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Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

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arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks

CellDet: Dual-Task Cell Detection Network for IHC-Stained Image Analysis

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CellDet: Dual-Task Cell Detection Network for IHC-Stained Im...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ji, Wei Tian, Weiwei Pan, Wenbin Sun, Siqi Jiang, Hui Feng, Rui Zhang, Yuejie Zhao, Rui-Wei Jin, Gang Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Fudan University Academy for Engineering and Technology Shanghai China Navy Military Medical University Changhai Hospital Shanghai China

ISBN: (纸本)9781665401265

Cell detection on immunohistochemistry stained (IHC-stained) images plays an essential role in computer assisted prediction of tumor progression and treatment response. Currently available cell detection datasets provide either point level or bounding box level annotations for deep object detection network training. And these widely used networks usually employ standard pyramid structured multi-scale feature fusion. However, we find that these methods have obvious limitation when facing large amounts of cells in similar scale with severe overlapping. To address this problem, we propose a novel CellDet network with (1) Scale Consistency Feature Fusion Module (SCFFM) and (2) Dual Task Detection Module to simultaneously exploit the complementary information from both point and bounding box annotations. In order to verify the effectiveness our proposed method, We make efforts to relabel the public SHIDC-B-Ki-67 dataset with bounding box annotations. Extensive experimental results show that the proposed CellDet outperforms other state-of-the-art cell detection methods with a remarkable margin. We will release our source code and dataset in https://***/JiweiMaster/celldet. © 2021 IEEE.

关键词： Object detection

Multiple Autoencoder-Based Anomaly Detection with Fuzzy Rough Sets

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SSRN

SSRN 2024年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Yue, Xiaodong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Anomaly detection is a practical and essential research topic with a wide range of applications. However, existing anomaly detection methods may face challenges when handling high-dimensional data with complex distributions. In this study, we propose a multiple autoencoder-based anomaly detection method with the aid of fuzzy rough sets. Specifically, the autoencoder is first improved by introducing the kernel fuzzy relation to enhance its representation capability in low-dimensional space. Then, the theory of fuzzy rough sets is employed to perform anomaly detection in the learned low-dimensional representation by fusing multi-view proximity-based information. Finally, to handle complex data, multiple autoencoders are utilized to collaboratively detect anomalies by integrating local anomaly information from different perspectives. Comparative experiments conducted on the selected datasets reveal that the proposed method is superior to state-of-the-art methods, improving over classical autoencoder by 5.58% in terms of the AUC-ROC index. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

COMBINATORIAL PROPERTIES FOR A CLASS OF SIMPLICIAL COMPLEXES EXTENDED FROM PSEUDO-FRACTAL SCALE-FREE WEB

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arXiv 2023年

作者： Xie, Zixuan Wang, Yucheng Xu, Wanyue Zhu, Liwang Li, Wei Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China School of Software Fudan University Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China Academy for Engineering and Technology Fudan University Shanghai200433 China

Simplicial complexes are a popular tool used to model higher-order interactions between elements of complex social and biological systems. In this paper, we study some combinatorial aspects of a class of simplicial complexes created by a graph product, which is an extension of the pseudo-fractal scale-free web. We determine explicitly the independence number, the domination number, and the chromatic number. Moreover, we derive closed-form expressions for the number of acyclic orientations, the number of root-connected acyclic orientations, the number of spanning trees, as well as the number of perfect matchings for some particular cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Fractals

Software Module Clustering Using the Hierarchical Clustering Combination Method 7

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Software Module Clustering Using the Hierarchical Clustering...

7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Xia, Hong Zhang, Yongkang Chen, Yanping Zhang, Hengshan Wang, Zhongmin Wang, Fengwei School of Computer Science and Technology Xi'An Univ. of P. and Telecom. Shaanxi Key Lab. of Network Data Analysis and Intelligent Processing Xi'an China Xi'An University of Posts and Telecommunications School of Computer Science and Technology Xi'an China ZTECorporation Shenzhen China

ISBN: (纸本)9781665487115

The scale of software applications has increased dramatically. Hierarchical clustering is a good method for modular recovery of software architecture. Because the different evaluation criteria of software types and clustering results, a single hierarchical clustering algorithm cannot integrate the number of clusters, arbitrary decision-making, recovery quality and other indicators on different evaluation standards, there is no comprehensive clustering algorithm selection method. We propose a hierarchical clustering combination algorithm and use principal component analysis to combine the results of multiple hierarchical clusters, the combined result retain the basic information of each clustering algorithm as much as possible. The experimental results show that compared with the single software hierarchical clustering method and other combined methods, the result of our method is closer to expert decomposition, and it has good performance on a variety of indicators. © 2022 IEEE.

关键词： Cloud computing Software architecture Clustering methods Software algorithms Decision making Clustering algorithms Big Data

M3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning

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arXiv 2023年

作者： Li, Lei Yin, Yuwei Li, Shicheng Chen, Liang Wang, Peiyi Ren, Shuhuai Li, Mukai Yang, Yazheng Xu, Jingjing Sun, Xu Kong, Lingpeng Liu, Qi The University of Hong Kong Hong Kong National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Shanghai Ai Lab China

Instruction tuning has significantly advanced large language models (LLMs) such as ChatGPT, enabling them to align with human instructions across diverse tasks. However, progress in open vision-language models (VLMs) has been limited due to the scarcity of high-quality instruction datasets. To tackle this challenge and promote research in the vision-language field, we introduce the Multi-Modal, Multilingual Instruction Tuning (M3IT) dataset, designed to optimize VLM alignment with human instructions. Our M3IT dataset comprises 40 carefully curated datasets, including 2.4 million instances and 400 manually written task instructions, reformatted into a vision-to-text structure. key tasks are translated into 80 languages with an advanced translation system, ensuring broader accessibility. M3IT surpasses previous datasets regarding task coverage, instruction number and instance scale. Moreover, we develop Ying-VLM, a VLM model trained on our M3IT dataset, showcasing its potential to answer complex questions requiring world knowledge, generalize to unseen video tasks, and comprehend unseen instructions in Chinese. We have open-sourced the dataset to encourage further research. 1 © 2023, CC BY.

关键词： Large dataset

Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution

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arXiv 2024年

作者： Lin, Junxiong Tao, Zen Tong, Xuan Mai, Xinji Wang, Haoran Wang, Boyang Wang, Yan Zhao, Qing Yu, Jiawen Lin, Yuxuan Yan, Shaoqi Gao, Shuyong Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China East China University of Science and Technology Shanghai China Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model the image degradation process using blur kernels. However, this explicit modeling approach struggles to cover the complex and varied degradation processes encountered in the real world, such as high-order combinations of JPEG compression, blur, and noise. Implicit modeling for the degradation process can effectively overcome this issue, but a key challenge of implicit modeling is the lack of accurate ground truth labels for the degradation process to conduct supervised training. To overcome this limitations inherent in implicit modeling, we propose an Uncertainty-based degradation representation for blind Super-Resolution framework (USR). By suppressing the uncertainty of local degradation representations in images, USR facilitated self-supervised learning of degradation representations. The USR consists of two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a feature extraction network composed of Variable Depth Dynamic Convolution (VDDC) blocks. To extract Uncertainty-based Degradation Representation from LR images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with Uncertainty Suppression Loss to suppress the inherent model uncertainty of the Degradation Extractor. Furthermore, VDDC block integrates degradation information through dynamic convolution. Rhe VDDC also employs an Adaptive Intensity Scaling operation that adaptively adjusts the degradation representation according to the network hierarchy, thereby facilitating the effective integration of degradation information. Quantitative and qualitative experiments affirm the superiority of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

Can Language Models Understand Physical Concepts?

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arXiv 2023年

作者： Li, Lei Xu, Jingjing Dong, Qingxiu Zheng, Ce Liu, Qi Kong, Lingpeng Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Shanghai AI Lab China The University of Hong Kong Hong Kong

Language models (LMs) gradually become general-purpose interfaces in the interactive and embodied world, where the understanding of physical concepts is an essential prerequisite. However, it is not yet clear whether LMs can understand physical concepts in the human world. To investigate this, we design a benchmark VEC that covers the tasks of (i) Visual concepts, such as the shape and material of objects, and (ii) Embodied Concepts, learned from the interaction with the world such as the temperature of objects. Our zero (few)-shot prompting results show that the understanding of certain visual concepts emerges as scaling up LMs, but there are still basic concepts to which the scaling law does not apply. For example, OPT-175B performs close to humans with a zero-shot accuracy of 85% on the material concept, yet behaves like random guessing on the mass concept. Instead, vision-augmented LMs such as CLIP and BLIP achieve a human-level understanding of embodied concepts. Analysis indicates that the rich semantics in visual representation can serve as a valuable source of embodied knowledge. Inspired by this, we propose a distillation method to transfer embodied knowledge from VLMs to LMs, achieving performance gain comparable with that by scaling up parameters of LMs 134×. 1 © 2023, CC BY.

关键词： Semantics

Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

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Deep Representation Learning for Electron Ionization Mass Sp...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shibo Liu Xuan Zhang Anlei Jiao Shiwei Sun Longyang Dian Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Microbial Technology Shandong University Qingdao China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI’s 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC).

关键词： Representation learning Deep learning Accuracy Databases Source coding Ionization Predictive models Mass spectroscopy Electrons Software development management