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Scattering exceptional point in the visible

Light(Science & Applications) 2023年第10期12卷 2228-2235页

作者： Tao He Zhanyi Zhang Jingyuan Zhu Yuzhi Shi Zhipeng Li Heng Wei Zeyong Wei Yong Li Zhanshan Wang Cheng-Wei Qiu Xinbin Cheng MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai 200092China Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China Shanghai Frontiers Science Center of Digital Optics Shanghai 200092China Shanghai Professional Technical Service Platform for Full-Spectrum and High-Performance Optical Thin Film Devices and Applications Shanghai 200092China Department of Electronic Science and Technology Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China

Exceptional point (EP) is a special degeneracy of non-Hermitian systems. One-dimensional transmission systems operating at EPs are widely studied and applied to chiral conversion and sensing. Lately, two-dimensional systems at EPs have been exploited for their exotic scattering features, yet so far been limited to only the non-visible waveband. Here, we report a universal paradigm for achieving a high-efficiency EP in the visible by leveraging interlayer loss to accurately control the interplay between the lossy structure and scattering lightwaves. A bilayer framework is demonstrated to reflect back the incident light from the left side ( | r_(−1) | >0.999) and absorb the incident light from the right side ( | r_(+1) | < 10^(–4)). As a proof of concept, a bilayer metasurface is demonstrated to reflect and absorb the incident light with experimental efficiencies of 88% and 85%, respectively, at 532 nm. Our results open the way for a new class of nanoscale devices and power up new opportunities for EP physics.

关键词： visible exceptional incident

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Laser Wire Additive Manufacturing of Ti-6Al-4V Alloy and Its Machine Learning Study for Parameters Optimization(Invited)

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中国激光 2024年第4期51卷 182-198页

作者： Wu Junyi Zhang Bo Wang Weihua Li Weipeng Yao Xiyu Wang Dawei Xing Wei Yan Ming Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055GuangdongChina Songshan Lake Materials Laboratory Dongguan 523830GuangdongChina Institute of Physics Chinese Academy of SciencesBeijing 100190China Jiaxing Research Institute Southern University of Science and TechnologyShenzhen 518055GuangdongChina High Performance Computing Department National Supercomputing CenterShenzhen 518055GuangdongChina

Ti-6Al-4V is a benchmark Ti alloy. Laser wire additive manufacturing(LWAM) offers advanced manufacturing capability to the alloy for applications possibly including exploration of outer space. As a typical multiple-variable process, LWAM is complex, which, however, can be analyzed, predicated or even optimized by artificial intelligence(AI) methods such as machine learning(ML). In this study, printing parameters of the Ti-6Al-4V is firstly optimized using single-track-single-layer experiments, and then single-track-multiple-layer samples are printed, whose properties in terms of hardness and compressive strength are analyzed subsequently by both experiments and ML. The two ML approaches, artificial neural network(ANN) and support vector machine(SVM), are employed to predict the experimental results, whose coefficients of determination R2 show good values. Further optimized properties are realized by adopting genetic algorithm(GA) and simulated annealing(SA) approaches, which contribute to high mechanical properties achieved, for instance, an engineering compressive strength of about 1694 MPa. The results here indicate that important mechanical properties of the LWAM-prepared Ti alloys can be well predicted and enhanced using suitable ML approaches.

关键词： artificial printing additive

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Efficient Quantum Simulation Algorithms in the Path Integral Formulation

arXiv

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arXiv 2024年

作者： Shum, Serene Wiebe, Nathan University of Toronto Department of Physics Toronto Canada University of Toronto Department of Computer Science Toronto Canada Pacific Northwest National Laboratory High Performance Computing Division Richland United States Canadian Institute for Advanced Study Toronto Canada

We provide a new paradigm for quantum simulation that is based on path integration that allows quantum speedups to be observed for problems that are more naturally expressed using the path integral formalism rather than the conventional sparse Hamiltonian formalism. We provide a quantum algorithm for Lagrangians of the form ∑γ mγ/2 x 2γ − V (x), as well as two novel quantum algorithms based on Hamiltonian versions of the path integral formulation. The Lagrangian path integral algorithm is based on a new rigorous derivation of a discrete version of the Lagrangian path integral. We show that for the time evolution of a system with η particles in D + 1 dimensions for time T, our Lagrangian simulation algorithm requires a number of queries to an oracle that computes the discrete Lagrangian that scales in the continuum limit as Oe(ηDT2/ϵ), if V (x) is bounded and finite and the wave function obeys appropriate position and momentum cutoffs. This shows that Lagrangian dynamics can be efficiently simulated on quantum computers and opens up the possibility for quantum field theories for which the Hamiltonian is unknown to be efficiently simulated on quantum computers. Our first Hamiltonian path integral method breaks up the paths into short time steps. It is efficient under appropriate sparsity assumptions and requires a number of queries to oracles that give the eigenvalues and overlaps between the eigenvectors of the Hamiltonian terms that scales as to(1)/ϵo(1) for simulation time t and error ϵ. The second approach uses long-time path integrals for near-adiabatic systems and has query complexity that scales as O(1/√ϵ) if the energy eigenvalue gaps and simulation time is sufficiently long. © 2024, CC BY.

关键词： Quantum computers

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Data Logging and Non-invasive IoMT Approach for Rats Monitoring in laboratory Experiments 37th

Data Logging and Non-invasive IoMT Approach for Rats Monit...

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Barbosa, Bryan C. M. Garan, Steven A. Quintela, Barbara Melo Dantas, Mario R. Universidade Federal de Juiz de Fora Juiz de Fora Brazil Center for Research and Education in Aging University of California Berkeley and Lawrence Berkeley National Laboratory BerkeleyCA United States Computational Physiology and High Performance Computing Laboratory and Computer Science Department at Universidade Federal de Juiz de Fora Juiz de Fora Brazil Computer Science Graduate Program at Universidade Federal de Juiz de Fora Juiz de Fora Brazil

ISBN: (纸本)9783031290558

It is being observed that the use of Internet of Medical Things (IoMT) in health sciences research grows as the technology and miniaturization of devices occur. Those devices often times suffer from several issues such as: being invasive, lacking the ability to collect many signals concurrently and communication. The present work proposes an approach to the use of an ultra-small IoMT device to fulfill the functions of a bio-signals data collector, a mathematical models processor, a data transmitter-receptor through the network, and a mechanical actuators controller. Our test environment was conceived to be used in animal models. This is an ongoing project from which partial results indicate that, when functioning, the device will use real-time data for monitoring bio-signals and helping with decision-making as it interacts with the organism. The data collection is going to be made through low-cost non-invasive sensors. Therefore, it can favor installation, maintenance, access, and promote a better quality of life for the user. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Health Care and Management based on Block chain and Machine Learning

Health Care and Management based on Block chain and Machine ...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Bakh, Ruhi Sarkar, VeenaNair Auguskani, Jerlin Priya Lovelin Poornima, Galiveeti Shetty, Monali N. Raj, Meerjumla Govind Dr. Ambedkar Institute of Management Studies and Research India Iihmr University India Department of Nursing College Aldrab Jazan University Saudi Arabia High Performance Computing Lab School of Computer Science and Information Science Presidency University Bengaluru India Computer Science Engineering Fr. Crce India St. Martin and #39 s Engineering College Survey No. 563 Dulapally Kompally Telangana Secunderabad500100 India

ISBN: (纸本)9798350334357

With the coming of technology, we currently have an immense measure of information accessible in each field, making it conceivable to offer responses to many issues. We will utilize machine learning and block chain technology to resolve issues with healthcare information the executives in this review. With the guide of machine learning, separating just the relevant information from the data is plausible. Using prepared calculations, this is finished. The reliability of information trade turns into a test after this information has been put away. This is where block chain technology is valuable. Block chain technology's agreement guarantees that information is credible and exchanges are secure. By putting the patient at the focal point of the healthcare framework and upgrading the security and interoperability of health information, block chain technology can possibly further develop health care organization. This article fundamentally centres around settling issues with healthcare information the board using Block chain technology and including a few urgent highlights utilizing Machine Learning. The freshest technology, block chain, offers a strong and secure groundwork. With these offices, block chain has gotten some momentum in an assortment of use areas, including the food business, store network the board, energy, government support offices, and healthcare. © 2023 IEEE.

