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arXiv 2022年

作者： Winnicki, Anna Srikant, R. Coordinated Science Laboratory The Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States C3.ai Digital Transformation Institute United States

We provide performance guarantees for a variant of simulation-based policy iteration for controlling Markov decision processes that involves the use of stochastic approximation algorithms along with state-of-the-art techniques that are useful for very large MDPs, including lookahead, function approximation, and gradient descent. Specifically, we analyze two algorithms;the first algorithm involves a least squares approach where a new set of weights associated with feature vectors is obtained via least squares minimization at each iteration and the second algorithm involves a two-time-scale stochastic approximation algorithm taking several steps of gradient descent towards the least squares solution before obtaining the next iterate using a stochastic approximation algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Wheat Rust Disease Segmentation from Ground Imagery

Wheat Rust Disease Segmentation from Ground Imagery

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Hirra Anwar Muhammad Jawad Khan Muhammad Fayyaz Ajmal Saeed Mian Faisal Shafait School of Mechanical and Manufacturing Engineering NUST Islamabad Pakistan Department of Electrical Engineering Prince Sattam Bin Abdul Aziz University Al-Kharj Saudi Arabia Crop Diseases Research Institute National Agriculture Research Center Islamabad Pakistan University of Western Australia Perth Australia School of Electrical Engineering & Computer Science NUST Islamabad Pakistan Deep Learning Laboratory National Center of Artificial Intelligence Islamabad Pakistan

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Wheat rust disease is a major agricultural threat, causing significant yield losses globally. Precision agriculture and autonomous robot systems represent pivotal advancements in modern farming and disease detection, leveraging technology to enhance crop management, optimize resource use, and improve productivity. These systems rely heavily on accurate and comprehensive data to train effectively. However, current datasets often fall short of meeting the specific needs of these advanced agricultural technologies. This paper addresses this gap by introducing a primary contribution in the form of an open-source dataset focused on wheat rust disease segmentation for precision agriculture. Open-source datasets in literature are either general plant disease datasets or wheat disease classification datasets. Our proposed wheat rust segmentation dataset, Focussed Rust Disease Images (FRDI), provides close-up views of disease manifestations on wheat leaves, capturing the natural variability and complexity of diseases in field conditions, thereby meeting the requirements of precision agriculture systems. This facilitates detailed analysis and effective training of deep learning models designed for disease detection and segmentation tasks for real-world agricultural environments. Furthermore, in our work we present an improved wheat rust disease detection and segmentation technique which is based on the concept of co-salient feature extraction for disease identification within complex and cluttered field images. By integrating localized and generic contextual information, our proposed approach effectively identifies key features and intricate patterns associated with wheat rust disease.

关键词： Precision agriculture Training Deep learning Representation learning Productivity Image segmentation Plant diseases Object detection Feature extraction Diseases

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CLDG: Contrastive Learning on Dynamic Graphs

arXiv

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arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

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Heterogeneously coupled waveguides for pure single photon generation 13

Heterogeneously coupled waveguides for pure single photon ge...

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13th International Conference on Information Optics and Photonics, CIOP 2022

作者： Ding, Xiangyan Ma, Jing Tan, Liying Helmy, Amr S. Kang, Dongpeng School of Astronautics National Key Laboratory of Science and Technology on Tunable Laser Harbin Institute of Technology 92 West Dazhi Street Harbin150001 China The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada

ISBN: (纸本)9781510660632

Single-photon sources are the cornerstone of the optical quantum information process, including quantum computing, simulation, metrology, etc. The practical application requires these sources to be high purity and indistinguishability. A readily available approach is to use heralded single-photon sources to generate photon pairs via spontaneous parametric down-conversion (SPDC). However, the purity of photon pairs generated by SPDC is usually limited by the spectral correlations because of energy conservation. Extra spectral filters and group velocity matching (GVM) can be used to generate spectral uncorrelated photon pairs on-chip. However, the material dispersion of most materials cannot meet the GVM requirements, and extra components will reduce the integration. In this work, we develop a strategy to generate pure single photon on-chip by mode coupling in heterogeneously coupled waveguides. This strategy can tailor the group velocity directly, or shape biphoton wave function through a new degree of freedom that is the non-constant overlap between interacting modes. This allows the generation of high-purity single photons totally on-chip. Design examples based on lithium niobate waveguide are provided to generate spectrally pure photon pairs. This strategy is generic that it is not limited to any material and waveguide system, providing new possibilities in the generation of quantum photons. © 2022 SPIE.

