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arXiv 2024年

作者： Pi, Xiangyu Zhu, Lipeng Mao, Haobin Xiao, Zhenyu Xia, Xiang-Gen Zhang, Rui School of Electronic and Information Engineering State Key Laboratory of CNS/ATM Beihang University Beijing100191 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

The effective utilization of unlicensed spectrum is regarded as an important direction to enable the massive access and broad coverage for next-generation wireless local area network (WLAN). Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face huge challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP’s antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Moreover, to further reduce the overhead of antenna movement, we propose an offline solution for APV and AOM design based on statistical channel state information (CSI). In addition, we further extend the proposed scheme from uni-polarized to dual-polarized modes for all antennas. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA sch

关键词： Convex optimization

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Towards Certifying '∞ Robustness using Neural Networks with '∞-dist Neurons 38

Towards Certifying '∞ Robustness using Neural Networks with...

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38th International Conference on Machine Learning, ICML 2021

作者： Zhang, Bohang Cai, Tianle Lu, Zhou He, Di Wang, Liwei Key Laboratory of Machine Perception MOE School of EECS Peking University China Department of Electrical and Computer Engineering Princeton University United States Zhongguancun Haihua Institute for Frontier Information Technology China Department of Computer Science Princeton University United States Microsoft Research Center for Data Science Peking University China

ISBN: (纸本)9781713845065

It is well-known that standard neural networks, even with a high classification accuracy, are vulnerable to small '∞-norm bounded adversarial perturbations. Although many attempts have been made, most previous works either can only provide empirical verification of the defense to a particular attack method, or can only develop a certified guarantee of the model robustness in limited scenarios. In this paper, we seek for a new approach to develop a theoretically principled neural network that inherently resists '∞ perturbations. In particular, we design a novel neuron that uses '∞-distance as its basic operation (which we call '∞-dist neuron), and show that any neural network constructed with '∞-dist neurons (called '∞-dist net) is naturally a 1-Lipschitz function with respect to '∞-norm. This directly provides a rigorous guarantee of the certified robustness based on the margin of prediction outputs. We then prove that such networks have enough expressive power to approximate any 1-Lipschitz function with robust generalization guarantee. We further provide a holistic training strategy that can greatly alleviate optimization difficulties. Experimental results show that using '∞-dist nets as basic building blocks, we consistently achieve state-of-the-art performance on commonly used datasets: 93.09% certified accuracy on MNIST (∊ = 0.3), 35.42% on CIFAR-10 (∊ = 8/255) and 16.31% on TinyImageNet (∊ = 1/255). Copyright © 2021 by the author(s)

关键词： Neurons

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Mining data impressions from deep models as substitute for the unavailable training data

arXiv

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arXiv 2021年

作者： Nayak, Gaurav Kumar Mopuri, Konda Reddy Jain, Saksham Chakraborty, Anirban Department of Computational and Data Sciences Indian Institute of Science Bangalore India Department of Computer Science and Engineering Indian Institute of Technology Tirupati

Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen data. However, in absence of training data, the utility of a trained model is merely limited to either inference or better initialization towards a target task. In this paper, we go further and extract synthetic data by leveraging the learnt model parameters. We dub them data Impressions, which act as proxy to the training data and can be used to realize a variety of tasks. These are useful in scenarios where only the pretrained models are available and the training data is not shared (e.g., due to privacy or sensitivity concerns). We show the applicability of data impressions in solving several computer vision tasks such as unsupervised domain adaptation, continual learning as well as knowledge distillation. We also study the adversarial robustness of lightweight models trained via knowledge distillation using these data impressions. Further, we demonstrate the efficacy of data impressions in generating data-free Universal Adversarial Perturbations (UAPs) with better fooling rates. Extensive experiments performed on benchmark datasets demonstrate competitive performance achieved using data impressions in absence of original training data. Copyright © 2021, The Authors. All rights reserved.

关键词： Distillation

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Analysis of Phylogeny Tracking Algorithms for Serial and Multiprocess Applications

arXiv

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arXiv 2024年

作者： Moreno, Matthew Andres Papa, Santiago Rodriguez Dolson, Emily University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data Science University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Michigan State University East Lansing United States

Since the advent of modern bioinformatics, the challenging, multifaceted problem of reconstructing phylogenetic history from biological sequences has hatched perennial statistical and algorithmic innovation. Studies of the phylogenetic dynamics of digital, agent-based evolutionary models motivate a peculiar converse question: how to best engineer tracking to facilitate fast, accurate, and memory-efficient lineage reconstructions? Here, we formally describe procedures for phylogenetic analysis in both serial and distributed computing scenarios. With respect to the former, we demonstrate reference-counting-based pruning of extinct lineages. For the latter, we introduce a trie-based phylogenetic reconstruction approach for "hereditary stratigraphy" genome annotations. This process allows phylogenetic relationships between genomes to be inferred by comparing their similarities, akin to reconstruction of natural history from biological DNA sequences. Phylogenetic analysis capabilities significantly advance distributed agent-based simulations as a tool for evolutionary research, and also benefit application-oriented evolutionary computing. Such tracing could extend also to other digital artifacts that proliferate through replication, like digital media and computer viruses. Copyright © 2024, The Authors. All rights reserved.

关键词： Phylogenetic

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Advancements in Cybersecurity Using Deep Learning Techniques Attack Detection for Trojan Horses

Advancements in Cybersecurity Using Deep Learning Techniques...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Hosanna Princye P Ahila A A. Anna Lakshmi N. Ragavendran K. E. Purushothaman G. Uma Maheswari R. Saravanakumar Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bangalore Anekal India Department of Information Technology R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Software Analyst Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Today's computer networks are threatened by Trojan horse assaults and other cybersecurity dangers. We propose and evaluate deep learning methods using the Kaggle-hosted Trojan Detection dataset to detect Trojan horse assaults. This project uses these approaches. Our malware detection technology uses CNN classification for accuracy. We clean and enhance the dataset to make the model more versatile. Due to extensive testing and optimization, Trojan horse detection rates reach 98.47%. We found that the deep learning architecture accurately identifies malware. We also discuss CNN model features that illuminate Trojan horse attacks. The technique offers complete cyber security and expands our knowledge of harmful software. Deep learning may boost cybersecurity defenses against emerging attacks like Trojan horses, according to our study.

关键词： Deep learning Training Accuracy Feature extraction Safety Trojan horses Convolutional neural networks computer crime Testing Python

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Estimating Mean Viral Load Trajectory from Intermittent Longitudinal data and Unknown Time Origins

arXiv

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arXiv 2023年

作者： Woodbridge, Yonatan Mandel, Micha Goldberg, Yair Huppert, Amit The Gertner Institute for Epidemiology & Health Policy Research Sheba Medical Center Ramat Gan Israel Department of Computer Science Holon Institute of Technology Holon Israel Department of Statistics and Data Science The Hebrew University of Jerusalem Jerusalem Israel Faculty of Industrial Engineering and Management Technion - Israel Institute of Technology Haifa Israel Faculty of Medicine Tel Aviv University Israel

Viral load (VL) in the respiratory tract is the leading proxy for assessing infectiousness potential. Understanding the dynamics of disease-related VL within the host is very important and help to determine different policy and health recommendations. However, often only partial followup data are available with unknown infection date. In this paper we introduce a discrete time likelihood-based approach to modeling and estimating partial observed longitudinal samples. We model the VL trajectory by a multivariate normal distribution that accounts for possible correlation between measurements within individuals. We derive an expectation-maximization (EM) algorithm which treats the unknown time origins and the missing measurements as latent variables. Our main motivation is the reconstruction of the daily mean SARS-Cov-2 VL, given measurements performed on random patients, whose VL was measured multiple times on different days. The method is applied to SARS-Cov-2 cycle-threshold-value data collected in Israel. © 2023, CC BY.

关键词： SARS

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Development and Characterization of Zinc Dry Electrodes for Wearable Electrophysiology

Development and Characterization of Zinc Dry Electrodes for ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Cassia Rizq Alessandro D’Amico Aidan Truel Joelle Faybishenko Min Suk Lee Jeong-Hoon Kim Gert Cauwenberghs Virginia R. de Sa Department of Bioengineering University of California San Diego La Jolla USA Department of Cognitive Science University of California San Diego La Jolla USA Halıcıoğlu Data Science Institute University of California San Diego La Jolla USA Department of Electrical and Computer Engineering University of California San Diego La Jolla USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Zinc dry electrodes were fabricated and investigated for wearable electrophysiology recording. Results from electrochemical impedance spectroscopy and electromyography functionality testing show that zinc electrodes are suitable for use in electrophysiology. Two electrode configurations were tested: a standard disc and a custom tripolar concentric ring configuration. However, no functional benefit was observed with the tripolar concentric ring electrodes as compared to the disc electrodes.

关键词： Electrodes Electromyography Recording engineering in medicine and biology Zinc Standards Testing Electrochemical impedance spectroscopy

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FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows

arXiv

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arXiv 2023年

作者： Qu, Yuxiao Rao, Jinmeng Gao, Song Zhang, Qianheng Chao, Wei-Lun Su, Yu Miller, Michelle Morales, Alfonso Huber, Patrick R. Department of Computer Science Carnegie Mellon University United States Geospatial Data Science lab University of Wisconsin Madison United States Department of Computer Science and Engineering Ohio State University United States Center for Integrated Agricultural Systems University of Wisconsin Madison United States Department of Planning and Landscape Architecture University of Wisconsin Madison United States Institute of the Environment University of California Davis United States

Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with their multidimensional interactions and decisions, presents significant challenges. This paper proposes FLEE-GNN, a novel Federated Learning System for Edge-Enhanced Graph Neural Network, designed to overcome these challenges and enhance the analysis of geospatial resilience of multicommodity food flow network, which is one type of spatial networks. FLEE-GNN addresses the limitations of current methodologies, such as entropy-based methods, in terms of generalizability, scalability, and data privacy. It combines the robustness and adaptability of graph neural networks with the privacy-conscious and decentralized aspects of federated learning on food supply network resilience analysis across geographical regions. This paper also discusses FLEE-GNN’s innovative data generation techniques, experimental designs, and future directions for improvement. The results show the advancements of this approach to quantifying the resilience of multicommodity food flow networks, contributing to efforts towards ensuring global food security using AI methods. The developed FLEE-GNN has the potential to be applied in other spatial networks with spatially heterogeneous sub-network distributions. © 2023, CC BY.

关键词： Geographical regions

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Eigenvectors of the De Bruijn Graph Laplacian: A Natural Basis for the Cut and Cycle Space

arXiv

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arXiv 2024年

作者： Philippakis, Anthony Mallinar, Neil Pandit, Parthe Belkin, Mikhail GV 5 Cambridge Ctr CambridgeMA02142 United States The Broad Institute of MIT and Harvard 415 Main St CambridgeMA02142 United States Department of Computer Science and Engineering UC San Diego 3235 Voigt Dr San DiegoCA92093 United States Center for Machine Intelligence and Data Science IIT Bombay KReSIT Building IIT Powai Maharashtra Mumbai400076 India Halıcıoglu Data Science Institute UC San Diego 3234 Matthews Ln San DiegoCA92093 United States

We study the Laplacian of the undirected De Bruijn graph over an alphabet A of order k. While the eigenvalues of this Laplacian were found in 1998 by Delorme and Tillich [1], an explicit description of its eigenvectors has remained elusive. In this work, we find these eigenvectors in closed form and show that they yield a natural and canonical basis for the cut- and cycle-spaces of De Bruijn graphs. Remarkably, we find that the cycle basis we construct is a basis for the cycle space of both the undirected and the directed De Bruijn graph. This is done by developing an analogue of the Fourier transform on the De Bruijn graph, which acts to diagonalize the Laplacian. Moreover, we show that the cycle-space of De Bruijn graphs, when considering all possible orders of k simultaneously, contains a rich algebraic structure, that of a graded Hopf algebra. © 2024, CC BY.

关键词： Undirected graphs

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DEEP LEARNING FOR ACCELERATED AND ROBUST MRI RECONSTRUCTION: A REVIEW

arXiv

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arXiv 2024年

作者： Heckel, Reinhard Jacob, Mathews Chaudhari, Akshay Perlman, Or Shimron, Efrat Technical University of Munich Department of Computer Engineering Munich Germany University of Iowa Department of Electrical and Computer Engineering Iowa CityIA United States Stanford University Department of Radiology Department of Biomedical Data Science StanfordCA United States Tel Aviv University Department of Biomedical Engineering Tel Aviv Israel Tel Aviv University Sagol School of Neuroscience Tel Aviv Israel Technion - Israel Institute of Technology Department of Electrical and Computer Engineering Department of Biomedical Engineering Haifa Israel

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction. It focuses on DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. These include end-to-end neural networks, pre-trained networks, generative models, and self-supervised methods. The paper also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling subtle bias. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices. © 2024, CC BY.

关键词： Image reconstruction

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