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Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms

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arXiv 2022年

作者： Li, Yang Lin, Qingfeng Liu, Ya-Feng Ai, Bo Wu, Yik-Chung Shenzhen Research Institute of Big Data Shenzhen518172 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves a close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

Non-Hermitian lattice fermions in the 2D Gross-Neveu-Yukawa model

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Physical Review D 2024年第3期110卷 034502-034502页

作者： Xingyu Guo Chen-Te Ma Hui Zhang Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Department of Physics and Astronomy Iowa State University Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Asia Pacific Center for Theoretical Physics Pohang University of Science and Technology School of Physics and Telecommunication Engineering South China Normal University The Laboratory for Quantum Gravity and Strings Department of Mathematics and Applied Mathematics University of Cape Town Physics Department Center for Exploration of Energy and Matter Indiana University

We work the lattice fermions and non-Hermitian formulation in the 2D Gross-Neveu-Yukawa (GNY) model and demonstrate the numerical implementation for two flavors by the hybrid Monte Carlo. Our approach has a notable advantage in dealing with chiral symmetry on a lattice by avoiding the Nielsen-Ninomiya theorem, due to the nonsymmetrized finite-difference operator. We restore the hypercubic symmetry by averaging over all possible orientations with the proper continuum limit. Our study is the first simulation for the interacting fermion formulated in a non-Hermitian way. We compare the numerical solution with the one-loop resummation. The resummation results matches with the numerical solution in ⟨ϕ⟩, ⟨ϕ2⟩, ⟨Tr(ψ¯1ψ1+ψ¯2ψ2)/2⟩, and ⟨Tr(ψ¯1ψ1+ψ¯2ψ2)ϕ/2⟩. We also used the one-loop resummation to provide the renormalization group flow and asymptotic safety in the 2D GNY model.

关键词： Nonperturbative effects in field theory Perturbation theory Chiral symmetry Hybrid Monte Carlo algorithm Lattice field theory

Artificial intelligence in cancer target identification and drug discovery

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Signal Transduction and Targeted Therapy 2022年第6期7卷 1951-1974页

作者： Yujie You Xin Lai Yi Pan Huiru Zheng Julio Vera Suran Liu Senyi Deng Le Zhang College of Computer Science Sichuan UniversityChengdu 610065China Laboratory of Systems Tumor Immunology Friedrich-Alexander-Universität Erlangen-Nürnberg(FAU)and Universitätsklinikum ErlangenErlangen 91052Germany Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesRoom D5131068 Xueyuan AvenueShenzhen University TownShenzhen 518055China School of Computing Ulster UniversityBelfast BT151EDUK Institute of Thoracic Oncology Department of Thoracic SurgeryWest China HospitalSichuan UniversityChengdu 610065China Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as ***,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover ***,we describe the scope of artificial intelligence biology analysis for novel anticancer target ***,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence ***,we showcase the applications of artificial intelligence approaches in cancer target identification and drug *** together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

关键词： drugs cancer drug

Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A Survey

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： Computer vision

Prediction of multi-parameters in the inverse heat conduction problems

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Prediction of multi-parameters in the inverse heat conductio...

2020 International Conference on Physics, Mechanics and Mathematical Science, PMMS 2020

作者： Abdelhamid, Talaat Chen, Rongliang Alam, Mahbub Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China Institute for Turbulence-Noise-Vibration Interaction and Control Shenzhen Graduate School Harbin Institute of Technology Shenzhen China Physics and Mathematical Engineering Department Faculty of Electronic Engineering Menoufiya University Menouf Egypt Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing Shenzhen China

This paper studies the prediction of the spatial-dependent-heat transfer coefficient γ(x) and- heat flux q(x), using the modified conjugate gradient method (MCGM). The mathematical formulation of the problem well defined and the existence of the minimizer is investigated. We establish the sensitivity and adjoint equations for computing the gradient with respect to γ(x) and q(x). The proposed algorithm 4.1 is derived for reconstructing γ(x) and q(x) using the MCGM. The numerical experiments are examined to show the efficiency and accuracy of the proposed method. Finally, some conclusions and remarks are given. © Published under licence by IOP Publishing Ltd.

关键词： Conjugate gradient method

A general framework for lifelong localization and mapping in changing environment

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arXiv 2021年

作者： Zhao, Min Guo, Xin Song, Le Qin, Baoxing Shi, Xuesong Lee, Gim Hee Sun, Guanghui Gaussian Robotics Shanghai China Intel Labs China Beijing China Computer Vision and Robotic Perception Lab Department of Computer Science School of Computing National University of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology China

The environment of most real-world scenarios such as malls and supermarkets changes at all times. A prebuilt map that does not account for these changes becomes out-of-date easily. Therefore, it is necessary to have an up-todate model of the environment to facilitate long-term operation of a robot. To this end, this paper presents a general lifelong simultaneous localization and mapping (SLAM) framework. Our framework uses a multiple session map representation, and exploits an efficient map updating strategy that includes map building, pose graph refinement and sparsification. To mitigate the unbounded increase of memory usage, we propose a maptrimming method based on the Chow-Liu maximum-mutualinformation spanning tree. The proposed SLAM framework has been comprehensively validated by over a month of robot deployment in real supermarket environment. Furthermore, we release the dataset collected from the indoor and outdoor changing environment with the hope to accelerate lifelong SLAM research in the community. Our dataset is available at https://***/sanduan168/lifelong-SLAM-dataset. © 2021, CC BY.

