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Large Tunneling Magnetoresistance in Nonvolatile 2D Hybrid Spin Filters

Physical Review Letters 2025年第7期134卷 077001-077001页

作者： Xiaoyu Wang Lihao Zhang Miao He Qi Li Wenqin Song Kunlin Yang Shuxi Wang Takashi Taniguchi Kenji Watanabe MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Shaanxi Province Key Laboratory of Advanced Materials and Mesoscopic Physics School of Physics Xi’an Jiaotong University Xi’an 710049 China Anhui Key Laboratory of Low-Energy Quantum Materials and Devices High Magnetic Field Laboratory Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei 230031 China Science Island Branch of Graduate School University of Science and Technology of China Hefei 230026 China Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech University Nanjing 211816 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research Center for Electronic and Optical Materials National Institute for Materials Science Tsukuba 305-0044 Japan

Ferromagnetic semiconductors offer an efficient way to achieve high spin polarization via spin filtering effect. Large tunneling magnetoresistance (TMR) can then be realized when multiple spin filters are put in series, as recently demonstrated in van der Waals 2D A-type antiferromagnets such as CrI3 and CrSBr. However, the interlayer antiferromagnetic ground state of these magnets inherently results in a high resistance state at zero field, and this volatile behavior limits potential applications. Here we fabricate hybrid spin filters using 2D ferromagnetic metal Fe3GeTe2 and semiconductor CrBr3, which are nonvolatile as two magnets are magnetically decoupled. We achieve large TMR of around 100%, with its temperature dependence well fitted by the extended Jullière model. Additionally, the devices allow spin injection tuned through bias voltage, and TMR polarity reversals are observed. Our work opens a new route to develop 2D magnetic semiconductor based spintronics.

关键词： Van der Waals forces

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Privacy protection on sliding window of data streams

Privacy protection on sliding window of data streams

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3rd International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2007

作者： Wang, Weiping Li, Jianzhong Ai, Chunyu Li, Yingshu National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology Department of Computer Science Georgia State University

ISBN: (纸本)1424413176

In many applications, transaction data arrive in the form of high speed data streams. These data contain a lot of information about customers that needs to be carefully managed to protect customers' privacy. In this paper, we consider the problem of preserving customer's privacy on the sliding window of transaction data streams. This problem is challenging because sliding window is updated frequently and rapidly. We propose a novel approach, SWAF (Sliding Window Anonymization Framework), to solve this problem by continuously facilitating kanonymity on the sliding window. Three advantages make SWAF practical: (1) Small processing time for each tuple of data steam. (2) Small memory requirement. (3) Both privacy protection and utility of anonymized sliding window are carefully considered. Theoretical analysis and experimental results show that SWAF is efficient and effective.

关键词： Data privacy

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Nanoscaffold Ba_0.6Sr_0.4TiO₃:Nd₂O₃ ferroelectric memristors crossbar array for neuromorphic computing and secure encryption

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Journal of Materiomics 2025年第5期11卷

作者： Zhang, Weifeng Xu, Jikang Wang, Yongrui Zhang, Yinxing Wang, Yu Li, Pengfei Jia, Yongqing Zhao, Zhen Li, Changliang Yang, Biao Hou, Yue Guo, Zhenqiang Huang, Zeze Qi, Yincheng Yan, Xiaobing Institute of Life Science and Green Development Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province College of Electronic and Information Engineering Hebei Baoding 071002 China Hebei Key Lab of Optic-Electronic Information and Materials College of Physics Science & Technology Hebei Baoding 071002 China Department of Electronic and Communication Engineering Intelligent Vision Computing Research Institute North China Electric Power University Hebei Baoding 071003 China

