检索结果-内蒙古大学图书馆

TechRxiv 2023年

作者： Hadikhani, Parham Lai, Daphne Teck Ching Ong, Wee-Hong Nadimi-Shahraki, Mohammad H. The School of Digital Science Universiti Brunei Darussalam Brunei Faculty of Computer Engineering Big Data Research Center Najafabad Branch Islamic Azad University Najafabad Iran

Most deep clustering methods despite providing complex networks to learn better from data, use a shallow clustering method. These methods have difficulty in finding good clusters due to the lack of ability to handle between local search and global search to prevent premature convergence. In other words, they do not consider different aspects of the search and it causes them to get stuck in the local optimum. In addition, the majority of existing deep clustering approaches perform clustering with the knowledge of the number of clusters, which is not practical in most real scenarios where such information is not available. To address these problems, this paper presents a novel automatic deep sparse clustering approach based on an evolutionary algorithm called Multi-Trial Vector-based Differential Evolution (MTDE). Sparse auto-encoder is first applied to extract embedded features. Manifold learning is then adopted to obtain representation and extract the spatial structure of features. Afterward, MTDE clustering is performed without prior information on the number of clusters to find the optimal clustering solution. The proposed approach was evaluated on various datasets, including images and time-series. The results demonstrate that the proposed method improved MTDE by 18.94 % on average and compared to the most recent deep clustering algorithms, is consistently among the top three in the majority of datasets. © 2023, CC BY.

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

Homophily Within and Across Groups

arXiv

引用

arXiv 2024年

作者： Rizi, Abbas K. Michielan, Riccardo Stegehuis, Clara Kivelä, Mikko DTU Compute Technical University of Denmark Kongens Lyngby2800 Denmark Copenhagen Center for Social Data Science University of Copenhagen Denmark Department of Computer Science School of Science Aalto University FI-00076 Finland Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Traditional social network analysis often models homophily—the tendency of similar individuals to form connections—using a single parameter, overlooking finer biases within and across groups. We present an exponential family model that integrates both local and global homophily, distinguishing between strong homophily within tightly knit cliques and weak homophily spanning broader community interactions. By modeling these forms of homophily through a maximum entropy approach and deriving the network behavior under percolation, we show how higher-order assortative mixing influences network dynamics. Our framework is useful for decomposing homophily into finer levels and studying the spread of information and diseases, influence dynamics, and innovation diffusion. We demonstrate that the interaction between different levels of homophily results in complex percolation thresholds. We tested our model on various datasets with distinct homophily patterns, showcasing its applicability. These homophilic connections significantly affect the effectiveness of intervention and mitigation strategies. Hence, our findings have important implications for improving public health measures, understanding information dissemination on social media, and optimizing intervention strategies. © 2024, CC BY.

关键词： Percolation (computer storage)

来源：评论

学校读者我要写书评

暂无评论

Does graph distillation see like vision dataset counterpart? 23

Does graph distillation see like vision dataset counterpart?

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Beining Yang Kai Wang Qingyun Sun Cheng Ji Xingcheng Fu Hao Tang Yang You Jianxin Li School of Computer Science and Engineering Beihang University and Advanced Innovation Center for Big Data and Brain Computing Beihang University National University of Singapore Carnegie Mellon University

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condensation methods primarily focus on optimizing the feature matrices of condensed graphs while overlooking the impact of the structure information from the original graphs. To investigate the impact of the structure information, we conduct analysis from the spectral domain and empirically identify substantial Laplacian Energy Distribution (LED) shifts in previous works. Such shifts lead to poor performance in cross-architecture generalization and specific tasks, including anomaly detection and link prediction. In this paper, we propose a novel Structure-broadcasting Graph dataset Distillation (SGDD) scheme for broadcasting the original structure information to the generation of the synthetic one, which explicitly prevents overlooking the original structure information. Theoretically, the synthetic graphs by SGDD are expected to have smaller LED shifts than previous works, leading to superior performance in both cross-architecture settings and specific tasks. We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98.6% test accuracy of training on the original graph dataset with 1,000 times saving on the scale of the graph. Moreover, we empirically evaluate there exist 17.6% ~ 31.4% reductions in LED shift crossing 9 datasets. Extensive experiments and analysis verify the effectiveness and necessity of the proposed designs. The code is available in the https://***/RingBDStack/SGDD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Hybrid CNN-GRU model for Session-based Recommender Systems

A Hybrid CNN-GRU model for Session-based Recommender Systems

引用

International Conference on computer and Communications (ICCC)

