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arXiv 2024年

作者： Yang, Yifan Ban, Hao Huang, Minhui Ma, Shiqian Ji, Kaiyi Department of Computer Science and Engineering University at Buffalo United States AI Research for Monetization Meta United States Department of Computational Applied Math and Operations Research Rice University United States

Bilevel optimization has recently attracted considerable attention due to its abundant applications in machine learning problems. However, existing methods rely on prior knowledge of problem parameters to determine stepsizes, resulting in significant effort in tuning stepsizes when these parameters are unknown. In this paper, we propose two novel tuning-free algorithms, D-TFBO and S-TFBO. D-TFBO employs a double-loop structure with stepsizes adaptively adjusted by the "inverse of cumulative gradient norms" strategy. S-TFBO features a simpler fully single-loop structure that updates three variables simultaneously with a theory-motivated joint design of adaptive stepsizes for all variables. We provide a comprehensive convergence analysis for both algorithms and show that D-TFBO and S-TFBO respectively require (Equation presented) and (Equation presented) iterations to find an ϵ-accurate stationary point, (nearly) matching their well-tuned counterparts using the information of problem parameters. Experiments on various problems show that our methods achieve performance comparable to existing well-tuned approaches, while being more robust to the selection of initial stepsizes. To the best of our knowledge, our methods are the first to completely eliminate the need for stepsize tuning, while achieving theoretical guarantees. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj Computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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Generalized paths and cycles in semicomplete multipartite digraphs

arXiv

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arXiv 2024年

作者： Bang-Jensen, Jørgen Wang, Yun Yeo, Anders Dept. of Math. and Computer Science University of Southern Denmark Odense Denmark Dept. of Math. and Computer Science University of Southern Denmark Odense Denmark School of Mathematics Shandong University Jinan250100 China Department of Mathematics and Applied Mathematics University of Johannesburg Auckland Park2006 South Africa

A digraph is semicomplete if it has no pair of non-adjacent vertices. It is complete if every pair of distinct vertices induce a 2-cycle. It is well-known and easy to show that even the following version of the directed travelling salesman problem is NP-complete: Given a strongly connected complete digraph D = (V, A), a cost function w: A → {0, 1} and a natural number K;decide whether D has a directed Hamiltonian cycle of cost at most K. We study the following variant of this problem for {0, 1}-weighted semicomplete digraphs where the set of arcs which have cost 1 form a collection of vertex-disjoint complete digraphs. A digraph is semicomplete multipartite if it can be obtained from a semicomplete digraph D by choosing a collection of vertex-disjoint subsets X1, . . ., Xc of V (D) and then deleting all arcs both of whose end-vertices lie inside some Xi. Let D be a semicomplete digraph with a cost function w as above, where w(a) = 1 precisely when a is an arc inside one of the subsets X1, . . ., Xc and let D∗ be the corresponding semicomplete multipartite digraph that we obtain by deleting all arcs inside the Xi’s. Then every cycle C of D corresponds to a generalized cycle Cg of D∗ which is either the cycle C itself if w(C) = 0 or a collection of two or more paths that we obtain by deleting all arcs of cost 1 on C. Similarly we can define a generalized path P g in a semicomplete multipartite digraph. The purpose of this paper is to study structural and algorithmic properties of generalized paths and cycles in semicomplete multipartite digraphs. This allows us to identify classes of directed {0, 1}-weighted TSP instances that can be solved in polynomial time as well as others for which we can get very close to the optimum in polynomial time. Along with these results we also show that two natural questions about properties of cycles meeting all partite sets in semicomplete multipartite digraphs are *** Codes 05C20, 05c38, 05c45, 05c85 © 2024, CC BY.

关键词： Cost functions

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Collaborative Control of Aerial Robots for Inferring Human Intent from Gaze Following

Collaborative Control of Aerial Robots for Inferring Human I...

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Control Technology and Applications (CCTA),

作者： Vishnu S. Chipade Alia Gilbert Daniel Harari Dimitra Panagou Department of Aerospace Engineering University of Michigan Ann Arbor MI USA Department of Robotics University of Michigan Ann Arbor MI USA Department of Computer Science and Applied Math Weizmann AI Center Weizmann Institute of Science Israel

In an ideal human-robot collaboration, autonomous robots work side-by-side with humans in a joint workspace, often performing complementary tasks to the humans. A robotic ability to infer human intention and goals directly from human behavior will facilitate the collaboration and maximize its efficiency. In this paper, we focus on inferring which object the human wants picked up next, based on what the human is looking at, by visually following the human gaze and head orientation. We develop a coordination protocol for a team of aerial robots to extract effective human head and gaze cues. The aerial robots are controlled to navigate around the human and collect data that improves the detection of the human's gaze and hence the intended object to be picked up. The effectiveness of the approach is shown using simulations in AirSim, a photo-realistic simulator.

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Retraction Note: An efficient quantum based D2D computation and communication approach for the internet of things

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Mekala, M. S. Srivastava, Gautam Chun-Wei Lin, Jerry Dhiman, Gaurav Park, Ju H. Jung, Ho-Youl Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea RLRC for Autonomous Vehicle Parts and Materials Innovation Yeungnam University Gyeongsan Korea Department of Math and Computer Science Brandon University Brandon Canada Western Norway University of Applied Sciences Gaurav Dhiman Bergen Norway Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical Engineering Yeungnam University Gyeongsan Korea Research Centre for Interneural Computing China Medical University Taichung Taiwan University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

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SIMAP: A simplicial-map layer for neural networks

arXiv

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arXiv 2024年

作者： Gonzalez-Diaz, Rocio Gutiérrez-Naranjo, Miguel A. Paluzo-Hidalgo, Eduardo The Department of Applied Math I School of Engineering University of Sevilla Seville Spain The Department of Computer Science and Artificial Intelligence School of Engineering University of Sevilla Seville Spain The Department of Quantitative Methods Universidad Loyola Andalucía Campus Sevilla Dos Hermanas Seville Spain

In this paper, we present SIMAP, a novel layer integrated into deep learning models, aimed at enhancing the interpretability of the output. The SIMAP layer is an enhanced version of Simplicial-Map Neural Networks (SMNNs), an explainable neural network based on support sets and simplicial maps (functions used in topology to transform shapes while preserving their structural connectivity). The novelty of the methodology proposed in this paper is two-fold: Firstly, SIMAP layers work in combination with other deep learning architectures as an interpretable layer substituting classic dense final layers. Secondly, unlike SMNNs, the support set is based on a fixed maximal simplex, the barycentric subdivision being efficiently computed with a matrix-based multiplication algorithm. © 2024, CC BY.

关键词： Deep learning

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INFLUENCE OF THE DISCRETIZATION STEP ON THE POSITIVITY OF CONFORMABLE FRACTIONAL LINEAR SYSTEMS

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UPB Scientific Bulletin, Series A: applied mathematics and Physics 2023年第1期85卷 77-86页

作者： Benuettou, Kamel Ghezzar, Mohammed Amine Bougada, Djillali Abdelhamid Ibn Badis University Mostaganem ACSY Team-Laboratory of Pure and Applied Math-ematics Department of Mathematics and Computer Science Faculty SEI BP 227/118 Mostaganem27000 Algeria National Higher School of Mathematics ACSY Team-Laboratory of Pure and Applied Mathematics Algeria

The effectiveness of this work lies in the influence of the discretization step on the positivity of the one-dimensional conformable fractional linear systems. A piecewise constant approximation method which converts a continuous fractional order derivative into a discrete time equation is presented as an alternative approach. It aims at investigating whether, how and when this step affects the positive continuous times models. To accomplish this research, a new test was developed and implemented in which the positivity conditions of the considered system was examined both before and after being exposed to the sampling step. i.e., under which conditions the one-dimensional discrete-time linear system obtained by discretization from the one-dimensional continuous time linear systems will be also positive if the one-dimensional continuous time linear system is positive. The new method presented is suggested to test the influence of the discretization step on the positivity of this class of systems. Finally, we discuss some numerical examples to illustrate the validity of the provided results. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Linear systems

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Energy grid management system with anomaly detection and Q-learning decision modules

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computers and Electrical Engineering 2023年 107卷

作者： Syu, Jia-Hao Srivastava, Gautam Fojcik, Marcin Cupek, Rafal Lin, Jerry Chun-Wei Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Mathematics and Computer Science Brandon University Brandon Canada Research Center for Interneural Computing China Medical University Taichung Taiwan Department of Computer Science and Math Lebanese American University Beirut Lebanon Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Stability and security issues in energy management have become widespread research topics, in which artificial intelligence techniques are often embedded in management systems to efficiently manage the smart grid. In this paper, we propose an energy grid management system with anomaly detection and Q-learning decision modules (EMSAD). The anomaly detection module is a multitask learning network that simultaneously classifies suppliers and predicts actual supply quantities. The Q-learning decision module then determines the operating reserve and subsidies to manage the energy grid. Experimental results show that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers with F1-scores from 73.3% to 100.0%. The robustness evaluation also shows that EMSAD maintains high performance even in unseen environments without fine-tuning. Thus, the simulation results demonstrate the security, efficiency, transferability, and robustness of the proposed EMSAD in smart grid energy management. © 2023 Elsevier Ltd

关键词： Energy management

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Parsoffensive: Persian Offensive Comments Dataset

Parsoffensive: Persian Offensive Comments Dataset

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International Conference on Web Research (ICWR)

作者： Ali Nazarizadeh Minoo Sayyadpour Omid Ebrahimkhani Amirmasoud Iravani Golnaz Ghannadan Shirazi Mojgan Behravan Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Dept. Math and Computer Science Kharazmi University Tehran Iran Department of English Translation Studies Faculty of Persian Literature and Foreign Languages Allameh Tabataba'i University Tehran Iran Department of Linguistics Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Iran Department of Engineering Faculty of Computer Engineering Alzahra University Tehran Iran Department of IT& Technology IU International University of Applied science Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Offensive language detection is essential for maintaining respectful and inclusive online environments. By implementing detection systems, platforms aim to protect users from harmful content, curb the spread of hate speech, and promote positive interactions within digital communities. Leveraging natural language processing (NLP) techniques, this area has become a key focus in computational linguistics research and development. To address this challenge, we created the ParsOffensive dataset, consisting of approximately 8,433 Persian comments labeled as Offensive or Neutral. These comments were sourced from Instagram and labeled by two linguists. Machine learning models were trained on this dataset, with the ParsBert model achieving 90% accuracy in detecting offensive speech in Persian.

关键词： Accuracy Social networking (online) Computational modeling Prevention and mitigation Hate speech Text categorization Natural language processing Web sites Multimedia communication Research and development

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Graph Fourier MMD for Signals on Graphs

Graph Fourier MMD for Signals on Graphs

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Leone, Samuel Venkat, Aarthi Huguet, Guillaume Tong, Alexander Wolf, Guy Krishnaswamy, Smita Applied Math Program New HavenCT United States Yale University Computational Biology & Bioinformatics Program New HavenCT United States Université de Montréal Mila - Quebec Ai Institute Dept. of Math. & Stat Montreal Canada Université de Montréal Mila-Quebec Ai Institute Dept. of Comp. Sci. & Oper. Res. Montreal Canada Yale University Department of Computer Science Department of Genetics New HavenCT United States

ISBN: (纸本)9798350328851

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes the difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called gene localization score which helps select genes for cellular state space characterization. © 2023 IEEE.

关键词： Genes

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