Recent advancements in AI have spurred interest in ferroelectric memristors for neuromorphic chips due to their ability to precisely control resistive states through polarization flip-flop without electroforming. However, oxygen vacancies in these devices often cause high leakage current, low endurance, and dispersed switching voltages. Here, we introduce a silicon-based integrated (Ba_0.6Sr_0.4TiO₃)_0.5(Nd₂O₃)_0.5 (BSTN) nanoscaffolded ferroelectric thin film memristor with a vertically self-assembled nanocomposite structure (VSNs) optimally oriented on La_0.67Sr_0.33MnO₃/SrTiO₃/P[sbnd]Si substrates. This device demonstrates a widely tunable ferroelectric domain range (0°–180°), high remnant polarization (21.04 μC/cm²), and a greater number of unitary states (16 states or 4 bits). It exhibits high durability, enduring over 10⁹ switching cycles. The switching mechanism combines ferroelectric polarization and oxygen vacancy migration, enabling the simulation of biological synaptic functions via bi-directional conductance tunability. Additionally, we implemented a low-power (0.57 pJ per event) multi-factor secure encryption system for smart locks using 16×16 BSTN memristor crossbar arrays and a pressure sensor. Under multiple factors (disordered inputs, specific users, and corresponding passwords) the system recognized passwords with 97.6% accuracy and a 3.8% loss rate after 500 iterations. Overall, this work establishes a robust foundation for advancing multilevel storage, neuromorphic computing, and AI chip applications based on ferroelectric memristors. © 2025 The Authors

关键词： Ferroelectric memristor Interface channel Multi-factor secure encryption system Nanoscaffolded

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LMP-GAN: Out-Of-Distribution Detection For Non-Control Data Malware Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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Multiple automatic base selection: Protein-ligand docking based on incremental construction without manual intervention

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 1997年第4期11卷 369-384页

作者： Rarey, M Kramer, B Lengauer, T German National Research Center for Information Technology (gmd) Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven D-53754 Sankt Augustin Germany German National Research Center for Information Technology (gmd) Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven D-53754 Sankt Augustin Germany German National Research Center for Information Technology (gmd) Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven D-53754 Sankt Augustin Germany

A possible way of tackling the molecular docking problem arising in computer-aided drug design is the use of the incremental construction method. This method consists of three steps: the selection of a part of a molecule a so-called base fragment, the placement of the base fragment into the active site of a protein, and the subsequent reconstruction of the complete drug molecule. Assuming that a part of a drug molecule is known, which is specific enough to be a good base fragment,the method is proven to be successful for a large set of docking examples. In addition, it leads to the fastest algorithms for flexible docking published so far. In most real-world applications of docking, large sets of ligands have to be tested for affinity to a given protein. Thus, manual selection of a base fragment is not practical. On the other hand, the selection of a base fragment is critical in that only few selections lead to a low-energy structure. We overcome this limitation by selecting a representative set of base fragments instead of a single one. In this paper, we present a set of rules and algorithms to automate this selection. In addition, we extend the incremental construction method to deal with multiple fragmentations of the drug molecule. Our results show that with multiple automated base selection, the quality of the docking predictions is almost as good as with one manually preselected base fragment. In addition, the set of solutions is more diverse and alternative binding modes with low scores are found. Although the run time of the overall algorithm increases, the method remains fast enough to search through large ligand data sets.

关键词： molecular docking flexible docking protein-ligand interaction molecular flexibility conformational analysis drug design

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Dual-layered file cache on cc-NUMA system 20

Dual-layered file cache on cc-NUMA system

引用

20th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2006

作者： Zhou, Yingchao Meng, Dan Ma, Jie National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate School Chinese Academy of Sciences China

ISBN: (纸本)1424400546

CC-NUMA is a widely adopted and deployed architecture of high performance computers. These machines are attractive for their transparent access to local and remote memory. However, the prohibitive latency gap between local and remote access deteriorates applications' performance seriously due to memory access stalls. File system cache, especially, being shared by all processes, inevitably triggers many remote accesses. To address this problem, we suggest and implement a mechanism that uses local memory to cache remote file cache, of which the main purpose is to improve data locality. Using realistic workload on a two-node cc-NUMA machine, we show that the cost of such a mechanism is as low as 0.5%, the performance can be increased 14.3% at most, and the local hit ratio can be improved as much as 40%. © 2006 IEEE.

关键词： Buffer storage

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Hybrid deep learning model to predict the ultimate tensile strength of friction stir welded joints

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Engineering Applications of Artificial Intelligence 2025年 154卷

作者： Mishra, Akshansh Miloudi, Abdelkader Sefene, Eyob Messele Arroussi, Chaaben Chekalil, Ismail Muthanna, Bassam Gamal Nasser School of Industrial and Information Engineering Politecnico Di Milano Milan Italy Sidi Bel Abbes Algeria Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology Bahir Dar University P.O. Box 26 Bahir Dar Ethiopia LPTPM Departement of Mechanical Engineering Faculty of Technology Hassiba BenBouali University of Chlef P.O. Box. 151 Hay Salem Chlef02000 Algeria Interdisciplinary Research Center for Intelligent Manufacturing & Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Mechatronics Engineering Faculty of Engineering and Computing University of Science and Technology Aden Yemen

Accurate prediction of tensile strength in friction stir welded joints is essential to improve the reliability of welded structures. Traditional Artificial Neural Networks (ANNs) often struggle to capture the complex relationships between welding parameters, leading to limited predictive accuracy. To address this challenge, a hybrid model was developed, combining Long Short-Term Memory (LSTM) and dense layers to leverage the ability of LSTM to learn sequential patterns and dependencies between inputs such as rotational speed, feed rate, and tool tilt angle. This hybrid model was compared to a traditional ANN model using a dataset containing welding parameters and tensile strength results. Performance indicators, including correlation coefficient and Mean Square Error (MSE), showed that the LSTM + Dense hybrid model achieved a higher correlation coefficient (0.9803) and lower MSE on the validation sets, indicating improved predictive ability. Analyses of error distribution and convergence of training and validation losses also showed that the hybrid model had higher consistency and reliability with reduced overfitting, even when dealing with outliers. The LSTM + Dense model was shown to significantly outperform the traditional ANN, making it a powerful tool for predicting tensile strength in welding applications with complex and nonlinear data. Although the accuracy and reliability of the proposed hybrid model have improved, limitations remain, including the need for larger datasets to enhance generalization and potential challenges in real-time implementation. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Microscopic-based analysis of nuclei in spheroids via SUNSHINE: An on-chip workflow integrating optical clearing, fluorescence calibration and supervoxel segmentation

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Computers in Biology and Medicine 2025年 187卷 109761-109761页

作者： Lin, Chia-Hsiang Leng, Zi-Chao Yu, Chien-Hsin Deori Bharali, Lui Kirtan Lin, Cheng-Li Mao, Bin-Hsu Tu, Ting-Yuan Department of Electrical Engineering National Cheng Kung University Tainan City70101 Taiwan Institute of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Tainan City70101 Taiwan Department of Biomedical Engineering College of Engineering National Cheng Kung University Tainan City70101 Taiwan Department of Biotechnology and Medical Engineering National Institute of Technology Odisha Rourkela769008 India Department of Orthopedic Surgery National Cheng Kung University Hospital Tainan City704 Taiwan Skeleton Materials and Biocompatibility Core Lab Research Center of Clinical Medicine National Cheng Kung University Hospital Tainan City704 Taiwan National Cheng Kung University Hospital Tainan City704 Taiwan Musculoskeletal Research Center Innovative Headquarter National Cheng Kung University Tainan City70101 Taiwan Miin Wu School of Computing National Cheng Kung University Tainan City70101 Taiwan Meta-nanoPhotonics Center National Cheng Kung University Tainan City70101 Taiwan Medical Device Innovation Center National Cheng Kung University Tainan City70101 Taiwan

