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Procrustean entanglement concentration in quantum-classical networking

Physical Review Applied 2024年第4期21卷 044027-044027页

作者： Hsuan-Hao Lu Muneer Alshowkan Jude Alnas Joseph M. Lukens Nicholas A. Peters Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA

The success of a future quantum internet will rest in part on the ability of quantum and classical signals to coexist in the same optical fiber infrastructure, a challenging endeavor given the orders of magnitude differences in flux of single-photon-level quantum fields and bright classical traffic. We theoretically describe and experimentally implement Procrustean entanglement concentration for polarization-entangled states contaminated with classical light, showing significant mitigation of crosstalk noise in dense wavelength-division multiplexing. Our approach leverages a pair of polarization-dependent loss emulators to attenuate highly polarized crosstalk that results from imperfect isolation of conventional signals copropagating on shared fiber links. We demonstrate our technique both on the tabletop and over a deployed quantum local area network, finding a substantial improvement of two-qubit entangled state fidelity from approximately 75% to over 92%. This local filtering technique could be used as a preliminary step to reduce asymmetric errors, potentially improving the overall efficiency when combined with more complex error-mitigation techniques in future quantum repeater networks.

关键词： Entanglement manipulation Optical quantum information processing Quantum entanglement Quantum networks Quantum optics Quantum repeaters Internet Multilayer & multiplex networks

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City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage 25th

City-Scale Point Cloud Stitching Using 2D/3D Registration fo...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Yao, Shizeng AliAkbarpour, Hadi Seetharaman, Guna Palaniappan, Kannappan Department of Electrical Engineering and Computer Science University of Missouri - Columbia ColumbiaMO United States US Naval Research Laboratory WashingtonDC United States

ISBN: (纸本)9783030687922

3D city-scale point cloud stitching is a critical component for large data collection, environment change detection, in which massive amounts of 3D data are captured under different times and conditions. This paper proposes a novel point cloud stitching approach, that automatically and accurately stitches multiple city-scale point clouds, which only share relatively small overlapping areas, into one single model for a larger geographical coverage. The proposed method firstly employs 2D image mosaicking techniques to estimate 3D overlapping areas among multiple point clouds, then applies 3D point cloud registration techniques to estimate the most accurate transformation matrix for 3D stitching. The proposed method is quantitatively evaluated on city-scale reconstructed point cloud dataset and real-world city LiDAR dataset, in which, our method outperforms other competing methods with significant margins and achieved the highest precision score, recall score, and F-score. Our method makes an important step towards automatic and accurate city-scale point cloud data stitching, which could be used in a variety of applications. © 2021, Springer Nature Switzerland AG.

关键词： Linear transformations

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Adam-family methods for nonsmooth optimization with convergence guarantees

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The Journal of Machine Learning research 2024年第1期25卷 2398-2450页

作者： Nachuan Xiao Xiaoyin Hu Xin Liu Kim-Chuan Toh Institute of Operations Research and Analytics National University of Singapore Singapore School of Computer and Computing Science Hangzhou City University Hangzhou China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we present a comprehensive study on the convergence properties of Adam-family methods for nonsmooth optimization, especially in the training of nonsmooth neural networks. We introduce a novel two-timescale framework that adopts a two-timescale updating scheme, and prove its convergence properties under mild assumptions. Our proposed framework encompasses various popular Adam-family methods, providing convergence guarantees for these methods in training nonsmooth neural networks. Furthermore, we develop stochastic subgradient methods that incorporate gradient clipping techniques for training nonsmooth neural networks with heavy-tailed noise. Through our framework, we show that our proposed methods converge even when the evaluation noises are only assumed to be integrable. Extensive numerical experiments demonstrate the high efficiency and robustness of our proposed methods.

关键词： nonsmooth optimization stochastic subgradient methods Adam nonconvex optimization gradient clipping

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Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots via Dynamic Feedback Linearization in Forward and Backward Motion

Trajectory Tracking of Tractor-Trailer Wheeled Mobile Robots...

