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MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion

MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Im...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yuan, Manman Jia, Weiming Luo, Xiong Ye, Jiazhen Zhu, Peican Li, Junlin Inner Mongolia University School of Computer Science Hohhot010000 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi'an710072 China Inner Mongolia Autonomous Region People's Hospital Department of Imaging Medicine Hohhot010017 China

ISBN: (纸本)9798350386226

The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to infer interactions. In this paper, we deal with a more arduous challenge, hypergraph modelling with synchronization between brain regions, and design a novel framework, i.e., A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion (MHSA), to tackle this challenge. Specifically, our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest (ROIs) and designs a multi-scale feature fusion mechanism to integrate dynamic connectivity features of functional magnetic resonance imaging (fMRI) from both the temporal and spectrum domains. To evaluate and op-timize the direct contribution of each ROI to phase synchronization in the temporal domain, we structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix. Experiments on the real-world dataset indicate the effectiveness of our strategy. The code is available at https://***/Jia-Weiming/MHSA. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Solar Radio Spectrum Classification by Deep Conventional Neural Network Based on Transfer Learning

Solar Radio Spectrum Classification by Deep Conventional Neu...

引用

2023 International conference on New Trends in Computational Intelligence, NTCI 2023

作者： Zhao, Haiyan Wuyouteng Cheng, Jun College of Computer Science and Technology Inner Mongolia Minzu University Inner Mongolia China National Astronomical Observatories Cas Key Laboratory of Solar Activity Beijing China

ISBN: (纸本)9798350380859

The damage to communication facilities and navigation equipments may be caused by the violent solar radio explosion. With the emergence of high-performance observation equipments, solar data shows a tremendous growth trend. Deep learning, especially convolutional neural network, is used to classify solar radio spectrum images because it performs well in the processing of massive data, especially image data. In response to the lack of solar image datasets, a transfer learning method based on four pre-trained models with ImageNet image dataset is improved to solve the problem of classifying and recognizing three categories of solar spectral images. The experimental results show that MobileNet based on this model can achieve 98.95% accuracy, while the training speed of the model is improved also. © 2023 IEEE.

关键词： Convolutional neural networks

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OpenVenus: An Open Service Interface for HPC Environment Based on SLURM 1

引用

7th International conference on Smart Computing and Communication, SmartCom 2022

作者： Wan, Meng Cao, Rongqiang Wang, Yangang Wang, Jue Li, Kai Wang, Xiaoguang Yang, Qinmeng Computer Network Information Center Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the emergence of more and more "AI + Field + HPC" applications, it is urgent to solve the problem of scheduling and management of high-performance Computing (HPC) resources, as well as the fast and efficient "cloud service" of HPC applications. This engineering problem is particularly critical because it affects the progress of scientific research, the development period of the research platform, and the learning cost of scientists. To solve the problem, a set of reusable life cycle processes for HPC resources are designed. Based on the life cycle, we propose an open service interface based on HPC, which reduces the startup time under multiple refreshes and abnormal retries by using the mode of contention lock. The active interruption of users is a typical scenario in the startup phase. Furthermore, a read-write strategy with an overlay based on Singularity is implemented to save storage space and improve running speed. In order to evaluate the serviceability and performance of the proposed interface, we deploy the service on the Venus platform and make a startup comparison experiment. In addition, the reduction of storage for 100 users is also tested. The experimental results show that under the HPC environment with SLURM, the proposed open-service interface can effectively shorten 46% startup time of applications and services and reduce 25% storage at least for each user of the Venus platform. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer software reusability

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THperf: Enabling Accurate Network Latency Measurement for Tianhe-2 System 24

THperf: Enabling Accurate Network Latency Measurement for Ti...

