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Current and future directions in network biology

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

Post-storm vehicle routing for distribution grid restoration: An OLUCT based learning approach

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Post-storm vehicle routing for distribution grid restoration...

2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Shuai, Hang He, Haibo Wen, Jinyu University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI02881 United States Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan China

ISBN: (纸本)9781728155081

Natural disasters such as storms usually bring significant damages to distribution grids. Many distribution grids are not equipped with sensors that can pinpoint the location of actual faults. This further complicated the outage restoration process in distribution grids. This paper focuses on the optimal routing of repair vehicles to restore outages in the grid as fast as possible after a storm. First, the vehicle routing problem is formulated as a sequential stochastic optimization problem. In the optimization model, the belief state of the power grid is updated according to the phone calls from customers and the information collected by the repair vehicles. Second, an Open Loop Upper Confidence Bound for Trees (OLUCT) algorithm based stochastic lookahead policy is utilized to achieve the realtime dispatching of the repair vehicles. Simulation results show that the proposed approach can effectively navigate the vehicle to repair all outages. © 2020 IEEE.

关键词： Optimization

Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications

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arXiv 2022年

作者： Zheng, Xi Fang, Jun Wang, Hongwei Wang, Peilan Li, Hongbin The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

We consider the problem of channel estimation and joint active and passive beamforming for reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We show that, with a well-designed frame-based training protocol, the received pilot signal can be organized into a low-rank third-order tensor that admits a canonical polyadic decomposition (CPD). Based on this observation, we propose two CPD-based methods for estimating the cascade channels associated with different subcarriers. The proposed methods exploit the intrinsic low-rankness of the CPD formulation, which is a result of the sparse scattering characteristics of mmWave channels, and thus have the potential to achieve a significant training overhead reduction. Specifically, our analysis shows that the proposed methods have a sample complexity that scales quadratically with the sparsity of the cascade channel. Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels. Simulation results show that the proposed CPD-based channel estimation methods attain mean square errors that are close to the Cramér-Rao bound (CRB) and present a clear advantage over the compressed sensing-based method. In addition, the proposed joint beamforming method can effectively utilize the estimated channel parameters to achieve superior beamforming performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel estimation

Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework

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arXiv 2023年

作者： Zheng, Jingheng Ni, Wanli Tian, Hui Gündüz, Deniz Quek, Tony Q.S. Han, Zhu The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Modena41100 Italy The Singapore University of Technology and Design 487372 Singapore The Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resource at the BS. To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL. Specifically, each device sends both local gradients and data samples to the BS for training a shared global model. To improve communication efficiency over the same time-frequency resources, we integrate over-the-air computation for aggregation and non-orthogonal multiple access for transmission by designing a novel transceiver structure. To gain deep insights, we conduct convergence analysis by deriving a closed-form optimality gap for SemiFL and extend the result to two extra cases. In the first case, the BS uses all accumulated data samples to calculate the CL gradient, while a decreasing learning rate is adopted in the second case. Our analytical results capture the destructive effect of wireless communication and show that both FL and CL are special cases of SemiFL. Then, we formulate a non-convex problem to reduce the optimality gap by jointly optimizing the transmit power and receive beamformers. Accordingly, we propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers. Extensive simulation results on two real-world datasets corroborate our theoretical analysis, and show that the proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on the MNIST dataset compared to state-of-the-art benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

Anisotropic subwavelength grating perturbation enables zero crosstalk in a leaky mode

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arXiv 2022年

作者： Kabir, Md Faiyaz Mia, Md Borhan Ahmed, Ishtiaque Jaidye, Nafiz Ahmed, Syed Z. Kim, Sangsik Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Department of Physics and Astronomy Texas Tech University LubbockTX79409 United States School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Electromagnetic coupling via exponentially decaying evanescent field or radiative wave is a primary characteristic of light, allowing optical signal/power transfer but limiting integration density in a photonic circuit. A leaky mode combines both evanescent field and radiative wave, causing stronger coupling and thus not ideal for dense integration. Here we show that a leaky mode with anisotropic perturbation rather can achieve completely zero crosstalk realized by subwavelength grating (SWG) metamaterials. The oscillating fields in the SWGs enable coupling coefficients in each direction to counteract each other, resulting in completely zero crosstalk. We experimentally demonstrate such an extraordinarily low coupling between closely spaced identical leaky SWG waveguides, suppressing the crosstalk by ≈40 dB compared to conventional strip waveguides, corresponding to ≈100 times longer coupling length. This leaky-SWG suppresses the crosstalk of transverse–magnetic (TM) mode, which is challenging due to its low confinement, and marks a novel approach in electromagnetic coupling applicable to other spectral regimes and generic devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Crosstalk

Coherent Acoustic Phonons in Bi2Se3 Ultrathin Films Probed by Femtosecond, Optical Pump-Probe Spectroscopy

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Coherent Acoustic Phonons in Bi2Se3 Ultrathin Films Probed b...

CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Cheng, Jing Niherysh, Kiryl A. Francis, Peter Chakraborty, Debamitra Lombardi, Floriana Erts, Donats Prischepa, Serghej L. Laszcz, Adam Komissarov, Ivan V. Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623-1299 United States Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg Sweden Institute of Chemical Physics University of Latvia Riga Latvia Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Dipartimento di Fisica "E.R. Caianiello" Università degli Studi di Salerno Sa FiscianoI-84084 Italy Lukasiewicz Research Network Institute of Microelectronics and Photonics WarszawaPL-02668 Poland

ISBN: (纸本)9781957171395

The dynamics of coherent acoustic phonons in the Bi2Se3 layered crystal system are investigated. The findings reveal that the frequency evolution of breathing modes with the number of the material quintuple layers and the dispersion relation both can be explained by a modified chain model with a nonuniform coupling force. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Ultrathin films

Multimodal Self-Supervised Learning for Lesion Localization

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Multimodal Self-Supervised Learning for Lesion Localization

IEEE International Symposium on Biomedical Imaging

作者： Hao Yang Hong-Yu Zhou Cheng Li Weijian Huang Jiarun Liu Yong Liang Guangming Shi Hairong Zheng Qiegen Liu Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Peng Cheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing China Department of Computer Science The University of Hong Kong Pokfulam China Pazhou Laboratory (Huangpu) Guangzhou China Department of Electronic Information Engineering Nanchang University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation information is lacking. Nonetheless, localizing diseases accurately without detailed positional annotations remains a challenge. Although existing methods have attempted to utilize local information to achieve fine-grained semantic alignment, their capability in extracting the fine-grained semantics of the comprehensive context within reports is limited. To address this problem, a new method is introduced that takes full sentences from textual reports as the basic units for local semantic alignment. This approach combines chest X-ray images with their corresponding textual reports, performing contrastive learning at both global and local levels. The leading results obtained by this method on multiple datasets confirm its efficacy in the task of lesion localization.

关键词： Location awareness Deep learning Annotations Semantics Robustness Lesions Task analysis

Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach

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arXiv 2022年

作者： Zheng, Xi Wang, Peilan Fang, Jun Li, Hongbin The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we show that the received signals can be expressed as a low-rank third-order tensor that admits a tensor rank decomposition, also known as canonical polyadic decomposition (CPD). A structured CPD-based method is then developed to estimate the channel parameters. Our analysis reveals that the training overhead required by our proposed method is as low as O(U2), where U denotes the sparsity of the cascade channel. Simulation results are provided to illustrate the efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

Darlington pair of quantum thermal transistors

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Physical Review B 2021年第4期104卷 045405-045405页

作者： Ravi T. Wijesekara Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

Recent progress in manipulating individual quantum systems has led to the development of quantum thermal transistors, which control the thermal conductivity between two of its terminals according to an input signal on its third terminal. With several models for individual thermal transistors already developed, the next natural step is to investigate how multiple thermal transistors can interconnect to build useful composite devices. In electronics literature, the Darlington pair is a two-transistor configuration commonly used to construct electronic amplifiers with higher gain than is possible with a single transistor. We create the electronic Darlington pair's thermal equivalent using two individual thermal transistors in a similar configuration. Unlike previously studied models, multitransistor configurations like this contain internal transistor interconnections whose temperatures cannot be biased externally but are determined by the individual transistors' internal dynamics. We refine previous models to incorporate these transistor-transistor interactions and introduce an intermediate thermal bath to facilitate the thermal energy exchange between the Darlington pair transistors. We investigate temperature-based and optical field-based control strategies of the Darlington pair in terms of both steady-state and transient thermal flow characteristics through numerical simulations. Under both control strategies, the thermal Darlington pair's steady-state performance exhibits superior thermal amplification, sensitivity, and thermodynamic efficiency than an equivalent single thermal transistor. Our results closely mirror those expected from the corresponding electronic Darlington pair. Hence, we envision that we may readily adapt the intermediate bath formalism we developed in this work to translate a wide variety of useful electronic multitransistor configurations to their thermal equivalents.

关键词： Thermal properties Nanostructures Transistors