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Spin Correlations in the Parent Phase of Li1−xFexODFeSe

Physical Review Letters 2025年第1期134卷 016501-016501页

作者： Hongliang Wo Bingying Pan Die Hu Yu Feng A. D. Christianson Jun Zhao State Key Laboratory of Surface Physics and Department of Physics Fudan University Shanghai 200433 China College of Physics and Optoelectronic Engineering Ocean University of China Qingdao Shandong 266100 China Institute of High Energy Physics Chinese Academy of Sciences (CAS) Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Shanghai Research Center for Quantum Sciences Shanghai 201315 China Institute of Nanoelectronics and Quantum Computing Fudan University Shanghai 200433 China

Elucidating spin correlations in the parent compounds of high-temperature superconductors is crucial for understanding superconductivity. We used neutron scattering to study spin correlations in Li1−xFexODFeSe, an insulating material with reduced electron carriers compared to its superconducting counterpart (Tc=41 K), serving as the undoped parent compound. Our findings show a reduced total fluctuating moment in this insulator relative to FeSe and 122 iron pnictides, likely due to increased interlayer distances from intercalation, which enhance fluctuations and reduce the intensity of spin excitations. Moreover, we observed a V-shaped spin wavelike excitation dispersion, contrasting with the twisted hourglass pattern in the superconducting counterpart. Electron doping shifts spin excitation from (π,0) point to an incommensurate position towards (π,π) direction below 65 meV. This transition from V-shaped to hourglasslike dispersion, akin to behaviors in hole-doped cuprates, suggests a potential shared mechanism in magnetism and superconductivity across these diverse systems.

关键词： Neutron scattering

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Cutting-edge RNA quantitative detection methods for rapid and on-site analysis

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TrAC - Trends in Analytical Chemistry 2025年 190卷

作者： Dong, Tao Yu, Chenghui Han, Feng Mao, Qi Jing, Weixuan Yang, Zhaochu Matos Pires, Nuno Miguel Correia, José Higino Lin, Qijing Hu, Fei Yao, Honghong Hu, Kui Xu, Tao Jiang, Zhuangde Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Nan'an District Chongqing400067 China X Multidisciplinary Research Institute School of Instrument Science and Technology Xi'an Jiaotong University Xi'an710049 China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an710049 China Department of Industrial Electronics University of Minho Campus Azurem Guimaraes4800 Portugal Department of Pharmacology School of Medicine Southeast University Jiangsu Nanjing China Department of Cardiothoracic Surgery Chongqing Fifth People's Hospital China The Third People's Hospital of Nan ‘an District Chongqing China

RNA, a vital biomarker, is crucial for molecular diagnostics, encompassing pathogen detection, gene expression, tumor diagnosis, prognosis, and drug efficacy. Since the COVID-19 outbreak, its role in clinical settings has grown. This review examines established RNA detection methods like qPCR, sequencing, Nanostring, and fluorescence in situ hybridization (FISH), highlighting recent progress and trends. It also focuses on new Point-of-Care Testing (POCT) methods developed over the last five years, categorizing them by the necessity for reverse transcription and nucleic acid amplification. The review emphasizes advances in multiplex detection tailored for POCT, aiming to update researchers and clinicians on the latest RNA detection technologies. Among various RNA detection methods, the amplification-free approach is preferable for POCT applications compared to those requiring reverse transcription and sequence-dependent amplification, as they simplify the detection process by reducing technical complexity while retaining high sensitivity. © 2025

关键词： Transcription

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Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

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Machine Intelligence research 2024年第2期21卷 257-271页

作者： Bo-Jing Feng Xi Cheng Hao-Nan Xu Wen-Fang Xue Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China

order to help investors understand the credit status of target corporations and reduce investment risks,the corporate credit rating model has become an important evaluation tool in the financial *** models are based on statistical learning,machine learning and deep learning especially graph neural networks(GNNs).However,we found that only few models take the hierarchy,heterogeneity or unlabeled data into account in the actual corporate credit rating ***,we propose a novel framework named hierarchical heterogeneous graph neural networks(HHGNN),which can fully model the hierarchy of corporate features and the heterogeneity of relationships between *** addition,we design an adversarial learning block to make full use of the rich unlabeled samples in the financial *** experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.

