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Twist-angle and thickness-ratio tuning of plasmon polaritons in twisted bilayer van der Waals films

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arXiv 2023年

作者： Wang, Chong Xie, Yuangang Ma, Junwei Hu, Guangwei Xing, Qiaoxia Huang, Shenyang Song, Chaoyu Wang, Fanjie Lei, Yuchen Zhang, Jiasheng Mu, Lei Zhang, Tan Huang, Yuan Qiu, Cheng-Wei Yao, Yugui Yan, Hugen School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Department of Physics Fudan University Shanghai200433 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China

Stacking bilayer structures is an efficient way to tune the topology of polaritons in in-plane anisotropic films, e.g., by leveraging the twist angle (TA). However, the effect of another geometric parameter, film thickness ratio (TR), on manipulating the plasmon topology in bilayers is elusive. Here, we fabricate bilayer structures of WTe2 films, which naturally host in-plane hyperbolic plasmons in the terahertz range. Plasmon topology is successfully modified by changing the TR and TA synergistically, manifested by the extinction spectra of unpatterned films and the polarization dependence of the plasmon intensity measured in skew ribbon arrays. Such TR- and TA-tunable topological transitions can be well explained based on the effective sheet optical conductivity by adding up those of the two films. Our study demonstrates TR as another degree of freedom for the manipulation of plasmonic topology in nanophotonics, exhibiting promising applications in bio-sensing, heat transfer and the enhancement of spontaneous emission. © 2023, CC BY.

关键词： Polariton

A Fast Hybrid Deep Neural Network Model for pushing behavior detection in human crowds

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A Fast Hybrid Deep Neural Network Model for pushing behavior...

ACS/IEEE International Conference on computer systems and Applications

作者： Ahmed Alia Mohammed Maree Mohcine Chraibi Institute for Advanced Simulation Forschungszentrum Jülich Jülich Germany Department of Computer Simulation for Fire Protection and Pedestrian Traffic University of Wuppertal Wuppertal Germany Department of Management Information Systems An-Najah National University Nablus Palestine Department of Information Technology Arab American University Jenin Palestine

ISBN: (纸本)9798350310092

Deep learning technology is regarded as one of the latest advances in data science and analytics due to its learning abilities from the data [1]. As a result, deep learning is widely applied in the human crowd analysis domain [2]. Although it has achieved remarkable success in this area, a fast and robust model for pushing behavior detection in the human crowd is unavailable. This paper proposes a model that allows crowd-monitoring systems to detect pushing behavior early, helping organizers make timely decisions before dangerous situations appear. This particularly becomes more challenging when applied to real-time video streams of crowded events, which the proposed model accomplishes with reasonable time latency. To achieve this, the model employs a hybrid deep neural network.

关键词： Deep learning Computational modeling Neurons Streaming media Feature extraction Real-time systems Motion detection

Automatic-SSD: Full Hardware Automation over New Memory for High Performance and Energy Efficient PCIe Storage Cards

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Automatic-SSD: Full Hardware Automation over New Memory for ...

IEEE International Conference on computer-Aided Design

作者： Gyuyoung Park Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology Daejeon Korea

ISBN: (数字)9781665423243

We propose Automatic-SSD that converts all storage management logic into hardware, which enable energy efficient, high performance fast memory based block storage. To achieve low operating power, Automatic-SSD directly reads or writes host-side data to underlying backend storage media without internal DRAM caches. To realize such DRAM-less approach with better performance and make it more energy efficient, Automatic-SSD also removes the internal processor(s) and firmware execution therein by fully automating the backend request management and data transfers over all pipelined hardware modules. We prototype Automatic-SSD on a middle-end FPGA custom board, employing massive numbers of phase change memories as representative of new memory technologies. Our evaluation results show that, compared to a conventional firmware-based approach, Automatic-SSD shows up 28.8× and 25.4× better bandwidth and latency behaviors, respectively, while consuming only 5% of the total energy, on overage.

