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Characteristic Decomposition: From Regular Sets to Normal Sets

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Journal of Systems science & Complexity 2019年第1期32卷 37-46页

作者： MOU Chenqi WANG Dongming LMIB–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University LMIB–SKLSDE–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Centre National de la Recherche Scientifique

In this paper it is shown how to transform a regular triangular set into a normal triangular set by computing the W-characteristic set of their saturated ideal and an algorithm is proposed for decomposing any polynomial set into ?nitely many strong characteristic pairs, each of which is formed with the reduced lexicographic Gr?bner basis and the normal W-characteristic set of a characterizable ideal.

关键词： Characteristic decomposition characteristic pair normal set regular set W-characteristic set

Cross-diffusion induced instability on networks

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arXiv 2023年

作者： Kuehn, Christian Soresina, Cinzia Department of Mathematics Technical University of Munich Garching b. München85748 Germany Munich Data Science Institute Technical University of Munich Garching b. München85748 Germany Complexity Science Hub Vienna Vienna1080 Austria Department of Mathematics and Scientific Computing University of Graz Graz8010 Austria

The concept of Turing instability, namely that diffusion can destabilize the uniform steady state, is well known either in the context of partial differential equations (PDEs) or in networks of dynamical systems. Recently reaction–diffusion equations with cross-diffusion terms have been investigated, showing an analogous effect called cross-diffusion induced instability. In this paper, we extend this concept to networks of dynamical systems, showing that the spectrum of the graph Laplacian determines the instability appearance, as well as the spectrum of the Laplace operator in reaction–diffusion equations. We extend to network dynamics a particular network model for competing species, coming from the PDEs context. In particular, the influence of different topology structures on the cross-diffusion induced instability is highlighted, considering regular rings and lattices, and also small-world, Erdős–Réyni, and Barabási–Albert *** Codes 92C42, 92C15, 92C17 Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations

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ACS Nanoscience Au 2023年第3期3卷 211-221页

作者： Kuschnerus, Inga C. Wen, Haotian Ruan, Juanfang Zeng, Xinrui Su, Chun-Jen Jeng, U-Ser Opletal, George Barnard, Amanda S. Liu, Ming Nishikawa, Masahiro Chang, Shery L. Y. School of Materials Science and Engineering University of New South Wales Sydney 2052 NSW Australia Electron Microscope Unit Mark Wainwright Analytical Centre University of New South Wales Sydney 2052 NSW Australia National Synchrotron Radiation Research Center Hsinchu 30076 Taiwan Department of Chemical Engineering National Tsing Hua University Hsinchu 30013 Taiwan CSIRO Data61 Clayton 3168 VIC Australia School of Computing Australian National University Acton 2601 ACT Australia Daicel Corporation Osaka 530-0011 Japan

Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3-5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood. Here, we present a novel characterization method of combining machine learning with direct cryo-transmission electron microscopy imaging to characterize the unique colloidal behavior of DNDs. Together with small-angle X-ray scattering and mesoscale simulations we show and explain the clear differences in the aggregation behavior between positively and negatively charged DNDs. Our new method can be applied to other complex particle systems, which builds essential knowledge for the safe implementation of nanoparticles in drug delivery. © 2023 The Authors. Published by American Chemical Society.

关键词： coarse-grained molecular dynamics modeling Cryo-TEM detonation nanodiamonds machine learning SAXS

Learning trivializing gradient flows for lattice gauge theories

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Physical Review D 2023年第5期107卷 L051504-L051504页

作者： Simone Bacchio Pan Kessel Stefan Schaefer Lorenz Vaitl Computation-based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus Machine Learning Group Technische Universität Berlin Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin Germany John von Neumann-Institut für Computing NIC Deutsches Elektronen-Synchrotron DESY Germany

We propose a unifying approach that starts from the perturbative construction of trivializing maps by Lüscher and then improves on it by learning. The resulting continuous normalizing flow model can be implemented using common tools of lattice field theory and requires several orders of magnitude fewer parameters than any existing machine learning approach. Specifically, our model can achieve competitive performance with as few as 14 parameters while existing deep-learning models have around 1 million parameters for SU(3) Yang-Mills theory on a 162 lattice. This has obvious consequences for training speed and interpretability. It also provides a plausible path for scaling machine-learning approaches toward realistic theories.

关键词： Lattice gauge theory Metropolis algorithm Monte Carlo methods

Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

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Journal of Computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

Towards Secure Semantic Communications in the Presence of Intelligent Eavesdroppers

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arXiv 2025年

作者： Tang, Shunpu Chen, Yuhao Yang, Qianqian Zhang, Ruichen Niyato, Dusit Shi, Zhiguo College of Information Science and Electronic Engineering Zhejiang University Hangzhou China College of Control Science and Engineering China College of Computing and Data Science Nanyang Technological University Singapore

Semantic communication (SemCom) has emerged as a promising paradigm for enhancing communication efficiency in sixth-generation (6G) networks. However, the broadcast nature of wireless channels makes SemCom systems vulnerable to eavesdropping, which poses a serious threat to data privacy. Therefore, we investigate secure SemCom systems that preserve data privacy in the presence of eavesdroppers. Specifically, we first explore a scenario where eavesdroppers are intelligent and can exploit semantic information to reconstruct the transmitted data based on advanced artificial intelligence (AI) techniques. To counter this, we introduce novel eavesdropping attack strategies that utilize model inversion attacks and generative AI (GenAI) models. These strategies effectively reconstruct transmitted private data processed by the semantic encoder, operating in both glass-box and closed-box settings. Existing defense mechanisms against eavesdropping often cause significant distortions in the data reconstructed by eavesdroppers, potentially arousing their suspicion. To address this, we propose a semantic covert communication approach that leverages an invertible neural network (INN)-based signal steganography module. This module covertly embeds the channel input signal of a private sample into that of a non-sensitive host sample, thereby misleading eavesdroppers. Without access to this module, eavesdroppers can only extract host-related information and remain unaware of the hidden private content. We conduct extensive simulations under various channel conditions in image transmission tasks. Numerical results show that while conventional eavesdropping strategies achieve a success rate of over 80% in reconstructing private information, the proposed semantic covert communication effectively reduces the eavesdropping success rate to 0%. Furthermore, results demonstrate that eavesdroppers can reconstruct host images with relatively good quality, achieving a peak signal-to-noise ratio

关键词： Sensitive data

CASSOCK: Viable Backdoor Attacks against DNN in the Wall of Source-Specific Backdoor Defences

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arXiv 2022年

作者： Wang, Shang Gao, Yansong Fu, Anmin Zhang, Zhi Zhang, Yuqing Susilo, Willy Liu, Dongxi School of Computer Science and Engineering Nanjing University of Science and Technology China CSIRO’s Data61 Australia University of Western Australia Australia University of Chinese Academy of Sciences China School of Computing and Information Technology University of Wollongong Australia

As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor attacks (SSBAs). Compared to traditional SABAs, SSBAs are more advanced in that they have superior stealthier in bypassing mainstream countermeasures that are effective against SABAs. Nonetheless, existing SSBAs suffer from two major limitations. First, they can hardly achieve a good trade-off between ASR (attack success rate) and FPR (false positive rate). Besides, they can be effectively detected by the state-of-the-art (SOTA) countermeasures (e.g., SCAn [40]). To address the limitations above, we propose a new class of viable source-specific backdoor attacks coined as CASSOCK. Our key insight is that trigger designs when creating poisoned data and cover data in SSBAs play a crucial role in demonstrating a viable source-specific attack, which has not been considered by existing SSBAs. With this insight, we focus on trigger transparency and content when crafting triggers for poisoned dataset where a sample has an attacker-targeted label and cover dataset where a sample has a ground-truth label. Specifically, we implement CASSOCKTrans that designs a trigger with heterogeneous transparency to craft poisoned and cover datasets, presenting better attack performance than existing SSBAs. We also propose CASSOCKCont that extracts salient features of the attacker-targeted label to generate a trigger, entangling the trigger features with normal features of the label, which is stealthier in bypassing the SOTA defenses. While both CASSOCKTrans and CASSOCKCont are orthogonal, they are complementary to each other, generating a more powerful attack, called CASSOCKComp, with further improved attack performance and stealthiness. To demonstrate their viability, we perform a comprehensive evaluation of the three CASSOCK-based attacks on four popular datasets (i.e., MNIST, CIFAR10, GTSRB and LFW) and three

关键词： Deep neural networks

An asymptotically compatible coupling formulation for nonlocal interface problems with jumps

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arXiv 2022年

作者： Glusa, Christian D'Elia, Marta Capodaglio, Giacomo Gunzburger, Max Bochev, Pavel B. Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Data Science and Computing Group Sandia National Laboratories LivermoreCA United States Computational Physics and Methods Group Los Alamos National Laboratory Los AlamosNM United States Department of Scientific Computing Florida State University TallahasseeFL United States

We introduce a mathematically rigorous formulation for a nonlocal interface problem with jumps and propose an asymptotically compatible finite element discretization for the weak form of the interface problem. After proving the well-posedness of the weak form, we demonstrate that solutions to the nonlocal interface problem converge to the corresponding local counterpart when the nonlocal data are appropriately prescribed. Several numerical tests in one and two dimensions show the applicability of our technique, its numerical convergence to exact nonlocal solutions, its convergence to the local limit when the horizons vanish, and its robustness with respect to the patch test. Copyright © 2022, The Authors. All rights reserved.

关键词： Finite element method

Distilling the Unknown to Unveil Certainty

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arXiv 2023年

作者： Zhao, Zhilin Cao, Longbing Zhang, Yixuan Lin, Kun-Yu Zheng, Wei-Shi The Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia The DataX Research Centre Macquarie University SydneyNSW2109 Australia The China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing Hangzhou Dianzi University Zhejiang Hangzhou310005 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China The School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou510275 China

Out-of-distribution (OOD) detection is essential in identifying test samples that deviate from the in-distribution (ID) data upon which a standard network is trained, ensuring network robustness and reliability. This paper introduces OOD knowledge distillation, a pioneering learning framework applicable whether or not training ID data is available, given a standard network. This framework harnesses unknown OOD-sensitive knowledge from the standard network to craft a certain binary classifier adept at distinguishing between ID and OOD samples. To accomplish this, we introduce Confidence Amendment (CA), an innovative methodology that transforms an OOD sample into an ID one while progressively amending prediction confidence derived from the standard network. This approach enables the simultaneous synthesis of both ID and OOD samples, each accompanied by an adjusted prediction confidence, thereby facilitating the training of a binary classifier sensitive to OOD. Theoretical analysis provides bounds on the generalization error of the binary classifier, demonstrating the pivotal role of confidence amendment in enhancing OOD sensitivity. Extensive experiments spanning various datasets and network architectures confirm the efficacy of the proposed method in detecting OOD samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks