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arXiv 2024年

作者： Wang, Ziyue Li, Yang Liu, Ya-Feng Ma, Junjie The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Shenzhen Research Institute of Big Data Shenzhen518172 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

This paper studies the device activity detection problem in a massive multiple-input multiple-output (MIMO) system for near-field communications (NFC). In this system, active devices transmit their signature sequences to the base station (BS), which detects the active devices based on the received signal. In this paper, we model the near-field channels as correlated Rician fading channels and formulate the device activity detection problem as a maximum likelihood estimation (MLE) problem. Compared to the traditional uncorrelated channel model, the correlation of channels complicates both algorithm design and theoretical analysis of the MLE problem. On the algorithmic side, we propose two computationally efficient algorithms for solving the MLE problem: an exact coordinate descent (CD) algorithm and an inexact CD algorithm. The exact CD algorithm solves the one-dimensional optimization subproblem exactly using matrix eigenvalue decomposition and polynomial root-finding. By approximating the objective function appropriately, the inexact CD algorithm solves the one-dimensional optimization subproblem inexactly with lower complexity and more robust numerical performance. Additionally, we analyze the detection performance of the MLE problem under correlated channels by comparing it with the case of uncorrelated channels. The analysis shows that when the overall number of devices N is large or the signature sequence length L is small, the detection performance of MLE under correlated channels tends to be better than that under uncorrelated channels. Conversely, when N is small or L is large, MLE performs better under uncorrelated channels than under correlated ones. Simulation results demonstrate the computational efficiency of the proposed algorithms and verify the correctness of the analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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DEEP NEURAL TANGENT KERNEL AND LAPLACE KERNEL HAVE THE SAME RKHS 9

DEEP NEURAL TANGENT KERNEL AND LAPLACE KERNEL HAVE THE SAME ...

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9th International Conference on Learning Representations, ICLR 2021

作者： Chen, Lin Xu, Sheng Simons Institute for the Theory of Computing University of California Berkeley United States Department of Statistics and Data Science Yale University United States

We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere Sd−1. Additionally, we prove that the exponential power kernel with a smaller power (making the kernel less smooth) leads to a larger RKHS, when it is restricted to the sphere Sd−1 and when it is defined on the entire Rd © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Laplace transforms

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A dynamic model of social media ad information diffusion in uncertain environment

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Soft computing 2024年 1-16页

作者： Jin, Meiling Ning, Yufu Liu, Fengming Li, Zhen Zheng, Haoran Gao, Yichang Zhou, Jian School of Management Shanghai University Shanghai200444 China School of Information Engineering Shandong Youth University of Political Science Jinan250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan250103 China Business School Shandong Normal University Jinan250014 China School of Logistics and Maritime Studies Bahrain Polytechnic Isa Town Bahrain School of Economics Shanghai University Shanghai200444 China Confucius Institute University of Bahrain Zallaq Bahrain

In the social media environment, ad (advertise) information diffusion can effectively increase the ad promotion effect and promote product marketing. Therefore, it is essential to study the mechanism of ad information diffusion in social media. This study aims to help cope with the complexity and uncertainty in social media ad information diffusion systems by identifying the causal relationships behind ad information diffusion behavior and clarifying the feedback mechanisms of system factors, and then testing the validity of the system model through simulation. Specifically, this study innovatively combines system dynamics and uncertainty theory to construct a dynamic model of ad information diffusion system. Particularly, the uncertainty effect of environmental noise on the ad information diffusion system is considered and portrayed as a Liu process. This study can explore the diffusion mechanism of ad information more precisely, so as to better serve the ad promotion industry. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： System theory

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Modulated Spike-Time Dependent Plasticity (Stdp)-Based Learning for Spiking Neural Network (Snn): A Review

SSRN

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SSRN 2024年

作者： Rahman, Nazeerah Abdul Yusoff, Nooraini Khamis, Nurulaqilla Faculty of Data Science & Computing Universiti Malaysia Kelantan UMK City Campus Pengkalan Chepa Malaysia Institute for Artificial Intelligence & Big Data Universiti Malaysia Kelantan City Campus Pengkalan Chepa Kelantan Kota Bharu16100 Malaysia