Multicellular spheroids (MCSs) are increasingly employed as 3D cell culture models in biomedical research due to their ability to effectively replicate in vivo cell interactions, making them suitable for high-throughput drug screening. Accurate cell counting is critical for data normalization, therapeutic evaluation, and exploration of culture conditions;however, affordable software solutions for 3D cell counting using microscopic images are limited. To fill this gap, we created SUNSHINE, an innovative on-chip analytical workflow that uniquely merges optical clearing, histogram matching (HM)-assisted fluorescence calibration, and simple linear iterative clustering (SLIC) supervoxel segmentation. This tool offers an efficient method for analyzing the characteristics and counts of fluorescence-labeled nuclei within MCSs. While optical clearing improves the penetration depth of microscopic imaging, deeper regions of thicker samples often yield faint fluorescence signals. SUNSHINE resolves this issue through the HM image post-processing algorithm. Moreover, SLIC is an effective alternative to traditional contour-wise segmentation, enabling the identification of irregularly shaped fluorescent nuclei. We found that SUNSHINE generated results comparable to commercial software like Imaris and machine learning (ML)-based tools, such as StarDist and Cellpose, in our analysis of the effects of seeding density and cell type on spheroid growth. We also used it to measure the volume and spatial distribution of nuclei, focusing on the hypoxic and peripheral regions of spheroids. Overall, this study finds that SUNSHINE serves as a valuable and economical approach for characterizing cellular activity and interactions in 3D, diminishing the reliance on costly proprietary software. © 2025

关键词： Cell culture

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Spectro-polarimetry of GRB 180427A: evidence for distinct emission sites with varying polarisation

arXiv

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arXiv 2025年

作者： Sonawane, Rushikesh Iyyani, Shabnam Gupta, Soumya Chattopadhyay, Tanmoy Bhattacharya, Dipankar Bhalerao, Varun B. Vadawale, Santosh V. Dewangan, G.C. Centre for High Performance Computing Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram695551 India School of Physics Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram695551 India Homi Bhabha National Institute Anushakti Nagar Maharashtra Mumbai400094 India Kavli Institute of Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Department of Physics Ashoka University Haryana Sonepat131029 India Indian Institute of Technology Bombay Mumbai India Physical Research Laboratory Gujarat Ahmedabad380009 India Inter-University Center for Astronomy and Astrophysics Maharashtra Pune411007 India

The dynamics of the origin of gamma-ray emissions in gamma-ray bursts (GRBs) remains an enigma. Through a joint analysis of GRB 180427A, observed by the Fermi Gamma-ray Space Telescope and AstroSat’s Cadmium Zinc Telluride Imager, we identify emissions from two distinct regions with varying polarisation properties. Time-resolved polarisation analysis reveals a synchronous evolution of the polarisation angle (PA) and fraction (PF) with two emission pulses, peaking with a delay of 4.82 ± 0.12 s. Spectral analysis indicates that the first pulse is dominated by blackbody radiation, while the second pulse exhibits a non-thermal spectrum (power law with an exponential cutoff). Using a bottom-to-top approach through simulations, we decouple the polarisation properties of the individual spectral components, revealing polarisation fractions of 25% - 40% for the blackbody spectrum and 30% - 60% for the non-thermal spectrum. At a redshift of z ∼ 0.05, the blackbody emission originates from the jet photosphere at 1011 cm, whereas the non-thermal emission arises from an optically thin region at 1015 cm. The changing dominance of these emissions explains the observed PA shift of 60◦ ±22.3◦. The spectral cutoff at 1 MeV suggests pair opacity due to the jet’s low bulk Lorentz factor (Γ ∼ tens). The high polarisation and hard spectral slopes (α > −0.5) imply a top-hat jet structure observed off-axis, near the jet’s edge. This off-axis viewing introduces anisotropy in the radiation within the viewing cone (1/Γ), accounting for the observed polarisation. © 2025, CC BY.

关键词： Gamma rays

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Analysis of moment invariants under general linear transformation

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Science China(Information Sciences) 2010年第7期53卷 1305-1311页

作者： DIAO LuHong1, LI Hua2, ZHANG Sen1 & LIU Lei1 1College of Applied Sciences, Beijing University of technology, Beijing 100124, China 2national center of intelligent computing, institute of computing technology, Chinese Academy of Science, Beijing 100080, China 1. College of Applied Sciences Beijing University of Technology Beijing 100124 China2. National Center of Intelligent Computing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China

This paper presents the necessary and sufficient condition of moment invariants under general linear transformation. Based on this condition, arbitrary order and dimensional moment invariant sets can be constructed. D... 详细信息

关键词： moment invariants independence linear transformation

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