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Well defined phase boundaries and superconductivity with high T_(c)in PbSe single crystal

Chinese Physics B 2025年第4期34卷 78-84页

作者： Jiawei Hu Yanghao Meng He Zhang Wei Zhong Hang Zhai Xiaohui Yu Binbin Yue Fang Hong Key Laboratory of Materials Physics Institute of Solid State PhysicsHFIPSChinese Academy of SciencesHefei 230031China University of Science and Technology of China Hefei 230026China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 10019China Center for High Pressure Science and Technology Advanced Research Beijing 100094China State Key Laboratory of Superhard Materials International Center for Computational Method and Softwareand Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy MaterialsJilin UniversityChangchun 130012China Songshan Lake Materials Laboratory Dongguan 523808China

Lead chalcogenides represent a significant class of materials that exhibit intriguing physical phenomena,including remarkable thermoelectric properties and *** this study,we present a comprehensive investigation on the superconductivity of PbSe single crystal under high *** signature of superconducting(SC)transition starts to appear at 7.2 K under 16.5 *** further compression,the SC temperature(T_(c))decreases,and it is reduced to 3.5 K at 45.0 *** negative pressure dependent behavior of T_(c)is consistent with the trend of T_(c)-P relations observed in other lead *** highest T_(c)is 8.0 K observed at 20.5 GPa during decompression process,which is also the highest record among all other PbSe derivatives,such as doped samples,superlattices,and so *** phase boundaries of the structural and electronic transitions are well defined by Raman spectroscopy,and then phase diagrams are plotted for both compression and decompression *** work corrects the previous claim of positive pressure dependence of T_(c)in PbSe and provides clear phase diagrams for intrinsic superconductivity in PbSe under pressure.

关键词： superconductivity high-pressure electrical transport Raman spectroscopy phase diagrams

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Visual Acuity Consistent Foveated Rendering towards Retinal Resolution

arXiv

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arXiv 2025年

作者： Zhang, Zhi Gai, Meng Li, Sheng School of Software and Microelectronics Peking University China School of Computer Science Peking University China National Key Laboratory of Intelligent Parallel Technology China

Prior foveated rendering methods often suffer from a limitation where the shading load escalates with increasing display resolution, leading to decreased efficiency, particularly when dealing with retinal-level resolutions. To tackle this challenge, we begin with the essence of the human visual system (HVS) perception and present visual acuity-consistent foveated rendering (VaFR), aiming to achieve exceptional rendering performance at retinal-level resolutions. Specifically, we propose a method with a novel log-polar mapping function derived from the human visual acuity model, which accommodates the natural bandwidth of the visual system. This mapping function and its associated shading rate guarantee a consistent output of rendering information, regardless of variations in the display resolution of the VR HMD. Consequently, our VaFR outperforms alternative methods, improving rendering speed while preserving perceptual visual quality, particularly when operating at retinal resolutions. We validate our approach using both the rasterization and ray-casting rendering pipelines. We also validate our approach using different binocular rendering strategies for HMD devices. In diverse testing scenarios, our approach delivers better perceptual visual quality than prior foveated rendering while achieving an impressive speedup of 6.5×-9.29× for deferred rendering of 3D scenarios and an even more powerful speedup of 10.4×-16.4× for ray-casting at retinal resolution. Additionally, our approach significantly enhances the rendering performance of binocular 8K path tracing, achieving smooth frame rates. Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

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Open source oriented cross-platform survey

引用

Information and software Technology 2025年 182卷

作者： Yao, Simeng Zhang, Xunhui Zhang, Yang Wang, Tao College of Computer Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex & Critical Software Environment Changsha410073 China National Key Laboratory of Parallel and Distributed Computing Changsha410073 China

Context: Open-source software development has become a widely adopted approach to software creation. However, developers’ activities extend beyond social coding platforms (e.g., GitHub), encompassing social Q&A platforms (e.g., StackOverflow) and social media platforms (e.g., Twitter). Therefore, cross-platform research is essential for a deeper understanding of the nature of software development activities. Objective: This paper focuses on open-source platforms and systematically summarizes relevant cross-platform research. It aims to assess the current state of cross-platform research and provide insights into the challenges and future developments in this field. Method: This paper reviews 69 cross-platform research papers related to open-source software from 2013 to 2024, with a focus on several key areas, including platform interconnections, research themes, experimental design methods, challenges and research opportunities. Results: Through the analysis of 69 papers, we found that cross-platform research primarily involves platforms such as social coding, social Q&A, and social media. Researchers typically rely on information traces, including user personal info, technical info, project/post/bug report metadata, interaction info, to facilitate connections between platforms. Cross-platform research in the open-source domain mainly focuses on problem classification and feature extraction. The predominant research methods include data-driven approaches, qualitative studies, modeling and machine learning, and tool development and implementation. Despite these advancements, common challenges remain, such as subjective evaluation bias in manual data classification, insufficient data source coverage, and inaccurate data recognition. Future research opportunities may focus on increasing the diversity of data sources, improving data recognition accuracy, optimizing data classification methods, and clarifying user skill requirements. Conclusions: Based on our findings, w

