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arXiv 2024年

作者： Jha, Bhargav Bopardikar, Shaunak D. Von Moll, Alexander Casbeer, David Michigan State University East LansingMI48864 United States Air Force Research Laboratory WPAFB OH45433 United States Department of Electrical and Computer Engineering Michigan State University United States Control Science Center 2210 8th St.

This paper presents joint motion planning of a vehicle with an attachedrotating turret. The turret has a limited range as well as the field of *** objective is capture a maneuvering target such that at the terminal time itis withing the field-of-view and range limits. Catering to it, we present aminimum effort guidance law that commensurate for the turn rate abilities ofthe vehicle and the turret. The guidance law is obtained using linearizationabout the collision triangle and admits an analytical solution. Simulationresults are presented to exemplify the cooperation between the turret and thevehicle. © 2024, CC BY.

关键词： Motion planning

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Prediction of severe preeclampsia in machine learning

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Medicine in Novel Technology and Devices 2022年第3期15卷 148-153页

作者： Xinyuan Zhang Yu Chen Stephen Salerno Yi Li Libin Zhou Xiaoxi Zeng Huafeng Li Department of Applied Mechanics College of Architecture and EnvironmentSichuan Province Biomechanical Engineering LaboratorySichuan UniversityChengdu610065SichuanChina Department of Biostatistics University of MichiganAnn ArborMIUSA Medical Big Data Center Sichuan UniversityChengdu610065SichuanChina Department of Computer Science University of WisconsinMadisonWIUSA West China Second University Hospital Sichuan UniversityChengdu610041China Department of Applied Mechanics Sichuan UniversityChengdu610065SichuanChina

This study aimed to find out the blood data characteristics of patients and explore the correlation between severe preeclampsia and blood index *** assistance for the early attention direction of severe preeclampsia diagnosis and treatment.19,653 pregnant women presenting to the West China Second University Hospital,Sichuan University from January 2017 to April *** screening,a total of 248 patients,124 severe preeclampsia cases,and 124 controls were selected for this ***-three blood examination variables were obtained from routine blood work,hepatic,renal and coagulation function *** gradient boosting machine(light GBM),decision tree and random forest were used for date *** randomly divided 35%of the original data as a testing set to conduct internal validation of the performance of the prediction *** area under receiver operating characteristic curve(AUC)was used as the main score to compare the three ***,a binary classification light GBM model based on aspartate aminotransferase,direct bilirubin and activated partial thromboplastin time ratio can predict severe preeclampsia with sensitivity of 88.37%,specificity of 77.27%,AUC of 89.74%and positive predictive value of 65.96%.We believe relevant quantifiable indicators can establish an effective prediction model,which can provide guidance for early detection and prevention of severe preeclampsia.

关键词： Preeclampsia Screening Prediction Blood examination Data characteristics

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Federated Multi-armed Bandits Under Byzantine Attacks

arXiv

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arXiv 2022年

作者： Saday, Artun Demirel, Ilker Yıldırım, Yigit Tekin, Cem Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Multi-armed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward. Federated multi-armed bandits (FMAB) is an emerging framework where a cohort of learners with heterogeneous local models play a MAB game and communicate their aggregated feedback to a server to learn a globally optimal arm. Two key hurdles in FMAB are communication-efficient learning and resilience to adversarial attacks. To address these issues, we study the FMAB problem in the presence of Byzantine clients who can send false model updates threatening the learning process. We analyze the sample complexity and the regret of β-optimal arm identification. We borrow tools from robust statistics and propose a median-of-means (MoM)-based online algorithm, Fed-MoM-UCB, to cope with Byzantine clients. In particular, we show that if the Byzantine clients constitute less than half of the cohort, the cumulative regret with respect to β-optimal arms is bounded over time with high probability, showcasing both communication efficiency and Byzantine resilience. We analyze the interplay between the algorithm parameters, a discernibility margin, regret, communication cost, and the arms’ suboptimality gaps. We demonstrate Fed-MoM-UCB’s effectiveness against the baselines in the presence of Byzantine attacks via experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Federated learning

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Max-Min Beamformer for THz Wideband Communications

Max-Min Beamformer for THz Wideband Communications

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IEEE International Conference on Communications Workshops, ICC

作者： Boyu Ning Weidong Mei Lipeng Zhu Zhi Chen Rui Zhang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Electrical and Computer Engineering National University of Singapore The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters. However, the conventional analog beamforming based on array response vector to steer a narrow beam may suffer from severe performance loss in wideband systems due to the “beam squint” effect over frequency. To tackle this issue, we propose a new max-min beamformer design in this paper for THz wideband communication systems, which aims to maximize the minimum beam gain over the entire frequency band. To solve this challenging problem, an augmented Lagrange method (ALM) is proposed to obtain a high-quality solution with low complexity. Numerical results show that the proposed beam design can yield considerably better performance than the conventional narrowband beamformer, especially in the case of large angle of departure (AoD) from the base station (BS) to the user.