关键词： Microprogramming Nonvolatile memory Hardware Phase change random access memory Memory management Power demand Media

Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning

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arXiv 2022年

作者： Jegham, Imen Rekik, Islem Université de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Horizon School of Digital Technologies Sousse4023 Tunisia BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Decrypting intelligence from the human brain construct is vital in the detection of particular neurological disorders. Recently, functional brain connectomes have been used successfully to predict behavioral scores. However, state-of-the-art methods, on one hand, neglect the topological properties of the connectomes and, on the other hand, fail to solve the high inter-subject brain heterogeneity. To address these limitations, we propose a novel regression graph neural network through meta-learning namely Meta-RegGNN for predicting behavioral scores from brain connectomes. The parameters of our proposed regression GNN are explicitly trained so that a small number of gradient steps combined with a small training data amount produces a good generalization to unseen brain connectomes. Our results on verbal and full-scale intelligence quotient (IQ) prediction outperform existing methods in both neurotypical and autism spectrum disorder cohorts. Furthermore, we show that our proposed approach ensures generalizability, particularly for autistic subjects. Our Meta-RegGNN source code is available at https://***/basiralab/Meta-RegGNN. © 2022, CC BY-NC-ND.

关键词： Forecasting

Non-Hermitian Chiral Heat Transport

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Physical Review Letters 2023年第26期130卷 266303-266303页

作者： Guoqiang Xu Xue Zhou Ying Li Qitao Cao Weijin Chen Yunfeng Xiao Lan Yang Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing 400067 China Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China International Joint Innovation Center Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University100871 Beijing China Department of Electrical and Systems Engineering Washington University St. Louis Missouri 63130 USA

Exceptional point (EP) has been captivated as a concept of interpreting eigenvalue degeneracy and eigenstate exchange in non-Hermitian physics. The chirality in the vicinity of EP is intrinsically preserved and usually immune to external bias or perturbation, resulting in the robustness of asymmetric backscattering and directional emission in classical wave fields. Despite recent progress in non-Hermitian thermal diffusion, all state-of-the-art approaches fail to exhibit chiral states or directional robustness in heat transport. Here we report the first discovery of chiral heat transport, which is manifested only in the vicinity of EP but suppressed at the EP of a thermal system. The chiral heat transport demonstrates significant robustness against drastically varying advections and thermal perturbations imposed. Our results reveal the chirality in heat transport process and provide a novel strategy for manipulating mass, charge, and diffusive light.

关键词： Diffusion Heat transfer Metamaterials Non-Hermitean systems

Super-Robust Nonadiabatic Holonomic Quantum Computation in coherence-protected Superconducting Circuits

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arXiv 2024年

作者： Wang, Yuan-Sheng Su, Zhaofeng Chen, Xiaosong Yung, Man-Hong School of Systems Science Beijing Normal University Beijing100875 China School of Electronics and Information Engineering Suzhou Vocational University Suzhou215104 China School of Computer Science and Technology University of Science and Technology of China Hefei230094 China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Hefei230026 China Institute for Advanced Study in Physics School of Physics Zhejiang University Hangzhou310058 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The schmeme of nonadiabatic holonomic quantum computation (NHQC) offers an error-resistant method for implementing quantum gates, capable of mitigating certain errors. However, the conventional NHQC schemes often entail longer operations concerning standard gate operations, making them more vulnerable to the effects of quantum decoherence. In this research, we propose an implementation of the Super-Robust NHQC scheme within the Decoherence-Free Subspace (DFS). SR-NHQC has demonstrated robustness against Global Control Errors (GCEs). By utilizing capacitance-coupled transmon qubits within a DFS, our approach enables universal gate operations on a scalable two-dimensional square lattice of superconducting qubits. Numerical simulations demonstrate the practicality of SR-NHQC in DFS, showcasing its superiority in mitigating GCEs and decoherence effects compared to conventional NHQC schemes. Our work presents a promising strategy for advancing the reliability of quantum computation in real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data

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arXiv 2021年

作者： Ye, Jiexia Zhao, Juanjuan Zheng, Furong Xu, Chengzhong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China State Key Lab of IOTSC Department of Computer Science University of Macau China

