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An Improved Algorithm for Finding Maximum Outerplanar Subgraphs

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arXiv 2023年

作者： Calinescu, Gruia Kaul, Hemanshu Kudarzi, Bahareh Department of Computer Science Illinois Institute of Technology ChicagoIL60616 United States Department of Applied Mathematics Illinois Institute of Technology ChicagoIL60616 United States

We study the NP-complete Maximum Outerplanar Subgraph problem. The previous best known approximation ratio for this problem is 2/3. We propose a new approximation algorithm which improves the ratio to 7/10. © 202... 详细信息

关键词： Approximation algorithms

Uncertainty-Aware Graph Self-Training with Expectation-Maximization Regularization

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arXiv 2025年

作者： Wang, Emily Chen, Michael Li, Chao School of Information Technology Lakeside College Ohio United States Department of Applied Mathematics North River University Manchester United Kingdom Department of Computer Science Eastern Asia Institute of Technology Beijing China

In this paper, we propose a novel uncertainty-aware graph self-training approach for semi-supervised node classification. Our method introduces an Expectation-Maximization (EM) regularization scheme to incorporate an uncertainty mechanism during pseudo-label generation and model retraining. Unlike conventional graph self-training pipelines that rely on fixed pseudo-labels, our approach iteratively refines label confidences with an EM-inspired uncertainty measure. This ensures that the predictive model focuses on reliable graph regions while gradually incorporating ambiguous nodes. Inspired by prior work on uncertainty-aware self-training techniques [5], our framework is designed to handle noisy graph structures and feature spaces more effectively. Through extensive experiments on several benchmark graph datasets, we demonstrate that our method outperforms strong baselines by a margin of up to 2.5% in accuracy while maintaining lower variance in performance across multiple runs. Copyright © 2025, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

MATLAB Simulation of 500 W Direct Methanol Fuel Cell Stack 2nd

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MATLAB Simulation of 500 W Direct Methanol Fuel Cell Stack

2nd International Conference on Materials, Design and Manufacturing for Sustainable Environment, ICMDMSE 2022

作者： Babu, S. Prem Kumar, T. Divya, V.V. Paulinga Prakash, R. Jeriel, D. Department of Mechanical Engineering PSG College of Technology Coimbatore641004 India Department of Mechanical Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India Department of Mathematics With Computer Applications PSG College of Arts & Science Coimbatore641014 India Department of Mechanical Engineering VEL Tech Multitech Chennai600062 India

ISBN: (纸本)9789811930522

This paper aims about the mathematical model analysis of 500 W direct methanol fuel cell (DMFC) using MATLAB SIMULINK. Three dynamic models, Input I-stdy-ideal, Input I-stdy, InputI-dynshrt4, were included with simulation time of 4900 ms, 3900 ms and 2040 ms, respectively. Ideal cell load with steady state, real fuel cell load with steady and real fuel cell load with transient state had been studied in detail. Pattern of output voltage, current density, power density and temperature distribution with respect to time has been found. Decreasing voltage pattern and increasing current density pattern are in line with typical VI (Voltage-Current density) curve. The optimum cell conditions obtained at 3100 ms are 25 V, 23 A/cm2 which gives the maximum power density of 575 W/cm2 at the operating temperature of 315 K. One of the major advantages of DMFC is obtaining maximum efficiency at the operating temperature which is closed to ambient conditions unlike proton exchange membrane fuel cell (PEMFC). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Current density

Credible Intervals for Probability of Failure with Gaussian Processes

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arXiv 2023年

作者： Sorokin, Aleksei Rao, Vishwas Department of Applied Mathematics Illinois Institute of Technology ChicagoIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

Efficiently approximating the probability of system failure has gained increasing importance as expensive simulations begin to play a larger role in reliability quantification tasks in areas such as structural design, power grid design, and safety certification among others. This work derives credible intervals on the probability of failure for a simulation which we assume is a realizations of a Gaussian process. We connect these intervals to binary classification error and comment on their applicability to a broad class of iterative schemes proposed throughout the literature. A novel iterative sampling scheme is proposed which can suggest multiple samples per batch for simulations with parallel implementations. We empirically test our scalable, open-source implementation on a variety simulations including a Tsunami model where failure is quantified in terms of maximum wave hight. Copyright © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

CLEARING SECTIONS OF LATTICE LIABILITY NETWORKS

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arXiv 2025年

作者： Ghrist, Robert Gould, Julian Lopez, Miguel Riess, Hans Department of Mathematics and Electrical & Systems Engineering University of Pennsylvania Philadelphia PA Department of Mathematics University of Pennsylvania Philadelphia PA Applied Mathematics & Computational Science University of Pennsylvania Philadelphia PA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

