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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ben Mhenni, Ramzi Bourguignon, Sebastien Idier, Jerome Ecole Cent Nantes CNRS LS2N 1 Rue Noe F-44321 Nantes France

ISBN: (纸本)9781509066315

We propose a new greedy sparse approximation algorithm, called SLS for Single L1 Selection, that addresses a least squares optimization problem under a cardinality constraint. The specificity and increased efficiency of SLS originate from the atom selection step, based on exploiting l(1)-norm solutions. At each iteration, the regularization path of a least-squares criterion penalized by the l(1) norm of the remaining variables is built. Then, the selected atom is chosen according to a scoring function defined over the solution path. Simulation results on difficult sparse deconvolution problems involving a highly correlated dictionary reveal the efficiency of the method, which outperforms popular greedy algorithms when the solution is sparse.

关键词： Sparse optimization greedy algorithm l(1)-norm regularization deconvolution

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G-Graphic Delta-Matroids and Their Applications

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COMBINATORICA 2023年第5期43卷 963-983页

作者： Kim, Donggyu Lee, Duksang Oum, Sang-il Inst Basic Sci IBS Discrete Math Grp Daejeon South Korea Korea Adv Inst Sci & Technol Dept Math Sci Daejeon South Korea Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon South Korea

For an abelian group G, a G-labelled graph is a graph whose vertices are labelled by elements of G. We prove that a certain collection of edge sets of a G-labelled graph forms a delta-matroid, which we call a G -graphic delta-matroid, and provide a polynomial-time algorithm to solve the separation problem, which allows us to apply the symmetric greedy algorithm of Bouchet to find a maximum weight feasible set in such a delta-matroid. We present two algorithmic applications on graphs;MAXIMUM WEIGHT PACKING OF TREES OF ORDER NOT DIVISIBLE BY k and MAXIMUM WEIGHT S -TREE PACKING. We also discuss various properties of G-graphic deltamatroids.

关键词： Delta-matroid Group-labelled graph greedy algorithm Tree packing

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An algorithm Based on Self-balancing Binary Search Tree to Generate Balanced, Intra-homogeneous and Inter-homogeneous Learning Groups

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 190-197页

作者： Ben Ammar, Ali Minalla, Amir Abdalla Univ Tabuk Univ Coll Tayma Tabuk Saudi Arabia

This paper presents an algorithm, based on the self-balancing binary search tree, to form learning groups. It aims to generate learning groups that are intra-homogeneous (student performance similarity within the group), interhomogeneous (group performance similarity between groups), and of balanced size. The algorithm mainly uses the 2-3 tree and the 2-3-4 tree as two implementations of a self-balancing binary search tree to form student blocks with close GPAs (grade point averages) and balanced sizes. Then, groups are formed from those blocks in a greedy manner. The experiment showed the efficiency of the proposed algorithm, compared to traditional forming methods, in balancing the size of the groups and improving their intra- and inter-homogeneity by up to 26%, regardless of the used version of the self-balancing binary search tree (2-3 or 2-3-4). For small samples of students, the use of the 23-4 tree was distinguished for improving intra- and interhomogeneity compared to the 2-3 tree. As for large samples of students, experiments showed that the 2-3 tree was better than the 2-3-4 tree in improving the inter-homogeneity, while the 2-3-4 tree was distinguished in improving the intra-homogeneity.

关键词： -Learning group formation balanced size groups homogeneous groups self-balancing binary search trees greedy algorithm

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TBCELF: Temporal Budget-Aware Influence Maximization 24

TBCELF: Temporal Budget-Aware Influence Maximization

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7th ACM India Joint International Conference on Data Science and Management of Data (CODS-COMAD) / 11th ACM IKDD CODS Conference / 29th COMAD Conference

作者： Zahoor, Aaqib Gillani, Iqra Altaf Bashir, Janibul NIT Srinagar Srinagar Jammu & Kashmir India

ISBN: (纸本)9798400716348

Influence Maximization addresses the challenge of identifying a small group of disseminators, known as seeds, essential for achieving maximal influence spread, particularly in viral marketing. This problem has now transitioned to the realm of temporal networks. Some approaches estimate influence spread and apply greedy or heuristic methods for seed selection, while others adapt to evolving networks over time. Our proposed approach, TBCELF, offers a two-fold solution. Firstly, it optimizes temporal seed selection, extending the principles of cost-effective lazy forward optimization. Secondly, it imposes a budget constraint, ensuring efficient seed selection within budgetary limits. We evaluate TBCELF on the manufacturing dataset and random graphs. Results show a 56.41% improvement in influence spread compared to the natural extension of the greedy algorithm to temporal networks, which highlights the improvement in seed quality by our proposed algorithm.

关键词： Temporal Networks Influence Maximization greedy algorithm NP Hardness Approximation

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PATIENT-ADAPTIVE AND LEARNED MRI DATA UNDERSAMPLING USING NEIGHBORHOOD CLUSTERING 49

PATIENT-ADAPTIVE AND LEARNED MRI DATA UNDERSAMPLING USING NE...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Gautam, Siddhant Li, Angqi Ravishankar, Saiprasad Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Biomed Engn E Lansing MI 48824 USA

ISBN: (纸本)9798350344868;9798350344851

There has been much recent interest in adapting undersampled trajectories in MRI based on training data. In this work, we propose a novel patient-adaptive MRI sampling algorithm based on grouping scans within a training set. Scan-adaptive sampling patterns are optimized together with an image reconstruction network for the training scans. The training optimization alternates between determining the best sampling pattern for each scan (based on a greedy search or iterative coordinate descent (ICD)) and training a reconstructor across the dataset. The eventual scan-adaptive sampling patterns on the training set are used as labels to predict sampling design using nearest neighbor search at test time. The proposed algorithm is applied to the fastMRI knee multicoil dataset and demonstrates improved performance over several baselines.

关键词： Sampling optimization iterative coordinate descent greedy algorithm machine learning computational imaging

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A Decentralized Approach to Intrusion Detection in Dynamic Networks of the Internet of Things Based on Multiagent Reinforcement Learning with Interagent Interaction

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2023年第8期57卷 1025-1032页

作者： Kalinin, M. O. Tkacheva, E. I. Peter Great St Petersburg Polytech Univ St Petersburg 195251 Russia

The application of multiagent reinforcement learning technology to solve the problem of intrusion detection in the Internet of Things (IoT) systems is considered. Three models of a multiagent intrusion detection system are implemented: a completely decentralized system, a system with the transfer of forecast data, and a system with the transfer of observation data. The experimental results are given in comparison with the Suricata open-code intrusion detection system. The considered architectures of multiagent systems are shown to be free from the shortcomings of the existing solutions.

关键词： agent decentralized system Internet of Things greedy algorithm cybersecurity machine learning multiagent reinforcement learning intrusion detection transfer of observation data transfer of prediction data DQN

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greedy Budget Allocation for Optimizing the Performance of Water Distribution Systems under Intermittent Supply Conditions

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2025年第7期151卷

作者： Perelman, Gal Xing, Lu Housh, Mashor Kandiah, Venu Fishbain, Barak Shafiee, Ehsan Technion Israel Inst Technol Fac Civil & Environm Engn IL-320003 Haifa Israel Xylem Inc Decis Intelligence Solut 301 Water St SE Washington DC 20003 USA Univ Haifa Dept Nat Resources & Environm Management IL-3498838 Haifa Israel DHI Water & Environm Inc 141 Union Blvd 320 Lakewood CO 80228 USA Xylem Inc Digital Solut 301 Water St SE Washington DC 20003 USA

Intermittent water supply is a prevalent strategy employed in water distribution systems (WDS) facing deteriorating conditions. However, this approach can have several drawbacks, including insufficient supply, pressure drops, water losses due to leakages, and unequal service levels. Further, these issues are often exacerbated when consumers establish private storage facilities and increase the peak demand, which leads to a feedback loop of worsening conditions. Thus, when a budget is available, restoring the system's functionality as fast as possible is crucial. The current study presents a method to optimize the rehabilitation of intermittent water supply by improving system design through investments and operational control settings. The method was developed for the challenge presented in the battle of the intermittent water supply (BIWS), where network performance is evaluated through nine different objectives over a five year horizon of planning and rehabilitation. The proposed method is based on a greedy optimization approach that was specifically tailored to the challenge of optimizing WDS under extreme hydraulic conditions. To overcome the formidable computational burden in the BIWS challenge, several heuristics are presented for reducing the search space. The results obtained reflect a dramatic improvement in the network performance, with 97.8% of the consumers having continuous supply and water loss reduced from 47% to 23.7% of the total inflow. We also present a generic greedy approach that can be applied to any water network for various decision-making problems.

关键词： Water distribution systems greedy algorithm Optimization Intermittent water supply Network rehabilitation Battle of the intermittent Water supply

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ADAPTIVE SENSOR SELECTION WITH DETERMINISTIC PRIORS FOR DOA TRACKING 49

ADAPTIVE SENSOR SELECTION WITH DETERMINISTIC PRIORS FOR DOA ...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Majumder, Kaushani Pillai, Sibi Raj B. Eldar, Yonina C. Mulleti, Satish Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India Weizmann Inst Sci Fac Math & Comp Sci Rehovot Israel

ISBN: (纸本)9798350344868;9798350344851

Compressive sensing (CS) techniques for estimating the direction-of-arrival (DoA) stand apart from traditional approaches due to their ability to derive DoA information from just a single snapshot, eliminating the need for a large number of snapshots. This research addresses the challenge of adaptively choosing sensors for each snapshot during DoA tracking. We have devised a greedy algorithm for sensor selection, incorporating a submodular cost function based on our proposed deterministic prior models for DoA. Notably, we show that this selection algorithm is equally efficient compared to the conventional greedy method that relies on exact knowledge of the DOAs. We also introduce a modified version of a conventional CS-reconstruction algorithm that takes advantage of prior information to reduce the required number of measurements and computational time. We demonstrate that the tracking accuracy is improved when using the deterministic priors for sensor selection and subsequent reconstruction.

关键词： Sensor selection direction of arrival estimation greedy algorithm DoA tracking deterministic priors

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Optimal Sensor Placement for Multilateration Using Alternating greedy Removal and Placement

Optimal Sensor Placement for Multilateration Using Alternati...

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IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Frisch, Daniel Li, Kailai Hanebeck, Uwe D. Karlsruhe Inst Technol KIT Intelligent Sensor Actuator Syst Lab ISAS Inst Anthropomat & Robot Karlsruhe Germany

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

We present a novel algorithm for optimal sensor placement in multilateration problems. Our goal is to design a sensor network that achieves optimal localization accuracy anywhere in the covered region. We consider the discrete placement problem, where the possible locations of the sensors are selected from a discrete set. Thus, we obtain a combinatorial optimization problem instead of a continuous one. While at first, combinatorial optimization sounds like more effort, we present an algorithm that finds a globally optimal solution surprisingly quickly.

关键词： Multilateration Dilution of Precision Fisher Information Sensor Placement Combinatorial Optimization greedy algorithm

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Multifidelity uncertainty quantification using reduced-basis methods with application to the Reynolds-averaged Navier-Stokes equations

Multifidelity uncertainty quantification using reduced-basis...

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AIAA SciTech Forum

作者： Parish, Eric J. Geraci, Gianluca Krath, Elizabeth Blonigan, Patrick Sandia Natl Labs Computat Data Sci Livermore CA 94550 USA Sandia Natl Labs Optimizat & Uncertainty Quantificat Albuquerque NM USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This work investigates the use of the reduced-basis method for multifidelity uncertainty quantification (UQ) with application to the Reynolds-Averaged Navier-Stokes equations. We examine the use of a greedy algorithm that leverages easily computable error estimates for constructing a reduced basis. This reduced basis is used to define a Galerkin reduced-order model that leverages entropy-variable transforms to maintain robustness. We embed our reduced-order model within sampling multifidelity UQ methods and assess its utility for more-efficiently propagating parametric uncertainties with the Spalart-Allmaras turbulence model. Results are presented on several hypersonic turbulent flow configurations.

关键词： Reynolds Averaged Navier Stokes Uncertainty Quantification greedy algorithm Entropy Flow Conditions Spalart Allmaras Turbulence Model Stress Distribution Finite Volume Method Direct Numerical Simulation Turbulent Boundary Layer

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