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Enhanced ferromagnetism in an artificially stretched lattice in quasi-two-dimensional Cr2Ge2Te6

Physical Review B 2025年第2期111卷 L020402-L020402页

作者： Hiroshi Idzuchi Andres E. Llacsahuanga Allcca Anh Khoa Augustin Lu Mitsuhiro Saito Subhadip Das John F. Ribeiro Michel Houssa Ruishen Meng Kazutoshi Inoue WPI Advanced Institute for Materials Research (AIMR) Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University Sendai 980-8577 Japan Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Mathematics for Advanced Materials Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology Sendai 980-8577 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Institute of Engineering Innovation The University of Tokyo Tokyo 113-8656 Japan Institute of Physics and Astronomy and Villum Centers for Dirac Materials and for Hybrid Quantum Materials and Devices Aarhus University 8000 Aarhus-C Denmark School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy Katholieke Universiteit Leuven 3001 Leuven Belgium

Crystal lattice is key in understanding magnetic interactions between atoms in solids. As the effective control of the lattice via tensile stress is limited, there are only a few demonstrations of controlling magnetic properties by expanding the lattice. This study reveals a clear correlation between enhanced magnetism, where Curie temperature (TC) is doubled from ∼60 to ∼120 K, and lattice expansion in prototypical van der Waals magnet Cr2Ge2Te6 with sputtered dielectric overlayers NiO and MgO. We ascribe the mechanism of this TC increase to a change in exchange interactions induced by strain, studied by several experimental probes and computational approaches.

关键词： Van der Waals forces

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Phase engineering of Ni-Mn binary layered oxide cathodes for sodiumion batteries

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Journal of Energy Chemistry 2024年第4期91卷 501-511页

作者： Feifei Hong Xin Zhou Xiaohong Liu Guilin Feng Heng Zhang Weifeng Fan Bin Zhang Meihua Zuo Wangyan Xing Ping Zhang Hua Yan Wei Xiang College of Materials and Chemistry&Chemical Engineering Chengdu University of TechnologyChengdu 610059SichuanChina College of Chemical Engineering Sichuan UniversityChengdu 610065SichuanChina Research Institute for Electronic Science(RIES)and Division of Information Science and Technology Graduate School of Information Science and TechnologyHokkaido UniversityN20W10SapporoHokkaido 001-0020Japan School of Materials Science and Engineering Suzhou University of Science and TechnologySuzhou 215009JiangsuChina Yibin Libode New Materials Co. Ltd.Yibin 644200SichuanChina Engineering Research Center of the Ministry of Education for Advanced Battery Materials School of Metallurgy and EnvironmentCentral South UniversityChangsha 410083HunanChina

Nickel-manganese binary layered oxides with high working potential and low cost are potential candidates for sodium-ion batteries,but their electrochemical properties are highly related to compositional *** composite materials with various phase structures of P3,P2/P3,P2,P2/O3,and P2/P3/O3 were synthesized by manipulating the sodium content and calcination conditions,leading to the construction of a synthetic phase diagram for Na_(x)Ni_(0.25)Mn_(0.75)O_(2)(0.45≤x≤1.1).Then,we compared the electrochemical characteristics and structural evolution during the desodiation/sodiation process of P2,P2/P3,P2/03,and P2/P3/O3-Na_(x)Ni_(0.25)Mn_(0.75)O_(2).Among them,P2/P3-Na0.75Ni0.25Mn0.75O2exhibits the best rate capability of 90.9 mA h g^(-1)at 5 C,with an initial discharge capacity of 142.62 mA h g^(-1)at 0.1 C and a capacity retention rate of 78.25%after 100 cycles at 1 C in the voltage range of 2-4.3 *** observed superior sodium storage performance of P2/P3 hybrids compared to other composite phases can be attributed to the enhanced Na^(+)transfer dynamic,reduction of the Jahn-teller effect,and improved reaction reversibility induced by the synergistic effect of P2 and P3 *** systematic research and exploration of phases in Na_(x)Ni_(0.25)Mn_(0.75)O_(2)provide new sights into high-performance nickel-manganese binary layered oxide for sodium-ion batteries.

关键词： Phase engineering Ni-Mn layered oxide Cathode Sodium-ion batteries

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China

ISBN: (纸本)9798350386226

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei - specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC - improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN. © 2024 IEEE.

关键词： Deep Learning Digital Pathology Foundation Model Nuclei Segmentation Segment Anything Model

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An Intelligent Reinforcement Learning–Based Method for Threat Detection in Mobile Edge Networks

An Intelligent Reinforcement Learning–Based Method for Thre...

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作者： Saeed, Muhammad Yousaf He, Jingsha Zhu, Nafei Farhan, Muhammad Dev, Soumyabrata Gadekallu, Thippa Reddy Almadhor, Ahmad College of Computer Science Beijing University of Technology Beijing China Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal Pakistan School of Computer Science University College Dublin Dublin Ireland Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia

Traditional techniques for detecting threats in mobile edge networks are limited in their ability to adapt to evolving threats. We propose an intelligent reinforcement learning (RL)–based method for real-time threat detection in mobile edge networks. Our approach enables an agent to continuously learn and adapt its threat detection capabilities based on feedback from the environment. Through experiments, we demonstrate that our technique outperforms traditional methods in detecting threats in dynamic edge network environments. The intelligent and adaptive nature of our RL-based approach makes it well suited for securing mission-critical edge applications with stringent latency and reliability requirements. We provide an analysis of threat models in multiaccess edge computing and highlight the role of on-device learning in enabling distributed threat intelligence across heterogeneous edge nodes. Our technique has the potential, significantly enhancing threat visibility and resiliency in next-generation mobile edge networks. Future work includes optimizing sample efficiency of our approach and integrating explainable threat detection models for trustworthy human–AI collaboration. © 2024 John Wiley & Sons Ltd.

关键词： Reinforcement learning

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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CPM-Based Tunable Phase-Attached Radar–Communications (PARC)

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2025年 3卷 520-538页

作者： Sahin, Cenk McCormick, Patrick M. Metcalf, Justin G. Jakabosky, John Blunt, Shannon D. Perrins, Erik S. Owen, Jonathan Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States The University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States The University of Oklahoma Advanced Radar Research Center NormanOK73019 United States Naval Research Laboratory Radar Division WashingtonDC20375 United States

Motivated by the increasing need for efficient use of the electromagnetic spectrum (EMS) in congested and contested environments, a codesigned dual-function radar-communication (DFRC) waveform framework was introduced, which combines the desirable features of a pulsed radar transmission (i.e., constant amplitude and continuous phase) with the ability to embed information in the phase of the waveform via continuous-phase modulation (CPM). This CPM-based phase-attached radar-communication (PARC) waveform operates in a pulse-agile mode, which introduces a coupling of the fast- and slow-time dimensions through what is known as range-sidelobe modulation (RSM). The flexibility of CPM-based PARC via its multiple tunable parameters provides the ability to control this radar performance degradation at the expense of bit error rate (BER) and/or data throughput. Furthermore, the severity of RSM can likewise be mitigated via mismatched filter pulse compression on receive to reduce the variance of the pulse compression responses. Here, we evaluate the radar and communication performance trade space as a function of the CPM-based PARC parameters when assuming both matched and mismatched filter pulse compression at the radar receiver. The efficacy of the CPM-based PARC framework for both radar and communications is experimentally validated in an open-air environment using a radar in a quasi-monostatic configuration and a communication receiver in the field of view of the radar. © 2023 IEEE.

关键词： Matched filters

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A cloud service composition method using a fuzzy-based particle swarm optimization algorithm

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Multimedia Tools and Applications 2024年第19期83卷 56275-56302页

作者： Nazif, Habibeh Nassr, Mohammad Al-Khafaji, Hamza Mohammed Ridha Jafari Navimipour, Nima Unal, Mehmet Department of Mathematics Payame Noor University Tehran Iran Communication Technology Engineering Department Tartous University Tartus Syria Dpartment of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Mubarak Al-Abdullah Kuwait Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babil Hillah51001 Iraq Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey

In today's dynamic business landscape, organizations heavily rely on cloud computing to leverage the power of virtualization and resource sharing. Service composition plays a vital role in cloud computing, combining multiple cloud services to fulfill complex user requests. Service composition in cloud computing presents several challenges. These include service heterogeneity, dynamic service availability, QoS (Quality of Service) constraints, and scalability issues. Traditional approaches often struggle to handle these challenges efficiently, leading to suboptimal resource utilization and poor service performance. This work presents a fuzzy-based strategy for composing cloud services to overcome these obstacles. The fact that service composition is NP-hard has prompted the use of a range of metaheuristic algorithms in numerous papers. Therefore, Particle Swarm Optimization (PSO) has been applied in this paper to solve the problem. Implementing a fuzzy-based PSO for service composition requires defining the fuzzy membership functions and rules based on the specific service domain. Once the fuzzy logic components are established, they can be integrated into the PSO algorithm. The simulation results have shown the high efficiency of the proposed method in decreasing the latency, cost, and response time. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Fuzzy logic

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Spectral period doubling and encoding of dissipative optical solitons via gain control

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PhotoniX 2024年第1期5卷 290-305页

作者： Kangwen Yang Yi Zhou Yuqing Ling Kevin K.Tsia Heping Zeng Kenneth K.Y.Wong Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and SystemMinistry of EducationSchool of Optical Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Advanced Biomedical Instrumentation Centre Hong Kong Science ParkHong KongChina State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200062China

Period-doubling bifurcation,as an intermediate state between order and chaos,is ubiquitous in all disciplines of nonlinear ***,previous experimental observations of period doubling in ultrafast fiber lasers are mainly restricted to self-sustained steady state,controllable manipulation and dynamic switching between period doubling and other intriguing dynamical states are still largely ***,we propose to expand the vision of dissipative soliton periodic doubling,which we illustrate experimentally by reporting original spontaneous,collisional,and controllable spectral period doubling in a polarization-maintaining ultrafast fiber ***,the spontaneous period doubling can be observed in both single-and *** mechanism of the switchable state and periodic doubling was revealed by numerical ***,state transformation of individual solitons can be resolved during the collision of triple solitons involving stationary,oscillating,and period ***,controllable deterministic switching between period doubling and other dynamical states,as well as exemplifying the application of period-doubling-based digital encoding,is achieved under programmable pump *** results open a new window for unveiling complex Hopf bifurcation in dissipative systems and bring useful insights into nonlinear science and applications.

关键词： Period doubling Mode locking Ultrafast fiber lasers

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Designing low-diameter interconnection networks with multi-ported host-switch graphs

Designing low-diameter interconnection networks with multi-p...

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作者： Yasudo, Ryota Nakano, Koji Koibuchi, Michihiro Matsutani, Hiroki Amano, Hideharu Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Department of Information and Computer Science Keio University Yokohama Japan

A host-switch graph was originally proposed as a graph that represents a network topology of a computer systems with 1-port host computers and (Formula presented.) -port switches. It has been studied from both theoretical and practical aspects in terms of the diameter, the average shortest path length, and the performance of real applications. In recent high-performance computing systems, however, a host computer is connected to multiple switches by using InfiniBand, NVSwitch, or Omni-Path, and consequently they provide high bandwidths. Since a host-switch graph cannot represent such systems, this article extends a host-switch graph so that it can represent such systems. As a result, a host-switch graph can include multi-ported hosts. Furthermore, we propose to use multi-port hosts for reducing the diameter. We show that the diameter minimization is equivalent to solving the degree diameter problem for bipartite graphs of diameter three. Our experimental results show that we can drastically reduce the diameter as well as increasing the bandwidth and improves performance of MPI applications by up to 162% as compared with networks with single-ported hosts. © 2020 John Wiley & Sons, Ltd.

关键词： Graph theory

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Anomaly detection in collider physics via factorized observables

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Physical Review D 2024年第5期110卷 055012页

作者： Eric M. Metodiev Jesse Thaler Raymond Wynne Center for Theoretical Physics USA Department of Electrical Engineering and Computer Science Division of Physics Mathematics and Astronomy

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called force: factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test force on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions.

关键词： Search strategy Signatures with jets Deep learning Machine learning

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