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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第8期70卷 5910-5934页

作者： Zhang, Ning Wang, Ziao Wang, Weina Wang, Lele Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Univ Oxford Dept Stat Oxford OX1 3LB England Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA

Motivated by various data science applications including de-anonymizing user identities in social networks, we consider the graph alignment problem, where the goal is to identify the vertex/user correspondence between two correlated graphs. Existing work mostly recovers the correspondence by exploiting the user-user connections. However, in many real-world applications, additional information about the users, such as user profiles, might be publicly available. In this paper, we introduce the attributed graph alignment problem, where additional user information, referred to as attributes, is incorporated to assist graph alignment. We establish both the achievability and converse results on recovering vertex correspondence exactly, where the conditions match for certain parameter regimes. Our results span the full spectrum between models that only consider user-user connections and models where only attribute information is available.

关键词： Social networking (online) Information theory Topology Computational modeling Web sites US Government Source coding Graph theory statistics inference algorithms

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Joint Batching and Scheduling for High-Throughput Multiuser Edge AI With Asynchronous Task Arrivals

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 13782-13795页

作者： Cang, Yihan Chen, Ming Huang, Kaibin Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211111 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China

Edge artificial intelligence (AI) in the sixth-generation networks will provide inference services at the network edge to enrich the capabilities of mobile devices and lengthen their battery lives. As a well-known technique in computing, batching can boost the computation throughput at an edge server by assembling multiple tasks into a batch that is fed into a pre-trained prediction model. This reduces the memory-access frequency and hence accelerates the execution of each task. In this paper, we study joint batching and (task) scheduling to maximise the throughput (i.e., the number of completed tasks) under the practical assumptions of heterogeneous task arrivals and deadlines. The design aims to optimise the number of batches, their starting time instants, and the task-batch association that determines batch sizes. The joint optimisation problem is complex due to multiple coupled variables as mentioned and numerous constraints including heterogeneous tasks arrivals and deadlines, the causality requirements on multi-task execution, and limited radio resources. Our approach of solving the formulated mixed-integer problem is to transform it into a convex problem via integer relaxation method and l(0)-norm approximation. This results in an efficient alternating optimization algorithm for finding a close-to-optimal solution. Specifically, it iterates between solving two sub-problems, optimal task-batch association and optimal batch starting time. The former is a linear program whose solution can be found using a derived scheme of greedy task selection while that of the latter is derived in closed form. In addition, we also design the optimal algorithm from leveraging spectrum holes, which are caused by fixed bandwidth allocation to devices and their asynchronized multi-batch task execution, to admit unscheduled tasks so as to further enhance throughput. Simulation results demonstrate that the proposed framework of joint batching and resource allocation can substantiall

关键词： Task analysis Throughput Resource management Servers Computational modeling inference algorithms Wireless communication Edge AI edge inference batching scheduling radio resource allocation

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Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 9482-9497页

作者： Lee, Daegyu Seong, Hyunki Kang, Gyuree Han, Seungil Shim, D. Hyunchul Yoon, Yoonjin Korea Adv Inst Sci & Technol KAIST Sch Elect Engn Daejeon 34141 South Korea Autonomous Driving Strateg Technol Dev Team Hyundai Motor Co Seoul 06797 South Korea Korea Adv Inst Sci & Technol Civil & Environm Engn Daejeon 34141 South Korea

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment. Our fully autonomous software architecture, proven successful in winning urban autonomous multi-vehicle races in South Korea, is ready for deployment on urban robot taxis. Our traffic analysis addresses multi-agent scenarios and resolves competitive conflicts among robot taxi companies, crucial for smart city integration and optimizing autonomous vehicle performance in complex urban settings.

关键词： Autonomous vehicles Planning Location awareness Laser radar inference algorithms Urban areas Roads traffic information

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AMARL: An Attention-Based Multiagent Reinforcement Learning Approach to the Min-Max Multiple Traveling Salesmen Problem

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 9758-9772页

作者： Gao, Hao Zhou, Xing Xu, Xin Lan, Yixing Xiao, Yongqian Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

In recent years, the multiple traveling salesmen problem (MTSP or multiple TSP) has received increasing research interest and one of its main applications is coordinated multirobot mission planning, such as cooperative search and rescue tasks. However, it is still challenging to solve MTSP with improved inference efficiency as well as solution quality in varying situations, e.g., different city positions, different numbers of cities, or agents. In this article, we propose an attention-based multiagent reinforcement learning (AMARL) approach, which is based on the gated transformer feature representations for min-max multiple TSPs. The state feature extraction network in our proposed approach adopts the gated transformer architecture with reordering layer normalization (LN) and a new gate mechanism. It aggregates fixed-dimensional attention-based state features irrespective of the number of agents and cities. The action space of our proposed approach is designed to decouple the interaction of agents' simultaneous decision-making. At each time step, only one agent is assigned to a non-zero action so that the action selection strategy can be transferred across tasks with different numbers of agents and cities. Extensive experiments on min-max multiple TSPs were conducted to illustrate the effectiveness and advantages of the proposed approach. Compared with six representative algorithms, our proposed approach achieves state-of-the-art performance in solution quality and inference efficiency. In particular, the proposed approach is suitable for tasks with different numbers of agents or cities without extra learning, and experimental results demonstrate that the proposed approach realizes powerful transfer capability across tasks.

