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arXiv 2024年

作者： Benito, María Karchev, Konstantin Leane, Rebecca K. Põder, Sven Smirnov, Juri Trotta, Roberto Tartu Observatory University of Tartu Observatooriumi 1 Tõravere61602 Estonia Theoretical and Scientific Data Science SISSA Trieste Italy SLAC National Accelerator Laboratory Stanford University StanfordCA94039 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University StanfordCA94039 United States National Institute of Chemical Physics and Biophysics Rävala 10 Tallinn10143 Estonia Department of Mathematical Sciences University of Liverpool LiverpoolL69 7ZL United Kingdom Imperial College London Physics Department Blackett Laboratory Prince Consort Rd LondonSW7 2AZ United Kingdom Italian Research Center on High Performance Computing Big Data and Quantum Computing & National Institute for Nuclear Physics Sezione di Trieste Italy

Dark Matter (DM) can become captured, deposit annihilation energy, and hence increase the heat flow in exoplanets and brown dwarfs. Detecting such a DM-induced heating in a population of exoplanets in the inner kpc of the Milky Way thus provides potential sensitivity to the galactic DM halo parameters. We develop a Bayesian Hierarchical Model to investigate the feasibility of DM discovery with exoplanets and examine future prospects to recover the spatial distribution of DM in the Milky Way. We reconstruct from mock exoplanet datasets observable parameters such as exoplanet age, temperature, mass, and location, together with DM halo parameters, for representative choices of measurement uncertainty and the number of exoplanets detected. We find that detection of O(100) exoplanets in the inner Galaxy can yield quantitative information on the galactic DM density profile, under the assumption of 10% measurement uncertainty. Even as few as O(10) exoplanets can deliver meaningful sensitivities if the DM density and inner slope are sufficiently large. © 2024, CC BY.

关键词： Hierarchical systems

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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

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IEEE Network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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All-optical Helicity-Independent Switching State Diagram in Gd-Fe-Co Alloys

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Physical Review Applied 2021年第5期15卷 054065-054065页

作者： Jiaqi Wei Boyu Zhang Michel Hehn Wei Zhang Gregory Malinowski Yong Xu Weisheng Zhao Stéphane Mangin Fert Beijing Institute Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) School of Integrated Circuit Science and Engineering Beihang University Beijing 100191 China Université de Lorraine CNRS IJL F-54000 Nancy France

Ultrafast magnetization switching induced by a single femtosecond laser pulse, under no applied magnetic field has attracted a lot of attention in the last 10 years because of its high potential for low-energy and ultrafast memory applications. Single-pulse helicity-independent switching has mostly been demonstrated for Gd-based materials. It is now necessary to optimize the pulse duration and the energy needed to switch a Gd-Fe-Co magnet depending on the alloy thickness and composition. Here we experimentally report state diagrams showing the magnetic state obtained after one single pulse depending on the laser pulse duration and fluence for various Gd-Fe-Co thin films with different compositions and thicknesses. We demonstrate that these state diagrams share similar characteristics: the fluence window for switching narrows for longer pulse duration and for the considered pulse-duration range the critical fluence for single-pulse switching increases linearly as a function of the pulse duration while the critical fluence required for creating a multidomain state remains almost constant. Calculations based on the atomistic spin model qualitatively reproduce the experimental state diagrams and their evolution. By studying the effect of the composition and the thickness on the state diagram, we demonstrate that the best energy efficiency and the longest pulse duration for switching are obtained for composition around the magnetic compensation.

关键词： Light-induced magnetic effects Magnetic domains Magnetization switching Spin dynamics Spintronics Ultrafast magnetic effects Ferrimagnets Transition-metal rare-earth alloys

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Individual Tree Crown Delineation Based on Deep Learning for Arid Areas Using High-Resolution Satellite Imagery

Individual Tree Crown Delineation Based on Deep Learning for...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jinzhuang Shi Hui Li Yuanyuan Lin Linhai Jing Kongwen Zhang International Research Center of Big Data for Sustainable Development Goals Beijing China College of Mathematics and System Sciences Xinjiang University Xinjiang China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing C Hainan Key Laboratory of Earth Observation Hainan Aerospace Information Research Institute Wenchang China School of Earth Sciences and Resources China University of Geosciences Beijing China School of Computing University of the Fraser Valley Abbotsford Canada

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In this work, we explored the potential of the individual tree crown (ITC) delineation and individual tree species classification using deep learning using a Worldview-3 scene covering the Wushen Banner area, Ordos City, where is a typical arid region located in the hinterland of the Mu Us Desert. Combined with field survey data, an ITC delineation sample set and an individual tree species sample set were established. The Mask R-CNN model was employed for ITC delineation, and ResNet-18, GoogLeNet, and DenseNet-40 network models were considered for individual tree species classification. The results showed that the highest accuracy of ITC delineation using MASK R-CNN reached 78.9%. The training accuracies of individual tree species classification models were above 90%, and ResNet-18 achieved the highest classification accuracy. The trained ResNet-18 model was employed to classify tree species from the ITC delineation results of two testing images, and the overall accuracies were higher than 90%.