Short-term OD flow (i.e. the number of passenger traveling between stations) prediction is crucial to traffic management in metro systems. Due to the delayed effect in latest complete OD flow collection, complex spatiotemporal correlations of OD flows in high dimension, it is more challengeable than other traffic prediction tasks of time series. Existing methods need to be improved due to not fully utilizing the real-time passenger mobility data and not sufficiently modeling the implicit correlation of the mobility patterns between stations. In this paper, we propose a Completion based Adaptive Heterogeneous Graph Convolution Spatiotemporal Predictor. The novelty is mainly reflected in two aspects. The first is to model real-time mobility evolution by establishing the implicit correlation between observed OD flows and the prediction target OD flows in high dimension based on a key data-driven insight: the destination distributions of the passengers departing from a station are correlated with other stations sharing similar attributes (e.g. geographical location, region function). The second is to complete the latest incomplete OD flows by estimating the destination distribution of unfinished trips through considering the realtime mobility evolution and the time cost between stations, which is the base of time series prediction and can improve the model’s dynamic adaptability. Extensive experiments on two real world metro datasets demonstrate the superiority of our model over other competitors with the biggest model performance improvement being nearly 4%. In addition, the data complete framework we propose can be integrated into other models to improve their performance up to 2.1%. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

Quantum Clock Synchronization Network with Silicon-chip Dual-Pumped Entangled Photon Source

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arXiv 2024年

作者： Li, Jiaao Han, Hui Huang, Xiaopeng Tang, Bangying Guo, Kai Huang, Jinquan Xiong, Siyu Yu, Wanrong Zhang, Zhaojian Yang, Junbo Liu, Bo Chen, Huan Lu, Zhenkun College of Electronic Information Guangxi Minzu University Nanning530006 China College of Science National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Strategic Assessments and Consultation Institute Academy of Military Sciences Beijing100091 China Institute of Systems Engineering Academy of Military Sciences Beijing100039 China School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China School of Mathematical Sciences Sichuan Normal University Chengdu610068 China

In this paper, we propose a quantum clock synchronization (QCS) network scheme with silicon-chip dual-pumped entangled photon source. This scheme couples two pump beams into the silicon-based waveguide, where degenerate and non-degenerate spontaneous four-wave mixing (SFWM) occurs, generating entanglement between one signal channel and three idler channels. The entangled photons are distributed to remote users through the wavelength division multiplexing strategy to construct an entanglement distribution network, and the round-trip QCS is adopted to realize a QCS network that can serve multiple users. A proof-of-principle QCS network experiment is implemented among the server and multiple users (Alice, Bob, and Charlie) for 11.1 hours, where Alice and Charlie are 10 km away from the server and Bob is 25 km away from the server. The lowest time deviations (TDEV) between the server and each user (Alice, Bob, and Charlie) are 1.57 ps, 0.82 ps and 2.57 ps at the average time of 8000 s, 8000 s and 800 s respectively. The results show that the QCS network scheme with dual-pumped SFWM photon source proposed by us achieves high accuracy, and the channel resources used by n users are reduced by about 30% compared with other round-trip QCS schemes. © 2024, CC BY.

关键词： Synchronization

Supervised Multi-topology Network Cross-Diffusion for Population-Driven Brain Network Atlas Estimation 23rd

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Supervised Multi-topology Network Cross-Diffusion for Popula...

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Mhiri, Islem Mahjoub, Mohamed Ali Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Ecole Nationale dIngénieurs de Sousse LATIS - Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse4023 Tunisia

ISBN: (纸本)9783030597276

Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field with untapped potentials in mapping a population of brain networks in health and disease. Although limited, existing BNA estimation methods have several limitations. First, they primarily rely on a similarity network diffusion and fusion technique, which only considers node degree as a topological measure in the cross-network diffusion process, thereby overlooking rich topological measures of the brain network (e.g., centrality). Second, both diffusion and fusion techniques are implemented in fully unsupervised manner, which might decrease the discriminative power of the estimated BNAs. To fill these gaps, we propose a supervised multi-topology network cross-diffusion (SM-netFusion) framework for estimating a BNA satisfying : (i) well-representativeness (captures shared traits across subjects), (ii) well-centeredness (optimally close to all subjects), and (iii) high discriminativeness (can easily and efficiently identify discriminative brain connections that distinguish between two populations). For a specific class, given the cluster labels of the training data, we learn a weighted combination of the topological diffusion kernels derived from degree, closeness and eigenvector centrality measures in a supervised manner. Specifically, we learn the cross-diffusion process by normalizing the training brain networks using the learned diffusion kernels. This normalization well captures shared networks between individuals at different topological scales, improving the representativeness and centeredness of the estimated multi-topology BNA. Our SM-netFusion produces the most centered and representative template in comparison with its variants and state-of-the-art methods and further boosted the classification of autistic subjects by 5 to 15%. SM-netFusion presents the first work for supervised network cross-diffusion based on graph topological measures, which can be further

关键词： Network topology