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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： Xu, Yunyang Wu, Ke Dong, Dezun Li, Cunlu Xiao, Liquan College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9798350319934

To prevent the network from becoming a potential performance bottleneck, two approaches commonly adopted in recent high-performance Computing systems are offloading the network stack and Remote Direct Memory Access. Notably, these technologies require precise measurement of network latency due to the increasing granularity of congestion control mechanisms. This demand is more urgent for the Tianhe system because of the huge volume and performance requirements. However, the Tianhe system lacks an accurate network latency measurement tool due to its customizable nature. In this paper, we propose THperf, a network round-trip latency measurement tool for the Tianhe network architecture that can accurately obtain network latency information without the need for hardware support. We perform accurate network latency measurement at various traffic sizes with THperf and analyze the obtained the latency as a percentage of all latency. In addition, we compare THperf to other measurement tools and discover that THperf can eliminate a significant amount of latency. Using THperf, we further measure and compare the performance of different Submission Queues in the Tianhe network, demonstrating that the hardware descriptor submission method outperforms the software descriptor submission method in terms of latency. We believe that THperf can be used as a controlled benchmark for more granular congestion control algorithms. © 2022 IEEE.

关键词： Network architecture

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Visual recognition of wheel hubs with convolutional neural network 3

Visual recognition of wheel hubs with convolutional neural n...

引用

3rd International conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Dai, Yining Fang, Zaojun Zhong, Caiming Ningbo University Faculty of Electrical Engineering and Computer Science NingBo Zhejiang China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology NingBo Zhejiang China

ISBN: (纸本)9798350331578

The traditional recognition methods of wheel hubs are mainly based on extracted feature matching. In practical production, their accuracy, robustness and processing speed are usually greatly affected. To overcome these problems, this paper proposes a recognition method based on convolutional neural network. The basic steps include two parts: wheel image pre-processing and wheel model classification. The image processing method, mainly using the detection algorithm of hough circles, obtains the center coordinates and radius of the wheel. Then it maps the ring-shaped wheel in right-angle coordinates to polar coordinates by the center coordinates and radius. This stepcan extract the ring-shaped feature information of the wheel image and reduce the influence generated by redundant features. Then a network architecture with an improved Resnet is designed to classify the wheel models. Finally, the wheel model recognition algorithm is evaluated, and the effectiveness of the method is verified through the comparison experiments of SVM, KNN and other models. The experiments show that the recognition accuracy can reach about 99.8% for 10 kinds of wheels. © 2023 IEEE.

关键词： Wheels

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Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-Aware Network 26th

Elongated Physiological Structure Segmentation via Spatial a...

引用

26th International conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Zhang, Yinglin Xi, Ruiling Fu, Huazhu Towey, Dave Bai, RuiBin Higashita, Risa Liu, Jiang Univ Nottingham Ningbo China Sch Comp Sci Ningbo 315100 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Tomey Corp Nagoya Aichi 4510051 Japan ASTAR Inst High Performance Comp IHPC Singapore Singapore

ISBN: (纸本)9783031439001;9783031439018

Robust and accurate segmentation for elongated physiological structures is challenging, especially in the ambiguous region, such as the corneal endothelium microscope image with uneven illumination or the fundus image with disease interference. In this paper, we present a spatial and scale uncertainty-aware network (SSU-Net) that fully uses both spatial and scale uncertainty to highlight ambiguous regions and integrate hierarchical structure contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps using Monte Carlo dropout to approximate Bayesian networks. Based on these spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA) module to guide the model to focus on ambiguous regions. Second, we extract the uncertainty under different scales and propose the multiscale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction. Finally, we visualize the uncertainty map of final prediction, providing interpretability for segmentation results. Experiment results show that the SSU-Net performs best on cornea endothelial cell and retinal vessel segmentation tasks. Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more accurate and robust.

关键词： Uncertainty Medical Image Segmentation Elongated Physiological Structure Deep Learning

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Exploring Large Language Models Text Style Transfer Capabilities 27

Exploring Large Language Models Text Style Transfer Capabili...

