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arXiv 2025年

作者： Bednarczuk, E. Brezhneva, O. Leśniewski, K. Prusińska, A. Tret’Yakov, A. Warsaw University of Technology Poland Department of Mathematics Miami University OxfordOH United States System Research Institute Polish Academy of Sciences Poland Faculty of Science University of Siedlce Poland Dorodnicyn Computing Center Federal Research Center "Computer Science and Control " Russian Academy of Sciences Moscow119333 Russia

We present recent advances in the analysis of nonlinear equations with singular operators and nonlinear optimization problems with constraints given by singular mappings. The results are obtained within the framework of p-regularity theory, which has developed successfully over the last forty years. We illustrate the theory with its applications to degenerate problems in various areas of mathematics. In particular, we address the problem of describing the tangent cone to the solution set of nonlinear equations in a singular case. The structure of p-factor operators is used to propose optimality conditions and construct numerical methods for solving degenerate nonlinear equations and optimization problems. The methods presented in the paper can be considered as the first numerical approaches targeting solutions of degenerate problems, such as the Van der Pol differential equation, boundary-value problems with a small parameter, partial differential equations where Poincaré’s method of small parameter fails, nonlinear degenerate dynamical systems, and others. There are various practical applications for the theory of p-regularity, including structural engineering, composite materials, and material design. For instance, the theory can be applied to analyze the behavior of materials with irregular or complex properties. By considering higher-order derivatives, it becomes possible to model and predict the response of materials to external forces, such as stress or temperature variations. In geophysics, the p-regularity theory can be utilized to analyze and interpret complex data obtained from seismic surveys, gravity measurements, or electromagnetic surveys. The theory also finds applications in the analysis of nonlinear differential equations arising in control systems, geometric and topological analysis, biomechanics, and many other fields. The theory offers a valuable approach to analyzing and understanding nonlinear phenomena in situations where classical regularity

关键词： Boundary value problems

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Forecasting Air Quality Index in Thailand Using Ensemble Method

Forecasting Air Quality Index in Thailand Using Ensemble Met...

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International computer science and Engineering Conference (ICSEC)

作者： Saksiri Lertnilkarn Suphakant Phimoltares Department of Mathematics and Computer Science Faculty of Science Advanced Virtual and Intelligent Computing (AVIC) Research Center Chulalongkorn University Pathumwan Bangkok Thailand

ISBN: (纸本)9781665462730

Air pollution is one of the most serious problems in many regions of the world. Thailand also has had to face this trouble unavoidably, especially in the northern region of Thailand, the area that has been highly contaminated by air pollution for so many years. In this paper, an ensemble method was introduced to forecast the level of air quality index (AQI) in the northern part of Thailand. The ensemble method, in this study, is a technique gaining the results from majority vote of outputs of three classification models—k-nearest neighbors, random forest, and support vector machine. The proposed model compared the voted accuracy with the accuracies of existing classification models. It made use of the 2018 - 2021 data from seven stations in four provinces of Northern Thailand. In the end, the proposed model yielded 99.68% - 99.84% accuracy rate on average higher than most of the performance of the other comparative models.

关键词： Support vector machines Training Atmospheric modeling Computational modeling Predictive models Air pollution Prediction algorithms

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image computing and computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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The RQR algorithm

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Linear Algebra and Its Applications 2025年

作者： Camps, Daan Mach, Thomas Vandebril, Raf Watkins, David S. National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory United States University of Potsdam Institute of Mathematics Karl-Liebknecht-Str. 24–25 Potsdam14476 Germany Department of Computer Science KU Leuven Belgium Department of Mathematics Washington State University United States

Pole-swapping algorithms, generalizations of bulge-chasing algorithms, have been shown to be a viable alternative to the bulge-chasing QZ algorithm for solving the generalized eigenvalue problem for a matrix pencil A−λB. It is natural to try to devise a pole-swapping algorithm that solves the standard eigenvalue problem for a single matrix A. This paper introduces such an algorithm and shows that it is competitive with Francis's bulge-chasing QR algorithm. © 2025 Elsevier Inc.

关键词： Eigenvalues and eigenfunctions

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Anonymous Distributed Localisation via Spatial Population Protocols

arXiv

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arXiv 2024年

作者： Gąsieniec, Leszek Kuszner, Lukasz Latif, Ehsan Parasuraman, Ramviyas Spirakis, Paul Stachowiak, Grzegorz Department of Computer Science University of Liverpool United Kingdom Institute of Informatics Physics and Informatics University of Gdańsk Poland AI4STEM Education Center Department of Mathematics Science and Social Studies Education University of Georgia AthensGA United States School of Computing University of Georgia AthensGA United States Institute of Computer Science University of Wroclaw Poland

In the distributed localization problem (DLP), n anonymous robots (agents) A0, . . . ,An-1 begin at arbitrary positions p0, . . . , pn-1 ∈ S, where S is a Euclidean space. Initially, each agent Ai operates within its own coordinate system in S, which may be inconsistent with those of other agents. The primary goal in DLP is for agents to reach a consensus on a unified coordinate system that accurately reflects the relative positions of all points, p0, . . . , pn-1, in S. Extensive research on DLP has primarily focused on the feasibility and complexity of achieving consensus when agents have limited access to inter-agent distances, often due to missing or imprecise data. In this paper, however, we examine a minimalist, computationally efficient model of distributed computing in which agents have access to all pairwise distances, if needed. Specifically, we introduce a novel variant of population protocols, referred to as the spatial population protocols model. In this variant each agent can memorise one or a fixed number of coordinates, and when agents Ai and Aj interact, they can not only exchange their current knowledge but also either determine the distance dij between them in S (distance query model) or obtain the vector -→vij spanning points pi and pj (vector query model). We examine three DLP scenarios, proposing and analysing several types of distributed localisation protocols, including: 1. Self-stabilising localisation protocol with distance queriesWe propose and analyse self-stabilising localisation protocol based on pairwise distance adjustment. We also discuss several hard instances in this scenario, and suggest possible improvements for the considered protocol, 2. Leader-based localisation protocol with distance queries We propose and analyse several leaderbased protocols which stabilise in o(n) parallel time. These protocols rely on efficient solution to multi-contact epidemic, an extension of one-way epidemic in population protocols, and 3. Self-stabil

关键词： Multi agent systems

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Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy

arXiv

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arXiv 2024年

作者： Guan, Zhenyu Kong, Xiangyu Zhong, Fangwei Wang, Yizhou Institute for Artificial Intelligence Peking University Beijing China Computer School Beijing Information Science & Technology University Beijing China School of Artificial Intelligence Beijing Normal University Beijing China Center on Frontiers of Computing Studies School of Computer Science Nat’l Eng. Research Center of Visual Technology State Key Lab of General Artificial Intelligence Peking University Beijing China State Key Laboratory of General Artificial Intelligence BIGAI Beijing China

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI’s potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection;2) goal-oriented negotiation with social reasoning;and 3) augmenting memory through self-play games for self-evolution without human in the loop. Copyright © 2024, The Authors. All rights reserved.

关键词： Strategic planning

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Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement

arXiv

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arXiv 2024年

作者： Lee, Ho Hin Saunders, Adam M. Kim, Michael E. Remedios, Samuel W. Remedios, Lucas W. Tang, Yucheng Yang, Qi Yu, Xin Bao, Shunxing Cho, Chloe Mawn, Louise A. Rex, Tonia S. Schey, Kevin L. Dewey, Blake E. Spraggins, Jeffrey M. Prince, Jerry L. Huo, Yuankai Landman, Bennett A. Department of Computer Science Vanderbilt University Nashville United States Department of Electrical and Computer Engineering Vanderbilt University Nashville United States Department of Computer Science Johns Hopkins University Baltimore United States Department of Biomedical Engineering Vanderbilt University Nashville United States Department of Ophthalmology and Visual Sciences Vanderbilt University Medical Center Nashville United States Department of Biochemistry Vanderbilt University Nashville United States Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Department of Cell and Developmental Biology Vanderbilt University Nashville United States

Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference. Approach: To tackle these limitations, we propose a process for creating high-resolution unbiased eye atlases. First, to restore spatial details from scans with a low through-plane resolution compared to a high in-plane resolution, we apply a deep learning-based super-resolution algorithm. Then, we generate an initial unbiased reference with an iterative metric-based registration using a small portion of subject scans. We register the remaining scans to this template and refine the template using an unsupervised deep probabilistic approach that generates a more expansive deformation field to enhance the organ boundary alignment. We demonstrate this framework using magnetic resonance images across four different tissue contrasts, generating four atlases in separate spatial alignments. Results: When refining the template with sufficient subjects, we find a significant improvement using the Wilcoxon signed-rank test in the average Dice score across four labeled regions compared to a standard registration framework consisting of rigid, affine, and deformable transformations. These results highlight the effective alignment of eye organs and boundaries using our proposed process. Conclusions: By combining super-resolution preprocessing and deep probabilistic models, we address the challenge of generating an eye atlas to serve as a standardized reference across a largely variable population. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

arXiv

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arXiv 2025年

作者： Meghanath, Ashtakala Das, Subham Behera, Bikash K. Khan, Muhammad Attique Al-Kuwari, Saif Farouk, Ahmed Department of Physics Indian Institute of Science Education and Research Thiruvananthapuram India Pvt. Ltd. WB Mohanpur741246 India Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Computer Science Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU† method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate QDCNN’s —key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework. © 2025, CC BY.

关键词： Convolutional neural networks

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Use Coupled LSTM Networks to Solve Constrained Optimization Problems

Use Coupled LSTM Networks to Solve Constrained Optimization ...

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作者： Chen, Zheyu Leung, Kin K. Wang, Shiqiang Tassiulas, Leandros Chan, Kevin Towsley, Don Imperial College London Department of Computing LondonSW7 2AZ United Kingdom Ibm T.J. Watson Research Center Yorktown HeightsNY10598 United States Yale University Institute for Network Science Electrical Engineering Department New HavenCT06520 United States Devcom Army Research Laboratory Army Research Directorate AdelphiMD20783 United States University of Massachusetts Department of Computer Science AmherstMA01003 United States

Gradient-based iterative algorithms have been widely used to solve optimization problems, including resource sharing and network management. When system parameters change, it requires a new solution independent of the previous parameter settings from the iterative methods. Therefore, we propose a learning approach that can quickly produce optimal solutions over a range of system parameters for constrained optimization problems. Two Coupled Long Short-Term Memory networks (CLSTMs) are proposed to find the optimal solution. The advantages of this framework include: (1) near-optimal solution for a given problem instance can be obtained in few iterations during the inference, (2) enhanced robustness as the CLSTMs can be trained using system parameters with distributions different from those used during inference to generate solutions. In this work, we analyze the relationship between minimizing the loss functions and solving the original constrained optimization problem for certain parameter settings. Extensive numerical experiments using datasets from Alibaba reveal that the solutions to a set of nonconvex optimization problems obtained by the CLSTMs reach within 90% or better of the corresponding optimum after 11 iterations, where the number of iterations and CPU time consumption are reduced by 81% and 33%, respectively, when compared with the gradient descent with momentum. © 2015 IEEE.

关键词： Gradient methods

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Multi-view Contrastive Learning for Medical Question Summarization

Multi-view Contrastive Learning for Medical Question Summari...

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International Conference on computer Supported Cooperative Work in Design

作者： Sibo Wei Xueping Peng Hongjiao Guan Lina Geng Ping Jian Hao Wu Wenpeng Lu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Australia Department of Blood Purification Qilu Hospital of Shandong University Jinan China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Most Seq2Seq neural model-based medical question summarization (MQS) systems have a severe mismatch between training and inference, i.e., exposure bias. However, this problem remains unexplored in the MQS task. To bridge this research gap and alleviate the problem of exposure bias, we propose a novel re-ranking training framework for MQS called Multi-view Contrastive Learning (MvCL). MvCL simultaneously considers the similarity scores between medical questions and candidate summaries as well as the average similarity scores between candidate summaries and other candidates within the same group, and utilizes contrastive learning to optimize the model’s ranking ability. Additionally, we propose a new multilevel inference approach to adapt to this training strategy. The approach first filters out candidate summaries that are dissimilar to the original medical question, and then selects the summary with the highest average similarity to other candidate summaries from the remaining candidates as the final output. We conducted extensive experiments, and the results demonstrate that our proposed MvCL framework achieves state-of-the-art results on the majority of evaluation metrics across four datasets. 1

关键词： Training Measurement Bridges Adaptation models Limiting Filters Federated learning

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