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International Conference on Control, Instrumentation, and Automation

作者： Mohammad Olyai Khalil Alipour Bahram Tarvirdizadeh Majid Sorouri Mohammad Ghamari Department of Mechatronics Eng. School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Advanced Service Robots (ASR) Laboratory University of Tehran Tehran Iran Department of Electronic Engineering Maynooth University Maynooth Co. Kildare Ireland Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

One type of wheeled mobile robot widely used in public transportation and for carrying high payloads is the tractor-trailer wheeled robots (TTWRs). This study considers a differentially-driven tractor under pure rolling conditions, which is subject to nonholonomic constraints. Controlling a TTWR in both backward and forward motion is challenging due to inherent instability. To address this issue and achieve trajectory tracking control for these systems, this paper employs dynamic feedback linearization (DFL) to overcome the limitations of static feedback linearization (SFL). The system’s response will be examined under various trajectories and initial conditions for both forward and backward motion.

关键词： Trajectory tracking Dynamics Wheels Kinematics Reinforcement learning Feedback linearization Trajectory Mobile robots Public transportation Payloads

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Path Following of a Tractor-Trailer System via Dynamic Extension in Forward and Backward Motion

Path Following of a Tractor-Trailer System via Dynamic Exten...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Mohammad Olyai Khalil Alipour Bahram Tarvirdizadeh Majid Sorouri Mohammad Ghamari Department of Mechatronics Eng. Advanced Service Robots (ASR) Laboratory School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology University of Tehran Tehran Iran Department of Electronic Engineering Maynooth University Maynooth Co. Kildare Ireland Department of Computer and Mechatronics Engineering Science and Research Branch Islamic Azad University Tehran Iran Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Tractor-trailer wheeled robots (TTWRs) have gained significant attention across various industries and control studies due to their complex dynamics and non-minimum phase behavior, which pose substantial control challenges. This study investigates the control of a differentially-driven tractor to follow a specified path under pure rolling conditions in both forward and backward motion. Notably, it has been established that using static feedback linearization (SFL) leads to instability during backward motion. In contrast, this research uses dynamic feedback linearization (DFL), also known as dynamic extension, for controlling the tractor-trailer system in both directions for the first time. Our findings demonstrate that the internal dynamics of the system remain stable in both forward and backward motions, enabling successful adherence to the desired path.

关键词： Industries Mechatronics Service robots Dynamics Wheels Optimal control Reinforcement learning Agricultural machinery Feedback linearization Mobile robots

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Maximizing the Thermal Performance of Microheaters for Non-Volatile Phase Change Photonics: A Comparative Study of Pulse Width Parameter Effects 23

Maximizing the Thermal Performance of Microheaters for Non-V...

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23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic systems, ITherm 2024

作者： Vasquez-Aza, Francis Sun, Hongyi Lian, Chuanyu Huang, Yi-Siou Vitale, Steven A. Takeuchi, Ichiro Hu, Juejun Youngblood, Nathan Ocampo, Carlos A. Rios Pavlidis, Georges University of Connecticut School of Mechanical Aerospace and Manufacturing Engineering StorrsCT06269 United States University of Maryland Department of Materials Science and Engineering College ParkMD20742 United States University of Maryland Institute for Research in Electronics and Applied Physics College ParkMD20742 United States Mit Lincoln Laboratory Advanced Materials and Microsystems Group LexingtonMA02139 United States University of Maryland Quantum Materials Center Department of Physics College ParkMD20742 United States Massachusetts Institute of Technology Department of Materials & Science Engineering CambridgeMA02139 United States The University of Pittsburgh Electrical & Computer Engineering Department PittsburghPA15213 United States

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364330

Guiding, manipulating, and detecting light is integral to modern photonic systems that can be designed for displays, memory, data transfer, and sensors. The prospect of realizing programmable yet zero-static power photonics has recently gained attention thanks to the rise of optical phase change materials (PCMs). PCMs have a unique set of properties that make them desirable for this task: nonvolatile retention and reversible switching between their amorphous and crystalline states, which, in addition, display distinct optical and electronic properties. Nonvolatile photonic devices have primarily relied on either electrical or optical switching of PCMs. electrical switching offers effortless CMOS integration;however, it is predominantly limited to binary output. In contrast, optical switching enables accurate multilevel control but is limited in scalability. This paper studies an alternative electro-thermal approach to nonvolatile photonics by controlling the PCM phase via doped silicon microheaters. The results highlight the significance of precise spatial and temporal temperature control to ensure optimal modulation of the PCM. Specifically, this paper investigates the impact of the electric pulse width (0.2 μs to 10 μs) on the maximum temperature for two different microheater designs. High resolution (≈410 nm) transient thermoreflectance imaging (TTI) is used to measure and analyze the surface temperature distribution across each microheater under pulsed biasing. To assess the impact of varying pulse widths, this study systematically examines their effects on maximum temperature, response time, and maximum power. Shorter pulse widths ( 1 μs) provide greater thermal stability but exhibit reduced maximum temperatures due to limited current levels. The experimental results demonstrate a nonlinear relationship between the peak temperature and the power supplied at sub-steady state thermalization time scales. For the specific microheater geometry, the optimal pulse wid

