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GECAM Localization of High Energy Transients and the Systematic Error

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arXiv 2022年

作者： Zhao, Yi Xue, Wang-Chen Xiong, Shao-Lin Wang, Yuan-Hao Liu, Jia-Cong Luo, Qi Zhang, Yan-Qiu Sun, Jian-Chao Zhao, Xiao-Yun Cai, Ce Xiao, Shuo Huang, Yue Li, Xiao-Bo Zhang, Zhen Liao, Jin-Yuan Yang, Sheng Qiao, Rui Guo, Dong-Ya Zheng, Chao Yi, Qi-Bin Xie, Sheng-Lun Guo, Zhi-Wei Li, Chao-Yang Wang, Chen-Wei Tan, Wen-Jun Wang, Yue Peng, Wen-Xi Zheng, Shi-Jie He, Jian-Jian Wang, Ping Wang, Jin Ma, Xiang Song, Xin-Ying Zhang, Hong-Mei Li, Bing Zhang, Peng Wu, Hong Du, Yan-Qi Liang, Jing Zhao, Guo-Ying Li, Xin-Qiao Wen, Xiang-Yang An, Zheng-Hua Sun, Xi-Lei Xu, Yan-Bing Zhang, Fan Zhang, Da-Li Gong, Ke Liu, Ya-Qing Liang, Xiao-Hua Liu, Xiao-Jing Gao, Min Wang, Jin-Zhou Song, Li-Ming Chen, Gang Zhang, Ke-Ke Han, Xing-Bo Wu, Hai-Yan Hu, Tai Geng, Hao Lu, Fang-Jun Zhang, Shu Zhang, Shuang-Nan Lu, Gao-Peng Zeng, Ming Yu, Heng Department of Astronomy Beijing Normal University Beijing100875 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China College of Physics Hebei Normal University 20 South Erhuan Road Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China Institute of Astrophysics Central China Normal University Wuhan430079 China College of Physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Province Baoding City071002 China Physics and Space Science College China West Normal University Nanchong637002 China College of Electronic and information Engineering Tongji University Shanghai201804 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai201304 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Earth and Space Sciences University of Science and Technology of China Hefei230026 China Ministry of Education Beijing100084 China Department of Engineering Physics Tsinghua University Beijing100084 China

Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a pair of microsatellites (i.e. GECAM-A and GECAM-B) dedicated to monitoring gamma-ray transients including gravitational waves high-energy electromagnetic counterparts, Gamma-ray Bursts, Soft Gamma-ray Repeaters, Solar Flares and Terrestrial Gamma-ray Flashes. Since launch in December 2020, GECAM-B has detected hundreds of astronomical and terrestrial events. For these bursts, localization is the key for burst identification and classification as well as follow-up observations in multi-wavelength. Here, we propose a Bayesian localization method with Poisson data with Gaussian background profile likelihood to localize GECAM bursts based on the burst counts distribution in detectors with different orientations. We demonstrate that this method can work well for all kinds of bursts, especially for extremely short ones. In addition, we propose a new method to estimate the systematic error of localization based on a confidence level test, which can overcome some problems of the existing method in literature. We validate this method by Monte Carlo simulations, and then apply it to a burst sample with accurate location and find that the mean value of the systematic error of GECAM-B localization is ∼ 2.5◦. By considering this systematic error, we can obtain a reliable localization probability map for GECAM bursts. Our methods can be applied to other gamma-ray monitors. Copyright © 2022, The Authors. All rights reserved.

关键词： Errors

Gut microbiota and diabetes: From correlation to causality and mechanism

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World Journal of Diabetes 2020年第7期11卷 293-308页

作者： Wei-Zheng Li Kyle Stirling Jun-Jie Yang Lei Zhang Microbiome-X Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Medicine and EngineeringKey Laboratory of Big Data-Based Precision MedicineBeihang UniversityBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Luddy School of Informatics Computing and EngineeringIndiana UniversityBloomingtonIN 47408United States The Crisis Technologies Innovation Lab Indiana UniversityThe Information Technology Services and the Pervasive Technology InstituteBloomingtonIN 47408United States Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine Jinan 250000Shandong ProvinceChina College of Life Science Qilu Normal UniversityJinan 250000Shandong ProvinceChina Microbiome Research Center Shandong Institutes for Food and Drug ControlJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Qilu Children's Hospital of Shandong UniversityJinan 250000Shandong ProvinceChina Microbiological Laboratory Lin Yi People’s HospitalLinyi 276000Shandong ProvinceChina Qingdao Human Microbiome Center The Affiliated Central Hospital of Qingdao UniversityQingdao 266000Shandong ProvinceChina Institute for Medical Dataology Cheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Department of Biostatistics School of Public HealthCheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Research Institute of PediatricsQilu Children's HospitalCheeloo College of MedicineShandong Universityand Jinan Children's HospitalJinan 250022Shandong ProvinceChina

In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for *** a global scale,diabetes causes a significant negative impact to the health status of human *** review covers type 1 diabetes and type 2 *** examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes *** appears that the human oral and gut microbiota are deeply interdigitated with *** is that *** studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and *** studies focus on the role and the potential impact of intestinal microflora in *** paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in *** microflora seems to have an endless capacity to impact and transform *** conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.

关键词： Diabetes Microbiota Causality Mechanism Type 1 diabetes Type 2 diabetes Insulin resistance Inflammation Metabolites

The 2025 Roadmap to Ultrafast Dynamics: Frontiers of Theoretical and Computational Modelling

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arXiv 2025年

作者： Caruso, Fabio Sentef, Michael A. Attaccalite, Claudio Bonitz, Michael Draxl, Claudia De Giovannini, Umberto Eckstein, Martin Ernstorfer, Ralph Fechner, Michael Grüning, Myrta Hübener, Hannes Joost, Jan-Philip Juraschek, Dominik M. Karrasch, Christoph Kennes, Dante Marvin Latini, Simone Lu, I-Te Neufeld, Ofer Perfetto, Enrico Rettig, Laurenz Pela, Ronaldo Rodrigues Rubio, Angel Rudzinski, Joseph F. Ruggenthaler, Michael Sangalli, Davide Schüler, Michael Shallcross, Samuel Sharma, Sangeeta Stefanucci, Gianluca Werner, Philipp Institute of Theoretical Physics and Astrophysics University of Kiel Leibnizstrasse 15 Kiel24116 Germany Institute for Theoretical Physics Bremen Center for Computational Materials Science University of Bremen Bremen28359 Germany Luruper Chaussee 149 Hamburg22761 Germany CNRS Aix-Marseille Université Centre Interdisciplinaire de Nanoscience de Marseille UMR 7325 Campus de Luminy Marseille13288 cedex 9 France Physics Department CSMB Adlershof Humboldt-Universität zu Berlin Berlin12489 Germany Università degli Studi di Palermo Dipartimento di Fisica e Chimica—Emilio Segrè PalermoI-90123 Italy Institute for Theoretical Physics University of Hamburg Notkestraße 9-11 Hamburg22607 Germany The Hamburg Centre for Ultrafast Imaging Hamburg Germany Fritz Haber Institute Max Planck Society Faradayweg 4-6 Berlin14195 Germany Institut für Optik und Atomare Physik Technische Universität Berlin Straße des 17 Juni 135 Berlin10623 Germany Max Planck Institute for the Structure and Dynamics of Matter Hamburg Germany School of Mathematics and Physics Queen’s University Belfast University Road BelfastBT7 1NN United Kingdom Max Planck Institute for the Structure and Dynamics of Matter Center for Free Electron Laser Science Hamburg22761 Germany Department of Applied Physics and Science Education Eindhoven University of Technology Eindhoven Netherlands Technische Universität Braunschweig Institut für Mathematische Physik Mendelssohnstr. 3 Braunschweig38106 Germany Institut für Theorie der Statistischen Physik RWTH Aachen University JARA – Fundamentals of Future Information Technology Aachen52056 Germany Max Planck Institute for the Structure and Dynamics of Matter Center for Free Electron Laser Science Hamburg22761 Germany Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Max Planck Institute for the Structure and Dynamics of Matter Center for Free-Electron Laser Science Luruper Chaussee 149 Hamburg22761 Germany Schulich Fac

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This Roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science. Contributions in this Roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green’s function approaches, quantum and ab initio simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Time-resolved spectroscopy

Association of structural MRI with Braak staging using neural network-based risk scoring