Modern financial networks involve complex obligations that transcend simple monetary debts: multiple currencies, prioritized claims, supply chain dependencies, and more. We present a mathematical framework that unifies and extends these scenarios by recasting the classical Eisenberg-Noe model of financial clearing in terms of lattice liability networks. Each node in the network carries a complete lattice of possible states, while edges encode nominal liabilities. Our framework generalizes the scalar-valued clearing vectors of the classical model to lattice-valued clearing sections, preserving the elegant fixed-point structure while dramatically expanding its descriptive power. Our main theorem establishes that such networks possess clearing sections that themselves form a complete lattice under the product order. This structure theorem enables tractable analysis of equilibria in diverse domains, including multi-currency financial systems, decentralized finance with automated market makers, supply chains with resource transformation, and permission networks with complex authorization structures. We further extend our framework to chain-complete lattices for term structure models and multivalued mappings for complex negotiation systems. Our results demonstrate how lattice theory provides a natural language for understanding complex network dynamics across multiple domains, creating a unified mathematical foundation for analyzing systemic risk, resource allocation, and network *** Codes 91G40, 06B23, 91D30, 68Q85 Copyright © 2025, The Authors. All rights reserved.

关键词： Lattice theory

Mathematical Model of Asset and Liability Management in the Presence of Normative and Internal Constraints 5

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Mathematical Model of Asset and Liability Management in the ...

5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhukova, Aleksandra Flerova, Anna Chernov, Aleksey Pilnik, Nikolay FRC CSC of RAS Dept. of Mathematical Modelling of Economic Systems MIPT Moscow Russia FRC CSC RAS Dept. of Mathematical Modelling of Economic Systems Moscow Russia Moscow Institute of Physics and Technology MIPT-School of Applied Mathematics and Computer Science Moscow Russia

ISBN: (纸本)9798350309126

In this paper we present the mathematical formulation of the problem of planning the Asset Liability Management if the Presence of Normative and Internal Constraints. As a nonlinear optimal control problem, it has specifics due to the state constraints. The maximum principle involves verification of regularity property in order to apply computational methods. We reformulate the problem as the problem with convex objective function and linear, but mixed system of constraints. This enables application of optimization methods to compute the optimal controls, and study the dependence of controls on the parameters that describe the external environments and the management objective. © 2023 IEEE.

关键词： Optimal control systems

Efficient Diabetes Detection using Hybrid Machine Learning Model

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Efficient Diabetes Detection using Hybrid Machine Learning M...

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

Coexisting Chaotic and Periodic Attractors in a Counterexample to the Kalman Conjecture 16

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Coexisting Chaotic and Periodic Attractors in a Counterexamp...

16th International Conference on Stability and Oscillations of Nonlinear Control Systems (Pyatnitskiy's Conference), STAB 2022

作者： Burkin, Igor M. Kuznetsov, Nikolay V. Mokaev, Timur N. Institute of Applied Mathematics and Computer Science Tula State University Tula Russia Institute for Problems in Mechanical Engineering RAS Saint-Petersburg Russia University of Jyvaskula Faculty of Information Technology Jyväskylä Finland Saint-Petersburg State University Department of Applied Cybernetics Saint-Petersburg Russia

ISBN: (纸本)9781665465861

In this paper, we use special numerical continuation procedures to construct a novel counterexample to the Kalman conjecture, based on the Fitts system. This counterexample represents a multistable configuration: the coexistence of two hidden chaotic attractors and two hidden limit cycles with a single stable equilibrium state. © 2022 IEEE.

关键词： Limit-cycles Nonlinear control systems Kalman filters Oscillators Numerical stability

Neural injective functions for multisets, measures and graphs via a finite witness theorem 23

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Neural injective functions for multisets, measures and graph...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Tal Amir Steven J. Gortler Ilai Avni Ravina Ravina Nadav Dym Faculty of Mathematics Technion - Israel Institute of Technology Haifa Israel School of Engineering and Applied Sciences Harvard University Cambridge Faculty of Mathematics Technion - Israel Institute of Technology Haifa Israel and Faculty of Computer Science Technion - Israel Institute of Technology Haifa Israel

Injective multiset functions have a key role in the theoretical study of machine learning on multisets and graphs. Yet, there remains a gap between the provably injective multiset functions considered in theory, which typically rely on polynomial moments, and the multiset functions used in practice, which rely on neural moments — whose injectivity on multisets has not been studied to *** this paper, we bridge this gap by showing that moments of neural networks do define injective multiset functions, provided that an analytic non-polynomial activation is used. The number of moments required by our theory is optimal essentially up to a multiplicative factor of two. To prove this result, we state and prove a finite witness theorem, which is of independent *** a corollary to our main theorem, we derive new approximation results for functions on multisets and measures, and new separation results for graph neural networks. We also provide two negative results: (1) moments of piecewise-linear neural networks cannot be injective multiset functions; and (2) even when moment-based multiset functions are injective, they can never be bi-Lipschitz.

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