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A discovery of two slow pulsars with FAST:“Ronin”from the globular cluster M15

Science China(Physics,Mechanics & Astronomy) 2024年第6期67卷 166-180页

作者： Dengke Zhou Pei Wang Di Li Jianhua Fang Chenchen Miao Paulo CCFreire Lei Zhang Dandan Zhang Huaxi Chen Yi Feng Yifan Xiao Jintao Xie Xu Zhang Chenwu Jin Han Wang Yinan Ke Xuerong Guo Rushuang Zhao Chenhui Niu Weiwei Zhu Mengyao Xue Yabiao Wang Jiafu Wu Zhenye Gan Zhongyi Sun Chengjie Wang Jie Zhang Junshuo Zhang Jinhuang Cao Wanjin Lu Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311121China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China University of Chinese Academy of Sciences Beijing 100049China New Cornerstone Science Laboratory Shenzhen 518054China Max Planck Institut f¨ur Radioastronomie Bonn 53121Germany Centre for Astrophysics and Supercomputing Swinburne University of TechnologyHawthorn 3122Australia School of Mathematical Sciences School of Physics and Electronic SciencesGuizhou Normal UniversityGuiyang 550001China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang 550001China School of Physics and Electronic Engineering Qilu Normal UniversityJinan 250200China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China College of Physical Science and Technology Central China Normal UniversityWuhan 430079China Tencent Youtu Lab Shanghai 201103China

Globular clusters harbor numerous millisecond pulsars,but long-period pulsars(P 100 ms)are rarely *** this study,we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundredmeter Aperture Spherical radio Telescope(FAST),aiming to detect the existence of long-period *** estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density(S_(min))of ***,we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 and3.960716 s,*** 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 *** to timing results,these two pulsars exhibit remarkably strong magnetic *** the magnetic fields were weakened during the accretion process,then a short duration of accretion might explain the strong magnetic fields of these pulsars.

关键词： pulsar globular cluster FAST

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Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Salicylic acid promotes quiescent center cell division through ROS accumulation and down-regulation of PLT1,PLT2,and WOX5

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Journal of Integrative Plant Biology 2021年第3期63卷 583-596页

作者： Zhuqing Wang Duoyan Rong Dixing Chen Yang Xiao Renyi Liu Shuang Wu Chizuko Yamamuro College of Life Sciences Fujian Agriculture and Forestry UniversityFuzhou 350002China FAFU‐UCR Joint Center for Horticultural Biology and Metabolomics Haixia Institute of Science and TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China Hunan Engineering Research Centre of Lily Germplasm Resource in Novation and Deep Processing College of Life Sciences and ChemistryHunan University of TechnologyZhuzhou 412007China Center for Agroforestry Mega Data Science Haixia Institute of Science and TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China

Salicylic acid(SA)plays a crucial role in plant ***,its function in plant development is poorly *** quiescent center(QC),which maintains columella stem cells(CSCs)in the root apical meristem and typically exhibits low levels of cell division,is critical for root growth and ***,we show that the Arabidopsis thaliana SA overaccumulation mutant constitutively activated cell death 1(cad1),which exhibits increased cell division in the QC,is rescued by additional mutations in genes encoding the SA biosynthetic enzyme SALICYLIC ACID INDUCTION DEFFICIENT2(SID2)or the SA receptor NONEXPRESSER OF PR GENES1(NPR1),indicating that QC cell division in the cad1 mutant is promoted by the NPR1-dependent SA signaling *** application of exogenous SA also promoted QC cell division in wild-type plants in a dose-dependent manner and largely suppressed the expression of genes involved in QC maintenance,including those encoding the APETALA2(AP2)transcription factors PLETHORA1(PLT1)and PLT2,as well as the homeodomain transcription factor WUSCHEL-RELATED HOMEOBOX5(WOX5).Moreover,we showed that SA promotes reactive oxygen species(ROS)production,which is necessary for the QC cell division phenotype in the cad1 *** results provide insight into the function of SA in QC maintenance.

关键词： PLT1 PLT2 QC cell division reactive oxygen species salicylic acid WOX5

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Flow characteristics in open channels with aquatic rigid vegetation

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Journal of Hydrodynamics 2020年第6期32卷 1100-1108页

作者： Yan-jie Wu He-fang Jing Chun-guang Li Ying-ting Song College of Civil and Hydraulic Engineering Hefei University of TechnologyHefei230009China School of Civil Engineering North Minzu UniversityYinchuan750021China Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province Yinchuan750021China Research Institute of Numerical Computation and Engineering Applications North Minzu UniversityYinchuan750021China State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin300350China

In order to study the flow characteristics in water bodies with rigid aquatic vegetation,series of laboratory experiments are carried out in an open channel,in which glass rods are used as plants with diameters of 6mm,8mm and 10mm,*** each diameter of glass rods,four typical cases are considered with various densities and arrangements of glass *** flow velocities in the four cases are measured by the 3-D laser Doppler velocimeter(LDV).The water surface slope,the flow velocity,the water head loss,the vegetation drag force and the hydraulic slope are calculated,analyzed and *** horizontal,vertical and total vegetation densities in the vegetation area are defined and the relationship between these physical parameters and the water surface slope are *** head loss and the hydraulic slope in the vegetation area are also calculated,compared and *** is indicated that the water surface slope and velocity,the head loss and the hydraulic slope in the vegetation area have a close relationship with the arrangement,the density,and the plant diameter of the vegetation.

