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Magnetar emergence in a peculiar gamma-ray burst from a compact star merger

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National science Review 2025年第3期12卷 125-131页

作者： Hui Sun Chenwei Wang Jun Yang Bin-Bin Zhang Shaolin Xiong Yi-Han Iris Yin Yuan Liu Ye Li Wangchen Xue Zhen-Yu Yan Chen Zhang Wenjun Tan Haiwu Pan Jiacong Liu Huaqing Cheng Yanqiu Zhang Jingwei Hu Chao Zheng Zhenghua An Ce Cai Zhiming Cai Lei Hu Chichuan Jin Dongyue Li Xinqiao Li Heyang Liu Mingjun Liu Wenxi Peng Liming Song Shengli Sun Xiaojin Sun Xilu Wang Xiangyang Wen Shuo Xiao Shuxu Yi Fan Zhang Wenda Zhang Xiaofeng Zhang Yonghe Zhang Donghua Zhao Shijie Zheng Zhixing Ling Shuang-Nan Zhang Weimin Yuan Bing Zhang National Astronomical Observatories Chinese Academy of Sciences Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences University of Chinese Academy of Sciences Chinese Academy of Sciences School of Astronomy and Space Science Nanjing University Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University) Ministry of Education Purple Mountain Observatory Chinese Academy of Sciences College of Physics and Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai Institute of Technical Physics Chinese Academy of Sciences Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Nevada Center for Astrophysics University of Nevada Department of Physics and Astronomy University of Nevada

The central engine that powers gamma-ray bursts（GRBs）, the most powerful explosions in the universe, is still not identified. Besides hyper-accreting black holes, rapidly spinning and highly magnetized neutron stars, known as millisecond magnetars, have been suggested to power both long and short GRBs. The presence of a magnetar engine following compact star mergers is of particular interest as it would provide essential constraints on the poorly understood equation of state for neutron stars. Indirect indications of a magnetar engine in these merger sources have been observed in the form of plateau features present in the X-ray afterglow light curves of some short GRBs. Additionally, some X-ray transients lacking gamma-ray bursts have been identified as potential magnetar candidates originating from compact star ***, smoking-gun evidence is still lacking for a magnetar engine in short GRBs, and associated theoretical challenges have been raised. Here we present a comprehensive analysis of the broad-band prompt emission data of the peculiar, very bright GRB 230307A. Despite its apparently long duration, the prompt emission and host galaxy properties are consistent with a compact star merger origin, as suggested by its association with a kilonova. Intriguingly, an extended X-ray emission component shows up as theγ-ray emission dies out, signifying the likely emergence of a magnetar central engine. We also identify an achromatic temporal break in the high-energy band during the prompt emission phase, which was never observed in previous bursts and reveals a narrow jet with a half opening angle of ~ 3.4°(RGRB/1015cm)-1/2,where RGRBis the GRB prompt emission radius.

关键词： gamma-ray burst magnetar X-ray emission compact binary merger

NeuVSA: A Unified and Efficient Accelerator for Neural Vector Search 31

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NeuVSA: A Unified and Efficient Accelerator for Neural Vecto...

31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Yuan, Ziming Dai, Lei Li, Wen Zhang, Jie Liang, Shengwen Wang, Ying Liu, Cheng Li, Huawei Li, Xiaowei Guo, Jiafeng Wang, Peng Chen, Renhai Zhang, Gong Shanxi University School of Computer and Information Technology China Peking University School of Computer Science China Institute of Computing Technology CAS State Key Lab of Processors China University of Chinese Academy of Sciences China Zhongguancun National Laboratory Beijing China Institute of Computing Technology CAS Key Lab of Network Data Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9798331506476