关键词： Corporate credit rating hierarchical relation heterogeneous graph neural networks adversarial learning

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Programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

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Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight Programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks Programming Predictive models Quantum circuit Physics Long short term memory

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge computing Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

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Design and implementation of communication system of the Dawning 6000 supercomputer

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中国计算机科学前沿 2010年第4期4卷 466-474页

作者： Qiang LI Bo LI Zhigang HUO Ninghui SUN National Research Center for Intelligent Computing Systems Beijing 100190China Key Laboratory of Computer System and Architecture Chinese Academy of SciencesBeijing 100190China Graduate University of Chinese Academy of Sciences Beijing 100190China National Research Center for Intelligent Computing Systems Beijing 100190China Key Laboratory of Computer System and Architecture Chinese Academy of SciencesBeijing 100190China

An increasing number of supercomputers adopt a heterogeneous architecture, consisting of both general purpose CPUs and specialized accelerators. Such design is beneficial for scalability and power, but on the other hand, heterogeneity brings new challenges in communication systems to connect heterogeneous components and provide support for programming. The communication system of the Dawning 6000 connectstwo kinds of heterogeneous processors, Loongson and AMD, and adopts a three layer architecture with an intranode layer between heterogeneous components. To efficiently connect heterogeneous components, the system forms a global address space and provides a mechanism for message transmission via an in-node global store; and employing Infiniband network, provides an OS-bypassing virtualization method to share an Infiniband card between nodes. To facilitate programming on heterogeneous processors, it supports unified parallel C (UPC), with a modified complier based on global address space. Also, aspecial collective network is implemented for collective operations. Results obtained from a prototype system prove these features to be both feasible and efficient.

关键词： hyper parallel processing (HPP) global address space (GAS) virtualization Dawning 6000 unified parallel C (UPC)

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3D Anatomical Structure-Guided Deep Learning for Accurate Diffusion Microstructure Imaging

3D Anatomical Structure-Guided Deep Learning for Accurate Di...

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IEEE International Symposium on Biomedical Imaging

作者： Xinrui Ma Jian Cheng Wenxin Fan Ruoyou Wu Yongquan Ye Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou Zhejiang China Pengcheng Laboratory Shenzhen Guangdong China United Imaging Healthcare Houston TX USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation; however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients.

关键词： Deep learning PSNR Accuracy Three-dimensional displays Magnetic resonance imaging Estimation Reconstruction algorithms Diffusion models Feature extraction Microstructure

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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The Odyssey Journey: Top-Tier Medical Resource Seeking for Specialized Disorder in China 25

The Odyssey Journey: Top-Tier Medical Resource Seeking for S...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Ka I Chan Siying Hu Yuntao Wang Xuhai Xu Zhicong Lu Yuanchun Shi Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Global Innovation Exchange (GIX) Institute Tsinghua University Beijing China Department of Computer Science City University of Hong Kong Hong Kong China Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China and School of Computer Science and Technology Qinghai University Xining China Google New York City New York USA Department of Computer Science George Mason University Fairfax Virginia USA Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China and Qinghai University Xining China

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Design and implementation of five-axis transformation function in CNC system

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Chinese Journal of Aeronautics 2014年第2期27卷 425-437页

作者： Wang Feng Lin Hu Zheng Liaomo Yang Lei Feng Jinjin Zhang Han Graduate University of Chinese Academy of Sciences National Engineering Research Center for High-End CNC Shenyang Institute of Computing Technology Chinese Academy of Sciences National Engineering Research Center for High-End CNC Shenyang Institute of Computing TechnologyChinese Academy of Sciences University of Science and Technology of China The 15th Institute of China Electronics Technology Group

To implement five-axis functions in CNC system, based on domestic system Lan Tian series, an improved design method for the system software structure is proposed in this paper. The numerical control kernel of CNC system is divided into the task layer and the motion layer. A five-axis transformation unit is integrated into the motion layer. After classifying five-axis machines into different types and analyzing their geometry information, the five-axis kinematic library is designed according to the abstract factory pattern. Furthermore, by taking CA spindle- tilting machine as an example, the forward and the inverse kinematic transformations are deduced. Based on the new software architecture and the five-axis kinematic library, algorithms of RTCP （rotation tool center point control） and 3D radius compensation for end-milling are designed and realized. The milling results show that, with five-axis functions based on such software struc- ture, the instructions with respect to the cutter＇s position and orientation can be directly carried out in the CNC system.

关键词： 3D cutter radiuscompensation CNC software structure Five-axis transformationfunction Motion control Rotation tool center pointcontrol

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