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International Conference on Machine Learning and Applications (ICMLA)

作者： Guojing Cong Shruti Kulkarni Seung–Hwan Lim Prasanna Date Shay Snyder Maryam Parsa Dominic Kennedy Catherine Schuman Computer Science and Math Division Oak Ridge National Laboratory) Oak Ridge TN USA Department of ECE George Mason University Fairfax VA USA Department of EECS University of Tennessee Knoxville TN USA

Graph convolutional networks leverage both graph structures and features on nodes and edges for improved learning performance in comparison with classical machine learning approaches. Spiking neuromorphic computers natively implement network-like computation and have been shown to be successful at implementing graph learning without features. Incorporating graph features brings the challenge of efficient feature representation and balancing the contribution of topology and features in learning. In this work, we present our design of a simulated network of spiking neurons to perform semi-supervised learning on graph data using both the graph structure and the node features. We explore various design choices, present preliminary results, and discuss the opportunities for using neuromorphic computers for this task in the future.

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Firefly Optimized Federated SVM Model for Breast Cancer Prediction

Firefly Optimized Federated SVM Model for Breast Cancer Pred...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Supriya Y Chemmalar Selvi G Gokul Yenduri Gautam Srivastava Thippa Reddy Gadekallu School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India Dept. of Math and Computer Science Brandon University Brandon Canada Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cancer is a leading cause of morbidity and mortality worldwide, with an estimation of 10 million deaths attributed to cancer each year. Among various types of cancer, Breast cancer is the most common cancer diagnosed among women, accounting for approximately 30% of all new cancer cases among women. Periodic clinical checks and self-tests will assist in the early identification of Breast cancer. Breast cancer detection at an early stage helps the patients to receive suitable treatment, which can increase the chances of their survivability. Automated clinical solutions, such as computer-Aided detection and AI-based algorithms, have shown increasing promise in improving the accuracy and efficiency of Breast cancer screening and diagnosis. Several serious challenges have to be addressed such as lack of standardization and regulation of these technologies, the need for large amounts of data for training and validation, as well as patients’ privacy and data security. To address these challenges, a novel Federated Learning architecture that, rather than sharing data, enables knowledge integration by sharing the model parameters of each client during the federated training process. In this paper, we proposed a federated Support Vector Machine for the early detection of Breast cancer. This work utilized the potential benefits of Federated Learning, Firefly algorithm, and Support Vector Machine to study the early detection of Breast cancer disease. The proposed model leverages a distributed computing approach, allowing the SVM model to be trained across multiple clients while preserving data privacy. In addition, we used the Firefly algorithm as a feature selection technique to identify the most relevant features for Breast cancer detection. We evaluated the performance of the proposed model using publicly available Breast cancer datasets namely Wisconsin dataset. The experimental results of this proposed work showed that our proposed model has achieved an accuracy of 95.68%.

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Applying Big Data Based Deep Learning System to Intrusion Detection

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Big Data Mining and Analytics 2020年第3期3卷 181-195页

作者： Wei Zhong Ning Yu Chunyu Ai Division of Math and Computer Science University of South Carolina UpstateSpartanburgSC 29303USA Department of Computing Sciences State University of New York College at BrockportBrockportNY 14420USA

With vast amounts of data being generated daily and the ever increasing interconnectivity of the world’s internet infrastructures,a machine learning based Intrusion Detection Systems(IDS)has become a vital component to protect our economic and national *** shallow learning and deep learning strategies adopt the single learning model approach for intrusion *** single learning model approach may experience problems to understand increasingly complicated data distribution of intrusion ***,the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of *** order to further enhance the performance of machine learning based IDS,we propose the Big Data based Hierarchical Deep Learning System(BDHDLS).BDHDLS utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the *** deep learning model in the BDHDLS concentrates its efforts to learn the unique data distribution in one *** strategy can increase the detection rate of intrusive attacks as compared to the previous single learning model *** on parallel training strategy and big data techniques,the model construction time of BDHDLS is reduced substantially when multiple machines are deployed.

关键词： intrusion detection deep learning convolution neural network fully connected feedforward neural network multi-level clustering algorithm

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Quantum Invariants of Links and 3-Manifolds with Boundary defined via Virtual Links: Calculation of some examples

arXiv

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arXiv 2022年

作者： Dye, Heather A. Kauffman, Louis H. Ogasa, Eiji Division of Science and Math McKendree University 701 College Rd. LebanonIL62254 United States Department of Mathematics Statistics and Computer Science University of Illinois at Chicago 851 South Morgan Street ChicagoIL60607-7045 United States Meijigakuin University Computer Science Kanagawa Yokohama244-8539 Japan

In the prequel of this paper, Kauffman and Ogasa introduced new topological quantum invariants of compact oriented 3-manifolds with boundary where the boundary is a disjoint union of two identical surfaces. The invariants are constructed via surgery on manifolds of the form F ×I where I denotes the unit interval. Since virtual knots and links are represented as links in such thickened surfaces, we are able also to construct invariants in terms of virtual link diagrams (planar diagrams with virtual crossings). These invariants are new, nontrivial, and calculable examples of quantum invariants of 3-manifolds with non-vacuous boundary. Since virtual knots and links are represented by embeddings of circles in thickened surfaces, we refer to embeddings of circles in the 3-sphere as classical links. Classical links are the same as virtual links that can be represented in a thickened 2-sphere and it is a fact that classical links, up to isotopy, embed in the collection of virtual links taken up to isotopy. We give a new invariant of classical links in the 3-sphere in the following sense: Consider a link L in S3 of two components. The complement of a tubular neighborhood of L is a manifold whose boundary consists in two copies of a torus. Our invariants apply to this case of bounded manifold and give new invariants of the given link of two components. Invariants of knots are also obtained. In this paper we calculate the topological quantum invariants of some examples explicitly. We conclude from our examples that our invariant is new and strong enough to distinguish some classical knots from one another. We examine links that are embedded in thickened surfaces and obtain invariants of three manifolds obtained by surgery on these thickened surfaces. One could take the viewpoint that the thickened surfaces are embedded in the three sphere and so also consider the three manifolds obtained by surgery on the links in the three sphere. These two points of view are distinct and gi

关键词： Surgery

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GPT-Based Automated Induction: Vulnerability Detection in Medical Software

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

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Beam Maps of the Canadian Hydrogen Intensity Mapping Experiment (CHIME) Measured with a Drone

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2025年第3期6卷 928-940页

作者： Tyndall, Will Reda, Alex Shaw, J. Richard Bandura, Kevin Chakraborty, Arnab Halpern, Mark Harris, Maile Kuhn, Emily MacEachern, Joshua Mena-Parra, Juan Newburgh, Laura Ordog, Anna Pinsonneault-Marotte, Tristan Polish, Anna Rose Saliwanchik, Ben Sanghavi, Pranav Siegel, Seth R. Whitmer, Audrey Wulf, Dallas Yale University Department of Physics New HavenCT06511 United States McGill University Department of Physics MontrealQC Canada McGill University Trottier Space Institute MontrealQC Canada University of British Columbia Department of Physics and Astronomy VancouverBC Canada West Virginia University Department of Computer Science and Electrical Engineering MorgantownWV United States West Virginia University Center for Gravitational Waves and Cosmology MorgantownWV United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States University of Toronto Dunlap Institute for Astronomy and Astrophysics TorontoON Canada David A. Dunlap Department of Astronomy and Astrophysics University of Toronto TorontoON Canada Department of Computer Science Math Physics and Statistics University of British Columbia Okanagan Campus KelownaBC Canada Dominion Radio Astrophysical Observatory Herzberg Astronomy and Astrophysics Research Center National Research Council Canada PentictonBC Canada Center for Astrophysics Harvard and Smithsonian CambridgeMA02138 United States Brookhaven National Laboratory Instrumentation Division UptonNY11973 United States Perimeter Institute for Theoretical Physics WaterlooON Canada

We present beam measurements of the CHIME telescope using a radio calibration source deployed on a drone payload. During test flights, the pulsing calibration source and the telescope were synchronized to GPS time, enabling in-situ background subtraction for the full N2 visibility matrix for one CHIME cylindrical reflector. We use the autocorrelation products to estimate the primary beam width and centroid location, and compare these quantities to solar transit measurements and holographic measurements where they overlap on the sky. We find that the drone, solar, and holography data have similar beam parameter evolution across frequency and both spatial coordinates. This paper presents the first drone-based beam measurement of a large cylindrical radio interferometer. Furthermore, the unique analysis and instrumentation described in this paper lays the foundation for near-field measurements of experiments like CHIME. © 2020 IEEE.

关键词： Drones

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FICK’S LAW SELECTS THE NEUMANN BOUNDARY CONDITION

arXiv

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arXiv 2023年

作者： Hilhorst, Danielle Kang, Seung-Min Kim, Ho-Youn Kim, Yong-Jung CNRS Laboratoire de Mathématiques University Paris-Saclay Cedex Orsay91405 France Department of Mathematical Sciences KAIST 291 Daehakro Yuseong-gu Daejeon34141 Korea Republic of Computer Electrical and Mathematical Sciences and Engineering Division 4700 King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We study the appearance of a boundary condition along an interface between two regions, one with constant diffusivity 1 and the other with diffusivity ϵ > 0, when ϵ → 0. In particular, we take Fick’s diffusion law in a context of reaction-diffusion equation with bistable nonlinearity and show that the limit of the reaction-diffusion equation satisfies the homogeneous Neumann boundary condition along the interface. This problem is developed as an application of heterogeneous diffusion laws to study the geometry effect of *** Codes 35K20, 35K57, 35K60, 82C24 Copyright © 2023, The Authors. All rights reserved.

关键词： Linear equations

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An Example of a Fractional Model of Enzymatic Reactions with Bi-Bi Random Mechanism

SSRN

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SSRN 2024年

作者： Nhan, Thái Anh Mai, Vinh Quang Department of Mathematics Thu Dau Mot University 06 Tran Van On Binh Duong Thu Dau Mot820000 Viet Nam Department of Mathematics and Computer Science Santa Clara University 500 El Camino Real Santa ClaraCA95053 United States Applied Math and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Enzymes are proteins capable of accelerating biochemical reactions in the metabolism process. A wide range of applications of enzymes has been developed in biotechnology, industry, pharmaceuticals, biofuels. In this paper, we give a main example which shows how to develop a fractional mathematical model that describes the enzymatic reactions with Bi-Bi random mechanism. © 2024, The Authors. All rights reserved.

关键词： Enzymes

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High-Gain Observer Design for Nonlinear Systems with Delayed Output Measurements using Time-Varying Gains

High-Gain Observer Design for Nonlinear Systems with Delayed...

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IEEE Conference on Decision and Control

作者： Ania Adil Ibrahima N’Doye Taous-Meriem Laleg-Kirati Laboratoire de Mathématiques Pures et Appliquées (LMPA) Mouloud Mammeri University of Tizi-Ouzou Tizi-Ouzou Algeria Computer Electrical and Mathematical Science & Engineering Division (CEMSE) KAUST Saudi Arabia The National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a high-gain observer design for nonlinear systems with delayed output measurements using time-varying gains. The proposed observer is endowed with an exponential stability guarantee and relies on the generalization of the Halanay-type inequalities. We establish that the estimated state and the adapted gain are exponentially bounded and prevent the oscillatory response of the estimates. The time-varying gain feature limits the constant high-gain values of the standard high-gain observer design to the minimum gain required to achieve stability. Furthermore, we derive an explicit relation between the maximum bound of the delay and the maximum gain parameter by using a Lyapunov-Krasovskii functional jointly with the time-varying Halanay inequality. Finally, a comparison with the standard high-gain observer is provided through numerical simulations to demonstrate the superiority of the proposed high-gain observer in rejecting the noise and reducing the peaking phenomena.

关键词： Observers Gain measurement Numerical simulation Stability analysis Robustness Numerical models Nonlinear systems

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ON q-DEFORMED CUBIC EQUATIONS: THE QUANTUM HEPTAGON AND NONAGON

arXiv

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arXiv 2024年

作者： Ovsienko, Valentin Ustinov, Alexey Centre National de la Recherche Scientifique Laboratoire de Mathématiques Université de Reims U.F.R. Sciences Exactes et Naturelles Moulin de la Housse - BP 1039 Reims51687 cedex 2 France Faculty of Computer Science HSE University Moscow Russia Khabarovsk Division of the Institute for Applied Mathematics Far-Eastern Branch Russian Academy of Sciences Russia

The recent notion of q-deformed irrational numbers is characterized by the invariance with respect to the action of the modular group PSL(2, ), or equivalently under the Burau representation of the braid group B3. The theory of q-deformed quadratic irrationals and quadratic equations with integer coefficients is known and entirely based on this invariance. In this paper, we consider the case of cubic irrationals. We show that irreducible cubic equations with three distinct real roots and cyclic Galois group C3 (or /3) acting by a third order element of PSL(2, ), have a canonical q-deformation, that we describe. This class of cubic equations contains well-known examples including the equations that describe regular 7- and 9-gons. Copyright © 2024, The Authors. All rights reserved.

关键词： Group theory

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