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Convex Analytic Theory for Convex Q-Learning

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Convex Analytic Theory for Convex Q-Learning

IEEE Conference on Decision and Control

作者： Fan Lu Prashant G. Mehta Sean P. Meyn Gergely Neu Department of Electrical and Computer Engineering University of Florida Gainesville FL Coordinated Science Laboratory and the Department Mechanical Science and Engineering University of Illinois at Urbana-Champaign (UIUC) Inria International Chair Paris France Department of Information and Communication Technologies Universitat Pompeu Fabra (Barcelona Spain)

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In recent years there has been a collective research effort to find new formulations of reinforcement learning that are simultaneously more efficient and more amenable to analysis. This paper concerns one approach that builds on the linear programming (LP) formulation of optimal control of Manne. A primal version is called logistic Q-learning, and a dual variant is convex Q-learning. This paper focuses on the latter, while building bridges with the former. The main contributions follow: (i) The dual of convex Q-learning is not precisely Manne’s LP or a version of logistic Q-learning, but has similar structure that reveals the need for regularization to avoid over-fitting. (ii) A sufficient condition is obtained for a bounded solution to the Q-learning LP. (iii) Simulation studies reveal numerical challenges when addressing sampled-data systems based on a continuous time model. The challenge is addressed using state-dependent sampling. The theory is illustrated with applications to examples from OpenAI gym. It is shown that convex Q-learning is successful in cases where standard Q-learning diverges, such as the LQR problem.

关键词： Sufficient conditions Q-learning Buildings Optimal control Sampled data systems Linear programming Continuous time systems

Spatiotemporal mode-locking and photonic flywheel in multimode microresonators

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arXiv 2022年

作者： Nie, Mingming Jia, Kunpeng Xie, Yijun Zhu, Shi-Ning Xie, Zhenda Huang, Shu-Wei National Laboratory of Solid State Microstructures School of Electronic Science and Engineering College of Engineering and Applied Sciences School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO80309 United States

Dissipative Kerr soliton (DKS) frequency combs - also known as microcombs - have arguably created a new field in cavity nonlinear photonics, with a strong cross-fertilization between theoretical, experimental, and technological research. Spatiotemporal mode-locking (STML) not only add new degrees of freedom to ultrafast laser technology, but also provide new insights for implementing analogue computers and heuristic optimizers with photonics. Here, we combine the principles of DKS and STML for the first time to demonstrate the STML DKS by developing an unexplored ultrahigh-quality-factor Fabry-Pérot microresonator based on graded index multimode fiber (GRIN-MMF). Using the intermodal stimulated Brillouin scattering, we can selectively excite either the eigenmode DKS or the STML DKS. Furthermore, we demonstrate an ultralow noise microcomb that enhances the photonic flywheel performance in both the fundamental comb linewidth and DKS timing jitter. The demonstrated fundamental comb linewidth of 400 mHz and DKS timing jitter of 500 attosecond (averaging times up to 25 μs) represent improvements of 25× and 2.5×, respectively, from the state-of-the-art. Our results show the potential of GRIN-MMF FP microresonators as an ideal testbed for high-dimensional nonlinear cavity dynamics and photonic flywheel with ultrahigh coherence and ultralow timing jitter. © 2022, CC BY.

关键词： Degrees of freedom (mechanics)

Low-Complexity Beam Training for Multi-RIS-Assisted Multi-User Communications

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arXiv 2024年

作者： Xu, Yuan Huang, Chongwen Wei, Li Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Hangzhou310027 China The School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electrical Engineering and Computer Science the KU 6G Center Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

In this paper, we investigate the beam training problem in the multi-user millimeter wave (mmWave) communication system, where multiple reconfigurable intelligent surfaces (RISs) are deployed to improve the coverage and the achievable rate. However, existing beam training techniques in mmWave systems suffer from high complexity (i.e., exponential order) and low identification accuracy. To address these problems, we propose a novel hashing multi-arm beam (HMB) training scheme that reduces the training complexity to the logarithmic order with the high accuracy. Specifically, we first design a generation mechanism for HMB codebooks. Then, we propose a demultiplexing algorithm based on the soft decision to distinguish signals from different RIS reflective links. Finally, we utilize a multi-round voting mechanism to align the beams. Simulation results show that the proposed HMB training scheme enables simultaneous training for multiple RISs and multiple users, and reduces the beam training overhead to the logarithmic level. Moreover, it also shows that our proposed scheme can significantly improve the identification accuracy by at least 20% compared to the existing beam training techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Millimeter waves