关键词： Urban areas Logic gates Feature extraction Transformers Task analysis inference algorithms Costs Attention mechanism multiagent reinforcement learning (MARL) the multiple traveling salesmen problem (MTSP) transfer capability

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On-Street Parking Occupancy inference Based on Payment Transactions

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 10680-10691页

作者： Assemi, Behrang Paz, Alexander Baker, Douglas Queensland Univ Technol QUT Gardens Point Sch Architecture & Built Environm Brisbane Qld 4000 Australia Queensland Univ Technol QUT Gardens Point Sch Civil & Environm Engn Brisbane Qld 4000 Australia

A record of spatial and temporal parking occupancy is critical to optimize on-street parking resources and to develop effective parking policies. Such data are often obtained through advanced and costly occupancy monitoring technologies. Moreover, it is usually challenging to integrate bay-level occupancies and parking data from other systems. Accurate occupancy-payment data are required for a wide range of analytical and practical purposes, including but not limited to investigating payment behavior, estimating and forecasting occupancy, and evaluating the efficiency and effectiveness of enforcement policies. This study proposes a metaheuristic optimization algorithm to integrate snapshots of bay-level parking occupancy, captured using simple cameras, with transactions from a conventional parking payment management system. The resulting integrated data were used to develop, calibrate and validate a parking occupancy estimation method utilizing parking payment data only. Details of the design, implementation, and validation of the proposed algorithm and modelling technique are provided. Logistic regression analysis was used to tune parameters of the data integration algorithm. Deep learning, gradient boosting and random forests were used to develop a model of parking occupancy. Evaluation of the algorithm indicated an accuracy of 76% of correct data integration;that is, individual bay occupancies integrated with the correct corresponding payment transactions. The best occupancy estimation model also showed a very high accuracy, with an R-2 above 94% and a root mean square error (RMSE) of 1.2 (occupied bays), when tested with a random sample from the integrated data.

关键词： Monitoring Estimation Data models Cameras Reliability Software algorithms Prediction algorithms Learning (artificial intelligence) prediction algorithms software algorithms inference algorithms heuristic algorithms

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Learned Factor Graphs for inference From Stationary Time Sequences

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 366-380页

作者： Shlezinger, Nir Farsad, Nariman Eldar, Yonina C. Goldsmith, Andrea J. Ben Gurion Univ Negev Sch ECE IL-8400711 Beer Sheva Israel Ryerson Univ Dept CS Toronto ON M5B 2K3 Canada Weizmann Inst Sci Fac Math & CS IL-7610001 Rehovot Israel Princeton Univ Dept EE Princeton NJ 08544 USA

The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algorithms have been derived to carry out inference at controllable complexity using recursive computations over the factor graph representing the underlying distribution. An alternative model-agnostic approach utilizes machine learning (ML) methods. Here we propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. In the proposed approach, neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence, rather than the complete inference task. By exploiting stationary properties of this distribution, the resulting approach can be applied to sequences of varying temporal duration. Learned factor graphs can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data, and can be applied to sequences of different lengths. Our experimental results demonstrate the ability of the proposed learned factor graphs to learn from small training sets to carry out accurate inference for sleep stage detection using the Sleep-EDF dataset, as well as for symbol detection in digital communications with unknown channels.

关键词： Signal processing algorithms inference algorithms Hidden Markov models Training Computational modeling Time series analysis Task analysis Factor graphs deep learning time sequences

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SV-SAE: Layer-Wise Pruning for Autoencoder Based on Link Contributions

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IEEE ACCESS 2025年 13卷 75666-75678页

作者： Rheey, Joohong Park, Hyunggon Ewha Womans Univ Dept Elect & Elect Engn Seoul 03760 South Korea

Autoencoders are a type of deep neural network and are widely used for unsupervised learning, particularly in tasks that require feature extraction and dimensionality reduction. While most research focuses on compressing input data, less attention has been given to reducing the size and complexity of the autoencoder model itself, which is crucial for deployment on resource-constrained edge devices. This paper introduces a layer-wise pruning algorithm specifically for multilayer perceptron-based autoencoders. The resulting pruned model is referred to as a Shapley Value-based Sparse AutoEncoder (SV-SAE). Using cooperative game theory, the proposed algorithm models the autoencoder as a coalition of interconnected units and links, where the Shapley value quantifies their individual contributions to overall performance. This enables the selective removal of less important components, achieving an optimal balance between sparsity and accuracy. Experimental results confirm that the SV-SAE reaches an accuracy of 99.25%, utilizing only 10% of the original links. Notably, the SV-SAE remains robust under high sparsity levels with minimal performance degradation, whereas other algorithms experience sharp declines as the pruning ratio increases. Designed for edge environments, the SV-SAE offers an interpretable framework for controlling layer-wise sparsity while preserving essential features in latent representations. The results highlight its potential for efficient deployment in resource-constrained scenarios, where model size and inference speed are critical factors.

关键词： Autoencoders Computational modeling Accuracy Neurons Image coding Deep learning inference algorithms Feature extraction Dimensionality reduction Convolutional neural networks Edge computing feature importance latent representation layer-wise pruning lightweight model Shapley value sparse autoencoder unstructured pruning

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Generalized Bilinear Factorization via Hybrid Vector Message Passing

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5675-5690页

作者： Jiang, Hao Yuan, Xiaojun Guo, Qinghua Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

Generalized bilinear factorization (GBF), in which two matrices are recovered from noisy and typically compressed measurements of their product, arises in various applications such as blind channel-and-signal estimation, image completion, and compressed video foreground and background separation. In this paper, we formulate the GBF problem by unifying several existing bilinear inverse problems, and establish a novel hybrid vector message passing (HVMP) algorithm for GBF. The GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, and exchanges matrix-variable messages in closed form. GBF-HVMP is advantageous over its counterparts in several aspects. For example, a matrix-variable message can characterize the correlations between the elements of the matrix, which is not possible in scalar-variable message passing;the hybrid of EP and VMP yields closed-form Gaussian messages associated with the bilinear constraints inherent in the GBF problem. We show that damping is unnecessary for GBF-HVMP to ensure convergence. We also show that GBF-HVMP performs close to the replica bound, and significantly outperforms state-of-the-art approaches in terms of both normalized mean squared error (NMSE) performance and computational complexity.

关键词： Message passing Approximation algorithms Signal processing algorithms Sparse matrices Covariance matrices Correlation Vectors Inverse problems inference algorithms Convergence Generalized bilinear factorization message passing variational free energy minimization expectation propagation variational message passing

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inference-Based Posteriori Parameter Distribution Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 3006-3017页

作者： Wang, Xuesong Li, Tianyi Cheng, Yuhu Chen, C. L. Philip China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Spac Minist Educ Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Xuzhou Key Lab Artificial Intelligence & Big Data Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Univ Macau Fac Sci & Technol Taipa 99999 Macau Sar Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Encouraging the agent to explore has always been an important and challenging topic in the field of reinforcement learning (RL). Distributional representation for network parameters or value functions is usually an effective way to improve the exploration ability of the RL agent. However, directly changing the representation form of network parameters from fixed values to function distributions may cause algorithm instability and low learning inefficiency. Therefore, to accelerate and stabilize parameter distribution learning, a novel inference-based posteriori parameter distribution optimization (IPPDO) algorithm is proposed. From the perspective of solving the evidence lower bound of probability, we, respectively, design the objective functions for continuous-action and discrete-action tasks of parameter distribution optimization based on inference. In order to alleviate the overestimation of the value function, we use multiple neural networks to estimate value functions with Retrace, and the smaller estimate participates in the network parameter update;thus, the network parameter distribution can be learned. After that, we design a method used for sampling weight from network parameter distribution by adding an activation function to the standard deviation of parameter distribution, which achieves the adaptive adjustment between fixed values and distribution. Furthermore, this IPPDO is a deep RL (DRL) algorithm based on off-policy, which means that it can effectively improve data efficiency by using off-policy techniques such as experience replay. We compare IPPDO with other prevailing DRL algorithms on the OpenAI Gym and MuJoCo platforms. Experiments on both continuous-action and discrete-action tasks indicate that IPPDO can explore more in the action space, get higher rewards faster, and ensure algorithm stability.

关键词： Task analysis Optimization Training Artificial neural networks Linear programming inference algorithms Markov processes Exploration inference parameter distribution reinforcement learning (RL)

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Optimal Accuracy-Privacy Trade-Off of inference as Service

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 4031-4046页

作者： Jin, Yulu Lai, Lifeng Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario (IAS). Instead of sending data directly to the server, the user will preprocess the data through a privacy-preserving mapping, which will increase privacy protection but reduce inference accuracy. To properly address the trade-off between privacy protection and inference accuracy, we formulate an optimization problem to find the privacy-preserving mapping. Even though the problem is non-convex in general, we characterize nice structures of the problem and develop an iterative algorithm to find the desired privacy-preserving mapping, with convergence analysis provided under certain assumptions. From numerical examples, we observe that the proposed method has better performance than gradient ascent method in the convergence speed, solution quality and algorithm stability.

关键词： Privacy Convergence Optimization Servers inference algorithms Data privacy Signal processing algorithms ADMM inference privacy

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