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Matthew I. Miller Shangran Qiu Prajakta S. Joshi Joyce Lee Rhoda Au Vijaya B. Kolachalama Boston University School of Medicine Boston MA USA College of Arts & Sciences Boston University Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA Boston University School of Public Health Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background The gold standard of Alzheimer’s disease (AD) diagnosis remains histopathologic examination of neuropathologic changes. However, the dependence on post-mortem microscopic analysis limits the role of pathology in clinical care. Magnetic resonance imaging (MRI) is routinely used to diagnose suspected dementia (DE) due to AD, but correlating neuroimaging to underlying pathologic changes remains challenging. Herein, we report the development of two risk scores derived from convolutional neural network (CNN) processing of MRI scans, which we then validate against Braak staging of neurofibrillary tangles from autopsy reports. Method Using T1-weighted MRIs from the National Alzheimer’s Coordinating Center (NACC), we trained two CNNs (Figure 1) to predict continuous scores for overall cognitive status (“COG” score) and AD probability (“ALZ” score). The COG score, ranging from 0-2, graded a subject’s cognitive status on a spectrum from normal cognition (NC, 0), to mild cognitive impairment (MCI, 1), and DE due to any cause (2). The ALZ score then graded each DE individual’s probability of AD from 0 to 1. These CNNs were then applied to a subset of participants with autopsy reports available within 6 months of MRI. CNN performance was evaluated by area under receiver operating characteristic curve (AUC). Differences in risk scores were assessed by Kruskal-Wallis H testing with post hoc Dunn testing. Result The CNN models for COG and ALZ score estimation were trained on 4,822 NACC participants. Thresholding of COG scores distinguished individuals with DE from those with NC and MCI (AUC = 0.87). ALZ scores distinguished individuals with AD from those with non-AD causes of DE (AUC = 0.77). Among 74 persons with available neuropathology data, COG scores differed significantly among Braak staging groups (p<0.001), increasing from a mean of 0.99±0.36 in Stage 1 to 1.47±0.41 in Stage 6 (p = 0.010). Significant differences in ALZ scores were also observed (p = 0.039) betwe

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Rethinking performance estimation in neural architecture search

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arXiv 2020年

作者： Zheng, Xiawu Ji, Rongrong Wang, Qiang Ye, Qixiang Li, Zhenguo Tian, Yonghong Tian, Qi Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University 361005 China National Institute for Data Science in Health and Medicine Xiamen University Peng Cheng Laboratory Shenzhen China University of Chinese Academy of Sciences China Noah's Ark Lab Huawei Technologies Peking University

Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space. Since searching an optimal BPE is extremely time-consuming as it requires to train a large number of networks for evaluation, we propose a Minimum Importance Pruning (MIP) approach. Given a dataset and a BPE search space, MIP estimates the importance of hyper-parameters using random forest and subsequently prunes the minimum one from the next iteration. In this way, MIP effectively prunes less important hyper-parameters to allocate more computational resource on more important ones, thus achieving an effective exploration. By combining BPE with various search algorithms including reinforcement learning, evolution algorithm, random search, and differentiable architecture search, we achieve 1, 000× of NAS speed up with a negligible performance drop comparing to the SOTA.1. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

Continuous black-box optimization with quantum annealing and random subspace coding

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arXiv 2021年

作者： Izawa, Syun Kitai, Koki Tanaka, Shu Tamura, Ryo Tsuda, Koji Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences University of Tokyo Kashiwa277-8561 Japan Department of Mechanical Engineering University of Tokyo Tokyo113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama223-8522 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds extremum of an unknown function by alternating inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply quantum annealing (QA) to overcome the difficulty in the continuous black-box optimization. As QA specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution of QA has to be translated back. Our method has the following three parts: 1) Random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector. 2) A quadratic unconstrained binary optimization (QUBO) defined by acquisition function based on nonnegative-weighted linear regression model which is solved by QA. 3) A penalization scheme to ensure that the QA solution can be translated back. It is shown in benchmark tests that its performance using D-Wave AdvantageTM quantum annealer is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up a new possibility of quantum annealing and other QUBO solvers including quantum approximate optimization algorithm (QAOA) using a gated-quantum computers, and expand its range of application to continuous-valued problems. © 2021, CC BY.