关键词： Data accuracy

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A SPECTRAL LEVENBERG-MARQUARDT-DEFLATION METHOD FOR MULTIPLE SOLUTIONS OF SEMILINEAR ELLIPTIC SYSTEMS

arXiv

引用

arXiv 2025年

作者： Li, Lin Zhou, Yuheng Xie, Pengcheng Li, Huiyuan School of Mathematics and Physics University of South China Hengyang China Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States State Key Laboratory of Computer Science Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China

Many nonlinear differential equations arising from practical problems may permit nontrivial multiple solutions relevant to applications, and these multiple solutions are helpful to deeply understand these practical problems and to improve some applications. Developing an efficient numerical method for finding multiple solutions is very necessary due to the nonlinearity and multiple solutions of these equations. Moreover, providing an efficient iteration plays an important role in successfully obtaining multiple solutions with fast and stable convergence. In the current paper, an efficient algorithm for finding multiple solutions of semilinear elliptic systems is proposed, where the trust region Levenberg-Marquardt method is firstly used to iterate the resulted nonlinear algebraic system. When the nonlinear term in these equations has only the first derivative, our algorithm can efficiently find multiple solutions as well. Several numerical experiments are tested to show the efficiency of our algorithm, and some solutions which have not been shown in the literature are also found and *** Codes 65N35, 65N22, 65F05, 65L10 Copyright © 2025, The Authors. All rights reserved.

关键词： Galerkin methods

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Revealing atomic strengthening mechanism in CoNiV medium-entropy alloy via machine learning-guided simulations

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Journal of Materials science and Technology 2025年 238卷 66-77页

作者： Li, Wenyue Liu, Xiongjun Liu, Leqing Du, Qing Lin, Deye Chen, Xin He, Dong Wang, Shudao Wu, Yuan Wang, Hui Jiang, Suihe Zhang, Xiaobin Lu, Zhaoping Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing100083 China School of Intelligent Manufacturing and Mechanical Engineering Hunan Institute of Technology Hengyang421002 China School of Materials Science and Engineering Ocean University of China Qingdao266100 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100094 China School of Materials Science and Engineering Dalian University of Technology Dalian116024 China

High/medium entropy alloys (H/MEAs) have shown unique strengthening behavior and mechanical properties because of the presence of massive local chemical orderings. Nevertheless, dynamic interactions between chemical short-range orders (CSROs) and dislocations, and the underlying atomic strengthening mechanism remain elusive. In this work, we first developed a novel machine learning-embedded atom method (ML-EAM) potential of the CoNiV system, trained on a comprehensive first-principles dataset, which enables accurate and efficient modeling of CSRO formation and dislocation dynamics. Then, we investigated the strengthening mechanisms of CSROs in CoNiV MEA through machine learning-augmented molecular dynamics (MD) simulations. Hybrid MD/Monte Carlo simulations reveal that CSRO domains possess an L12 (NiCo)3 V structure, whose size increases with lowering annealing temperatures. These domains significantly enhance strength by impeding dislocation motion through complex energy pathways, increasing depinning forces, and reducing mobility. Moreover, the MD simulations combined with theoretical analysis elucidate the competition between CSRO-assisted strengthening (via antiphase boundary formation) and solid solution weakening (via reduced atomic misfit volume). Phonon-drag effects are also amplified by CSROs, further resisting dislocation glide. Our results demonstrate that L12-CSROs strengthen CoNiV MEA primarily through antiphase boundary and phonon-drag contributions, providing new insights for designing high-performance multi-principal-element alloys via tailoring CSROs. © 2025

关键词： Turing machines

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Commutation of Geometry-Grids and Fast Discrete PDE Eigen-Solver GPA

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Chinese Annals of Mathematics,Series B 2023年第5期44卷 735-752页

作者： Jiachang SUN Jianwen CAO Ya ZHANG Haitao ZHAO Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy ofSciencesBeijing 100190China

A geometric intrinsic pre-processing algorithm(GPA for short)for solving largescale discrete mathematical-physical PDE in 2-D and 3-D case has been presented by Sun(in 2022–2023).Different from traditional preconditioning,the authors apply the intrinsic geometric invariance,the Grid matrix G and the discrete PDE mass matrix B,stiff matrix A satisfies commutative operator BG=GB and AG=GA,where G satisfies G^(m)=I,m<parallelism of geometric mesh pre-transformation is mainly proportional to the number of faces of polyhedron”is obtained through research,and it is further found that“commutative of grid mesh matrix and mass matrix is an important basis for the feasibility and reliability of GPA algorithm”.

关键词： Mathematical-physical discrete eigenvalue problems Commutative operator Geometric pre-processing algorithm Eigen-polynomial factorization

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LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

arXiv

引用

arXiv 2025年

作者： Wang, Jiaping Zhang, Simiao He, Qiao-Chu Chen, Yifan Computational Machine Intelligence Laboratory Hong Kong Baptist University Hong Kong Software Engineering Institute East China Normal University China School of Business Southern University of Science and Technology China

The machine learning and data science community has made significant while dispersive progress in accelerating transformer-based large language models (LLMs), and one promising approach is to replace the original causal attention in a generative pre-trained transformer (GPT) with exponentially decaying causal linear attention. In this paper, we present LeetDecoding, which is the first Python package that provides a large set of computation routines for this fundamental operator. The launch of LeetDecoding was motivated by the current lack of (Equation presented) clear understanding of the complexity regarding this operator, (Equation presented) a comprehensive collection of existing computation methods (usually spread in seemingly unrelated fields), and (Equation presented) CUDA implementations for fast inference on GPU. LeetDecoding's design is easy to integrate with existing linear-attention LLMs, and allows for researchers to benchmark and evaluate new computation methods for exponentially decaying causal linear attention. The usage of LeetDecoding does not require any knowledge of GPU programming and the underlying complexity analysis, intentionally making LeetDecoding accessible to LLM practitioners. The source code of LeetDecoding is provided at this GitHub repository, and users can simply install LeetDecoding by the command pip install leet-decoding. Copyright © 2025, The Authors. All rights reserved.

关键词： Program processors

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Htem: High-Throughput Toolkit for Elasticity Modeling

SSRN

引用

SSRN 2025年

作者： Yang, Zhen Xian, Jiawei Xu, Yuanji Lin, De-Ye Wang, Qingchun Gao, Xingyu Tian, Fuyang Song, Haifeng Institute for Applied Physics University of Science and Technology Beijing Beijing100083 China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100094 China CAEP Software Center for High Performance Numerical Simulation Institute of Applied Physics and Computational Mathematics Beijing100094 China

The elasticity under varying temperatures and pressures is particularly significant for understanding mechanical properties and structural phase transitions. Consequently, there is an increasing demand for tools capable of determining elasticity across wide temperature and pressure ranges, either numerically or analytically. We propose HTEM, a comprehensive toolkit that automates the workflow for input generation, elastic calculations, modeling, and visualization within an integrated framework. HTEM performs simulations with four computational modes, allowing users to balance accuracy and efficiency. It incorporates a semi-analytic model for robust, high-precision elastic modeling with finite data combinations, even using sparse elasticity at high temperatures. To validate the workflows of calculation and modeling, we perform HTEM to study the elasticity of Si across wide temperature and pressure ranges. Results show the high precision and high efficiency of HTEM. And modeling mitigates noise from constant pressure ensemble simulations. Furthermore, HTEM provides detailed visualizations of elasticity and its anisotropy as functions of temperature and pressure, providing comprehensive insights into how the intrinsic properties of materials evolve under varying conditions. © 2025, The Authors. All rights reserved.

关键词： Elasticity

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Compromising Rechargeable Sensor Networks in Marine Environment

引用

IEEE Transactions on Mobile Computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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MFF-YOLO: An Improved YOLO Algorithm Based on Multi-Scale Semantic Feature Fusion

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Tsinghua science and Technology 2025年第5期30卷 2097-2113页

作者： Junsan Zhang Chenyang Xu Shigen Shen Jie Zhu Peiying Zhang Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao China School of Information Engineering Huzhou University Huzhou China Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China National Research Center for Information Science and Technology Tsinghua University Beijing China

The YOLOv5 algorithm is widely used in edge computing systems for object detection. However, the limited computing resources of embedded devices and the large model size of existing deep learning based methods increase the difficulty of real-time object detection on edge devices. To address this issue, we propose a smaller, less computationally intensive, and more accurate algorithm for object detection. Multi-scale Feature Fusion-YOLO (MFF-YOLO) is built on top of the YOLOv5s framework, but it contains substantial improvements to YOLOv5s. First, we design the MFF module to improve the feature propagation path in the feature pyramid, which further integrates the semantic information from different paths of feature layers. Then, a large convolution-kernel module is used in the bottleneck. The structure enlarges the receptive field and preserves shallow semantic information, which overcomes the performance limitation arising from uneven propagation in Feature Pyramid Networks (FPN). In addition, a multi-branch downsampling method based on depthwise separable convolutions and a bottleneck structure with deformable convolutions are designed to reduce the complexity of the backbone network and minimize the real-time performance loss caused by the increased model complexity. The experimental results on PASCAL VOC and MS COCO datasets show that, compared with YOLOv5s, MFF-YOLO reduces the number of parameters by 7% and the number of FLoating point Operations Per second (FLOPs) by 11.8%. The mAP@0.5 has improved by 3.7% and 5.5%, and the mAP@0.5:0.95 has improved by 6.5% and 6.2%, respetively. Furthermore, compared with YOLOv7-tiny, PP-YOLO-tiny, and other mainstream methods, MFF-YOLO has achieved better results on multiple indicators.

关键词： YOLO Deformable models Accuracy computational modeling Image edge detection Semantics Neural networks Feature extraction Real-time systems Edge computing

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