关键词： Aquatic rigid vegetation flow velocity laser Doppler velocimeter(LDV) head loss hydraulic slope

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FaceBench: A Multi-View Multi-Level Facial Attribute VQA dataset for Benchmarking Face Perception MLLMs

arXiv

引用

arXiv 2025年

作者： Wang, Xiaoqin Ma, Xusen Hou, Xianxu Ding, Meidan Li, Yudong Chen, Junliang Chen, Wenting Peng, Xiaoyang Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China AIAC Xi’an Jiaotong-Liverpool University China Tsinghua University China The Hong Kong Polytechnic University Hong Kong City University of Hong Kong Hong Kong Sun Yat-sen University China

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely unexplored. To address this gap, we introduce FaceBench, a dataset featuring hierarchical multi-view and multi-level attributes specifically designed to assess the comprehensive face perception abilities of MLLMs. Initially, we construct a hierarchical facial attribute structure, which encompasses five views with up to three levels of attributes, totaling over 210 attributes and 700 attribute values. Based on the structure, the proposed FaceBench consists of 49,919 visual question-answering (VQA) pairs for evaluation and 23,841 pairs for fine-tuning. Moreover, we further develop a robust face perception MLLM baseline, Face-LLaVA, by training with our proposed face VQA data. Extensive experiments on various mainstream MLLMs and Face-LLaVA are conducted to test their face perception ability, with results also compared against human performance. The results reveal that, the existing MLLMs are far from satisfactory in understanding the fine-grained facial attributes, while our Face-LLaVA significantly outperforms existing open-source models with a small amount of training data and is comparable to commercial ones like GPT-4o and Gemini. The dataset will be released at https://***/CVI-SZU/FaceBench. Copyright © 2025, The Authors. All rights reserved.

关键词：

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VELO: A Vector database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework

arXiv

引用

arXiv 2024年

作者： Yao, Zhi Tang, Zhiqing Lou, Jiong Shen, Ping Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519087 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response delays and incur high costs, thereby impacting the Quality of Services (QoS) at the network edge. Leveraging vector database caching to store LLM request results at the edge can substantially mitigate response delays and cost associated with similar requests, which has been overlooked by previous research. Addressing these gaps, this paper introduces a novel Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework. Firstly, we propose the VELO framework, which ingeniously employs vector database to cache the results of some LLM requests at the edge to reduce the response time of subsequent similar requests. Diverging from direct optimization of the LLM, our VELO framework does not necessitate altering the internal structure of LLM and is broadly applicable to diverse LLMs. Subsequently, building upon the VELO framework, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and devise an algorithm grounded in Multi-Agent Reinforcement Learning (MARL) to decide whether to request the LLM in the cloud or directly return the results from the vector database at the edge. Moreover, to enhance request feature extraction and expedite training, we refine the policy network of MARL and integrate expert demonstrations. Finally, we implement the proposed algorithm within a real edge system. Experimental findings confirm that our VELO framework substantially enhances user satisfaction by concurrently diminishing delay and resource consumption for edge users utilizing LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Vectors

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A Diabetic Retinopathy Classification Method Based on Novel Attention Mechanism 17th

A Diabetic Retinopathy Classification Method Based on Novel ...

引用

17th International Conference on Intelligent Computing, ICIC 2021

作者： Zou, Jinfan Zhang, Xiaolong Lin, Xiaoli School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030845216

The convolutional neural network (CNN) has been effectively used to do feature extraction in medical image analysis including fundus images for diabetic retinopathy. However, some important detail features are easily ignored, which causes low prediction accuracy. To solve this problem, a CNN feature extraction method based on a novel pixel-level attention mechanism is proposed to achieve an efficient and accurate identification of diabetic retinopathy. Firstly, data normalization and data augmentation are used to improve the quality of datasets. Then, according to the structure characteristics of the two backbone networks Inception- ResnetV2 and EfficientNet-B5, the corresponding attention modules are introduced respectively. Finally, attention networks are trained as feature extractors of fundus images, and complementary deep attention feature descriptors are formed through feature fusion, which can effectively improve the accuracy of the classification model. The experimental results on EyePACS, Messidor and OIA datasets show that the proposed method outperforms the previous ones. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

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A Dynamic Transformer Network for Vehicle Detection

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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FDSR-INT: A Flexible On-Demand In-Band Telemetry Approach for Aerial Computing Networks

引用

IEEE Internet of Things Journal 2025年

作者： Xu, Jinghan Xu, Xiaolong Zhao, Juan Gao, Honghao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing The School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do 461-701 Korea Republic of

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy pathcutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed *** integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on *** interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead. © 2014 IEEE.

关键词： Bandwidth

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Training time minimization for federated edge learning with optimized gradient quantization and bandwidth allocation

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Frontiers of Information Technology & Electronic engineering 2022年第8期23卷 1247-1263页

作者： Peixi LIU Jiamo JIANG Guangxu ZHU Lei CHENG Wei JIANG Wu LUO Ying DU Zhiqin WANG State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China China Academy of Information and Communications Technology Beijing 100191China Shenzhen Research Institute of Big Data Shenzhen 518172China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Zhejiang Provincial Key Laboratory of Information Processing Communication and NetworkingHangzhou 310027China

Training a machine learning model with federated edge learning(FEEL)is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge *** this study,the training time minimization problem is investigated in a quantized FEEL system,where heterogeneous edge devices send quantized gradients to the edge server via orthogonal *** particular,a stochastic quantization scheme is adopted for compression of uploaded gradients,which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication *** training time is modeled by taking into account the communication time,computation time,and the number of communication *** on the proposed training time model,the intrinsic trade-off between the number of communication rounds and per-round latency is ***,we analyze the convergence behavior of the quantized FEEL in terms of the optimality ***,a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap,based on which the closed-form expressions for the number of communication rounds and the total training time are *** by the total bandwidth,the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization *** this end,an algorithm based on alternating optimization is proposed,which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection *** different learning tasks and models,the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.

关键词： Federated edge learning Quantization optimization Bandwith allocation Training time minimization

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