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IEEE International Symposium on Biomedical Imaging

作者： Vaibhavi S. Itkyal Armin Iraji Zening Fu Vince D. Calhoun Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University GA Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University GA Department of Neuroscience Emory University GA Department of Computer Science Georgia State University Atlanta GA Department of Electrical and Computer Engineering Georgia Institute of Technology GA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

White matter (WM) has traditionally been undervalued in functional brain connectivity studies due to its weaker blood-oxygenation-level-dependent (BOLD) signals compared to gray matter (GM). Conventional atlas-based approaches often overlook the individualized aspects of functional networks, limiting their applicability for WM. In this study, we introduce the first WM intrinsic connectivity network (ICN) templates derived from a large dataset of over 100,000 scans, demonstrating the feasibility of estimating individualized WM networks using multivariate-spatially constrained ICA (scICA). By combining WM and GM ICNs, our work provides an expanded framework for evaluating functional network connectivity (FNC) across both tissue types, offering new insights into the integrative role of WM in functional connectivity. This approach holds particular promise for enhancing the understanding of connectivity alterations in clinical populations, such as individuals with schizophrenia.

关键词： Limiting Cognitive processes Grey matter Independent component analysis Schizophrenia Biomarkers Functional analysis White matter Biomedical imaging

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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IEEE International Conference on Big data

作者： Zheyi Qin Randy Paffenroth Anura P. Jayasumana Electrical and Computer Engineering Colorado State University Fort Collins USA Department of Data Science Program Worcester Polytechnic Institute Worcester MA USA

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph’s topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph’s topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

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ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting

arXiv

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arXiv 2025年

作者： Yu, Guoqi Li, Yaoming Wang, Juncheng Guo, Xiaoyu Aviles-Rivero, Angelica I. Yang, Tong Wang, Shujun Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong Data Structures Laboratory School of Computer Science Peking University Beijing China DAMTP University of Cambridge Cambridge United Kingdom

Recent advancements have progressively incorporated frequency-based techniques into deep learning models, leading to notable improvements in accuracy and efficiency for time series analysis tasks. However, the Mid-Frequency Spectrum Gap in the real-world time series, where the energy is concentrated at the low-frequency region while the middle-frequency band is negligible, hinders the ability of existing deep learning models to extract the crucial frequency information. Additionally, the shared Key-Frequency in multivariate time series, where different time series share indistinguishable frequency patterns, is rarely exploited by existing literature. This work introduces a novel module, Adaptive Mid-Frequency Energy Optimizer, based on convolution and residual learning, to emphasize the significance of mid-frequency bands. We also propose an Energy-based Key-Frequency Picking Block to capture shared Key-Frequency, which achieves superior inter-series modeling performance with fewer parameters. A novel Key-Frequency Enhanced Training strategy is employed to further enhance Key-Frequency modeling, where spectral information from other channels is randomly introduced into each channel. Our approach advanced multivariate time series forecasting on the challenging Traffic, ECL, and Solar benchmarks, reducing MSE by 4%, 6%, and 5% compared to the previous SOTA iTransformer. Code is available at this GitHub Repository: https://***/Levi-Ackman/ReFocus. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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5G-Enabled V2X Communication with Cloud-Powered XGBoost Algorithm for Electric Transportation

5G-Enabled V2X Communication with Cloud-Powered XGBoost Algo...

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International Conference on Communications and Signal Processing

作者： J Gnana Jeslin M Sujatha S. Aghalya C. Jehan M.V Ishwarya P Vedasundara Vinayagam Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering St. Joseph’s College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Multitech Dr.Rangarajan Dr.Sagunthala Engineering College Chennai Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The opportunity for electric transportation system optimization has never been greater with the combination of cloud-based machine learning algorithms and 5G-enabled Vehicle-to-Everything (V2X) connectivity. To improve the effectiveness, reliability, and longevity of electric vehicle (EV) networks, it provides a new architecture combining V2X communication enabled by 5G and an XGBoost algorithm driven by the cloud. Vehicles may exchange data in real time and make decisions with the help of infrastructure and the cloud via the seamless communication made possible by 5G networks’ high-speed, low-latency connection. The XGBoost algorithm, hosted on cloud servers, can use this data to forecast several metrics, including traffic flow, energy usage, and the best way to charge. Cloud computing allows more sophisticated analysis and prediction models by moving the processing load from individual cars to large, powerful servers. It improves transportation efficiency, decreases congestion, minimizes energy usage, and optimizes charging infrastructure via simulation and real-world experiments.