引用

27th European conference on Artificial Intelligence, ECAI 2024

作者： Li, Weijie Gu, Zhentao Fan, Xiaochao Deng, Wenjun Yang, Yong Zhao, Xinyuan Diao, Yufeng Yang, Liang College of Computer Science and Technology Xinjiang Normal University China School of Software Xinjiang University China College of Computer Science and Technology Inner Mongolia Minzu University China School of Computer Science and Technology Dalian University of Technology China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education Dalian China

ISBN: (纸本)9781643685489

The emergence of Large Language Models (LLMs) provides a new solution to text generation tasks that involve high complexity, such as text style transfer (TST) tasks. However, previous studies have not fully explored the TST capabilities of different LLMs, and have faced issues with a lack of uniform standards in the human evaluation stage. This makes the results of human evaluation difficult to reproduce and less credible. To address this, this paper designs a prompt template to guide the cutting-edge LLMs to perform effective text style transfer and carries out an in-depth comparative analysis of various small-scale language models. In the stage of human evaluation, this paper eschews the conventional rating system, opting instead for a comparative human assessment methodology, which we refer to as duel-ranking. This method determines the relative ranking of models through mutual comparison, serving as an alternative to direct scoring. Detailed evaluation instructions are provided herein, to enhance the reproducibility of this method and ensure consistency throughout the evaluation process. This manual evaluation process reveals that GPT-3.5 and GPT-4 exhibit excellent performance in the TST tasks. © 2024 The Authors.

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PSA-Swin Transformer: Image Classification on Small-Scale Datasets∗

PSA-Swin Transformer: Image Classification on Small-Scale Da...

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2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Shao, Chao Jiang, Shaochen Li, Yongming School of Computer Science and Technology Xinjiang University Urumqi830049 China

ISBN: (纸本)9781665410205

This paper introduces the PSA-Swin Transformer, a novel framework for image classification on small-scale datasets, highlighting the challenges of training effective models in resource-limited environments. Recognizing the limitations of current deep learning methods that rely heavily on large-scale datasets and extensive pre-training, we propose a method for handling small datasets. Our model can effectively han-dle smaller data volumes without the need for pre-training weights. The key to our approach is in the introduction of an efficient positional embedding (EPE) module, which improves parameter utilization and network expressiveness through a grouped convolutional architecture and shuffling operations for dynamic information exchange. In addition, we integrated the Polarized Self-Attention (PSA) module in Windows Multi-Head Self-Attention (W-MSA) and named the new module PSA-W-MSA;PSA addresses the complexity of learning element-specific attention by combining polarization filtering with augmentation techniques. Through a series of experiments on the Mini-Imagenet dataset, the PSA-Swin Transformer demonstrates notable performance, especially in environments where high-quality annotated data is scarce or costly to acquire. Our research results are expected to make progress in areas that require efficient and accurate image classification using limited resources. © 2024 IEEE.

关键词： Deep learning

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Clothed Human performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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SIMPLE: A GRADIENT ESTIMATOR FOR k-SUBSET SAMPLING 11

SIMPLE: A GRADIENT ESTIMATOR FOR k-SUBSET SAMPLING

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11th International conference on Learning Representations, ICLR 2023

作者： Ahmed, Kareem Zeng, Zhe Niepert, Mathias Van den Broeck, Guy Dept. of Computer Science UCLA United States Dept. of Computer Science Stuttgart University Germany

k-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity. The challenge lies in rendering k-subset sampling amenable to end-to-end learning. This has typically involved relaxing the reparameterized samples to allow for backpropagation, with the risk of introducing high bias and high variance. In this work, we fall back to discrete k-subset sampling on the forward pass. This is coupled with using the gradient with respect to the exact marginals, computed efficiently, as a proxy for the true gradient. We show that our gradient estimator, SIMPLE, exhibits lower bias and variance compared to state-of-the-art estimators, including the straight-through Gumbel estimator when k = 1. Empirical results show improved performance on learning to explain and sparse linear regression. We provide an algorithm for computing the exact ELBO for the k-subset distribution, obtaining significantly lower loss compared to SOTA. © 2023 11th International conference on Learning Representations, ICLR 2023. All rights reserved.

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