检索结果-内蒙古大学图书馆

Stable Menus of Public Goods: A Matching Problem

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Fish, Sara Gonczarowski, Yannai A. Hart, Sergiu School of Engineering and Applied Sciences Harvard University United States Department of Economics Department of Computer Science Harvard University United States Federmann Center for the Study of Rationality Bogen Department of Economics Einstein Institute of Mathematics The Hebrew University of Jerusalem

We study a matching problem between agents and public goods, in settings without monetary transfers. Since goods are public, they have no capacity constraints. There is no exogenously defined budget of goods to be provided. Rather, each provided good must justify its cost, leading to strong complementarities in the "preferences" of goods. Furthermore, goods that are in high demand given other already-provided goods must also be provided. The question of the existence of a stable solution (a menu of public goods to be provided) exhibits a rich combinatorial structure. We uncover sufficient conditions and necessary conditions for guaranteeing the existence of a stable solution, and derive both positive and negative results for strategyproof stable matching. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

Integrability of polynomial vector fields and a dual problem

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Petek, Tatjana Romanovski, Valery G. Faculty of Electrical Engineering and Computer Science University of Maribor Koroska cesta 46 MariborSI-2000 Slovenia Institute of Mathematics Physics and Mechanics Jadranska 19 LjubljanaSI-1000 Slovenia Faculty of Natural Science and Mathematics University of Maribor Koroska cesta 160 MariborSI-2000 Slovenia Center for Applied Mathematics and Theoretical Physics Mladinska 3 MariborSI-2000 Slovenia

We investigate the integrability of polynomial vector fields through the lens of duality in parameter spaces. We examine formal power series solutions annihilated by differential operators and explore the properties of the integrability variety in relation to the invariants of the associated Lie group. Our study extends to differential operators on affine algebraic varieties, highlighting the intrinsic connection between these operators and local analytic first integrals. To illustrate the duality the case of quadratic vector fields is considered in detail. © 2024, CC BY.

关键词： Choquet integral

Rlsnake: A hybrid reinforcement learning approach for road detection 24

学校读者我要写书评

暂无评论

Rlsnake: A hybrid reinforcement learning approach for road d...

2021 24th ISPRS Congress Commission III: Imaging Today, Foreseeing Tomorrow

作者： Botteghi, N. Sirmacek, B. Ünsalan, C. Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Smart Cities School of Creative Technology Saxion University of Applied Sciences Netherlands Electrical and Electronics Engineering Faculty of Engineering Marmara University Turkey

Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives. All rights reserved.

关键词： Reinforcement learning

Promotion/Inhibition Effects in Networks: A Model with Negative Probabilities

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Dong, Anqi Georgiou, Tryphon T. Tannenbaum, Allen The Department of Mechanical and Aerospace Engineering University of California IrvineCA92697 United States The Department of Computer Science and Applied Mathematics & Statistics The State University of New York Stony Brook United States

MSC Codes 92F99, 49M29, 90C30, 93-08, 90C25Biological networks often encapsulate promotion/inhibition as signed edge-weights of a graph. Nodes may correspond to genes assigned expression levels (mass) of respective proteins. The promotion/inhibition nature of co-expression between nodes is encoded in the sign of the corresponding entry of a sign-indefinite adjacency matrix, though the strength of such co-expression (i.e., the precise value of edge weights) cannot typically be directly measured. Herein we address the inverse problem to determine network edge-weights based on a sign-indefinite adjacency and expression levels at the nodes. While our motivation originates in gene networks, the framework applies to networks where promotion/inhibition dictates a stationary mass distribution at the nodes. In order to identify suitable edge-weights we adopt a framework of "negative probabilities," advocated by P. Dirac and R. Feynman, and we set up a likelihood formalism to obtain values for the sought edge-weights. The proposed optimization problem can be solved via a generalization of the well-known Sinkhorn algorithm;in our setting the Sinkhorn-type "diagonal scalings" are multiplicative or inverse-multiplicative, depending on the sign of the respective entries in the adjacency matrix, with value computed as the positive root of a quadratic polynomial. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

Decentralized Optimal Control for Linear Stochastic Systems with Control Signals subject to Unknown Noises

学校读者我要写书评

暂无评论

Decentralized Optimal Control for Linear Stochastic Systems ...

International Conference on Control and Automation (ICCA)

作者： Zhaorong Zhang Juanjuan Xu Minyue Fu Xun Li School of Computer Science and Technology Shandong University Qingdao China School of Control Science and Engineering Shandong University Jinan China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Applied Mathematics the Hong Kong Polytechnic University Hong Kong SAR China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Decentralized strategies have been extensively applied to LQ optimal control problems, whereas, stochastic systems with unknown random parameters have not been comprehensively studied. In this paper, we consider a class of stochastic systems with a decentralized configuration consisting of multiple controllers which have access to Gaussian noises with unknown statistical information. The stabilizing and optimal control strategies are acquired by designing a novel stochastic approximation algorithm recursively evaluating the zero points of certain matrix equations, which is confirmed to be equivalent with solving the corresponding Riccati equations. The proof of convergence and boundness of the proposed algorithm is presented.