关键词： Measurement Cloud computing Machine learning algorithms Transportation Signal processing algorithms Prediction algorithms Real-time systems

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Learnware Specification via Label-Aware Neural Embedding 39

Learnware Specification via Label-Aware Neural Embedding

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Wei Mao, Jun-Xiang Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education China Information Technology and Data Management Department of China Mobile Communications Group Zhejiang Co. Ltd China

ISBN: (纸本)157735897X

The learnware paradigm aims to establish a learnware dock system of numerous well-trained machine learning models, enabling users to reuse existing helpful models for their tasks instead of starting from scratch. Each learnware in the system is a well-established model submitted by its developer, associated with a specification generated by the learnware dock system. The specification characterizes the specialty of the corresponding model, enabling it to be identified accurately for new task requirements. Existing specification generation methods are mostly based on the Reduced Kernel Mean Embedding (RKME) technique, which uses the Maximum Mean Discrepancy (MMD) in the Reproducing Kernel Hilbert Space (RKHS) to seek a reduced set that characterizes the model’s capabilities. However, existing RKME-based methods mainly utilize feature information to generate specifications by assuming the existence of the ground-truth labeling function, while leaving the label information, which is capable of providing rich semantic characterization, untouched. Furthermore, the quality of the generated specifications heavily relies on the choice of the kernels, which makes it prohibitive to adapt to all real-world scenarios. In this paper, to overcome the above limitations, we propose a novel specification approach named LANE, i.e., Label-Aware Neural Embedding. In LANE, the neural embedding space is utilized to replace the RKHS, effectively circumventing the step of kernel selection and thereby addressing the dependency on kernels in existing RKME-based specification methods. More importantly, LANE uses the label information as additional supervision to enhance the generation process, resulting in specifications of superior quality. Extensive experiments demonstrate the effectiveness and superiority of the proposed LANE approach in the learnware paradigm. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Hilbert spaces

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ViTime: A Visual Intelligence-Based Foundation Model for Time Series Forecasting

arXiv

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arXiv 2024年

作者： Yang, Luoxiao Wang, Yun Fan, Xinqi Cohen, Israel Chen, Jingdong Zhao, Yue Zhang, Zijun Department of Automation Xi’an University of Technology China Department of Data Science City University of Hong Kong Hong Kong Electrical and Computer Engineering Technion Israel School of Marine Science and Technology Northwest Polytechnical University China The Manchester Metropolitan University United Kingdom

Time series forecasting (TSF) possesses great practical values in various fields, including power and energy, transportation, etc. TSF methods have been studied based on knowledge from classical statistics to modern deep learning. Yet, all of them were developed based on one fundamental concept, the numerical data fitting. Thus, the models developed have been long known for being problem-specific and lacking application generalizability. A TSF foundation model serving TSF tasks across different applications can reverse such an impression. The central question is then how to develop such a TSF foundation model. This paper offers a pioneering study in developing a TSF foundation model and proposes a vision intelligence-powered framework, ViTime, for the first time. In ViTime, a method synthesizing authentic time series periodic and trend patterns is developed to enrich sample pattern diversity. A deep architecture operating TSF in image metric space is designed to achieve significantly enhanced TSF generalizability. Extensive experiments demonstrate ViTime’s SOTA performance across multiple settings. In zero-shot scenarios, ViTime outperforms TimesFM by 9-15%. With just 10% fine-tuning data, ViTime surpasses both foundation models and fully-supervised benchmarks trained on complete datasets, with this performance gap widening further at 100% fine-tuning. Additionally, ViTime exhibits exceptional robustness, handling missing data without imputation and outperforming TimesFM by 20-30% under various data perturbations. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series

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Incentivizing Participation in SplitFed Learning: Convergence Analysis and Model Versioning

Incentivizing Participation in SplitFed Learning: Convergenc...

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International Conference on Distributed Computing Systems

作者： Pengchao Han Chao Huang Xingyan Shi Jianwei Huang Xin Liu School of Information Engineering Guangdong University of Technology Guangzhou China Department of Computer Science The University of California Davis USA School of Data Science The Chinese University of Hong Kong Shenzhen China School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

In SplitFed learning (SFL), a global model is split into two segments, where distributed clients train the first segment in a federated manner and a main server trains the other. Existing studies focus on algorithm development but ignore the important issue of incentives, without which self-interested clients may be unwilling to participate. We fill this gap by presenting a first incentive study in SFL. One challenge is that the design requires an understanding of how clients' participation affects the model performance. To this end, we provide a first convergence analysis for SFL considering partial client participation to guide the mechanism design. Another challenge is that monetary payment may not be viable for large distributed systems. To this end, we propose a model-versioning mechanism where the main server assigns different versions of models (of different qualities) to clients as incentives. The design is further complicated by clients' multi-dimensional private information. To this end, we design the model-versioning mechanism so that it decouples clients' decisions and admits a weakly dominant strategy at equilibrium. We prove that our mechanism is feasible, effective, and incentive compatible. Experimental results show that our mechanism greatly improves client participation and model accuracy compared to a benchmark.

