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arXiv 2024年

作者： Niu, Jiarui R. Wang, W.Y. Jiang, J.C. Qu, Y. Zhou, D.J. Zhu, W.W. Lee, K.J. Han, J.L. Zhang, B. Li, D. Cao, S. Fang, Z.Y. Feng, Y. Fu, Q.Y. Jiang, P. Jing, W.C. Li, J. Li, Y. Luo, R. Meng, L.Q. Miao, C.C. Miao, X.L. Niu, C.H. Pan, Y.C. Wang, B.J. Wang, F.Y. Wang, H.Z. Wang, P. Wu, Q. Wu, Z.W. Xu, H. Xu, J.W. Xu, L. Xue, M.Y. Yang, Y.P. Yuan, M. Yue, Y.L. Zhao, D. Zhang, C.F. Zhang, D.D. Zhang, J.S. Zhang, S.B. Zhang, Y.K. Zhu, Y.H. National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing100049 China The Nevada Center for Astrophysics University of Nevada Las VegasNV89154 United States Department of Physics and Astronomy University of Nevada Las VegasNV89154 United States Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Astronomy Peking University Beijing100871 China Yunnan Observatories Chinese Academy of Sciences P.O.Box110 Yunnan Kunming650011 China Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Yuhang District Zhejiang Province Hangzhou China School of Computer Science and Engineering Beihang University Beijing100191 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Department of Astronomy School of Physics and Materials Science Guangzhou University Guangzhou510006 China Institute of Astrophysics Central China Normal University Wuhan430079 China School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education China Lanzhou University Lanzhou730000 China Faculty of Electrical engineering and Computer Science Ningbo University Ningbo315211 China Pengcheng Laboratory Shenzhen518055 China South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650504 China National Space Science Center Chinese Academy of Sciences Beijing100190 China School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550025 China

We report the first detection of polarization angle (PA) orthogonal jumps, a phenomenon previously only observed from radio pulsars, from a fast radio burst (FRB) source FRB 20201124A. We find three cases of orthogonal jumps in over two thousand bursts, all resembling those observed in pulsar single pulses. We propose that the jumps are due to the superposition of two orthogonal emission modes that could only be produced in a highly magnetized plasma, and they are caused by the line of sight sweeping across a rotating magnetosphere. The shortest jump timescale is of the order of one-millisecond, which hints that the emission modes come from regions smaller than the light cylinder of most pulsars or magnetars. This discovery provides convincing evidence that FRB emission originates from the complex magnetosphere of a magnetar, suggesting an FRB emission mechanism that is analogous to radio pulsars despite a huge luminosity difference between two types of objects. Copyright © 2024, The Authors. All rights reserved.

关键词： Polarization

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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A prototype for software refactoring recommendation system

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International Journal of Performability engineering 2020年第7期16卷 1095-1104页

作者： Gao, Yuan Zhang, Youchun Lu, Wenpeng Luo, Jie Hao, Daqing China National-Local Joint Engineering Lab of Next Generation Internet Data Processing Technology University of Electronic Science and Technology of China Chengdu610054 China China School of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Luoyang Bearing Research Institute Co. Ltd. Luoyang471039 China

Software refactoring is used to reduce the costs and risks of software evolution. Automated software refactoring tools can reduce risks caused by manual refactoring, improve efficiency, and reduce difficulties of software refactoring. Researchers have made great efforts to research how to implement and improve automated software refactoring tools. However, results of automated refactoring tools often deviate from the intentions of the implementer. To this end, in this paper, we proposed and implemented a prototype tool for a software refactoring recommendation system based on previous research. The tool provides users with an optimized software refactoring scheme and users realize refactoring intentions by interacting with the tool. The tool has been evaluated to be effective, especially for users who are inexperienced and non- English speaking. © 2020 Totem Publisher, Inc. All rights reserved.

关键词： Recommender systems

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A discovery of Two Slow Pulsars with FAST: "Ronin" from the Globular Cluster M15

arXiv

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arXiv 2023年

作者： Zhou, Dengke Wang, Pei Li, Di Fang, Jianhua Miao, Chenchen Freire, Paulo C.C. Zhang, Lei Zhang, Dandan Chen, Huaxi Feng, Yi Xiao, Yifan Xie, Jintao Zhang, Xu Jin, Chenwu Wang, Han Ke, Yinan Guo, Xuerong Zhao, Rushuang Niu, Chenhui Zhu, Weiwei Xue, Mengyao Wang, Yabiao Wu, Jiafu Gan, Zhenye Sun, Zhongyi Wang, Chengjie Zhang, Junshuo Cao, Jinhuang Lu, Wanjin Zhang, Jie Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China University of Chinese Academy of Sciences Beijing100049 China New Cornerstone Science Laboratory Shenzhen518054 China Max Planck Institut für Radioastronomie Bonn53121 Germany Centre for Astrophysics and Supercomputing Swinburne University of Technology Hawthorn3122 Australia School of Mathematical Sciences School of Physics and Electronic Sciences Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang550001 China School of Physics and Electronic Engineering Qilu Normal University Jinan250200 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China College of Physical Science and Technology Central China Normal University Wuhan430079 China Tencent Youtu Lab Shanghai201103 China

Globular clusters harbor numerous millisecond pulsars, but long-period pulsars (P 100 ms) are rarely found. In this study, we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), aiming to detect the existence of long-period pulsars. We estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density (Smin) of pulsars. Subsequently, we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 seconds and 3.960716 seconds, respectively. On the P − P diagram, both pulsars are positioned below the spin-up line, suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later on. According to timing results, these two pulsars exhibit remarkably strong magnetic fields. If the magnetic fields were weakened during the accretion process, then a short duration of accretion might explain the strong magnetic fields of these pulsars. © 2023, CC BY.

关键词： Magnetic fields

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Effects of annotation granularity in deep learning models for histopathological images

Effects of annotation granularity in deep learning models fo...

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Jiangbo Shi Zeyu Gao Haichuan Zhang Pargorn Puttapirat Chunbao Wang Xiangrong Zhang Chen Li National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Department of Pathology the First Affiliated Hospital of Xi'an Jiaotong University Institute of Intelligent Information Processing Xidian University

ISBN: (纸本)9781728118680

Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machine learning, especially deep learning have established robust and accurate classifiers. They are being used to analyze histopathological slides and assist pathologists in diagnosis. Most machine learning systems rely heavily on annotated data sets to gain experiences and knowledge to correctly and accurately perform various tasks such as classification and segmentation. Generally, annotations made in pathology-related datasets have inherited annotation methods from natural scene images. This work investigates different granularity of annotations in histopathological data set including image-wise, bounding box, ellipse-wise, and pixel-wise to verify the influence of annotation in pathological slide on deep learning models. We design corresponding experiments to test classification and segmentation performance of deep learning models based on annotations with different annotation granularity. In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer granularity annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows not only that finer-grained annotation can improve the performance of deep learning models, but also help they extract more accurate phenotypic information from histopathological slides. The accurate and spatially precise acquisitions of phenotypic information can improve the reliability of the model prediction. Intelligence systems trained on granular annotations may help pathologists inspecting certain regions a

关键词： histopathological image annotation granularity deep learning classification semantic segmentation ANNOTATIONS Machine Learning Taxonomy Slide Pathologists Slide dataset slump

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Action Recognition Combining Trajectory Feature with KNR 2

Action Recognition Combining Trajectory Feature with KNR

引用

2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Hu, Yang Liu, Benyong College of Big Data and Information Engineering Guizhou University Guiyang Guizhou550025 China Guizhou University Institute of Intelligent Information Processing Guiyang Guizhou550025 China

ISBN: (纸本)9781538618035

D338 trajectory feature is combined with kernel-based nonlinear representor (KNR) for video-based human action recognition. Initially, feature points are filtered through the Shi-Tomasi discrimination criterion after uniform sampling. Then dense trajectory is formed by tracking features based on optical flow field. For generating features of the same dimension, Fisher vectors (FVs) are used to encode features of different dimensions. Finally, these aligned FVs are taken as features for KNR training and classification. To verify the performance of combination, Gaussian kernel with the same parameter is applied to KNR and support vector machine (SVM). Experimental results on the KTH action database show that the proposed method can significantly improve classification performance with respect to SVM. © 2018 IEEE.

关键词： Support vector machines

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BASSET: Bandpass-Adaptive Single-pulse SEarch Toolkit – Optimized Sub-Band Pulse Search Strategies for Faint Narrow-Band FRBs

arXiv

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arXiv 2025年

作者： Cao, J.H. Wang, P. Li, D. Pan, Q.H. Mao, K. Niu, C.H. Zhang, Y.K. Qu, Q.Y. Lu, W.J. Zhang, J.S. Zhu, Y.H. Wang, Y.D. Chen, H.X. Chen, X.L. Gügercinoğlu, E. Fang, J.H. Feng, Y. Gao, H. Huang, Y.F. Li, J. Miao, C.C. Tsai, C.W. Yao, J.M. You, S.P. Zhao, R.S. Liu, Q.Z. Weng, S.M. Yew, S.H. Zhang, J. Zhang, L. Zhou, D.K. Zhu, W.W. National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Tsinghua University Beijing100084 China Zhejiang Lab Zhejiang Hangzhou311121 China Central China Normal University Wuhan430079 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China Department of Astronomy Beijing Normal University Beijing100875 China School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210023 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi830011 China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China College of Physics and Electronic Engineering Qilu Normal University Jinan250200 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia

The existing single-pulse search algorithms for fast radio bursts (FRBs) do not adequately consider the frequency bandpass pattern of the pulse, rendering them incomplete for the relatively narrow-spectrum detection of pulses. We present a new search algorithm for narrow-band pulses to update the existing standard pipeline, Bandpass-Adaptive Single-pulse SEarch Toolkit (BASSET). The BASSET employs a time-frequency correlation analysis to identify and remove the noise involved by the zero-detection frequency band, thereby enhancing the signal-to-noise ratio (SNR) of the pulses. The BASSET algorithm was implemented on the FAST real dataset of FRB 20190520B, resulting in the discovery of additional 79 pulses through reprocessing. The new detection doubles the number of pulses compared to the previously known 75 pulses, bringing the total number of pulses to 154. In conjunction with the pulse calibration and the Markov Chain Monte Carlo (MCMC) simulated injection experiments, this work updates the quantified parameter space of the detection rate. Moreover, a parallel-accelerated version of the BASSET code was provided and evaluated through simulation. BASSET has the capacity of enhancing the detection sensitivity and the SNR of the narrow-band pulses from the existing pipeline, offering high performance and flexible applicability. BASSET not only enhances the completeness of the low-energy narrow-band pulse detection in a more robust mode, but also has the potential to further elucidate the FRB luminosity function at a wider energy scale. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Cl2⋅−Mediates Direct and Selective Conversion of Inert C(sp3)−H Bonds into Aldehydes/Ketones

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Angewandte Chemie 2023年第36期135卷

作者： Dr. Qinhua Zhang Dr. Bo An Dr. Yu Lei Zhixiao Gao Dr. Haonan Zhang Prof. Sheng Xue Prof. Xin Jin Prof. Wengang Xu Zihan Wu Prof. Mingbo Wu Prof. Xin Yang Prof. Wenting Wu State Key Laboratory of Heavy Oil Processing Institute of New Energy College of Chemical Engineering China University of Petroleum (East China) Qingdao 266580 P. R. China Contribution: Data curation (lead) Investigation (lead) Methodology (lead) Software (lead) Writing - original draft (lead) Contribution: Data curation (supporting) Software (supporting) Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology School of Environmental Science and Engineering Sun Yat-sen University Guangzhou 510275 China Contribution: Data curation (supporting) Investigation (supporting) Contribution: Resources (supporting) Software (supporting) Contribution: Data curation (supporting) Validation (supporting) Institute for Translational Medicine The Affiliated Hospital of Qingdao University College of Medicine Qingdao University Qingdao 266021 P. R. China Contribution: Conceptualization (supporting) Resources (supporting) Contribution: Formal analysis (supporting) Resources (supporting) Contribution: Investigation (supporting) Methodology (supporting) Contribution: Data curation (supporting) Contribution: Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - review & editing (lead) Contribution: Investigation (equal) Methodology (equal) Writing - review & editing (equal) Contribution: Formal analysis (lead) Project administration (lead) Supervision (lead) Writing - review & editing (lead)

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Optimization of Grouping Evacuation Strategy in High-rise Building Fires Based on Graph Theory and Computational Experiments

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1104-1112页

作者： Yuling Hu Xiwei Liu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044China State Key Laboratory fo Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing 100190China Qingdao Academy of Intelligent Industries Qingdao 266109China

It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.

关键词： Computational experiments grouping evacuation graph theory high-rise building fire management strategies

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Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation

Constrained Multi-scale Dense Connections for Accurate Biome...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiawei Zhang Yanchun Zhang Shanfeng Zhu Xiaowei Xu Shanghai key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China College of Engineering and Science Victoria University Melbourne Australia ISTBI and Shanghai Institute of Artificial Intelligence Algorithms Fudan University Shanghai China Nanjing University Institute of Artificial Intelligence Biomedicine Nanjing China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Guangdong Provincial People’s Hospital Guangdong Cardiovascular Institute Guangzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical image segmentation plays a critical role in clinical diagnosis and medical intervention. Recently, a variety of deep neural networks have boosted the biomedical image segmentation performance with a large margin, which adopts dense connections to explore rich representations in multiple scales. In multi-scale dense connections, features from all or most scales are fused or iteratively aggregated. In this paper, we propose constrained multi-scale dense connections (CMDC) for accurate biomedical image segmentation, which only fuse features from the nearest scales containing the most relevant appearance or semantic information. Based on CMDC, we further construct constraint multi-scale dense networks (CMD-Net) by applying CMDC to existing segmentation networks. Experiments across various architectures (including FCN-8s, U-Net, and DeepLabV3) and datasets (including GlaS, CRAG, KID, and ECS) demonstrate that CMD-Net not only outperforms existing schemes on both accuracy and efficiency but also can be easily generalized to a variety of segmentation networks. In addition, CMD-Net achieves state-of-the-art performance on two instance segmentation datasets, GlaS and CRAG.

关键词： Decoding Image segmentation Image resolution Biomedical imaging Glands Fuses Cancer

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