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Sequential Bayesian Neural Subnetwork Ensembles

arXiv

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arXiv 2022年

作者： Jantre, Sanket Bhattacharya, Shrijita Urban, Nathan M. Yoon, Byung-Jun Maiti, Tapabrata Balaprakash, Prasanna Madireddy, Sandeep Computational Science Initiative Brookhaven National Laboratory Department of Statistics and Probability Michigan State University United States Department of Electrical and Computer Engineering Texas A&M University United States Computing and Computational Sciences Directorate Oak Ridge National Laboratory United States Mathematics and Computer Science Division Argonne National Laboratory United States

Deep ensembles have emerged as a powerful technique for improving predictive performance and enhancing model robustness across various applications by leveraging model diversity. However, traditional deep ensemble methods are often computationally expensive and rely on deterministic models, which may limit their flexibility. Additionally, while sparse subnetworks of dense models have shown promise in matching the performance of their dense counterparts and even enhancing robustness, existing methods for inducing sparsity typically incur training costs comparable to those of training a single dense model, as they either gradually prune the network during training or apply thresholding post-training. In light of these challenges, we propose an approach for sequential ensembling of dynamic Bayesian neural subnetworks that consistently maintains reduced model complexity throughout the training process while generating diverse ensembles in a single forward pass. Our approach involves an initial exploration phase to identify high-performing regions within the parameter space, followed by multiple exploitation phases that take advantage of the compactness of the sparse model. These exploitation phases quickly converge to different minima in the energy landscape, corresponding to high-performing subnetworks that together form a diverse and robust ensemble. We empirically demonstrate that our proposed approach outperforms traditional dense and sparse deterministic and Bayesian ensemble models in terms of prediction accuracy, uncertainty estimation, out-of-distribution detection, and adversarial robustness. © 2022, CC BY.

关键词： Prediction models

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An Explainable Convolutional Neural Networks for Automatic Segmentation of the Left Ventricle in Cardiac MRI 11

An Explainable Convolutional Neural Networks for Automatic S...

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11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Liu, Jun Deng, Feng Yuan, Geng Lin, Xue Song, Houbing Wang, Yanzhi Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science and Technology University Beijing China Department of Electrical and Computer Engineering College of Engineering Northeastern University BostonMA United States Embry-Riddle Aeronautical University Daytona BeachFL United States

ISBN: (纸本)9781643682402

Recently, the study on model interpretability has become a hot topic in deep learning research area. Especially in the field of medical imaging, the requirements for safety are extremely high;Moreover, it is very important for the model to be able to explain. However, the existing solutions for left ventricular segmentation by convolutional neural networks are black boxes;explainable CNNs remains a challenge;explainable deep learning models has always been a task often overlooked in the entire data science lifecycle by data scientists or deep learning engineers. Because of very limited medical imaging data, most solutions currently use transfer learning methods to transfer the model which used on large-scale benchmark data sets (such as ImageNet) to fine tune medical imaging models. Consequently, a large amount of useless parameters are generated, resulting in further barrier for the model to provide a convincing explanation. This paper presents a novel method to automatically segment the Left Ventricle in Cardiac MRI by explainable convolutional neural networks with optimized size and parameters by our enhanced Deep Learning GPU Training System. It is very suitable for deployment on mobile devices. We simplify deep learning tasks on DIGITS systems, monitoring performance, and displaying the heat map of each layer of the network with advanced visualizations in real time. Our experiment results demonstrated that the proposed method is feasible and efficient. © 2022 The authors and IOS Press.

