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Maximizing the Spread of Effective Information in Social Networks

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第4期35卷 4062-4076页

作者： Zhang, Haonan Fu, Luoyi Ding, Jiaxin Tang, Feilong Xiao, Yao Wang, Xinbing Chen, Guihai Zhou, Chenghu Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Comp Sci & Engn Shanghai 200240 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100045 Peoples R China

Influence maximization through social networks has aroused tremendous interests nowadays. However, people's various expressions or feelings about a same idea often cause ambiguity via word of mouth. Consequently, the problem of how to maximize the spread of "effective information" still remains largely open. In this paper, we consider a practical setting where ideas can deviate from their original version to invalid forms during message passing, and make the first attempt to seek a union of users that maximizes the spread of effective influence, which is formulated as an Influence Maximization with Information Variation (IMIV) problem. To this end, we model the information as a vector, and quantify the difference of two arbitrary vectors as a distance by a matching function. We further establish a process where such distance increases with the propagation and ensure the recipient whose vector distance is less than a threshold can be effectively influenced. Due to the NP-hardness of IMIV, we greedily select users that can approximately maximize the estimation of effective propagation. Especially, for networks of small scales, we derive a condition under which all the users can be effectively influenced. Our models and theoretical findings are further consolidated through extensive experiments on real-world datasets.

关键词： Social networking (online) Mouth Smart phones Estimation approximation algorithms Time complexity Statistics Social network influence maximization information variation greedy algorithm

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Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

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IEEE TRANSACTIONS ON ROBOTICS 2023年第2期39卷 1458-1475页

作者： Huang, Qiangqiang Pu, Can Khosoussi, Kasra Rosen, David M. Fourie, Dehann How, Jonathan P. Leonard, John J. MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA MIT Dept Nucl Sci & Engn Cambridge MA 02139 USA CSIRO Robot & Autonomous Syst Grp DATA61 Brisbane Qld 4069 Australia Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Dept Math Boston MA 02115 USA NavAbility Boston MA 02110 USA MIT Dept Aeronaut & Astronaut Engn Cambridge MA 02139 USA

This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challenging non-Gaussian setting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association).

关键词： Simultaneous localization and mapping Inference algorithms approximation algorithms Task analysis Particle separators Belief propagation Random variables Bayes tree distribution estimation non-Gaussian normalizing flows SLAM

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GRLF-LAD Control Based Active Power Filter Operation With QSG-SOGI Algorithm for Grid Voltage Harmonics Disturbance Rejection

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IEEE ACCESS 2023年 11卷 135753-135763页

作者： Aijaz, Masiha Hussain, Ikhlaq Lone, Shameem Ahmad Chankaya, Mukul Natl Inst Technol Srinagar Dept Elect Engn Srinagar 190006 Jammu & Kashmir India Vellore Inst Technol Sch Elect Engn Vellore Campus Vellore 632014 Tamil Nadu India

This article presents application of generalized robust logarithmic family (GRLF) framework derived least absolute difference (LAD) Algorithm implemented for active power filters (APF) for PQ enhancement. The presented GRLF-LAD algorithm successfully eliminates lower as well as higher order errors from the harmonically rich nonlinear load currents and thus results in accurate fundamental component extraction. Weak power grids are characterised by distorted grid voltages and riddled with inherent harmonics. Conventional techniques such as synchronous reference frame (SRF) algorithm are unable to filter out the harmonics from grid voltages and this results in oscillations in D-Q components. This leads to generation of a distorted reference current and thus unsatisfactory APF performance. In this article, the grid voltages are processed by modified second-order generalized integrator with quadrature signal generator (QSG-SOGI) based algorithm. The harmonic rejection capabilty of QSG-SOGI is far superior than other tested algorithms. QSG-SOGI shows remarkable improvement of approximately 99.35% than SRF and 96.02% than SOGI in terms of percentage improvement. The performance of presented APF is verified experimentally on a developed prototype and tested rigorously against load variations, grid voltage variations as well as grid distortions. The GRLF-LAD algorithm is found to be approximately 36.04% faster than APA and about 45.16% faster than LMS in terms of dynamic response and about 11.19% better than LMS in accurate current component extraction. The total harmonic distortion (THD) content of presented algorithm is found to be well within the IEEE-519 std.