关键词： Annealing

Deciphering the Spatial Stickiness, Co-occurrence, and Co-change of Industries at the Scale of City

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International Journal on Smart and Sustainable Cities

International Journal on Smart and Sustainable Cities 2023年第2期01卷

作者： Lock Yue Chew Ning Ning Chung Hoai Nguyen Huynh Wen Xuan Sia Alvin Chua School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Data Science & Artificial Intelligence Research Centre Nanyang Technological University Singapore 639798 Singapore Centre for University Core Singapore University of Social Sciences Singapore 599494 Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way Connexis North Tower #16-16 Singapore 138632 Singapore Design & Planning Lab Urban Redevelopment Authority Singapore 179369 Singapore

This paper analyzes the location of industries in two metropolitan cities to investigate how firms are spatially distributed, and identify the prevailing trends in firm location choice. By defining a “Region Index” metric, we show how industries have concentrated in or diffused from the regions-of-interest over time. We apply this metric to the Central Area (CA) of Singapore and the London Region to decipher the relative tendency of industries to locate at CA as well as London. To analyze the spatial co-location of industries, we constructed a community network, which reveals subtle spatial relationships between firms. Extending this approach temporally to create a co-change network, we deduce the occurrences of industries in Singapore and London that move into or out of a spatial region synchronously. Based on this approach, we elucidate firm types that not only co-change but also co-occur in space and remain within a region-of-interest. Overall, we observe certain common and contrasting patterns of industry movements in the context of Singapore and London through the analysis. “Other Activities Auxiliary to Financial Services” is found to have the consistent tendency of diffusing out of CA and London throughout the period of study. Conversely, opposite movement patterns are observed where we see the proclivity of IT and digital service firms to locate in London but relocate out of CA, while the Media, Financial Services, and Life Insurance subsectors tend to diffuse out of London but concentrate in the CA of Singapore.

关键词： Spatial morphology cities spatial indices land use urban complexity spatial analysis networks

Fish vitality assessment under adversity stress based on multi-sensing fusion and deep learning techniques

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Aquaculture 2025年 609卷

作者： Yanfei Zhu Wenguan Zhang Yongjun Zhang Xiaoshuan Zhang College of Engineering China Agricultural University Beijing 100083 China College of Engineering Beijing Laboratory of Food Quality and Safety China Agricultural University Beijing China School of Information Engineering Shandong Youth University of Political Science Jinan 250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Jinan 250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan 250103 China Sanya Research Institute of China Agricultural University Sanya 572000 China

Accurately assessing the vitality of fish during Cryogenic Waterless Live Transportation (CWLT) is crucial for ensuring their survival rate and transport quality. To address this, this study developed a multi-sensing fusion measurement system, integrating the impedance sensor and image sensor to capture fish impedance, phase angle data and visual image data, which can provide reliable data support for vitality assessment. A multi-input deep learning model for cross-modal data fusion, called the whale optimization algorithm-based dual-channel convolutional network (WOA-DConvNet), was developed to assess fish vitality. The study first conducted a correlation analysis between impedance and phase angle data using blood index data, revealing the relationship between different frequency signals and fish vitality status. Cluster analysis and variance analysis were then performed to examine the significant differences in vitality states. The results show significant differences ( P < 0.05) in impedance and phase angle data between different vitality levels. Finally, the WOA-DConvNet model was used to evaluate the vitality of pearl gentian grouper during the live transport. The results showed that the model achieved excellent performance, achieving precision, recall, f1 score and accuracy of 91.81 %, 91.74 %, 91.76 % and 91.71 %. The core goal of this research is to explore the integrated application of artificial intelligence and advanced sensing technologies in the live transport process, aiming to develop a more precise and intelligent vitality assessment method and promote scientific monitoring and management during live fish transportation.

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Multi-graph Convolution Network with Jump Connection for Event Detection

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Multi-graph Convolution Network with Jump Connection for Eve...

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiangbin Meng Pengfei Wang Haoran Yan Liutong Xu Jiafeng Guo Yixing Fan School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Event detection is an important information extraction task in nature language processing. Recently, the method based on syntactic information and graph convolution network has been wildly used in event detection task and achieved good performance. For event detection, graph convolution network (GCN) based on dependency arcs can capture the sentence syntactic representations and the syntactic information, which is from candidate triggers to arguments. However, existing methods based on GCN with dependency arcs suffer from imbalance and redundant information in graph. To capture important and refined information in graph, we propose Multi-graph Convolution Network with Jump Connection (MGJ-ED). The multi-graph convolution network module adds a core subgraph splitted from dependency graph which selects important one-hop neighbors' syntactic information in breadth via GCN. Also the jump connection architecture aggregate GCN layers' representation with different attention score, which learns the importance of neighbors' syntactic information of different hops away in depth. The experimental results on the widely used ACE 2005 dataset shows the superiority of the other state-of-the-art methods.

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