关键词： Image segmentation

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Comments on "Transient Magnetic Shielding of a Planar Conductive Thin Screen via Exact Image Theory"

TechRxiv

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TechRxiv 2023年

作者： Štumpf, Martin The Lerch Laboratory of EM Research Department of Radio Electronics FEEC Brno University of Technology Technická 3082/12 Brno616 00 Czech Republic The Department of Computer Science Electrical and Space Engineering EISLAB Luleå University of Technology Luleå971 87 Sweden

关键词： Transient analysis

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Deep Neural Network Enhanced Chip-on-Board Hybrid-Packaged Photonic-electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-Board Hybrid-Packaged P...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jun Qin Qipeng Yang Changhao Han Yan Zhou Yunhao Zhang Yichen WU Yu Sun Junde Lu Siming Liu Yueqin Li Jian Sun Miao Min Weiwei Hu Zhixue He Lei Wang Shaohua Yu Haowen Shu Xingjun Wang Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Peking University Yangtze Delta Institute of Optoelectronics Nantong China Peng Cheng Laboratory Shenzhen China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

ISBN: (纸本)9798350369311

关键词： Electric potential Optical interconnections System performance Artificial neural networks Transceivers Silicon Electro-optical waveguides Photonics

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Learning Certifiably Robust Controllers Using Fragile Perception

arXiv

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arXiv 2022年

作者： Sun, Dawei Musavi, Negin Dullerud, Geir Shakkottai, Sanjay Mitra, Sayan Coordinated Science Laboratory University of Illinois UrbanaIL61801 United States The Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States

Advances in computer vision and machine learning enable robots to perceive their surroundings in powerful new ways, but these perception modules have well-known fragilities. We consider the problem of synthesizing a safe controller that is robust despite perception errors. The proposed method constructs a state estimator based on Gaussian processes with input-dependent noises. This estimator computes a high-confidence set for the actual state given a perceived state. Then, a robust neural network controller is synthesized that can provably handle the state uncertainty. Furthermore, an adaptive sampling algorithm is proposed to jointly improve the estimator and controller. Simulation experiments, including a realistic vision-based lane keeping example in CARLA, illustrate the promise of the proposed approach in synthesizing robust controllers with deep-learning-based perception. © 2022, CC BY.

关键词： Controllers

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Spectral evidence for local-moment ferromagnetism in the van der Waals metals Fe3GaTe2 and Fe3GeTe2

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Physical Review B 2024年第10期109卷 104410-104410页

作者： Han Wu Chaowei Hu Yaofeng Xie Bo Gyu Jang Jianwei Huang Yucheng Guo Shan Wu Cheng Hu Ziqin Yue Yue Shi Rourav Basak Zheng Ren T. Yilmaz Elio Vescovo Chris Jozwiak Aaron Bostwick Eli Rotenberg Alexei Fedorov Jonathan D. Denlinger Christoph Klewe Padraic Shafer Donghui Lu Makoto Hashimoto Junichiro Kono Alex Frano Robert J. Birgeneau Xiaodong Xu Jian-Xin Zhu Pengcheng Dai Jiun-Haw Chu Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Department of Physics University of Washington Seattle Washington 98195 USA Department of Materials Science and Engineering University of Washington Seattle Washington 98195 USA Theoretical Division and Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico USA Department of Advanced Materials Engineering for Information and Electronics Kyung Hee University Yongin 17104 Republic of Korea Department of Physics University of California at Berkeley Berkeley California 94720 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics University of California San Diego La Jolla California 92093USA National Synchrotron Light Source II Brookhaven National Lab Upton New York 11973 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Department of Material Science and NanoEngineering Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Materials Science and Engineering University of California Berkeley CaliforniaUSA

Magnetism in two-dimensional (2D) materials has attracted considerable attention recently for both fundamental understanding of magnetism and its tunability towards device applications. The isostructural Fe3GeTe2 and Fe3GaTe2 are two members of the Fe-based van der Waals (vdW) ferromagnet family, but exhibit very different Curie temperatures (TC) of 210 and 360 K, respectively. Here, by using angle-resolved photoemission spectroscopy and density functional theory, we systematically compare the electronic structures of the two compounds. Qualitative similarities in the Fermi surface can be found between the two compounds, with expanded hole pockets in Fe3GaTe2 suggesting additional hole carriers compared to Fe3GeTe2. Interestingly, we observe almost no band shift in Fe3GaTe2 across its TC of 360 K, compared to a small shift in Fe3GeTe2 across its TC of 210 K. The weak temperature-dependent evolution strongly deviates from the expectations of an itinerant Stoner mechanism. Our results suggest that itinerant electrons have minimal contributions to the enhancement of TC in Fe3GaTe2 compared to Fe3GeTe2, and that the nature of ferromagnetism in these Fe-based vdW ferromagnets must be understood with considerations of the electron correlations.

关键词： Magnetism Van der Waals systems Angle-resolved photoemission spectroscopy

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