Neural Vector Search (NVS) has exhibited superior search quality over traditional key-based strategies for information retrieval tasks. An effective NVS architecture requires high recall, low latency, and high throughput to enhance user experience and cost-efficiency. However, implementing NVS on existing neural network accelerators and vector search accelerators is sub-optimal due to the separation between the embedding stage and vector search stage at both algorithm and architecture levels. Fortunately, we unveil that Product Quantization (PQ) opens up an opportunity to break separation. However, existing PQ algorithms and accelerators still focus on either the embedding stage or the vector search stage, rather than both simultaneously. Simply combining existing solutions still follows the beaten track of separation and suffers from insufficient parallelization, frequent data access conflicts, and the absence of scheduling, thus failing to reach optimal recall, latency, and throughput. To this end, we propose a unified and efficient NVS accelerator dubbed NeuVSA based on algorithm and architecture co-design philosophy. Specifically, on the algorithm level, we propose a learned PQ-based unified NVS algorithm that consolidates two separate stages into the same computing and memory access paradigm. It integrates an end-to-end joint training strategy to learn the optimal codebook and index for enhanced recall and reduced PQ complexity, thus achieving smoother acceleration. On the architecture level, we customize a homogeneous NVS accelerator based on the unified NVS algorithm. Each sub-accelerator is optimized to exploit all parallelism exposed by unified NVS, incorporating a structured index assignment strategy and an elastic on-chip buffer to alleviate buffer conflicts for reduced latency. All sub-accelerators are coordinated using a hardware-aware scheduling strategy for boosted throughput. Experimental results show that the joint training strategy improves recall

关键词： Network embeddings

A Medical data-Effective Learning Benchmark for Highly Efficient Pre-training of Foundation Models

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arXiv 2024年

作者： Yang, Wenxuan Tan, Weimin Sun, Yuqi Yan, Bo Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Foundation models, pre-trained on massive datasets, have achieved unprecedented generalizability. However, is it truly necessary to involve such vast amounts of data in pre-training, consuming extensive computational resources? This paper introduces data-effective learning, aiming to use data in the most impactful way to pre-train foundation models. This involves strategies that focus on data quality rather than quantity, ensuring the data used for training has high informational value. data-effective learning plays a profound role in accelerating foundation model training, reducing computational costs, and saving data storage, which is very important as the volume of medical data in recent years has grown beyond many people's expectations. However, due to the lack of standards and comprehensive benchmark, research on medical data-effective learning is poorly studied. To address this gap, our paper introduces a comprehensive benchmark specifically for evaluating data-effective learning in the medical field. This benchmark includes a dataset with millions of data samples from 31 medical centers (dataDEL), a baseline method for comparison (MedDEL), and a new evaluation metric (NormDEL) to objectively measure data-effective learning performance. Our extensive experimental results show the baseline MedDEL can achieve performance comparable to the original large dataset with only 5% of the data. Establishing such an open data-effective learning benchmark is crucial for the medical foundation model research community because it facilitates efficient data use, promotes collaborative breakthroughs, and fosters the development of cost-effective, scalable, and impactful healthcare solutions. The benchmark can be accessed at GitHub Repository. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction 5

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China South China University of Technology School of Electronic and Information Engineering Guangzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350373820

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. © 2024 IEEE.

关键词： Trajectories

Review on guidance and control of aerospace vehicles: recent progress and prospect

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Aerospace Systems 2024年第2期7卷 175-192页

作者： Wang, Zian Cai, Pengcheng Gong, Zheng Zhang, Chengxi Zhao, Shunyi Wu, Jin Dong, Peng Department of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Key Laboratory of Advanced Control for Light Industry Processes Ministry of Education Jiangnan University Wuxi214122 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China

This paper expounds on the development status and relevant works of control and guidance methods of the aerospace vehicle in recent years. The control difficulties and the solutions in the related results are introduced briefly. Moreover, the guidance methods are then expounded in detail according to the flight phases of the whole flight mission. Guidance methods are usually included in each phase, and the corresponding trajectory design theories are also introduced where necessary. In addition, the potential future development direction prospects. Based on the above, a brief conclusion is then made as a summary. © shanghai Jiao Tong University 2024.

关键词： Space flight

FacialFlowNet: Advancing Facial Optical Flow Estimation with a Diverse dataset and a Decomposed Model

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arXiv 2024年

作者： Lu, Jianzhi He, Ruian Zhou, Shili Tan, Weimin Yan, Bo Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Facial movements play a crucial role in conveying altitude and intentions, and facial optical flow provides a dynamic and detailed representation of it. However, the scarcity of datasets and a modern baseline hinders the progress in facial optical flow research. This paper proposes FacialFlowNet (FFN), a novel large-scale facial optical flow dataset, and the Decomposed Facial Flow Model (DecFlow), the first method capable of decomposing facial flow. FFN comprises 9,635 identities and 105,970 image pairs, offering unprecedented diversity for detailed facial and head motion analysis. DecFlow features a facial semantic-aware encoder and a decomposed flow decoder, excelling in accurately estimating and decomposing facial flow into head and expression components. Comprehensive experiments demonstrate that FFN significantly enhances the accuracy of facial flow estimation across various optical flow methods, achieving up to an 11% reduction in Endpoint Error (EPE) (from 3.91 to 3.48). Moreover, DecFlow, when coupled with FFN, outperforms existing methods in both synthetic and real-world scenarios, enhancing facial expression analysis. The decomposed expression flow achieves a substantial accuracy improvement of 18% (from 69.1% to 82.1%) in micro-expressions recognition. These contributions represent a significant advancement in facial motion analysis and optical flow estimation. Codes and datasets can be found here. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical flows

Heterogeneous-attributes enhancement deep framework for network embedding

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Frontiers of computer science 2021年第6期15卷 121-131页

作者： Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230022China National Key Laboratory of Blind Signal Processing Chengdu 610041China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230022China

Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network ***,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute ***,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more *** that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining *** this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised *** extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.

关键词： network embedding heterogeneous-attributes deep framework inconsistent

Addressing Imbalance for Class Incremental Learning in Medical Image Classification

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arXiv 2024年

作者： Hao, Xuze Ni, Wenqian Jiang, Xuhao Tan, Weimin Yan, Bo Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Deep convolutional neural networks have made significant breakthroughs in medical image classification, under the assumption that training samples from all classes are simultaneously available. However, in real-world medical scenarios, there’s a common need to continuously learn about new diseases, leading to the emerging field of class incremental learning (CIL) in the medical domain. Typically, CIL suffers from catastrophic forgetting when trained on new classes. This phenomenon is mainly caused by the imbalance between old and new classes, and it becomes even more challenging with imbalanced medical datasets. In this work, we introduce two simple yet effective plug-in methods to mitigate the adverse effects of the imbalance. First, we propose a CIL-balanced classification loss to mitigate the classifier bias toward majority classes via logit adjustment. Second, we propose a distribution margin loss that not only alleviates the inter-class overlap in embedding space but also enforces the intra-class compactness. We evaluate the effectiveness of our method with extensive experiments on three benchmark datasets (CCH5000, HAM10000, and EyePACS). The results demonstrate that our approach outperforms state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

Object Detection based on the Collection of Geometric Evidence

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arXiv 2023年

作者： Wei, Hui Tang, Fu-Yu The Laboratory of Cognitive Algorithms and Modeling Shanghai Key Laboratory of Data Science Department of Computer Science Fudan University Shanghai201203 China

Artificial objects usually have very stable shape features, which are stable, persistent properties in geometry. They can provide evidence for object recognition. Shape features are more stable and more distinguishing than appearance features, color features, grayscale features, or gradient features. The difficulty with object recognition based on shape features is that objects may differ in color, lighting, size, position, pose, and background interference, and it is not currently possible to predict all possible conditions. The variety of objects and conditions renders object recognition based on geometric features very challenging. This paper provides a method based on shape templates, which involves the selection, collection, and combination discrimination of geometric evidence of the edge segments of images, to find out the target object accurately from background, and it is able to identify the semantic attributes of each line segment of the target object. In essence, the method involves solving a global optimal combinatorial optimization problem. Although the complexity of the global optimal combinatorial optimization problem seems to be very high, there is no need to define the complex feature vector and no need for any expensive training process. It has very good generalization ability and environmental adaptability, and more solid basis for cognitive psychology than other methods. The process of collecting geometric evidence, which is simple and universal, shows considerable prospects for practical use. The experimental results prove that the method has great advantages in response to changes in the environment, invariant recognition, pinpointing the geometry of objects, search efficiency, and efficient calculation. This attempt contributes to understanding of some types of universal processing during the process of object recognition. © 2023, CC BY-NC-SA.

关键词： Object recognition