, B.R. Fele, M. French, F. Hauert, S. Heinrich, M.K. Herbert-Read, J.E. Hoitt, J. Ioannou, C.C. Landgraf, T. Matchette, S.R. Polverino, G. Sankey, D.W.E. Scott, D.M. Sridhar, V.H. Strömbom, D. Trianni, V. Vo-Doan, T.T. King, A.J. Department of Biosciences Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Biology Lafayette College Easton PA United States Institute for Theoretical Biology Department of Biology Humboldt-Universität zu Berlin Berlin Germany Cluster of Excellence ‘Science of Intelligence’ Berlin Germany Department Fish Biology Fisheries and Aquaculture Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany Faculty of Life Sciences Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences Humboldt-Universität zu Berlin Berlin Germany Museum and Institute of Zoology Polish Academy of Science Warszawa Poland School of Biological Sciences University of Bristol Bristol United Kingdom School of Natural Sciences Macquarie University Sydney Australia Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Department of Collective Behavior Max Planck Institute of Animal Behavior Konstanz Germany School of Computing and Digital Media London Metropolitan University London United Kingdom Bristol Robotics Laboratory University of Bristol Bristol United Kingdom IRIDIA Université Libre de Bruxelles Brussels Belgium Department of Zoology University of Cambridge Cambridge United Kingdom Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Ecological and Biological Sciences University of Tuscia Viterbo Italy School of Natural and Environmental Science Newcastle University Newcastle upon Tyne United Kingdom Centre for Ecology and Conservation Faculty of Environment Science and Economy University of Exeter Penryn Campus Cornwall United Kingdom School of Animal Rural and Environmental Sciences Nottingham Trent University Nottingham United Kingdom Department for the Ecology of Animal Societies Max Planck Institute of Animal Behavior Konstanz Germany Ins

Biohybrid approaches (where living and engineered components are combined) provide new opportunities for advancing animal behaviour research and its applications. This review article and accompanying special issue explores how different types of novel technologies can be used in the field of animal behaviour from three perspectives: (1) comprehension, (2) application and (3) integration. Under the perspective of ‘comprehension,’ we present examples of how technologies like virtual animals or robots can be used in experimental settings to interact with living animals in a standardized manner. Such interactions can advance our understanding of fundamental topics such as mate choice, social learning and collective behaviour. Under ‘application,’ we investigate the potential for technologies to monitor, react and interact with animals in a variety of scenarios. For example, we discuss how drones can be used to keep large herbivores away from valuable crops and robotic predators to deter invasive species. Under ‘integration,’ we discuss possibilities for the coexistence of engineered and biological systems, augmenting the capacity or resilience of either or both components. Integration can be physical, for example, livestock can have sensors sit in their inner body for temperature monitoring, or within the environment, where sensors or robots monitor and interact with animals, such as a short-term earthquake forecasting method. Based upon these three themes, we discuss and classify existing biohybrid animal behaviour research, including the four articles included in our special issue. We also consider the ethics of this emerging field, highlight the advantages and potential issues associated with using technologies to create biohybrid systems and emphasize how such technologies can support the advancement of animal behaviour research. © 2025 The Association for the Study of Animal Behaviour

关键词： behavioural control behavioural monitoring biomimetic robotics ethorobotics interspecies interaction sensory integration

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Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements

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Physical Review C 2025年第5期111卷 054913-054913页