关键词： Deep learning Training Surveys Accuracy Satellites Urban areas Vegetation

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Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

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ACM Transactions on Recommender Systems 2024年第4期2卷 1-27页

作者： Zhiyong Cheng Jianhua Dong Fan Liu Lei Zhu Xun Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computing National University of Singapore Singapore Singapore School of Electronic and Information Engineering Tongji University Shanghai China School of Information Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China and Hefei Comprehensive National Science Center Hefei China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

关键词： Multi-Behavior Recommendation disentangled representation learning graph convolutional network multi-behavior recommendation multi-task learning

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Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits

arXiv

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arXiv 2021年

作者： Bai, Jiawang Wu, Baoyuan Zhang, Yong Li, Yiming Li, Zhifeng Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China PCL Research Center of Networks and Communications Peng Cheng Laboratory China School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data China Tencent AI Lab United States

To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Specifically, our goal is to misclassify a specific sample into a target class without any sample modification, while not significantly reduce the prediction accuracy of other samples to ensure the stealthiness. To this end, we formulate this problem as a binary integer programming (BIP), since the parameters are stored as binary bits (i.e., 0 and 1) in the memory. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method. Consequently, the flipped critical bits can be easily determined through optimization, rather than using a heuristic strategy. Extensive experiments demonstrate the superiority of our method in attacking DNNs. The code is available at: https://***/jiawangbai/TA-LBF. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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

arXiv

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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.

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dbDEMC 3.0:Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 446-454页

作者： Feng Xu Yifan Wang Yunchao Ling Chenfen Zhou Haizhou Wang Andrew E.Teschendorff Yi Zhao Haitao Zhao Yungang He Guoqing Zhang Zhen Yang Center for Medical Research and Innovation of Pudong Hospital Fudan University Pudong Medical CenterShanghai 201399China Institutes of Biomedical Science Shanghai Key Laboratory of Medical EpigeneticsInternational Co-laboratory of Medical Epigenetics and Metabolism(Ministry of Science and Technology)Fudan UniversityShanghai 200032China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China CAS Key Laboratory of Computational Biology Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Department of Liver Surgery Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730China Shanghai Fifth People’s Hospital Fudan UniversityShanghai 200240China

MicroRNAs(miRNAs)are important regulators in gene *** dysregulation of miRNA expression is widely reported in the transformation from physiological to pathological states of cells.A large number of differentially expressed miRNAs(DEMs)have been identified in various human cancers by using high-throughput technologies,such as microarray and *** mining of published studies with high-throughput experiment information,the database of DEMs in human cancers(dbDEMC)was constructed with the aim of providing a systematic resource for the storage and query of the *** we report an update of the dbDEMC to version 3.0,which contains two-fold more data entries than the second version and now includes also data from mice and *** dbDEMC 3.0 contains 3268 unique DEMs in 40 different cancer *** current datasets for differential expression analysis have expanded to 9 generalized ***,the current release integrates functional annotations of DEMs obtained by using experimentally validated *** annotations can be of great benefit to the intensive analysis of the roles of DEMs in *** summary,dbDEMC 3.0 provides a valuable resource for characterizing molecular functions and regulatory mechanisms of DEMs in human *** dbDEMC 3.0 is freely accessible at https://***/dbDEMC.

关键词： MicroRNA Cancer Differential expression Model organism database

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Block-Level Interference Exploitation Precoding without Symbol-by-Symbol Optimization

arXiv

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arXiv 2022年

作者： Li, Ang Shen, Chao Liao, Xuewen Masouros, Christos Swindlehurst, A. Lee School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China Shenzhen Research Institute of Big Data Shenzhen China Department of Electronic and Electrical Engineering University College London London United Kingdom Center for Pervasive Communications and Computing University of California Irvine United States

Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint. Copyright © 2022, The Authors. All rights reserved.

关键词： Quadratic programming

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MATNet: MRI Super-Resolution with Multiple Attention Mechanisms

MATNet: MRI Super-Resolution with Multiple Attention Mechani...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Longfeng Shen Fenglan Qin Yingjie Zhang Qiong Wang Bowen Hu Wei Zhao Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) Hefei Comprehensive National Science Center Institute of Artificial Integence Hefei China School of Computer Science and Technology Huaibei Normal University Huaibei Anhui China Anhui Big-Data Research Center on University Management Huaibei Anhui China People's Hospital of Huaibei City Anhui China

High-resolution magnetic resonance imaging (MRI) provides a clear anatomical structure for diagnosis, however, its high cost makes it unsuitable in practice. On the contrary, low-resolution MRI cannot provide fine structural information but is resource and time efficient. Super-resolution methods are high-resolution images obtained from low-resolution MRI. However, existing MRI super-resolution methods suffer from the following defects: (1) CNN-based super-resolution methods lack the ability to build relationships for remote features. (2) While models using transformer based on self-attentive mechanism have achieved a breakthrough in super-resolution, the single self-attentive model fails to fully utilize the useful features of the input image. Thus, in this paper, an MRI super-resolution network (MATNet) based on a multi-attention mechanism is proposed that can utilize multiple useful image features from different dimensions for image super-resolution, improved super-resolution performance. To enable the network to obtain noise-free image features before sampling the image, we also added a denoising module to it. Finally, we prove the effectiveness of the model using the IXI public dataset.

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