关键词： Power harmonic filters Active filters Harmonic analysis Heuristic algorithms approximation algorithms Distortion Voltage control Power quality active power filter adaptive controls polluted grid conditions grid voltage harmonic reduction

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Online Pricing Incentive to Sample Fresh Information

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第1期10卷 514-526页

作者： Li, Hongbo Duan, Lingjie Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore 487372 Singapore

Today mobile users such as drivers are invited by content providers (e.g., Tripadvisor) to sample fresh information of diverse paths to control the age of information (AoI). However, selfish drivers prefer to travel through the shortest path instead of the others with extra costs in time and gas. To motivate drivers to route and sample diverse paths, this paper is the first to propose online pricing for a provider to economically reward drivers for diverse routing and control the actual AoI dynamics over time and spatial path domains. This online pricing optimization problem should be solved without knowing drivers' costs and even arrivals, and is intractable due to the curse of dimensionality in both time and space. If there is only one non-shortest path, we leverage the Markov decision process (MDP) techniques to analyze the problem. Accordingly, we design a linear-time algorithm for returning optimal online pricing, where a higher pricing reward is needed for a larger AoI. If there are a number of non-shortest paths, we prove that pricing one path at a time is optimal, yet it is not optimal to choose the path with the largest current AoI. Then we propose a new backward-clustered computation method and develop an approximation algorithm to alternate different paths to price over time. Perhaps surprisingly, our analysis of approximation ratio suggests that our algorithm's performance approaches closer to optimum given more paths.

关键词： Pricing Vehicles Costs approximation algorithms Heuristic algorithms Routing Roads Age of information approximation error online multi-path pricing polynomial-time algorithms

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An approximation Cascade Scheme via Rational Fractions for Biomedical Data Analysis 18

An Approximation Cascade Scheme via Rational Fractions for B...

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18th IEEE International Conference on Computer Science and Information Technologies, CSIT 2023

作者： Izonin, Ivan Tkachenko, Roman Shcherbii, Ostap Berezsky, Oleh Krak, Iurii Oliinyk, Maksym Lviv Polytechnic National University Department of Artificial Intelligence Lviv Ukraine Lviv Polytechnic National University Department of Publishing Information Technologies Lviv Ukraine West Ukrainian National University Department of Computer Engineering Ternopil Ukraine Taras Shevchenko National University of Kyiv Department of Theoretical Cybernetics Kyiv Ukraine

ISBN: (纸本)9798350360462

The accuracy of solving the approximation tasks in the case of analysis of large volumes of tabular biomedical datasets by machine learning methods is not always high. This is explained by the complex, non-linear dependencies inside a multi-parametric dataset of a large volume, which combines medical-biological and engineering-technical features. The task of increasing the accuracy of the machine learning algorithm, ANN, or a certain mathematical model, in particular for the case when they are a 'black box' during the intellectual analysis of such data, is urgent. The paper proposed a new two-step method for the refinement of regression model parameters (a direct approach). It is based on the principles of approximation by rational fractions, which have many advantages compared to other known methods. Simulation of the developed method is implemented as a cascade of two machine learning algorithms. Experimental studies were carried out based on SGD and extended-input SGTM neural-like structure using a real-world large biomedical dataset. We show a significant increase in the approximation accuracy of the designed method in comparison with several known methods, including cascades. © 2023 IEEE.