关键词： Photons

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Deep-Neural-Network Discrimination of Multiplexed Superconducting-Qubit States

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Physical Review Applied 2022年第1期17卷 014024-014024页

作者： Benjamin Lienhard Antti Vepsäläinen Luke C.G. Govia Cole R. Hoffer Jack Y. Qiu Diego Ristè Matthew Ware David Kim Roni Winik Alexander Melville Bethany Niedzielski Jonilyn Yoder Guilhem J. Ribeill Thomas A. Ohki Hari K. Krovi Terry P. Orlando Simon Gustavsson William D. Oliver Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Quantum Engineering and Computing Group Raytheon BBN Technologies Cambridge Massachusetts 02138 USA MIT Lincoln Laboratory Lexington Massachusetts 02421 USA

Demonstrating a quantum computational advantage will require high-fidelity control and readout of multiqubit systems. As system size increases, multiplexed qubit readout becomes a practical necessity to limit the growth of resource overhead. Many contemporary qubit-state discriminators presume single-qubit operating conditions or require considerable computational effort, limiting their potential extensibility. Here, we present multiqubit readout using neural networks as state discriminators. We compare our approach to contemporary methods employed on a quantum device with five superconducting qubits and frequency-multiplexed readout. We find that fully connected feedforward neural networks increase the qubit-state-assignment fidelity for our system. Relative to contemporary discriminators, the assignment error rate is reduced by up to 25% due to the compensation of system-dependent nonidealities such as readout crosstalk, which is reduced by up to one order of magnitude. Our work demonstrates a potentially extensible building block for high-fidelity readout relevant to both near-term devices and future fault-tolerant systems.

关键词： Machine learning Measurement-based quantum computing Optoelectronics Quantum control Quantum information with solid state qubits Superconducting qubits Artificial neural networks Microwave techniques

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Global Stability and Optimal Control of Epidemics in Heterogeneously Structured Populations Exhibiting Adaptive Behaviour

SSRN

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SSRN 2023年

作者： Liu, Lixiang Chen, Shanshan Small, Michael Moore, Jack Murdoch Shang, Keke Department of Computer Science School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Complex Systems Group Department of Mathematics and Statistics The University of Western Australia Perth6009 Australia Mineral Resources Commonwealth Scientific and Industrial Research Organisation Kensington6151 Australia MOE Key Laboratory of Advanced Micro-Structured Materials School of Physics Science and Engineering Tongji University Shanghai200092 China Computational Communication Collaboratory Nanjing University Nanjing210093 China

During the outbreak of infectious diseases, individual always make some adaptive response to avoid infected the diseases. In this paper, we introduce SIRS model on scale-free networks that take into account behavioral memory and time delay to depict individuals’ adaptive feedback, which is better characterize the actual spread of epidemics. We address uniform persistence and find that the model has the sharp threshold property, that is, for the basic reproduction number less than one, the disease-free equilibrium is globally asymptotically stable, while for the basic reproduction number is above one, the endemic equilibrium is globally stable. We further analyze the effects of two control strategies (the vaccination of susceptible people and treatment of infected people) on epidemic dynamics by optimal control strategy. The existence and uniqueness of the optimal control strategy are obtained. Finally, we perform some stochastic network simulations that yield quantitative agreement with the deterministic mean-field approach. The final results show that the effect of time delay does not change the basic reproduction number, but it plays an important role in the process of disease transmission. The behavioral memory is able to affect the basic reproduction number, and it has important and positive implications for disease transmission and control. The work of this paper can provide a theoretical basis and reference for the prediction and prevention of infectious diseases. © 2023, The Authors. All rights reserved.

关键词： Diseases

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Tunable Magnetically Induced Transparency Spectra in Magnon-Magnon Coupled Y3Fe5O12/Permalloy Bilayers

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Physical Review Applied 2022年第4期17卷 044010-044010页

作者： Yuzan Xiong Jerad Inman Zhengyi Li Kaile Xie Rao Bidthanapally Joseph Sklenar Peng Li Steven Louis Vasyl Tyberkevych Hongwei Qu Zhili Xiao Wai K. Kwok Valentine Novosad Yi Li Fusheng Ma Wei Zhang Department of Physics Oakland University Rochester Michigan 48309 USA Department of Electronic and Computer Engineering Oakland University Rochester Michigan 48309 USA Materials Science Division Argonne National Laboratory Argonne Illinois 60439 USA Jiangsu Key Laboratory of Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210046 China Department of Physics and Astronomy Wayne State University Detroit Michigan 48201 USA Department of Electrical and Computer Engineering Auburn University Auburn Alabama 36849 USA

Hybrid magnonic systems host a variety of characteristic phenomena such as the magnetically induced transparency (MIT) and Purcell effect, which are considered useful for future coherent quantum-information processing. In this work, we experimentally demonstrate a tunable MIT effect in the Y3Fe5O12(YIG)/Permalloy(Py) magnon-magnon coupled system via changing the magnetic field orientations. By probing the magneto-optic effects of Py and YIG thin films, we identify clear features of MIT spectra induced by the mode hybridization between the uniform mode of Py and the perpendicular standing spin-wave modes of YIG. By changing the external magnetic field orientations, we observe a tunable coupling strength between the YIG’s spin-wave modes and the Py’s uniform mode, upon the application of an out-of-plane magnetic field. This observation is theoretically interpreted by a geometrical consideration of the Py and YIG magnetization under the oblique magnetic field even at a constant interfacial exchange coupling. Our findings show high promise for investigating tunable coherent phenomena with hybrid magnonic platforms.

关键词： Electromagnetically induced transparency Magneto-optical effect Magnons Quantum information with hybrid systems Spintronics Ferromagnets Magnetic insulators Magnetic multilayers

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Neural Signal Compression System with Spike Detection Using Compressed Sensing

Neural Signal Compression System with Spike Detection Using ...

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Cross Strait Quad-Regional Radio science and Wireless Technology Conference (CSRWTC)

作者： Ruihan Zheng Yu Xia Dongming Li Liyang Wang Hung Chun Li Peng Un Mak Mang I. Vai Sio Hang Pun State Key Laboratory of Analog and Mixed-Signal VLSL University of Macau Macau China Institution of Microelectronics University of Macau Macau China Joint Laboratory of Zhuhai UM Science and Technology Research Institution – Lingyange Semiconductor Incorporated Zhuhai Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

This article aims to demonstrate a signal compression method for the wireless invasive neural recording system. A compression system with spike detection for neural signals is proposed. The input signal is firstly detected in the spike detection part and then the intercepted spike segments are sent to the compression part. A compressed sensing technique is applied in the compression part, and the Minimum Euclidean or Manhattan Distance Cluster-based (MDC) matrix is adopted for compressing neural spike segments. During simulation, the compression rate can surpass 99% and the signal-to-noise distortion ratio is around 37 dB. Moreover, the proposed method is also contrasted with the direct compression of input neural signals. When the input neural signal is directly compressed, the compression ratio is 98%, and the signal-to-noise ratio distortion rate is about 28 dB. By employing spike detection and utilizing this MDC matrix, it becomes possible to compress non-sparse spike segments in the time domain, resulting in a higher compression rate and improved reconstruction performance.

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Unification of Finite Symmetries in Simulation of Many-body Systems on Quantum computers

arXiv

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arXiv 2024年

作者： Bastidas, Victor M. Fitzpatrick, Nathan Joven, K.J. Rossi, Zane M. Islam, Shariful Van Voorhis, Troy Chuang, Isaac L. Liu, Yuan Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. SunnyvaleCA94085 United States Quantinuum Terrington House 13-15 Hills Road CambridgeCB2 1NL United Kingdom Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics North Carolina State University RaleighNC27606 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC27606 United States Department of Computer Science North Carolina State University RaleighNC27606 United States

Symmetry is fundamental in the description and simulation of quantum systems. Leveraging symmetries in classical simulations of many-body quantum systems often results in an exponential overhead due to the exponentially growing size of some symmetry groups as the number of particles increases. Quantum computers hold the promise of achieving exponential speedup in simulating quantum many-body systems;however, a general method for utilizing symmetries in quantum simulations has not yet been established. In this work, we present a unified framework for incorporating symmetry groups into the simulation of many-body systems on quantum computers. The core of our approach lies in the development of efficient quantum circuits for symmetry-adapted projection onto irreducible representations of a group or pairs of commuting groups. We provide resource estimations for common groups, including the cyclic and permutation groups. Our algorithms demonstrate the capability to prepare coherent superpositions of symmetry-adapted states and to perform quantum evolution across a wide range of models in condensed matter physics and ab initio electronic structure in quantum chemistry. Specifically, we execute a symmetry-adapted quantum subroutine for small molecules in first quantization on noisy hardware, and demonstrate the emulation of symmetry-adapted quantum phase estimation for preparing coherent superpositions of quantum states in various irreps of a symmetry group. In addition, we present a discussion of major open problems regarding the use of symmetries in digital quantum simulations of many-body systems, paving the way for future systematic investigations into leveraging symmetries for practical quantum advantage. The broad applicability and rigorous resource estimation for symmetry transformations make our framework appealing for achieving provable quantum advantage on fault-tolerant quantum computers. The efficiency of the proposed symmetry-adapted subroutine holds the promi

关键词： Quantum computers

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Efficient simulations of Hartree–Fock equations by an accelerated gradient descent method

arXiv

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arXiv 2024年

作者： Ohno, Y. Del Maestro, A. Lakoba, T.I. Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Institute for Advanced Materials & Manufacturing University of Tennessee KnoxvilleTN37920 United States

We develop convergence acceleration procedures that enable a gradient descent-type iteration method to efficiently simulate Hartree–Fock equations for atoms interacting both with each other and with an external potential. Our development focuses on three aspects: (i) optimization of a parameter in the preconditioning operator;(ii) adoption of a technique that eliminates the slowest-decaying mode to the case of many equations (describing many atoms);and (iii) a novel extension of the above technique that allows one to eliminate multiple modes simultaneously. We illustrate performance of the numerical method for the 2D model of the first layer of helium atoms above a graphene sheet. We demonstrate that incorporation of aspects (i) and (ii) above into the "plain" gradient descent method accelerates it by at least two orders of magnitude, and often by much more. Aspect (iii) — a multiple-mode elimination — may bring further improvement to the convergence rate compared to aspect (ii), the single-mode elimination. Both single- and multiple-mode elimination techniques are shown to significantly outperform the well-known Anderson Acceleration. We believe that our acceleration techniques can also be gainfully employed by other numerical methods, especially those handling hard-core-type interaction potentials. Copyright © 2024, The Authors. All rights reserved.

关键词： Numerical methods

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