作者： R. Ehlers Y. Chen J. Mulligan Y. Ji A. Kumar S. Mak P. M. Jacobs A. Majumder A. Angerami R. Arora S. A. Bass R. Datta L. Du H. Elfner R. J. Fries C. Gale Y. He B. V. Jacak S. Jeon F. Jonas L. Kasper M. Kordell, II R. Kunnawalkam-Elayavalli J. Latessa Y.-J. Lee R. Lemmon M. Luzum A. Mankolli C. Martin H. Mehryar T. Mengel C. Nattrass J. Norman C. Parker J.-F. Paquet J. H. Putschke H. Roch G. Roland B. Schenke L. Schwiebert A. Sengupta C. Shen M. Singh C. Sirimanna D. Soeder R. A. Soltz I. Soudi Y. Tachibana J. Velkovska G. Vujanovic X.-N. Wang X. Wu W. Zhao Department of Physics University of California Berkeley California 94270 USA Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley California 94270 USA Laboratory for Nuclear Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics and Astronomy Vanderbilt University Nashville Tennessee 37235 USA Department of Statistical Science Duke University Durham North Carolina 27708 USA Department of Physics University of Regina Regina Saskatchewan S4S 0A2 Canada Department of Physics McGill University Montréal Québec H3A2T8 Canada Department of Physics and Astronomy Wayne State University Detroit Michigan 48201 USA Lawrence Livermore National Laboratory Livermore California 94550 USA Department of Computer Science Wayne State University Detroit Michigan 48202 USA Department of Physics Duke University Durham North Carolina 27708 USA GSI Helmholtzzentrum für Schwerionenforschung 64291 Darmstadt Germany Institute for Theoretical Physics Goethe University 60438 Frankfurt am Main Germany Frankfurt Institute for Advanced Studies 60438 Frankfurt am Main Germany Cyclotron Institute Texas A&M University College Station Texas 77843 USA Department of Physics and Astronomy Texas A&M University College Station Texas 77843 USA Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Daresbury Laboratory Daresbury Warrington Cheshire WA44AD United Kingdom Instituto de Fìsica Universidade de São Paulo C.P. 66318 05315-970 São Paulo SP Brazil. Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Oliver Lodge Laboratory University of Liverpo

The JETSCAPE Collaboration reports a new determination of the jet transport parameter q̂ in the quark-gluon plasma (QGP) using Bayesian inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). This multi-observable analysis extends the previously published JETSCAPE Bayesian inference determination of q̂, which was based solely on a selection of inclusive hadron suppression data. jetscape is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of q̂ utilizes active learning, a machine-learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation.

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Event extraction by associating event types and argument roles

arXiv

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arXiv 2021年

作者： Li, Qian Guo, Shu Wu, Jia Li, Jianxin Sheng, Jiawei Wang, Lihong Dong, Xiaohan Peng, Hao Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Department of Computing Macquarie University Sydney Australia Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying triggers and arguments under different role patterns). As different event types always own distinct extraction schemas (i.e., role patterns), previous work on EE usually follows an isolated learning paradigm, performing element extraction independently for different event types. It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles. This paper proposes a novel neural association framework for the EE task. Given a document, it first performs type classification via constructing a document-level graph to associate sentence nodes of different types, and adopting a graph attention network to learn sentence embeddings. Then, element extraction is achieved by building a universal schema of argument roles, with a parameter inheritance mechanism to enhance role preference for extracted elements. As such, our model takes into account type and role associations during EE, enabling implicit information sharing among them. Experimental results show that our approach consistently outperforms most state-of-the-art EE methods in both sub-tasks. Particularly, for types/roles with less training data, the performance is superior to the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Network comparison via encoding, decoding, and causality

arXiv

引用

arXiv 2022年

作者： Tian, Yang Hou, Hedong Xu, Guangzheng Zhang, Ziyang Sun, Pei Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Institut de Mathématiques d’Orsay 14 Rue Joliot-Curie Gif-sur-Yvette91190 France Department of Computer Science University College London LondonWC1E 6AE United Kingdom

Quantifying the relations (e.g., similarity) between complex networks paves the way for studying the latent information shared across networks. However, fundamental relation metrics are not well-defined between networks. As a compromise, prevalent techniques measure network relations in data-driven manners, which are inapplicable to analytic derivations in physics. To resolve this issue, we present a theory for obtaining an optimal characterization of network topological properties. We show that a network can be fully represented by a Gaussian variable defined by a function of the Laplacian, which simultaneously satisfies network-topology-dependent smoothness and maximum entropy properties. Based on it, we can analytically measure diverse relations between complex networks. As illustrations, we define encoding (e.g., information divergence and mutual information), decoding (e.g., Fisher information), and causality (e.g., Granger causality and conditional mutual information) between networks. We validate our framework on representative networks (e.g., random networks, protein structures, and chemical compounds) to demonstrate that a series of science and engineering challenges (e.g., network evolution, embedding, and query) can be tackled from a new perspective. An implementation of our theory is released as a multi-platform *** Codes 05C62, 05C80, 05C90, 68P01 © 2022, CC BY.

关键词： Complex networks

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