关键词： Health care

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Dynamic Scheduling for Heterogeneous Multicores

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SN computer Science 2021年第6期2卷 486页

作者： Vazquez, Ruben Edun, Ayobami Gordon-Ross, Ann Stitt, Greg Department of Electrical and Computer Engineering Univeristy of Florida Gainesville FL United States NSF Center for Space High-Performance and Resilient Computing (SHREC) at University of Florida Gainesville FL United States

Heterogeneous multicore systems can help adherence to design goals by providing a diverse set of hardware components to meet application requirements. Each core may also have tunable hardware that can reconfigured for different applications. However, scheduling becomes difficult in the presence of tunable hardware due to the additional constraint that an application must be scheduled to a core that offers the best configuration, based on the application’s requirements, to maximize the benefit of the tunable hardware. The scheduling decision can be made by exploring and evaluating each hardware configuration in the design space exhaustively or through the use of a heuristic to evaluate a subset of the design space. However, exhaustive and heuristic approaches do not scale well with the number of available hardware configurations. To improve upon exhaustive and heuristic approaches, in this paper, we present a dynamic scheduling methodology that uses machine learning to schedule applications to the application’s respective best core for reduced energy consumption for a system with configurable caches. We use an artificial neural network (ANN) to train our predictive model, using hardware counters, to predict the best core and a tuning heuristic to determine best configuration on non-best cores. If the best core is busy, our scheduler considers alternative idle cores, or the application is stalled depending on which decision is energy advantageous. Our experiments show that our proposed system can achieve a total energy savings of approximately 33% and 2.5% compared to a fixed-cache, base system when using a configurable instruction and data cache, respectively, while remaining competitive and exhibiting better scaling properties compared to systems adopting a heuristic approach. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Configurable caches Dynamic scheduling Embedded systems Heterogeneous systems Machine learning

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Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection

arXiv

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arXiv 2025年

作者： Marfo, William Tosh, Deepak K. Moore, Shirley V. Suetterlein, Joshua Manzano, Joseph Department of Computer Science United States High-Performance Computing Group United States University of Texas at El Paso El PasoTX79968 United States Pacific Northwest National Laboratory RichlandWA99354 United States

Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems, where the myriad of client configurations and network conditions can severely impact system efficiency and detection accuracy. While existing approaches attempt to address this through individual optimization techniques, they often fail to maintain the delicate balance between reduced overhead and detection performance. This paper presents an adaptive FL framework that dynamically combines batch size optimization, client selection, and asynchronous updates to achieve efficient anomaly detection. Through extensive profiling and experimental analysis on two distinct datasets—UNSW-NB15 for general network traffic and ROAD for automotive networks—our framework reduces communication overhead by 97.6% (from 700.0s to 16.8s) compared to synchronous baseline approaches while maintaining comparable detection accuracy (95.10% vs. 95.12%). Statistical validation using Mann-Whitney U test confirms significant improvements (p © 2025, CC BY.

关键词： Network security

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MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining

arXiv

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arXiv 2025年

作者： Wu, Ruiqi Su, Na Zhang, Chenran Ma, Tengfei Zhou, Tao Cui, Zhiting Tang, Nianfeng Mao, Tianyu Zhou, Yi Fan, Wen Wu, Tianxing Jing, Shenqi Fu, Huazhu School of Computer Science and Engineering Southeast University Nanjing China Department of Ophthalmology The First Affiliated Hospital of Nanjing Medical University Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of High-Performance Computing Agency for Science Technology and Research Singapore

Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievements, they significantly rely on large-scale private image-text data but pay less attention to the pretraining manner, which limits their further advancements. In this work, we introduce MM-Retinal V2, a high-quality image-text paired dataset comprising CFP, FFA, and OCT image modalities. Then, we propose a novel fundus vision-language pretraining model, namely KeepFIT V2, which is pretrained by integrating knowledge from the elite data spark into categorical public datasets. Specifically, a preliminary textual pretraining is adopted to equip the text encoder with primarily ophthalmic textual knowledge. Moreover, a hybrid image-text knowledge injection module is designed for knowledge transfer, which is essentially based on a combination of global semantic concepts from contrastive learning and local appearance details from generative learning. Extensive experiments across zero-shot, few-shot, and linear probing settings highlight the generalization and transferability of KeepFIT V2, delivering performance competitive to state-of-the-art fundus VLP models trained on large-scale private image-text datasets. Our dataset and model are publicly available via https://***/lxirich/MM-Retinal. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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On the properties of kullback-leibler divergence between multivariate Gaussian distributions 23

On the properties of kullback-leibler divergence between mul...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yufeng Zhang Jialu Pan Kenli Li Wanwei Liu Zhenbang Chen Xinwang Liu Ji Wang College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer State Key Laboratory for High Performance Computing Key laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Kullback-Leibler (KL) divergence is one of the most important measures to calculate the difference between probability distributions. In this paper, we theoretically study several properties of KL divergence between multivariate Gaussian distributions. Firstly, for any two n-dimensional Gaussian distributions Ɲ1 and Ɲ2, we prove that when KL(Ɲ2‖Ɲ1) ≤ ε (ε > 0) the supremum of KL (Ɲ1‖Ɲ2) is (1/2) ((-W0 (-e-(1+2ε)))-1 + log(-W0 (-e-(1+2ε))) - 1), where W0 is the principal branch of Lambert W function. For small ε, the supremum is ε + 2ε1.5 + O (ε2). This quantifies the approximate symmetry of small KL divergence between Gaussian distributions. We further derive the infimum of KL(Ɲ1‖Ɲ2) when KL(Ɲ2‖Ɲ1) ≥ M (M > 0). We give the conditions when the supremum and infimum can be attained. Secondly, for any three n-dimensional Gaussian distributions Ɲ1, Ɲ2, and Ɲ3, we theoretically show that an upper bound of KL (Ɲ1‖Ɲ3) is 3ε1 + 3ε2 + 2√ε1ε2 + o(ε1)+ o(ε2) when KL (Ɲ1‖Ɲ2) ≤ ε1 and KL(Ɲ2‖Ɲ3) ≤ ε2 (ε1, ε2 ≥ 0). This reveals that KL divergence between Gaussian distributions follows a relaxed triangle inequality. Note that, all these bounds in the theorems presented in this work are independent of the dimension n. Finally, we discuss several applications of our theories in deep learning, reinforcement learning, and sample complexity research.

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Eosinophil Detection with Modified YOLOv3 Model in Large Pathology Image 4

Eosinophil Detection with Modified YOLOv3 Model in Large Pat...

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4th International Conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Wang, Xinyue Che, Yonggang Jiang, Nan Chen, Weijian Lan, Long National University of Defense Technology State Key Laboratory of High Performance Computing College of Computer Changsha China Hunan Children's Hospital Department of Pathology Changsha China

ISBN: (纸本)9781665406925

Eosinophil detection and counting have an important role in the treatment of eosinophilic gastroenteritis. Generally, the physicians diagnose the eosinophilic gastroenteritis depending on the number of eosinophil cells in the corresponding pathology images. However, manually identifying eosinophils in the large pathology image is time-consuming and boring. The accuracy of Eosinophil counting significantly relies on the experiences of professional pathologists, the manual counting also prone to missing and misclassification due to fatigue. In this paper, we introduce a modified YOLOv3 object detection method to automatically count the eosinophil. Specifically, to detect the small and crowded eosinophils in the large pathology image, we carefully redesign the scale and size of candidate boxes and make our kernels more sensitive to tiny eosinophils in the feature maps. To verify our method, we collect and label a real eosinophil dataset with totally 8469 eosinophils being annotated and distributing over 255 pathology images from 11 different classes. We implemented our proposed method on our dataset and demonstrated the feasibility and bright prospect for applying object detection technique into the treatments of eosinophilic gastroenteritis. © 2021 IEEE.

关键词： Object detection

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