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The Journal of Machine Learning Research 2022年第1期23卷 4544-4598页

作者： Michael Puthawala Konik Kothari Matti Lassas Ivan Dokmanić Maarten De Hoop Department of Computational and Applied Mathematics Rice University Houston TX Department of Computer Science University of Illinois at Urbana-Champaign Champaign IL Department of Mathematics and Statistics University of Helsinki Finland Department of Mathematics and Computer Science University of Basel Basel Switzerland

Injectivity plays an important role in generative models where it enables inference; in inverse problems and compressed sensing with generative priors it is a precursor to well posedness. We establish sharp characterizations of injectivity of fully-connected and convolutional ReLU layers and networks. First, through a layerwise analysis, we show that an expansivity factor of two is necessary and sufficient for injectivity by constructing appropriate weight matrices. We show that global injectivity with iid Gaussian matrices, a commonly used tractable model, requires larger expansivity between 3.4 and 10.5. We also characterize the stability of inverting an injective network via worst-case Lipschitz constants of the inverse. We then use arguments from differential topology to study injectivity of deep networks and prove that any Lipschitz map can be approximated by an injective ReLU network. Finally, using an argument based on random projections, we show that an end-to-end--rather than layerwise--doubling of the dimension suffices for injectivity. Our results establish a theoretical basis for the study of nonlinear inverse and inference problems using neural networks.

关键词： bilipschitz networks injective generative models random neural networks deep learning universal approximation

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Numerical evaluation for the peristaltic flow in the proximity of double-diffusive convection of non-Newtonian nanofluid under the MHD

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International Journal of Thermofluids 2024年 23卷

作者： Riaz, Muhammad Bilal Hussain, Azad Saddiqa, Ayesha Jarad, Fahd IT4Innovations VSB – Technical University of Ostrava Ostrava Czech Republic Department of Computer Science and Mathematics Lebanese American University Byblos Lebanon Department of Mathematics University of Gujrat Gujrat50700 Pakistan Department of Mathematics Faculty of Arts and Sciences Çankaya University Ankara06790 Turkey Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Hawally32093 Kuwait

This article mainly studies the 2-D propagation of a non-compressible Eyring-Powell nanofluid flow through a stretched wedge under the Magneto-hydrodynamic effect. Equations for temperature, concentration, double-diffusive convection and momentum are taken into consideration. Since solving the dimensionless equations associated with our study is an uphill task, we utilize the MATLAB bvp4c solver to illustrate the graphical performance of different parameters. This manuscript may be significant in the projects in the field of industry and medicine. The manuscript's noteworthy features include the magnetic field, heat source-sink parameter, double diffusivity, and solar radiation process. The main finding is that the local fluid parameter k1 and magnetic field parameter M decelerate the velocity of nanofluid. Because different nanoparticles have different effects on fluids, the fluid's temperature exhibits multiple behaviors, therefore by escalating the Prandtl number initially, it increases and then decelerates due to the presence of nanoparticles. The concentration of fluid declines as the Schmidt number rises. The double diffusivity of Eyring-Powell nanofluid improves with magnification in the fluid's Schmidt number Sc and Prandtl number Pr. © 2024 The Author(s)

关键词： Prandtl number

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Splinets – ORTHOGONAL SPLINES AND FDA FOR THE CLASSIFICATION PROBLEM

arXiv

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arXiv 2023年

作者： Basna, Rani Nassar, Hiba Podgórski, Krzysztof Department of Statistics Lund University Sweden Cognitive Systems Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

This study introduces an efficient workflow for functional data analysis in classification problems, utilizing advanced orthogonal spline bases. The methodology is based on the flexible Splinets package, featuring a novel spline representation designed for enhanced data efficiency. Several innovative features contribute to this efficiency: 1) Utilization of Orthonormal Spline Bases – The workflow incorporates recently introduced orthonormal spline bases, known as splinets, ensuring a robust foundation for data analysis;2) Consideration of Spline Support Sets – The proposed spline object representation accounts for spline support sets, which refines the accuracy of data representation;3) Data-Driven Knot Selection – The workflow employs data-driven knot selection techniques for spline construction, optimizing the overall analysis process. To illustrate the effectiveness of this approach, we applied the workflow to the Fashion MINST dataset, a collection of two-dimensional images. We demonstrate the classification process and highlight significant efficiency gains. Particularly noteworthy are the improvements that can be achieved through the 2D generalization of our methodology, especially in scenarios where data sparsity and dimension reduction are critical factors. A key advantage of our workflow is the projection operation into the space of splines with arbitrarily chosen knots, allowing for versatile functional data analysis associated with classification problems. Moreover, this study explores some features of the Splinets package suited for functional data analysis. The algebra and calculus of splines use Taylor expansions at the knots within the support sets. Various orthonormalization techniques for B-splines are implemented, including the highly recommended dyadic method, which leads to the creation of splinets. Importantly, the locality of B-splines concerning support sets is preserved in the corresponding splinet. Using this locality, along with implemented

关键词： Interpolation

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Ontology-Driven IoT System for Monitoring Hypertension 25

Ontology-Driven IoT System for Monitoring Hypertension

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： de Souza, Pedro Lopes de Souza, Wanderley Lopes Pires, Luís Ferreira Moreira, João Luiz Rebelo da Silva Rodrigues, Ronitti Juner Ciferri, Ricardo Rodrigues Semantics Cybersecurity & Services Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede Netherlands Ubiquitous Computing Group Graduate Program in Computer Science Computing Department Federal University of São Carlos P.O. Box 676 São Carlos Brazil

ISBN: (纸本)9789897586484

Hypertension is a noncommunicable disease (NCD) that causes global concern, high costs and a high number of deaths. Internet of Things, Ubiquitous Computing, and Cloud Computing enable the development of systems for remote and real-time monitoring of patients affected with NCDs like hypertension. This paper reports on a system for monitoring hypertension patients that was built by employing these techniques. This system allows the vital signs of a patient (blood pressure, heart rate, body temperature) to be captured via sensors built in a wearable device similar to a wristwatch. These signals are transmitted to the patient's mobile device for processing, and the generated clinical data are sent to the cloud to be properly presented and analysed by the health professionals responsible for the patient. To deal with semantic interoperability issues that arise when multiple different devices and system components must interoperate, a semantic model was conceived for this system in terms of ontologies for diseases and devices. This paper also presents the semantic module that we developed and implemented in the cloud to perform reasoning based on this model, demonstrating the potential benefits of incorporating semantic technologies in our system. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Ontology

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CAPE: Commitment-based Privacy-Preserving Payment Channel Scheme in Blockchain

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Gai, Keke Guo, Yunwei Yu, Jing Chan, Weilin Zhu, Liehuang Zhang, Yinqian Meng, Weizhi The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The Institute of Information Engineering Chinese Academy of Sciences Beijing China The Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Applied Mathematics and Computer Science Cybersecurity Engineering Technical University of Denmark Kongens Lyngby Denmark

Ensuring scalability in cryptocurrency systems is significant in guaranteeing real-world utility along with the remarkable increment of cryptographic currency. As an alternative in solving scalability issue, payment channel allows users to deliver extensive offline transactions without uploading massive transaction details to the blockchain, such that increasing efficiency can be achieved. However, the implementation of payment channel still encounters privacy concerns when considering the publicly available transaction amounts and the potentials in mining associations between transaction parties. In this paper, we propose a novel payment channel scheme, entitled Commitment-based Anonymous Payment ChannEl (CAPE), to facilitate unlimited off-chain bidirectional payments while guaranteeing participants’ privacy. The proposed scheme adopts zero-knowledge proof (zk-SNARKs) and verifiable timed (VTD) commitments to ensure the anonymity of the relationship between on-chain and off-chain transactions, privacy of transaction amounts, and security of balances. We comprehensively formalize security definitions and present rigorous proofs for each security attribute. Experiment results further demonstrate the practical viability of CAPE. © 2025 IEEE. All rights reserved.

关键词： Differential privacy

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The Iterative Compromise Ranking Analysis (ICRA) - The New Approach to Make Reliable Decisions

The Iterative Compromise Ranking Analysis (ICRA) - The New A...

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Special Sessions in the Advances in Information Systems and Technologies Track, AIST 2022 and 17th Conference on Information Systems Management, ISM 2022 held as part of the Federated Conference on computer science and Information Systems, FedCSIS 2022

作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Shekhovtsov, Andrii Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

In the field of multi-criteria decision-making, compromise is often sought because it is highly desirable for decision-making. However, over the years, many methods have been developed for decision-making, between which discrepancies in the final rankings are often present. For this reason, it is worth noting the possibility of a compromise between different multi-criteria decision-making methods. One such solution is the Iterative Compromise Ranking Analysis (ICRA), which, by means of an iterative evaluation of the preferences of alternatives, leads to a compromise between the methods under consideration. This work presents an example of a solution to a theoretical decision problem, for which five methods were used: TOPSIS, VIKOR, MARCOS, MABAC and EDAS. In addition, an empirical analysis of the compromise solution was carried out to check the effect of parameters on the number of iterations needed to reach a compromise and the differences between the rankings proposed by the methods and the compromise ranking. The work showed that this is an interesting tool that can find its use in the field of multi-criteria decision-making as well as can be used to analyze the behaviour of multi-criteria decision-making methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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A computational evaluation of gender asymmetry in semantic change 43

A computational evaluation of gender asymmetry in semantic c...

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43rd Annual Meeting of the Cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Schnell, Eric Xu, Yang Cognitive Science and Mathematics Programs University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

A fundamental goal in cognitive and historical linguistic research on semantic change is to characterize the regularity in how word meanings change over time. We examine a common belief that has not yet been evaluated comprehensively, which asserts that gender of a word influences its direction of semantic change. By this account, female terms like mistress should undergo pejorative change in meaning systematically more so than male terms like master. We evaluate this claim in gender-marked word pairs in English and French respectively as languages without and with grammatical gender. Our results provide supporting evidence for gender asymmetry in semantic change of English words but not French words. Our study raises questions about the generality of the claim about gender asymmetry in semantic change and provides a scalable computational framework for understanding the social roots of word meaning change. © Cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Sentiment analysis

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A Sustainable Approach for Determining Compromise Ranking Based on Intuitonistic Fuzzy Score Functions

A Sustainable Approach for Determining Compromise Ranking B...

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作者： Paradowski, Bartosz Kizielewicz, Bartlomiej Więckowski, Jakub Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland National Institute of Telecommunications Szachowa 1 Warsaw04-894 Poland

ISBN: (纸本)9783031295690

Many real-world decision-making problems require some degree of uncertainty to be taken into account. For purpose of representing such problems, intuitionistic fuzzy sets are used, however, most well-known multi-criteria decision-making methods operate in a crisp environment. In this paper, we present an assessment of score functions that are used to convert fuzzy numbers into crisp ones. Five score functions were selected to assess their usefulness and effectiveness. Those functions were used to transform the theoretical fuzzy decision matrix problems into a crisp environment for evaluating alternatives using the Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS) method. In addition, the compromise between score functions is presented and compared with the other results. The research showed that score functions are useful tools when dealing with problems in an uncertain environment and might prove helpful for decision-makers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Recent advances and applications of deep learning methods in materials science

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npj Computational Materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction in *** contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

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Steganography Algorithm Based on Transparent Display Principle of Executable program Icon 8th

Steganography Algorithm Based on Transparent Display Princip...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Tian, Zuwei Gao, Zhichen College of Computer Science Hunan First Normal University Changsha410205 China Department of Applied Mathematics and Statistics College of Engineering and Applied Sciences Stony Brook University Stony BrookNY11794-2300 United States

ISBN: (纸本)9783031067631

Icon resource is a very important resource in PE file. In order to get the best visual effect when displaying icons under different screen backgrounds and different operating system environments, PE files often store multiple icons of different sizes and different color depths. Because the size of the icon is irregular, the number of bytes filled is often large. These filled byte spaces are redundant and can be used to hide information without affecting the execution result and performance of the program. This paper presents a steganography algorithm based on transparent display principle of executable program icon, which hides secret information in transparent areas of icons. The algorithm has good concealment. The display effect of the embedded icon is the same as that of the original icon. It does not cause any visual distortion neither change the functionality of the executable. The size of the executable and the performance of the executable will not change. It can be widely used in covert data communication. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Steganography

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