Reduction of Autocorrelation Times in Lattice Path Integral Quantum Monte Carlo via Direct Sampling of the Truncated Exponential Distribution

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arXiv 2023年

作者： Casiano-Diaz, Emanuel Barros, Kipton Li, Ying Wai Maestro, Adrian Del Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Theoretical Division Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Institute for Advanced Materials and Manufacturing University of Tennessee KnoxvilleTN37996 United States

In Monte Carlo simulations, proposed configurations are accepted or rejected according to an acceptance ratio, which depends on an underlying probability distribution and an a priori sampling probability. By carefully selecting the probability distribution from which random variates are sampled, simulations can be made more efficient, by virtue of an autocorrelation time reduction. In this paper, we illustrate how to directly sample random variates from a two dimensional truncated exponential distribution. We show that our direct sampling approach converges faster to the target distribution compared to rejection sampling. The direct sampling of one and two dimensional truncated exponential distributions is then applied to a recent Path Integral Monte Carlo (PIMC) algorithm for the simulation of Bose-Hubbard lattice models at zero temperature. The new sampling method results in improved acceptance ratios and reduced autocorrelation times of estimators, providing an effective speed up of the simulation. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

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MIM-GAN-based Anomaly Detection for Multivariate Time Series...

IEEE Conference on Vehicular Technology (VTC)

作者： Shan Lu Zhicheng Dong Donghong Cai Fang Fang Dongcai Zhao College of Information Science Technology Tibet University Lhasa China Tibet Autonomous Region Plateau Complex Environmental Information Transmission and Processing Engineering Research Center Advanced Technology Research Center of Tibet University Lhasa China College of Information Science and Technology Jinan University Guangzhou China Department of Electrical and Computer Engineering Western University London Canada

The loss function of Generative adversarial network (GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure (MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant-reconstruction score is composed of discrimination and reconstruction loss. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1-score.

关键词：

Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images

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arXiv 2023年

作者： Lin, Subin Ramani, Vasantha Martin, Miguel Arjunan, Pandarasamy Chong, Adrian Biljecki, Filip Ignatius, Marcel Poolla, Kameshwar Miller, Clayton Berkeley Education Alliance for Research in Singapore CREATE Tower 1 Create 6 Way 138602 Singapore Robert Bosch Centre for Cyber-physical Systems Indian Institute of Science Karnataka Bengaluru560012 India Department of the Built Environment College of Design and Engineering National University of Singapore 4 Architecture Drive 117566 Singapore Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Department of Architecture College of Design and Engineering National University of Singapore 4 Architecture Drive 117566 Singapore

This paper describes a dataset collected by infrared thermography, a non-contact, non-intrusive technique to acquire data and analyze the built environment in various aspects. While most studies focus on the city and building scales, an observatory installed on a rooftop provides high temporal and spatial resolution observations with dynamic interactions on the district scale. The rooftop infrared thermography observatory with a multi-modal platform capable of assessing a wide range of dynamic processes in urban systems was deployed in Singapore. It was placed on the top of two buildings that overlook the outdoor context of the National University of Singapore campus. The platform collects remote sensing data from tropical areas on a temporal scale, allowing users to determine the temperature trend of individual features such as buildings, roads, and vegetation. The dataset includes 1,365,921 thermal images collected on average at approximately 10-second intervals from two locations during ten months. © 2023, CC BY-SA.

关键词： Observatories

End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml

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arXiv 2025年

作者： Guglielmo, Giuseppe Di Du, Botao Campos, Javier Boltasseva, Alexandra Dixit, Akash V. Fahim, Farah Kudyshev, Zhaxylyk Lopez, Santiago Ma, Ruichao Perdue, Gabriel N. Tran, Nhan Yesilyurt, Omer Bowring, Daniel Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States University of Chicago ChicagoIL60637 United States

We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32 ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications. © 2025, CC BY.

关键词： Firmware

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition