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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Yu, Kai Xu, Yong Yang, Jian Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China The Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Terminus Group China

Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for fast MR imaging. However, current multi-contrast super-resolution (SR) methods tend to concatenate different contrasts directly, ignoring their relationships in different clues, e.g., in the high-intensity and low-intensity regions. In this study, we propose a separable attention network (comprising high-intensity priority attention and low-intensity separation attention), named SANet. Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image. SANet provides three appealing benefits: (1) It is the first model to explore a separable attention mechanism that uses the auxiliary contrast to predict the high-intensity and low-intensity regions regions, diverting more attention to refining any uncertain details between these regions and correcting the fine areas in the reconstructed results. (2) A multi-stage integration module is proposed to learn the response of multi-contrast fusion at multiple stages, get the dependency between the fused representations, and boost their representation ability. (3) Extensive experiments with various state-of-the-art multi-contrast SR methods on fastMRI and clinical in vivo datasets demonstrate the superiority of our model. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Coexistence of insulator-like paramagnon and metallic spin-orbit exciton modes in SrIrO3

arXiv

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arXiv 2024年

作者： Paris, Eugenio Zhang, W. Tseng, Y. Efimenko, A. Sahle, C. Strocov, V.N. Skoropata, E. Rolfs, K. Shang, T. Lyu, J. Pomjakushina, E. Medarde, M. Rønnow, H.M. Normand, B. Radovic, M. Schmitt, T. PSI Center for Photon Sciences Villigen-PSI CH-5232 Switzerland European Synchrotron Radiation Facility 71 Avenue des Martyrs Grenoble38043 France PSI Center for Neutron and Muon Sciences Villigen-PSI CH-5232 Switzerland School of Physics and Electronic Science East China Normal University Shanghai200241 China CAS Key Laboratory of Magnetic Materials and Devices Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China LausanneCH-1015 Switzerland PSI Center for Scientific Computing Theory and Data Villigen-PSI CH-5232 Switzerland

We probe the spectrum of elementary excitations in SrIrO3 by using heterostructured [(SrIrO3)m/(SrTiO3)l] samples to approach the bulk limit. Our resonant inelastic x-ray scattering (RIXS) measurements at the Ir L3-edge reveal a robust low-lying collective magnetic mode with an antiferromagnetic (AF) dispersion similar to the insulators Sr2IrO4 and Sr3Ir2O7, albeit with a large gap and much larger linewidth. At higher energies we find the spin-orbit exciton, also strongly broadened, but with an inverted dispersion and doubled periodicity that are controlled by the charge hopping. These results demonstrate that the AF paramagnon persists, somewhat counterintuitively, far into the metallic regime of the insulator-metal transition driven by the degree of confinement in the heterostructure. We conclude that these two excitations, which are contrasting but coexisting hallmarks of strong AF pseudospin and charge fluctuations in a spin-orbit-coupled Mott-Slater material, are properties intrinsic to the ground state of semimetallic perovskite SrIrO3. © 2024, CC BY.

关键词： Perovskite

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Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

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第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

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Large Language Models Could Be Rote Learners

arXiv

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arXiv 2025年

作者： Xu, Yuyang Hu, Renjun Ying, Haochao Wu, Jian Shi, Xing Lin, Wei College of Computer Science and Technology Zhejiang University China School of Data Science and Engineering East China Normal University China State Key Laboratory of Transvascular Implantation Devices The Second Affiliated Hospital Zhejiang University School of Medicine China School of Public Health Zhejiang University China Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence China Alibaba Cloud Computing

Multiple-choice question (MCQ) benchmarks are widely used for evaluating Large Language Models (LLMs), yet their reliability is undermined by benchmark contamination. In this study, we reframe contamination as an inherent aspect of learning and seek to disentangle genuine capability acquisition from superficial memorization in LLM evaluation. First, by analyzing model performance under different memorization conditions, we uncover a counterintuitive trend: LLMs perform worse on memorized MCQs than on non-memorized ones, indicating the coexistence of two distinct learning phenomena, i.e., rote memorization and genuine capability learning. To disentangle them, we propose TrinEval, a novel evaluation framework that reformulates MCQs into an alternative trinity format, reducing memorization while preserving knowledge assessment. Experiments validate TrinEval’s effectiveness in reformulation, and its evaluation reveals that common LLMs may memorize by rote 20.5% of knowledge points (in MMLU on average). Copyright © 2025, The Authors. All rights reserved.

关键词： Teaching

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A Tent Lévy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study

arXiv

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arXiv 2022年

作者： Yang, Qinwen Gao, Yuelin Song, Yanjie School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Mathematics and Information Science North Minzu University Yinchuan750021 China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan750021 China Ningxia Province Cooperative Innovation Center of Scientific Computing and Intelligent in Formation Processing North Minzu University Yinchuan750021 China College of Systems Engineering National University of Defense Technology Changsha410073 China

The "Curse of Dimensionality" induced by the rapid development of information science, might have a negative impact when dealing with big datasets. In this paper, we propose a variant of the sparrow search algorithm (SSA), called Tent Lévy flying sparrow search algorithm (TFSSA), and use it to select the best subset of features in the packing pattern for classification purposes. SSA is a recently proposed algorithm that has not been systematically applied to feature selection problems. After verification by the CEC2020 benchmark function, TFSSA is used to select the best feature combination to maximize classification accuracy and minimize the number of selected features. The proposed TFSSA is compared with nine algorithms in the literature. Nine evaluation metrics are used to properly evaluate and compare the performance of these algorithms on twenty-one datasets from the UCI repository. Furthermore, the approach is applied to the coronavirus disease (COVID-19) dataset, yielding the best average classification accuracy and the average number of feature selections, respectively, of 93.47% and 2.1. Experimental results confirm the advantages of the proposed algorithm in improving classification accuracy and reducing the number of selected features compared to other wrapper-based algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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Secure Intelligent Reflecting Surface Aided Integrated Sensing and Communication

arXiv

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arXiv 2022年

作者： Hua, Meng Wu, Qingqing Chen, Wen Dobre, Octavia A. Swindlehurst, A. Lee The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China The Department of Electronic Engineering Shanghai Institute of Advanced Communications and Data Sciences Shanghai Jiao Tong University Minhang200240 China The Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X5 Canada The Center for Pervasive Communications and Computing University of California at Irvine IrvineCA92697 United States

In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the accurate target location at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the target location is uncertain, a robust algorithm based on the S-procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality, and also show the tremendous pot

关键词： Channel state information

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Revealing Interfacial Properties of Inorganic Molecular Crystal Sb2o3 and Monolayer Mos2

SSRN

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SSRN 2023年

作者： Liu, Qin Zuo, Yang He, Jingyu Zeng, Minggang Yang, Tong Zhou, Jun Yang, Yulin Song, Ting Ting Wang, Shijie Yang, Ming College of Physics and Space Science China West Normal University Nanchong637002 China Department of Applied Physics The Hong Kong Polytechnic University Hung Hom Hong Kong Institute of High-Performance Computing A∗STAR 1 Fusionopolis Way Singapore138632 Singapore Institute of Materials Research and Engineering A ∗ STAR 2 Fusionopolis Way Singapore138634 Singapore Fujian Provincial Key Laboratory of Optoelectronic Technology and Devices School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Research Centre on Data Sciences & Artificial Intelligence The Hong Kong Polytechnic University Hung Hom Hong Kong

Inorganic molecule crystal Sb2O3 has been identified as a promising high-k dielectric for direct integration with the two-dimensional semiconductor MoS2. However, a comprehensive understanding of this van der Waals (vdW) interface is still lacking, impeding its applications in high-performance nanoelectronics. In this study, we employ first-principles calculations to elucidate the interfacial interaction and electronic properties of the Sb2O3/monolayer MoS2 interface. We find that a high-performance quasi-vdW interface can be formed between Sb2O3 and monolayer MoS2, as evidenced by weak interfacial interaction, a dangling-bond-free interface, insignificant electron-hole puddle redistribution, and the preserved semiconducting properties of monolayer MoS2. Notably, the interface exhibits a remarkable defect tolerance capability during integration, as Sb2O3 cluster vacancies (the dominant defect in Sb2O3) neither introduce mid-gap states nor significantly affect the interface properties. Besides, our study reveals an asymmetric type-I band alignment at the interface, where the conduction band offset is sufficiently large to minimize electron tunneling. Our work offers a comprehensive understanding of the vdW interface between Sb2O3 and monolayer MoS2, which could potentially advance the development of inorganic molecular crystals as high-k dielectrics for high-performance two-dimensional electronic devices. © 2023, The Authors. All rights reserved.

关键词： Antimony compounds

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Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network

Plot2API: Recommending Graphic API from Plot via Semantic Pa...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Zeyu Wang Sheng Huang Zhongxin Liu Meng Yan Xin Xia Bei Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely *** the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API *** collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

关键词： Deep learning visualization Semantics Neural networks data visualization Software Task analysis

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Optimizing the numbers of queries and replies in federated learning with differential privacy

arXiv

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arXiv 2021年

作者： Zhou, Yipeng Liu, Xuezheng Fu, Yao Wu, Di Li, Chao Yu, Shui The Department of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia The School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Co. Ltd China The School of Computer Science University of Technology Sydney Australia

Federated learning (FL) empowers distributed clients to collaboratively train a shared machine learning model through exchanging parameter information. Despite the fact that FL can protect clients' raw data, malicious users can still crack original data with disclosed parameters. To amend this flaw, differential privacy (DP) is incorporated into FL clients to disturb original parameters, which however can significantly impair the accuracy of the trained model. In this work, we study a crucial question which has been vastly overlooked by existing works: what are the optimal numbers of queries and replies in FL with DP so that the final model accuracy is maximized. In FL, the parameter server (PS) needs to query participating clients for multiple global iterations to complete training. Each client responds a query from the PS by conducting a local iteration. Our work investigates how many times the PS should query clients and how many times each client should reply the PS. We investigate two most extensively used DP mechanisms (i.e., the Laplace mechanism and Gaussian mechanisms). Through conducting convergence rate analysis, we can determine the optimal numbers of queries and replies in FL with DP so that the final model accuracy can be maximized. Finally, extensive experiments are conducted with publicly available datasets: MNIST and FEMNIST, to verify our analysis and the results demonstrate that properly setting the numbers of queries and replies can significantly improve the final model accuracy in FL with DP. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Larger Receptive Field and Context Information for Pose Estimation: Larger Gaussian Kernel

Larger Receptive Field and Context Information for Pose Esti...

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High Performance Big data and Intelligent Systems (HPBD&IS)

作者： Junxiao Ma Kai Yang Zunwang Ke Gang Wang Yugui Zhang Fengcai Cao Qi Zhou Yang Fei School of Information Beijing Forestry University Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China CGNPC Uranium Industry Development Co. Ltd. Beijing China School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Zhongke Shangyi Health Technology (Beijing) Co. Ltd. Beijing China School of Semiconductor Science and Technology South China Normal University GuangZhou China

The field of pose estimation has a wide range of application prospects in various industries in the current era. With the continuous development of deep learning techniques, the effects in the field of human pose estimation are constantly being optimized. However, due to the influence of factors such as occlusion and human posture variability, it is easy to lead to inaccurate identification of keypoints and even keypoints' missing and matching errors, there is still a lot of optimization space for the effect of pose estimation. In this paper, we propose an improvement to generate heatmaps in the training process: increase the radius of the Gaussian kernel and adjust the smoothness of Gaussian distribution to get a larger receptive field, encourage the model to use more context information in prediction, and control other parameters unchanged on the premise of using heatmap regression. Improve keypoint detection performance by optimizing this item. In the experiment, for the COCO-keypoint data set, we selected different methods published in recent years, and after our improvement, we obtained better accuracy and performance than the original method, which proves the validity of our method.

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