Computational models called Spiking Neural Networks (SNNs) are modelled after the intricate information processing discovered in the brain. A key learning principle, spike-time dependent plasticity (STDP), controls how the temporal connection between pre- and post-synaptic spikes affects synaptic weight changes. STDP is a Hebbian learning rule used in training algorithms for SNNs. SNNs encode information in the precise timing of spikes. Modified variants of STDP have recently evolved to improve the learning and adaptation of SNNs. These versions incorporate extra elements like neuromodulators and dendritic processing. This review focuses on the underlying principles, experimental results, and computational models to provide an in-depth overview of the developments in modulated STDP-based learning for SNNs. It also deals with the difficulties of modified STDP, such as computational complexity, parameter optimisation, scalability, and the quest for biological plausibility. This review is invaluable for researchers and practitioners interested in creating practical and biologically plausible learning algorithms for modulated STDP. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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InTime: Towards Performance Predictability In Byzantine Fault Tolerant Proof-of-Stake Consensus

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Proceedings of the ACM on Management of data 2025年第1期3卷 1-27页

作者： Weijie Sun Zihuan Xu Wangze Ni Lei Chen The Hong Kong University of Science and Technology Hong Kong Hong Kong Shenzhen Institute of Computing Sciences Shenzhen China Zhejiang University Hangzhou China The State Key Laboratory of Blockchain and Data Security Hangzhou China & Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China The Hong Kong University of Science and Technology Hong Kong Hong Kong & HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

Performance predictability, ensuring low latency variability, is crucial for the reliability and efficiency of blockchain consensus. Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS) consensus aims to achieve stable transaction processing latency by scheduling block generation at consistent intervals. However, BFT-PoS's incentive mechanisms grant all transaction tips to the block proposer, which can be exploited by delaying proposals to gain extra Maximal Extractable Value (MEV) rewards, thus undermining performance predictability. Existing solutions impose penalties for delays but lack a standard for measuring the extra rewards from delays or fail in malicious *** paper introduces InTime, a novel approach to safeguard performance predictability in BFT-PoS by economically motivating timely block proposals. We first introduce the untimely MEV ratio, a reliable metric to measure the extra rewards gained from proposal delays, facilitating our countermeasures against deliberate delays. Furthermore, we propose the arrival rate incentive (ARI), aligning rewards with transaction arrival timing among nodes to reduce potential MEV manipulation. To make ARI robust against malicious behaviors, we establish a committee time witness (CTW) workflow to accurately gather and verify transaction arrival times. Extensive experiments demonstrate that InTime can effectively reduce latency variability by up to 95.9%.

关键词： PoS consensus blockchain incentive design

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pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning 39

pFedES: Generalized Proxy Feature Extractor Sharing for Mode...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yi, Liping Yu, Han Ren, Chao Wang, Gang Liu, Xiaoguang Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, has attracted significant interest from industry and academia. To allow each data owner (FL client) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged. Existing MHPFL approaches either rely on the availability of a public dataset with special characteristics to facilitate knowledge transfer, incur high computational and communication costs, or face potential model leakage risks. To address these limitations, we propose a model-heterogeneous personalized Federated learning approach based on generalized proxy feature Extractor Sharing (pFedES) for supervised image classification tasks. (1) We devise a shared small proxy homogeneous feature extractor before each client’s heterogeneous local model. (2) Clients train them via the proposed iterative learning to enable the exchange of global generalized knowledge and local personalized knowledge. (3) The small proxy local homogeneous extractors produced after local training are uploaded to the server for aggregation to facilitate knowledge fusion across clients. We theoretically prove pFedES converges with a non-convex convergence rate O(1/T). Experiments on 3 benchmark datasets against 9 baselines demonstrate that pFedES performs state-of-the-art model accuracy while maintaining efficient communication and computation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Non-salient region erasure for time series augmentation

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Frontiers of Computer science 2022年第6期16卷 203-205页

作者： Pin LIU Xiaohui GUO Bin SHI Rui WANG Tianyu WO Xudong LIU State Key Lab of Software Development Environment(SKLSDE) Beihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data and Brain Computing(BDBC) Beijing100191China Hangzhou Innovation Institute Beihang UniversityHangzhou310052China School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an710049China

1 Introduction Time seriesaugmentationis an essential approachto solvethe overfitting problem on the time series classification(TSC)task[1,2].Although existing approaches perform better in mitigating this problem,none of them focus on protecting saliency regions on time *** key informative shapelets contained in these regions are the core basis for distinguishing categories(e.g.,upward spikes in ECG and high amplitude in Sensor).

