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Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series 40

Sequential Multi-Dimensional Self-Supervised Learning for Cl...

引用

40th International conference on Machine Learning

作者： Raghu, Aniruddh Chandak, Payal Alam, Ridwan Guttag, John Stultz, Collin M. MIT Cambridge MA 02139 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA USA

Self-supervised learning (SSL) for clinical time series data has received significant attention in recent literature, since these data are highly rich and provide important information about a patient's physiological state. However, most existing SSL methods for clinical time series are limited in that they are designed for unimodal time series, such as a sequence of structured features (e.g., lab values and vitals signs) or an individual high-dimensional physiological signal (e.g., an electrocardiogram). These existing methods cannot be readily extended to model time series that exhibit multimodality, with structured features and high-dimensional data being recorded at each timestep in the sequence. In this work, we address this gap and propose a new SSL method - Sequential Multi-Dimensional SSL - where a SSL loss is applied both at the level of the entire sequence and at the level of the individual high-dimensional data points in the sequence in order to better capture information at both scales. Our strategy is agnostic to the specific form of loss function used at each level - it can be contrastive, as in SimCLR, or non-contrastive, as in VICReg. We evaluate our method on two real-world clinical datasets, where the time series contains sequences of (1) high-frequency electrocardiograms and (2) structured data from lab values and vitals signs. Our experimental results indicate that pre-training with our method and then fine-tuning on downstream tasks improves performance over baselines on both datasets, and in several settings, can lead to improvements across different self-supervised loss functions.

关键词： Supervised learning

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MISA-MD: A New Design of Molecular Dynamics Software for GPU Architecture 24

MISA-MD: A New Design of Molecular Dynamics Software for GPU...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Chu, Genshen Chen, Tianyi Chen, Dandan Bai, He Li, Guannan Ren, Shuai Li, Yang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Research Center Zhejiang Lab Beijing China

ISBN: (纸本)9798350319934

Molecular dynamics is a significant method in computational material and many other fields, and is computationally intensive. In this paper, we present a new molecular dynamics software, MISA-MD, which is mainly designed for high computational performance on GPU architecture. The main contributions include a redesign of the atom data structure, communication optimizations, parallel strategies and a series of GPU optimizations on memory accessing. Compared to the CPU version, the presented version of MISA-MD can achieve more than 130 speedup when running on GPU hardware. Moreover, our MISAMD code also demonstrates 51 % performance improvement, when compared to the well-known software LAMMPS on GPU. © 2022 IEEE.

关键词： Graphics processing unit

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Testing the Unknown: A Framework for OpenMP Testing via Random program Generation

Testing the Unknown: A Framework for OpenMP Testing via Rand...

引用

high performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International conference for

作者： Ignacio Laguna Patrick Chapman Konstantinos Parasyris Giorgis Georgakoudis Cindy Rubio-González Center for Applied Scientific Computing Lawrence Livermore National Laboratory Department of Computer Science University of California Davis

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates.

关键词： Automatic programming high performance computing conferences computer bugs Production Encoding Grammar Testing

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Proposing a Low-Rank Approximation Method with Mathematical Guarantees for high-Dimensional Tensor Data

Proposing a Low-Rank Approximation Method with Mathematical ...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Hasegawa, Hiroki Yata, Kazuyoshi Okada, Yukihiko Kunimatsu, Jun University of Tsukuba Program in Service Engineering Tsukuba Japan Sciences University of Tsukuba Institute of Pure and Applied Tsukuba Japan University of Tsukuba Institute of Systems and Information Engineering Tsukuba Institute for Advanced Research Center for Artificial Intelligence Research Tsukuba Japan University of Tsukuba Institute of Medicine Tsukuba Japan