关键词： Photonic integration technology

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Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs

Cross-Modality Time-Variant Relation Learning for Generating...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jingyi Wang Jinfa Huang Can Zhang Zhidong Deng Department of Computer Science Tsinghua University Beijing China School of Electronic and Computer Engineering Peking University China Department of Computer Science Beijing National Research Center for Information Science and Technology (BNRist) THUAI State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomous navigation, and task planning of self-driving vehicles and mobile robots. In the process of temporal and spatial modeling during dynamic scene graph generation, it is particularly intractable to learn time-variant relations in dynamic scene graphs among frames. In this paper, we propose a Time-variant Relation-aware TRansformer (TR 2 ), which aims to model the temporal change of relations in dynamic scene graphs. Explicitly, we leverage the difference of text embeddings of prompted sentences about relation labels as the supervision signal for relations. In this way, cross-modality feature guidance is realized for the learning of time-variant relations. Implicitly, we design a relation feature fusion module with a transformer and an additional message token that describes the difference between adjacent frames. Extensive experiments on the Action Genome dataset prove that our TR2 can effectively model the time-variant relations. TR2 significantly outperforms previous state-of-the-art methods under two different settings by 2.1 % and 2.6% respectively.

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SSRN

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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience

arXiv

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arXiv 2025年

作者： Yin, Haoran Tang, Yanqun Bemani, Ali Kountouris, Marios Zhou, Yu Zhang, Xingyao Liu, Yuqing Chen, Gaojie Yang, Kai Liu, Fan Masouros, Christos Li, Shuangyang Caire, Giuseppe Xiao, Pei School of Electronics and Communication Engineering Sun Yat-sen University China Mathematical and Algorithmic Sciences Laboratory Huawei France Research and Development France Communication Systems Department EU-RECOM France Department of Computer Science and Artificial Intelligence University of Granada Spain Sun Yat-sen University China School of Information and Electronics Beijing Institute of Technology China National Mobile Communications Research Laboratory Southeast University Bangladesh Department of Electrical and Electronic Engineering University College London United Kingdom Department of Electrical Engineering and Computer Science Technical University of Berlin Germany 5G/6G Innovation Centre University of Surrey United Kingdom

Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development. Copyright © 2025, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Constrained Probabilistic Pareto Dominance for Expensive Constrained Multiobjective Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, Zhiyao Wang, Yong Sun, Guangyong Pang, Tong Tang, Ke School of Automation Central South University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering and the Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

This paper proposes a new parameterless constraint-handling technique, named constrained probabilistic Pareto dominance (CPPD), for expensive constrained multiobjective optimization problems (CMOPs). In CPPD, when comparing two solutions, in terms of each original objective, we design a new objective for each solution, which is the negative product of two probabilities calculated based on the predicted fitness mean values and the uncertainty information provided by Kriging models: 1) the probability that this solution satisfies all constraints, denoted as PoF, and 2) the probability that this solution is better than the other on the original objective, denoted as PoB. It is evident that for each solution, PoF and PoB indicate its feasibility and its optimality on the corresponding original objective, respectively. Then, Pareto dominance based on new objectives is executed. As a result, both competitive feasible solutions and promising infeasible solutions with good diversity can be preserved by CPPD. These two kinds of solutions can help the population to exploit the located feasible parts and to explore new feasible parts, respectively. Further, based on CPPD, we develop a Pareto-based Kriging-assisted constrained multiobjective evolutionary algorithm (called PEA) to deal with expensive CMOPs with two or three objectives. Finally, PEA is generalized to solve expensive constrained many-objective optimization problems, named PEA+. The effectiveness of CPPD, PEA, and PEA+ is verified by comprehensive experiments. IEEE

关键词： Evolutionary algorithms

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