关键词： Q-learning Automation Stochastic systems Gaussian noise Riccati equations Optimal control Approximation algorithms

IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lin, Junchao Ling, Zenan Feng, Zhanbo Xu, Jingwen Liao, Minxuan Zhou, Feng Hou, Tianqi Liao, Zhenyu Qiu, Robert C. School of EIC Huazhong University of Science and Technology China Department of Computer Science and Engineering Shanghai Jiao Tong University China Center for Applied Statistics School of Statistics Renmin University of China China Lagrange Mathematics and Computation Research Center of Huawei China School of Science Wuhan University of Technology China

Implicit graph neural networks (IGNNs), which exhibit strong expressive power with a single layer, have recently demonstrated remarkable performance in capturing long-range dependencies (LRD) in underlying graphs while effectively mitigating the over-smoothing problem. However, IGNNs rely on computationally expensive fixed-point iterations, which lead to significant speed and scalability limitations, hindering their application to large-scale graphs. To achieve fast fixed-point solving for IGNNs, we propose a novel graph neural solver, IGNN-Solver, which leverages the generalized Anderson Acceleration method, parameterized by a tiny GNN, and learns iterative updates as a graph-dependent temporal process. To improve effectiveness on large-scale graph tasks, we further integrate sparsification and storage compression methods, specifically tailored for the IGNN-Solver, into its design. Extensive experiments demonstrate that the IGNN-Solver significantly accelerates inference on both small- and large-scale tasks, achieving a 1.5× to 8× speedup without sacrificing accuracy. This advantage becomes more pronounced as the graph scale grows, facilitating its large-scale deployment in real-world applications. The code to reproduce our results is available at https://***/landrarwolf/IGNN-Solver. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the Data Set

学校读者我要写书评

暂无评论

Classification Models and Variable Selection for SME Credit ...

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sornthun Intharasompong Anuchit Jitpattanakul Sakorn Mekruksavanich Piyada Wongwiwat Wikanda Phaphan Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Computer Engineering School of Information and Communication University of Phayao Thailand Faculty of Science and Technology Suansunandha Rajabhat University Bangkok Thailand Research Group in Statistical Learning and Inference

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans.

关键词： Support vector machines Machine learning algorithms Input variables Predictive models Nearest neighbor methods Boosting Data models Robustness Bayes methods Risk management

VLM-Enabled Network Experimentation: Case Study of Chanel Capacity in System Limited by η-μ Fading

学校读者我要写书评

暂无评论

VLM-Enabled Network Experimentation: Case Study of Chanel Ca...

International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Dejan Milić Nenad Petrović Milan Jović Radiša Stefanović Mejdin Saliji Elida Suljović Faculty of Electronic Engineering University of Niš Niš Serbia Applied Engineering Sciences Academy of Vocational Studies Polytechnic Belgrade Serbia Faculty of Mathematics and Natural Sciences Cyril and Methodius University Skopje Republic of North Macedonia Informatics and Computer Science Technical School in Novi Pazar Novi Pazar Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

In this paper, a micro-MRC receiver with L branches operating in a correlated gamma-shadowed η-μ fading channel is considered. For such a system, the channel capacity (CC) is determined based on the maximum signal-to-noise ratio (SNR) at the L branches at the input of the MRC receiver. The results are presented graphically to illustrate the impact of different system parameters on performance and the improvements that result from the advantages of combined diversity. Additionally, we introduce the application of Large Language Models (LLMs) to make network experimentation more practical, using the previously presented expression as a case study. Moreover, a Vision Language Model (VLM) aided method aiming to make model-driven network design and experimentation more convenient is introduced and evaluated for the previously considered case study

关键词： Fading channels Wireless communication Visualization Channel capacity System performance Scalability Large language models Receivers Signal to noise ratio

WELL-POSEDNESS OF THE STOKES EQUATIONS ON A WEDGE WITH NAVIER-SLIP BOUNDARY CONDITIONS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Bravin, Marco Gnann, Manuel V. Knüpfer, Hans Masmoudi, Nader Roodenburg, Floris B. Sauer, Jonas Departmento de Matemática Aplicada y Ciencias de la Computacíon E.T.S.I. Industriales y de Telecomunicacíon Universidad de Cantabria Santander39005 Spain Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Institute for Applied Mathematics and IWR Heidelberg University INF 205 Heidelberg69120 Germany NYUAD Research Institute New York University Abu Dhabi Saadiyat Island P.O. Box Abu Dhabi129188 United Arab Emirates Courant Institute of Mathematical Sciences New York University 251 Mercer Street New YorkNY10012 United States Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628CD Netherlands Institute for Mathematics Faculty of Mathematics and Computer Science University of Jena Ernst-Abbe-Platz 2 Jena07737 Germany

We consider the incompressible and stationary Stokes equations on an infinite two-dimensional wedge with non-scaling invariant Navier-slip boundary conditions. We prove well-posedness and higher regularity of the Stokes problem in a certain class of weighted Sobolev spaces. The novelty of this work is the occurrence of two different scalings in the boundary condition, which is not treated so far for the Stokes system in unbounded wedge-type domains. These difficulties are overcome by first constructing a variational solution in a second order weighted Sobolev space and subsequently proving higher regularity up to the tip of the wedge by employing an iterative scheme. We believe that this method can be used for other problems with variational structure and multiple scales. Copyright © 2024, The Authors. All rights reserved.

关键词： Boundary conditions