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arXiv 2024年

作者： Overbeek, Jamie C. Partin, Alexander Brettin, Thomas S. Chia, Nicholas Narykov, Oleksandr Vasanthakumari, Priyanka Wilke, Andreas Zhu, Yitan Clyde, Austin Jones, Sara Gnanaolivu, Rohan Liu, Yuanhang Jiang, Jun Wang, Chen Knutson, Carter McNaughton, Andrew Kumar, Neeraj Fernando, Gayara Demini Ghosh, Souparno Sanchez-Villalobos, Cesar Zhang, Ruibo Pal, Ranadip Ryan Weil, M. Stevens, Rick L. Data Science and Learning Division Argonne National Laboratory LemontIL United States Computing Environment and Life Sciences Argonne National Laboratory LemontIL United States Department of Computer Science The University of Chicago ChicagoIL United States Frederick National Laboratory for Cancer Research FrederickMD United States Department of Quantitative Health Sciences Mayo Clinic RochesterMN United States Pacific Northwest National Laboratory RichlandWA United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States

Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. Issues like underspecified software environments, lack of workflow documentation, and omission of data preprocessing scripts create challenges for users attempting to reproduce or build upon these models. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP a

关键词： Prediction models

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Distributed identification of power system network branch events

Distributed identification of power system network branch ev...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Madurasinghe, Dulip Arunagirinathan, Paranietharan Venayagamoorthy, Ganesh K. Clemson University Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering ClemsonSC29634 United States School of Engineering University of Kwazulu-Natal Durban4041 South Africa

ISBN: (纸本)9781728155081

Online identification of power system network branches is critical in modern electric power system operation. Availability of phasor measurement units (PMUs) can be used to identify branch events. Due to complexity of the power system, a distributed cellular computational network (CCN) is proposed. Comparison of centralized and distributed neural network based power system network branch events identification is studied. IEEE 12-bus benchmark power system is simulated on real-time digital simulator platform for this study. CCN based distributed neural network approach is computationally efficient compared to centralized approach. © 2020 IEEE.

关键词： Phasor measurement units

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Joint User Scheduling and computing Resource Allocation Optimization in Asynchronous Mobile Edge computing Networks

arXiv

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arXiv 2024年

作者： Cang, Yihan Chen, Ming Pan, Yijin Yang, Zhaohui Hu, Ye Sun, Haijian Chen, Mingzhe The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211100 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The School of Electrical and Computer Engineering The University of Georgia AthensGA30602 United States The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy transfer

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Visible Light Positioning Based on a Single Luminaire: A Novel Visual Odometry Assisted Algorithm

Visible Light Positioning Based on a Single Luminaire: A Nov...

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IEEE International Conference on Communications (ICC)

作者： Zhiyu Zhu Yang Yang Mingzhe Chen Caili Guo Yipeng Bai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Visible light positioning (VLP) is a promising positioning technique, which, however, typically requires multiple luminaires to achieve accurate positioning. This paper proposes a novel visual odometry (VO) assisted visible light positioning algorithm (VO-VLP) in achieving positioning with only a single luminaire. In the considered model, a user equipped with a camera jointly uses geometric features in the captured images and coordinates information obtained via visible light communication (VLC) for positioning. The proposed VLP algorithm does not rely on any extra inertial measurement unit and relaxes the tilted angle limitation at the user. In particular, VO-VLP first uses the circle feature of a luminaire to obtain dual normal vectors of the luminaire. Then, the basic principle of VO is used to eliminate the wrong normal vector by exploiting the geometric features in two consecutive images captured when the user moves. Finally, the pose and location of the user are obtained by using an artificially marked point on the luminaire's contour. VO-VLP can achieve accurate positioning with only a single luminaire and a camera. Simulation results show that the proposed indoor positioning algorithm can achieve a 97th-percentile positioning accuracy of around 10 cm.

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Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liang, Feng Zhang, Zhen Lu, Haifeng Leung, Victor C.M. Guo, Yanyi Hu, Xiping Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518000 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen518107 China Gansu Provincial Key Laboratory of Wearable Computing School of information Science and Engineering Lanzhou University Gansu730000 China The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Frontier Cross Disciplinary Research Institute Shenzhen MSU-BIT University Shenzhen518000 China The School of Mechanical and Electrical Engineering Beijing Institute of Technology Beijing10081 China

With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In contrast to traditional distributed deep learning, the large-scale scenario poses new challenges that include fault tolerance, scalability of algorithms and infrastructures, and heterogeneity in data sets, models, and resources. Due to intensive synchronization of models and sharing of data across GPUs and computing nodes during distributed training and inference processes, communication efficiency becomes the bottleneck for achieving high performance at a large scale. This article surveys the literature over the period of 2018-2023 on algorithms and technologies aimed at achieving efficient communication in large-scale distributed DL at various levels, including algorithms, frameworks, and infrastructures. Specifically, we first introduce efficient algorithms for model synchronization and communication data compression in the context of large-scale distributed training. Next, we introduce efficient strategies related to resource allocation and task scheduling for use in distributed training and inference. After that, we present the latest technologies pertaining to modern communication infrastructures used in distributed deep learning, including GPU interconnects, programmable network devices, collective communication protocols, and communication topologies. We focus our discussion of these topics on examining the impact of the communication overhead in a large-scale and heterogeneous setting. Finally, we conduct a case study on the distributed training of large language models at a large scale to illustrate how to apply these technologies in real cases. This article aims to offer researchers a comprehensive understanding of the current landscape of large-scale distributed deep learning and to reveal promising future research directions toward communication-efficient solutions in this scope.

关键词： Scalability

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Joint Optimization of Transmit Power and Trajectory for UAV-Enabled Data Collection With Dynamic Constraints

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IEEE Transactions on Communications 2025年

作者： Zhang, Hongyun Li, Bin Rong, Yue Zeng, Yong Zhang, Rui School of Aeronautics and Astronautics Sichuan University Sichuan Chengdu610065 China The School of Electrical Engineering Computing and Mathematical Sciences Curtin University GPO Box U1987 PerthWA6845 Australia National Mobile Communications Research Laboratory Southeast University Jiangsu Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hon g Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

The unmanned aerial vehicle (UAV)-enabled data collection system with a rotary-wing UAV and multiple ground nodes (GNs) is investigated in this paper. The average transmission data rate is maximized through the coordinated optimization of the GNs’ transmit power and the UAV’s trajectory. In particular, the UAV dynamic constraints and physical constraints are imposed. The UAV dynamics, which are governed by a group of differential equations, are usually ignored in existing works. As a consequence, the planned trajectory cannot be fully tracked by the controller in real world applications, which could lead to severe performance degradation. Thus, a control-based method is devised to address this issue. Specifically, by adopting the state-space model from control theory, the data collection problem is established as a dynamic optimization problem subject to state constraints, in which both of the decision variables and constraints are infinite-dimensional in nature. The key idea of the solution method is to convert the infinite-dimensional dynamic program into a finite-dimensional static nonlinear problem. This is achieved by deriving the required gradients of the dynamic optimization problem based on the control parametrization scheme and an exact penalty function method. The effectiveness and superiority of the proposed design are validated via numerical experiments. © 1972-2012 IEEE.

关键词： State space methods

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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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Quantum advantage for differential equation analysis

引用

Physical Review A 2022年第2期105卷 022415-022415页

作者： Bobak Toussi Kiani Giacomo De Palma Dirk Englund William Kaminsky Milad Marvian Seth Lloyd Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Giacomo De Palma Scuola Normale Superiore 56126 Pisa Italy QuEra Computing Boston Massachusetts 02143 USA Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical and Computer Engineering Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

Quantum algorithms for differential equation solving, data processing, and machine learning potentially offer an exponential speedup over all known classical algorithms. However, there also exist obstacles to obtaining this potential speedup in useful problem instances. The essential obstacle for quantum differential equation solving is that outputting useful information may require difficult postprocessing, and the essential obstacle for quantum data processing and machine learning is that inputting the data is a difficult task just by itself. In this study, we demonstrate that, when combined, these difficulties solve one another. We show how the output of quantum differential equation solving can serve as the input for quantum data processing and machine learning, allowing dynamical analysis in terms of principal components, power spectra, and wavelet decompositions. To illustrate this, we consider continuous-time Markov processes on epidemiological and social networks. These quantum algorithms provide an exponential advantage over existing classical Monte Carlo methods.

关键词： Machine learning Quantum algorithms Quantum computation Markovian processes Numerical techniques

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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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