关键词： Analytical models Accuracy Mechanism design Benchmark testing Numerical models Servers Distributed computing

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Discern-XR: An Online Classifier for Metaverse Network Traffic

arXiv

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arXiv 2024年

作者： Manjunath, Yoga Suhas Kuruba Wissborn, Austin Szymanowski, Mathew Li, Mushu Zhao, Lian Zhang, Xiao-Ping Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University Toronto Canada Department of Computer Science and Engineering Lehigh University BethlehemPA United States Shenzhen Key Laboratory of Ubiquitous Data Enabling Tsinghua Shenzhen International Graduate School Tsinghua University China

In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FIA) are proposed to extract critical frame-related statistics from raw network data having only four application-level features. A novel Augmentation, Aggregation, and Retention Online Training (A2R-OT) algorithm is proposed to find an accurate classification model through online training methodology. In addition, we contribute to the real-world Metaverse dataset comprising virtual reality (VR) games, VR video, VR chat, augmented reality (AR), and mixed reality (MR) traffic, providing a comprehensive benchmark. Discern-XR outperforms state-of-the-art classifiers by 7% while improving training efficiency and reducing false-negative rates. Our work advances Metaverse network traffic classification by standing as the state-of-the-art solution. © 2024, CC BY.

关键词： Internet service providers

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A Machine Learning Model for Foreign Exchange Prediction 2nd

A Machine Learning Model for Foreign Exchange Prediction

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Ajibade, Temiloluwa Iyanuoluwa Jasser, Muhammed Basheer Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Adediran, Anthonia Oluwatosin Majeed, Anwar P. P. Abdul Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Department of Business Management Scholar School System Birmingham United Kingdom School of Engineering and Technology Sunway University Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The rise of deep learning algorithms has sparked interest in the financial domain. The foreign exchange market (FOREX) is the globally leading financial market with millions of active stakeholders. Research in deep learning for forecasting price movements has shown its efficacy in financial markets. Notably, LSTM have empirically proven the best performance among other machine learning techniques. However, studies have shown the lack of deep learning hybrid comparisons to fortify empirical claims, especially in the FX market characterized for its high volatility and variance among currency pairs. This study develops a generalized LSTM model to forecast the major pairs and conducts a comparative analysis with a secondary hybrid deep learning model to assess and solidify the robustness of deep learning methods in forex forecasting. The empirical scenario simulates a classification problem employing cross-validation techniques to optimize the models’ generalization rigorously. Evaluation of the model utilized the accuracy, precision, recall, and f1-score metrics. Results indicated the performance of the models from best to worst were LSTM, SRNN and followed by CNN indicating the importance of testing deep learning approaches in different empirical setups. Conclusively, this study highlights the need challenges in forex forecasting and raises the need for more robust assessments on machine learning models forecasting capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Recovery and generalization in over-realized dictionary learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 6060-6082页

作者： Jeremias Sulam Chong You Zhihui Zhu Department of Biomedical Engineering & Mathematical Institute for Data Science Johns Hopkins University Baltimore MD Department of Electrical Engineering & Computer Sciences University of California Berkeley CA Department of Electrical & Computer Engineering University of Denver Denver CO

In over two decades of research, the field of dictionary learning has gathered a large collection of successful applications, and theoretical guarantees for model recovery are known only whenever optimization is carried out in the same model class as that of the underlying dictionary. This work characterizes the surprising phenomenon that dictionary recovery can be facilitated by searching over the space of larger over-realized models. This observation is general and independent of the specific dictionary learning algorithm used. We thoroughly demonstrate this observation in practice and provide an analysis of this phenomenon by tying recovery measures to generalization bounds. In particular, we show that model recovery can be upper-bounded by the empirical risk, a model-dependent quantity and the generalization gap, reflecting our empirical findings. We further show that an efficient and provably correct distillation approach can be employed to recover the correct atoms from the over-realized model. As a result, our meta-algorithm provides dictionary estimates with consistently better recovery of the ground-truth model.

关键词： dictionary learning model recovery sparse models over-realization over-parameterization

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