关键词： Retail stores

Nonreciprocal Bundle Emissions of Quantum Entangled Pairs

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arXiv 2024年

作者： Bin, Qian Jing, Hui Wu, Ying Nori, Franco Lü, Xin-You School of Physics Institute for Quantum Science and Engineering Huazhong University of Science and Technology Wuhan Institute of Quantum Technology Wuhan430074 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wakoshi Saitama351-0198 Japan Center for Quantum Computing RIKEN Wakoshi Saitama351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109-1040 United States

Realizing precise control over multiquanta emission is crucial for quantum information processing, especially when integrated with advanced techniques of manipulating quantum states. Here, by spinning the resonator to induce the Sagnac effect, we can obtain nonreciprocal photon-phonon and photon-magnon super-Rabi oscillations under conditions of optically driving resonance transitions. Opening dissipative channels for such super-Rabi oscillations enables the realization of directional bundle emissions of entangled photon-phonon pairs and photon-magnon pairs by transferring pure multiquanta state to bundled multiquanta outside of the system. This nonreciprocal emission is a flexible switch that can be controlled with precision, and simultaneous emissions of different entangled pairs (such as photon-phonon or photon-magnon pairs) can even emerge but in opposite directions by driving the resonator from different directions. This ability to flexibly manipulate the system allows us to achieve directional entangled multiquanta emitters, and has also potential applications for building hybrid quantum networks and on-chip quantum communications. © 2024, CC BY.

关键词： Photons

Rethinking CNN Models for Audio Classification

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arXiv 2020年

作者： Palanisamy, Kamalesh Singhania, Dipika Yao, Angela Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India School of Computing National University of Singapore Singapore

In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used as strong baseline networks for audio classification. Even though there is a significant difference between audio Spectrogram and standard ImageNet image samples, transfer learning assumptions still hold firmly. To understand what enables the ImageNet pretrained models to learn useful audio representations, we systematically study how much of pretrained weights is useful for learning spectrograms. We show (1) that for a given standard model using pretrained weights is better than using randomly initialized weights (2) qualitative results of what the CNNs learn from the spectrograms by visualizing the gradients. Besides, we show that even though we use the pretrained model weights for initialization, there is variance in performance in various output runs of the same model. This variance in performance is due to the random initialization of linear classification layer and random mini-batch orderings in multiple runs. This brings significant diversity to build stronger ensemble models with an overall improvement in accuracy. An ensemble of ImageNet pretrained DenseNet achieves 92.89% validation accuracy on the ESC-50 dataset and 87.42% validation accuracy on the UrbanSound8K dataset which is the current state-of-the-art on both of these datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectrographs

Innovation, Research and Development in engineering Education. Activities of the Spanish Chapter of the IEEE Education Society: Directive Board of the IEEE Education Society Spanish Chapter 2020-2021

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Innovation, Research and Development in Engineering Educatio...

Technologies Applied to Electronics Teaching (TAEE)

作者： Ó scar Martí nez Bonastre Cristina Ferná ndez Lluí s Vicent Francisco Mur Manuel Castro Gabriel Diaz Jose Carpio Elio Sancristobal Sergio Martin Jose Sá nchez Francisco Javier Arcega Solsona Juan Suardiaz Muro Manuel Caeiro-Rodrí guez Martí n Llamas Inmaculada Plaza Baltasar Ferná ndez Manjó n Pedro Muñ oz-Merino Edmundo Tovar Telmo Zarraonandia Ayo Javier Garcia Zubia José Antonio Delgado Pení n Department of Statistics Mathematics and Computing Science Operations Research Centre Universidad Miguel Hern&#x00E1 ndez Elche Spain Department of Electronic Technology Universidad Carlos III de Madrid Madrid Spain UPF Barcelona School of Management Barcelona Spain Department of Electrical Electronic and Control Engineering UNED Madrid Spain Department of Electrical Engineering Universidad de Zaragoza Zaragoza Spain Department of Automatics Electrical Engineering and Electronic Technology Technical University of Cartagena Cartagena Spain atlanTTic Research Center University of Vigo Vigo Spain Department of Electronics and Communication Universidad de Zaragoza Zaragoza Spain Department of Software Engineering and Artificial Intelligence Complutense University of Madrid Madrid Spain Department of Telematic Engineering Universidad Carlos III de Madrid Madrid Spain Department of Computer Languages and Systems and Software Engineering Universidad Polit&#x00E9 cnica de Madrid Madrid Spain Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain Department of Electronic Engineering Universidad de Deusto Bilbao Spain Universitat Polit&#x00E8 cnica de Catalunya Barcelona Spain

ISBN: (数字)9781665421614

ISBN: (纸本)9781665421621

The Technical Committee of the CESEI (Spanish Chapter of the IEEE Education Society) is focused on the Innovation, Research and Development (I+R+D) of the education in engineering, mainly Electric and Computer engineering. As in other interdisciplinary fields, the I+R+D involves several sources of information and different stakeholders, each one with its own focus and purpose. As a result, sometimes it may be complex for practitioners and researchers to identify the more appropriate entities for their interests. This paper shows how the CESEI Technical Committee tries to solve this difficulty by providing updated information regarding the main publications (journals), events (conferences) and awards to the best academic works. The paper focuses special attention on the activities held during 2020–2021.

关键词： Technological innovation Collaboration Stakeholders engineering education Research and development