关键词： Sensor series series

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Machine Learning-Based Timeseries Analysis for Cryptocurrency Price Prediction: A Systematic Review and Research

Machine Learning-Based Timeseries Analysis for Cryptocurrenc...

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Networking and Communications (ICNWC), International Conference on

作者： Siva Kumar.A Gopirajan Pv Beulah Jackson Department of Data Science and Business Systems School of Computing SRM Institute of Science and TechnologyKattankulathur Campus Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

A virtual currency known as cryptocurrencies holds all business online. It’s virtual money that wouldn’t materialize like complicated conventional paper currency. Thus, this study emphasizes a distinction between distributed paper currency and cryptocurrencies, where these individuals may access information without outside interference. Because of its considerable market swings, such cryptocurrencies have an influence upon commerce as well as foreign diplomacy. Virtual currencies which are available in the market, such as Bitcoin (BTC), Ethereum (ETH), Terra (LUNA), Solana (SOL), Cardano (ADA), Tether (USDT), Binance coin (BNB), USD coin, XRP coin, Avalanche coin (AVAX) and Lite coin (LTC), etc. This study focussed on a detailed analysis of the literature about Machine Learning (ML) methods used for predictions. This proposed work also focused on implementing an efficient Machine Learning (ML)-based time series model for predicting BTC cryptocurrency prices. Long Short-Term Memory (LSTM) forecasting theory was established to accommodate the fluctuation of bitcoin prices and achieve great precision. The effectiveness of the LSTM in predicting the price of a cryptocurrency is demonstrated by this suggested study’s comparison between it and comparable time-series models.

关键词： Training Technological innovation Systematics Online banking Time series analysis Machine learning Bitcoin

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Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation

arXiv

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arXiv 2024年

作者： Čevora, Kate Glocker, Ben Bai, Wenjia Department of Computing Imperial College London United Kingdom Department of Brain Sciences Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom

Deep learning-based medical image segmentation has seen tremendous progress over the last decade, but there is still relatively little transfer into clinical practice. One of the main barriers is the challenge of domain generalisation, which requires segmentation models to maintain high performance across a wide distribution of image data. This challenge is amplified by the many factors that contribute to the diverse appearance of medical images, such as acquisition conditions and patient characteristics. The impact of shifting patient characteristics such as age and sex on segmentation performance remains relatively under-studied, especially for abdominal organs, despite that this is crucial for ensuring the fairness of the segmentation model. We perform the first study to determine the impact of population shift with respect to age and sex on abdominal CT image segmentation, by leveraging two large public datasets, and introduce a novel metric to quantify the impact. We find that population shift is a challenge similar in magnitude to cross-dataset shift for abdominal organ segmentation, and that the effect is asymmetric and dataset-dependent. We conclude that dataset diversity in terms of known patient characteristics is not necessarily equivalent to dataset diversity in terms of image features. This implies that simple population matching to ensure good generalisation and fairness may be insufficient, and we recommend that fairness research should be directed towards better understanding and quantifying medical image dataset diversity in terms of performance-relevant characteristics such as organ morphology. © 2024, CC BY.

关键词： Image segmentation

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AMIR: An Automated MisInformation Rebuttal System — A COVID-19 Vaccination datasets based Exposition

arXiv

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arXiv 2023年

作者： Sharma, Shakshi Datta, Anwitaman Sharma, Rajesh School of Artificial Intelligence Bennett University Greater Noida India College of Computing and Data Science Nanyang Technological University Singapore Institute of Computer Science University of Tartu Estonia

Misinformation has emerged as a major societal threat in the recent years in general;specifically in the context of the COVID-19 pandemic, it has wrecked havoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable solutions for combating misinformation are the need of the hour. This work explored how existing information obtained from social media and augmented with more curated fact checked data repositories can be harnessed to facilitate automated rebuttal of misinformation at scale. While the ideas herein can be generalized and reapplied in the broader context of misinformation mitigation using a multitude of information sources and catering to the spectrum of social media platforms, this work serves as a proof of concept, and as such, it is confined in its scope to only rebuttal of tweets, and in the specific context of misinformation regarding COVID-19. It leverages two publicly available datasets, viz. FaCov (fact-checked articles) [1] and misleading (social media Twitter) [2] data on COVID-19 Vaccination. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

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