关键词： approximation algorithms

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Offloading algorithms for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第7期34卷 2025-2039页

作者： Fresa, Andrea Champati, Jaya Prakash IMDEA Networks Inst Edge Networks Grp Madrid 28918 Spain

With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Learning (ML) inference from the data samples collected at the EDs, we study the problem of offloading inference jobs by considering the following novel aspects: 1) in contrast to a typical computational job, the processing time of an inference job depends on the size of the ML model, and 2) recently proposed Deep Neural Networks (DNNs) for resource-constrained devices provide the choice of scaling down the model size by trading off the inference accuracy. Considering that multiple ML models are available at the ED, and a powerful ML model is available at the ES, we formulate an Integer Linear Programming (ILP) problem with the objective of maximizing the total inference accuracy of n data samples at the ED subject to a time constraint T on the makespan. Noting that the problem is NP-hard, we propose an approximation algorithm Accuracy Maximization using LP-Relaxation and Rounding (AMR(2)) and prove that it results in a makespan at most 2T and achieves a total accuracy that is lower by a small constant from the optimal total accuracy implying that AMR(2) is asymptotically optimal. Further, if the data samples are identical we propose Accuracy Maximization using Dynamic Programming (AMDP), an optimal pseudo-polynomial time algorithm. Furthermore, we extend AMR(2) for the case of multiple ESs, where each ES is equipped with a powerful ML model. As proof of concept, we implemented AMR(2) on a Raspberry Pi, equipped with MobileNets, that is connected to a server equipped with ResNet, and studied the total accuracy and makespan performance of AMR(2) for image classification.

关键词： Data models Computational modeling Inference algorithms Costs Servers approximation algorithms Scheduling deep learning edge computing linear programming machine learning

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An Improved Dynamic Programming-Based Decentralized Algorithm for AC-OPF of Radially Connected Multi-Level Networks

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IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第2期38卷 1460-1473页

作者： Chen, Jiajun Zhu, Jianquan Ye, Hanfang Wang, Zeshuang Zeng, Kai Liu, Haixin Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China South China Univ Technol Guangdong Key Lab Green Energy Technol Guangzhou 510640 Peoples R China

In this paper, an improved dynamic programming (IDP)-based decentralized algorithm is proposed for the AC-optimal power flow (AC-OPF) of radially connected transmission-distribution-microgrid networks (RCTDMNs). Most of the existing decentralized algorithms need to iterate among networks, which may lead to additional computational and information exchange burden. Different from these algorithms, we decompose the original AC-OPF problem into small-scale subproblems based on dynamic programming (DP) theory, and then solve them in a decentralized manner without iteration among networks. In order to improve the computational performance of DP, its value functions are directly obtained by solving Galerkin projection equations (GPEs) instead of traversing the whole state space. Moreover, a parallel technique is proposed to further accelerate the proposed IDP algorithm when dealing with the AC-OPF problem of RCTDMNs. Case studies on various test systems demonstrate the effectiveness of the proposed method.

关键词： Heuristic algorithms Load flow Convergence Dynamic programming approximation algorithms Voltage Reactive power Improved dynamic programming decentralized optimization AC-OPF radially connected networks

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SLAM Using Cellular Multipath Component Delays and Angular Information with JPDA approximation 8

SLAM Using Cellular Multipath Component Delays and Angular I...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Chen, Junshi Whiton, Russ Tufvesson, Fredrik Lund University Dept. of Electrical and Information Technology Lund Sweden Terranet AB Lund Sweden Volvo Car Corporation GothenburgSE-405 31 Sweden

ISBN: (纸本)9798350397932

Advanced cellular communication systems provide increased potential for opportunistic high-accuracy positioning. In this paper, long-term evolution (LTE) downlink signals from two commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) parameters, like delays and angle-of-arrival (AOA) are extracted from the received signals on a snapshot-by-snapshot basis, and then associated across snapshots with a low complexity joint probability data association approximation algorithm. The associated parameters are used to jointly estimate the positions of the vehicle, the transmitters, and the virtual transmitters (VT) with a simultaneous localization and mapping (SLAM) algorithm. Both reflector and scatterer models are adopted, and clock and angular offsets are taken into account in the algorithm. The measurement results show the effectiveness of the data association algorithm and the accuracy of the SLAM algorithm. The vehicle's horizontal position error of SLAM fused with proprioception is less than 5.5 meters after a traversed distance of 530 meters, compared to that of the un-aided proprioception which is 15 meters. © 2023 IEEE.

关键词： approximation algorithms

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Policy Iteration Based Approximate Dynamic Programming Toward Autonomous Driving in Constrained Dynamic Environment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第5期24卷 5003-5013页

作者： Lin, Ziyu Ma, Jun Duan, Jingliang Li, Shengbo Eben Ma, Haitong Cheng, Bo Lee, Tong Heng Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Guangzhou Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore

In the area of autonomous driving, it typically brings great difficulty in solving the motion planning problem since the vehicle model is nonlinear and the driving scenarios are complex. Particularly, most of the existing methods cannot be generalized to dynamically changing scenarios with varying surrounding vehicles. To address this problem, this development here investigates the framework of integrated decision and control. As part of the modules, static path planning determines the reference candidates ahead, and then the optimal path-tracking controller realizes the specific autonomous driving task. An innovative and effective constrained finite-horizon approximate dynamic programming (ADP) algorithm is herein presented to generate the desired control policy for effective path tracking. With the generalized policy neural network that maps from the state to the control input, the proposed algorithm preserves the high effectiveness for the motion planning problem towards changing driving environments with varying surrounding vehicles. Moreover, the algorithm attains the noteworthy advantage of alleviating the typically heavy computational loads with the mode of offline training and online execution. As a result of the utilization of multi-layer neural networks in conjunction with the actor-critic framework, the constrained ADP method is capable of handling complex and multidimensional scenarios. Finally, various simulations have been carried out to show that the constrained ADP algorithm is effective.

关键词： Planning Autonomous vehicles Vehicle dynamics Task analysis Heuristic algorithms approximation algorithms Roads Autonomous driving approximate dynamic programming motion planning constrained optimization reinforcement learning

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Influence-Based Community Partition With Sandwich Method for Social Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第2期10卷 819-830页

作者： Ni, Qiufen Guo, Jianxiong Wu, Weili Wang, Huan Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China Beijing Normal Univ BNU Zhuhai BNU UIC Inst Artificial Intelligence & Future Net Zhuhai 519087 Guangdong Peoples R China Beijing Normal Univ BNU Zhuhai BNU HKBU United Int Coll Guangdong Key Lab AI & Multimodal Data Proc Zhuhai 519087 Guangdong Peoples R China Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China

Community partition is an important problem in many areas, such as biology networks and social networks. The objective of this problem is to analyze the relationships among data via the network topology. In this article, we consider the community partition problem under the independent cascade (IC) model in social networks. We formulate the problem as a combinatorial optimization problem that aims at partitioning a given social network into disjoint m communities. The objective is to maximize the sum of influence propagation of a social network through maximizing it within each community. The existing work shows that the influence maximization for community partition problem (IMCPP) is NP-hard. We first prove that the objective function of IMCPP under the IC model is neither submodular nor supermodular. Then, both supermodular upper bound and submodular lower bound are constructed and proved so that the sandwich framework can be applied. A continuous greedy algorithm and a discrete implementation are devised for upper and lower bound problems. The algorithm for both of the two problems gets a 1-1/e approximation ratio. We also present a simple greedy algorithm to solve the original objective function and apply the sandwich approximation framework to it to guarantee a data-dependent approximation factor. Finally, our algorithms are evaluated on three real datasets, which clearly verifies the effectiveness of our method in the community partition problem, as well as the advantage of our method against the other methods.

关键词： Social networking (online) Integrated circuit modeling approximation algorithms Greedy algorithms Linear programming Heuristic algorithms Upper bound Community partition influence maximization (IM) sandwich approximation framework social networks

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