ISBN: (纸本)9798350362480

The analysis of high-dimensional tensor data presents significant challenges for traditional methods such as CANDECOMP/PARAFAC (CP) decomposition and higher-Order Singular Value Decomposition (HOSVD) due to the size, complexity, and noise inherent in the data. Recent studies have emphasized the critical need for noise-robust approaches capable of reducing dimensionality while preserving data integrity, particularly for matrix-form data, underscoring the necessity of theoretical advancements in noise reduction. In this study, we propose a novel method, Noise-Reduced higher-Order Singular Value Decomposition (NR-HOSVD), which integrates noise reduction techniques into the HOSVD framework to enhance dimensionality reduction for matrix data. In comparative simulations with CP decomposition and HOSVD, NR-HOSVD consistently demonstrated lower relative errors as the rank increased, particularly for high-dimensional tensors, surpassing the performance of conventional methods beyond a certain rank threshold. Furthermore, NR-HOSVD exhibited superior stability and faster convergence on large-scale datasets compared to existing approaches. These findings suggest that NR-HOSVD provides a more accurate and efficient approach to high-dimensional tensor data analysis, making it particularly well-suited for applications in big data and machine learning. In the future, NR-HOSVD is expected to serve as a pivotal method for enhancing the accuracy and efficiency of high-dimensional tensor data analysis, with broad applicability anticipated in fields such as large-scale data processing and precision medicine. © 2024 IEEE.

关键词： Data integrity

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A Negative Sample Enhancement Strategy to Improve Contrastive Learning for Unsupervised Sentence Representation

A Negative Sample Enhancement Strategy to Improve Contrastiv...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Li, Hao Xie, Chunzhi Cao, Zhoulin Lee, Yan-Li Liu, Jia Gao, Zhisheng Xihua University School of Computer and Software Engineering Chengdu China

ISBN: (纸本)9798350362480

Contrastive learning has achieved remarkable success in sentence representation research within the field of natural language processing. Nevertheless, most existing studies focus primarily on the construction of negative samples while paying insufficient attention to the mechanisms for handling these samples. Such methods tend to treat all negative samples within a batch as equally important, neglecting the crucial role that negative samples play in semantic learning. This oversight can result in suboptimal model performance in semantic understanding. To address these issues, this study proposes a negative sample enhancement strategy that applies fine-grained processing to different types of constructed negative samples based on their importance. In the high-dimensional semantic space, hard negative samples and false negative samples are treated respectively - by increasing the distance between hard negative samples and positive samples, while treating false negative samples as pseudo-positive samples to enhance the attraction between them and the anchor sample. This strategy enables the model to perform more effective semantic differentiation and representation. Experimental results on the Semantic Textual Similarity (STS) task demonstrate that the proposed method outperforms existing baseline methods in unsupervised sentence representation learning. © 2024 IEEE.

关键词： Contrastive Learning

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Federated Kolmogorov-Arnold Networks for Health Data Analysis: A Study Using ECG Signal

Federated Kolmogorov-Arnold Networks for Health Data Analysi...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Zeleke, Sileshi Nibret Bochicchio, Mario University of Bari Department of Computer Science Bari Italy

ISBN: (纸本)9798350362480

With the increasing adoption of predictive healthcare systems, Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative model training, which is crucial for sensitive health data. This study investigates the integration of Kolmogorov-Arnold Network (KAN) with FL to enhance predictive healthcare applications. KAN's innovative implementation of spline-based activation functions offers enhanced flexibility, interoperability, and efficiency in capturing complex nonlinear data patterns with fewer parameters than traditional neural networks. When benchmarked against standard Multilayer Perceptrons (MLPs), KANs show significant performance improvements in both real-world and synthetic electrocardiogram (ECG) datasets. Notably, KAN's adaptive activation functions, particularly quadratic splines with a grid size of 30, effectively capture the complexities of time-series data, achieving a test accuracy of 93.73% and an F1-score of 92.90% on the MIT-BIH arrhythmia dataset. Furthermore, KAN outperformed MLP on synthetic ECG data, highlighting its potential for broader medical applications. Despite its higher computational overhead, federated KAN shows considerable promise in healthcare settings that require both privacy and high accuracy in modeling complex data patterns. The findings underscore the need for optimization to address computational inefficiencies in KAN-based FL environments while establishing a foundation for future applications of KAN in time-series-based medical diagnostics. © 2024 IEEE.

关键词： Multilayer neural networks

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Optimization Model and Simulation Platform for Task Scheduling between Edge Servers in Edge Computing Systems

Optimization Model and Simulation Platform for Task Scheduli...

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2024 International conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Zhang, Qi Xu, Weiqiang School of Computer Science and Technology of Zhejiang Sci-Tech University Hangzhou310018 China School of Information Science and Engineering of Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9798331507886

Edge computing has emerged as a popular paradigm to meet the growing demands of time-sensitive and compute-intensive tasks. In edge computing systems, the problem of task load imbalance among edge servers often leads to high response latency, which negatively affects the performance of individual server. Therefore, a well-designed scheduling strategy for edge task plays a crucial role in improving the overall system performance. There is some existing work on solving the task load imbalance problem by scheduling between devices and edges, or between edges and cloud servers. Unlike the existing work, in this paper we perform task scheduling between edge servers, using low load edges to assist high load edges for task processing. However, due to the multiple tasks that are processed on each edge server, the edge server state is complex, scheduling tasks between edge servers is a difficult problem. To address this problem, we consider modeling each type of task to be processed on edge servers as a queue, and formulate the optimization model with the constraint that the sum of edge service time within a time slot is less than the slot length. And then we model the task scheduling problem between edge servers as an optimization model. Since we need to validate our proposed model through the simulation platform, and the existing simulation platform does not support task scheduling between edge servers, we design a simulation platform with edge scheduling capabilities to validate the effectiveness of our proposed model. Through simulation experiments, it is verified that the edge task scheduling policy obtained by our optimization model can well improve the performance of the edge computing system. Moreover, since our simulation platform is modular developed based on Python programming, it can be easily applied to other optimization models. The code for our optimization model and simulation platform is available at https://***/AtticusZhang/PyEdgeSim. © 2024 IEEE.

关键词： Cloud platforms

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Federated Meta Continual Learning for Efficient and Autonomous Edge Inference 24th

Federated Meta Continual Learning for Efficient and Autono...

引用

24th International conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Bingze Ho, Stella Qu, Youyang Xu, Chenhao Luan, Tom H. Gao, Longxiang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Biomedical Engineering The University of Melbourne VIC Melbourne Australia School of Information Technology Deakin University VIC Geelong Australia School of Cyber Engineering Xidian University Xi’an China

ISBN: (纸本)9789819615476

With an increasing volume of data being collected and processed on edge, Federated Learning (FL) has become a dominant approach to train shared global models on distributed data held by diverse edge clients without exposing privacy. However, with expectations of high-quality user experience, instantaneous intelligent decisions must be made at the network edge to meet performance, security, and latency requirements. Intuitively, this challenge becomes more daunting as edge devices are constrained by limited computing and storage resources. Furthermore, edge clients need to continuously acquire, update, accumulate, and apply knowledge in domains requiring the processing of sensitive data and long-term model training. It has been proven that edge clients often forget previous tasks knowledge after learning new tasks, resulting in catastrophic forgetting. To mitigate this, we propose a Federated Meta Continual Learning (FMCL) approach. It requires edge clients not only to learn new tasks quickly but also to memorize rapidly. Specifically, we adopted a MAML (Model-Agnostic Meta-Learning) framework with memory replay, setting up buffers at edge clients to consolidate old tasks. In such a way, the model learns optimal initialization to quickly adapt to all seen tasks while preventing catastrophic forgetting. Extensive evaluation results indicate that FMCL performs well in alleviating catastrophic forgetting and achieves a good balance between learning new tasks and remembering existing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Enhancing Movement Directions Decoding in EEG-BCI through Force Feedback 30

Enhancing Movement Directions Decoding in EEG-BCI through Fo...

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30th International conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Li, Zhiying Zhao, Jinqiu Yu, Jingwan Sun, Chenyang Lu, Xiaohan Li, Xuehan Zhang, Mingming Chen, Yi-Feng Southern University of Science and Technology Department of Biomedicial Engineering Shenzhen China Harbin Institute of Technology Shenzhen Department of Mechanical Engineering Shenzhen China Southern University of Science and Technology Shenzhen Key Laboratory of Smart Healthcare Engineering Department of Biomedicial Engineering Shenzhen China

ISBN: (纸本)9798350391916

In advancing brain-computer interface (BCI), force feedback has demonstrated potential in enhancing neurophysiological interactions during motor tasks. This study investigated the impact of force feedback on brain activity and the accuracy of decoding movement direction. We developed an electroencephalogram (EEG)-based BCI paradigm with four levels of force feedback (i.e., 0 N, 8 N, 16 N, 24 N) applied during right-hand movements to left and right directions. Six participants were involved to ensure robust results. Three deep learning models - DeepConvNet, ShallowConvNet, and EEGNet - were used to decode movement directions. Findings from event-related desynchronization/event-related synchronization (ERD/ERS) and movement-related cortical potentials (MRCPs) indicated that increased force feedback significantly enhanced the brain's response to motor stimuli. The decoding results revealed that force feedback notably improved decoding accuracy of DeepConvNet and EEGNet, particularly under medium and high-intensity conditions. Specifically, three models demonstrated accuracy improvements of 11%, 4%, and 12% under high-intensity force feedback, respectively. These results suggest that specific force feedback enhances motor area responsiveness, improving movement intention decoding in BCI. Our study confirms the positive impact of force feedback on BCI performance, highlighting the potential of force feedback-based BCI systems. © 2024 IEEE.

关键词： Brain computer interface

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An MLIR-based Compiler Flow for System-Level Design and Hardware Acceleration 22

An MLIR-based Compiler Flow for System-Level Design and Hard...

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IEEE/ACM 41st International conference on computer Aided-Design (ICCAD)

作者： Agostini, Nicolas Bohm Curzel, Serena Amatya, Vinay Tan, Cheng Minutoli, Marco Castellana, Vito Giovanni Manzano, Joseph Kaeli, David Tumeo, Antonino PNNL Richland WA 99354 USA Politecn Milan Milan Italy Northeastern Univ Boston MA USA

ISBN: (纸本)9781450392174

The generation of custom hardware accelerators for applications implemented within high-level productive programming frameworks requires considerable manual effort. To automate this process, we introduce SODA-OPT, a compiler tool that extends the MLIR infrastructure. SODA-OPT automatically searches, outlines, tiles, and pre-optimizes relevant code regions to generate high-quality accelerators through high-level synthesis. SODA-OPT can support any high-level programming framework and domain-specific language that interface with the MLIR infrastructure. By leveraging MLIR, SODA-OPT solves compiler optimization problems with specialized abstractions. Backend synthesis tools connect to SODA-OPT through progressive intermediate representation lowerings. SODA-OPT interfaces to a design space exploration engine to identify the combination of compiler optimization passes and options that provides high-performance generated designs for different backends and targets. We demonstrate the practical applicability of the compilation flow by exploring the automatic generation of accelerators for deep neural networks operators outlined at arbitrary granularity and by combining outlining with tiling on large convolution layers. Experimental results with kernels from the PolyBench benchmark show that our high-level optimizations improve execution delays of synthesized accelerators up to 60x. We also show that for the selected kernels, our solution outperforms the current of state-of-the art in more than 70% of the benchmarks and provides better average speedup in 55% of them. SODA-OPT is an open source project available at https://***/sodalite/soda-opt.

关键词： MLIR high-Level Optimizations HLS Compilers

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