作者： Abebe Tamrat Tegene Qiao Liu Yanglei Gan Elias Jimale Melak Ayenew Habte Leka School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Center of Excellence of AI and Robotics Addis Ababa Science and Technology University Addis Ababa Ethiopia School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

In many e-commerce recommendation systems, user profiles are mostly not available. In such cases, the recommendation task considers user behaviors like click sessions or browsing history. Traditional recommender methods often rely on users’ past interactions for recommending user interests, which is difficult to apply to session-based recommender systems. However, these methods perform very poorly on this task. Recently, deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) showed outstanding performance in session-based recommendation problems. In this paper, we propose a hybrid CNN and gated recurrent unit (GRU) model that exploits the advantages of both CNN and GRU in modeling the rigid order requirement for interactions inside sessions and modeling the sequential long-term dependencies in session data, respectively. Our proposed hybrid model has two architectures: the first part consists of the CNN layers that take input from Item ID to learn local features and relationships in sessions, and the second part is a GRU layer that takes the output from the CNN layer to learn the long-term dependencies in that session. This hybrid technique enables the architecture for modeling prominent features in the session to provide accurate recommendations. Extensive experiments over a real-world dataset demonstrate that the proposed hybrid model performs well and outperforms those GRU and CNN-based models in the state-of-the-art recommendation task on both metrics used.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MMFNet: A Multi-modal and Multi-attention Fusion Network for Multi-task Skin Disease Classification

MMFNet: A Multi-modal and Multi-attention Fusion Network for...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlei Zhu Pengjiang Qian Chuang Wang Li Liu Ming Gao Weiwei Cai Eddie Yin-Kwee Ng School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Jiangnan University Wuxi China Big Data Center Affiliated Hospital of Jiangnan University Wuxi China School of Mechanical and Aerospace Engineering Nanyang Technological University Nanyang Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Skin diseases rank among the most prevalent ailments in humans, which underscores the critical importance of early detection and diagnosis. Considering clinical practice, the integration of information from various modalities holds significant potential to enhance diagnostic accuracy and precision. In this study, we propose a multi-modal and multi-attention fusion network for multi-task skin disease classification. We initially devise the Shifted Window Cross-attention Fusion (SWCF) module based on the shifted window self-attention mechanism, leveraging the advantages of the attention mechanism to learn the correlations between different modalities and fully integrate multi-modal features. Subsequently, taking into account the heterogeneity of the data, we employ the Heterogeneous data Cross-attention Fusion (HDCF) module using the idea of information decoupling and separation processing to integrate image and text data. Additionally, we suggest the Dynamic Loss Weight Allocation (DLWA) method for multi-task learning to refine the training procedure. We confirm the superiority of the proposed method on the publicly available multi-modal skin lesion dataset, Derm7pt. The average accuracy of multi-modal skin disease classification is 79.14%, surpassing the current state-of-the-art methods.

关键词： Training Attention mechanisms Accuracy Fuses Multitasking Dynamic scheduling Skin Resource management Lesions Diseases

来源：评论

学校读者我要写书评

暂无评论

Faithful Polarization Entanglement Distribution in the SeQUeNCe Quantum Network Simulator

Faithful Polarization Entanglement Distribution in the SeQUe...

引用

2024 Frontiers in Optics, FiO 2024

作者： Singal, Ansh Zang, Allen Ramesh, Anirudh Kolar, Alexander Chung, Joaquin Kettimuthu, Rajkumar Kumar, Prem Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Pritzker School of Molecular Engineering University of Chicago 5801 S Ellis Ave ChicagoIL60637 United States Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States

We simulate polarization entanglement distribution with the Fock state representation in the open-sourced quantum network simulator SeQUeNCe. Simulations are validated by comparing with experimental entanglement distribution results and unknown experimental parameters are estimated. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Polarization

来源：评论

学校读者我要写书评

暂无评论

Timing-Driven High-Level Synthesis for Continuous-Flow Microfluidic Biochips

Timing-Driven High-Level Synthesis for Continuous-Flow Micro...

引用

IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Ye Zhisheng Chen Youlin Pan Genggeng Liu Wenzhong Guo Tsung-Yi Ho Xing Huang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

As continuous-flow microfluidic biochips technology rapidly advances, its automated synthesis methods require adapting to increasingly complex and stringent biochemical applications. In particular, with the introduction of time-sensitive bioassays, in this work, we present a timing-driven high-level synthesis method to optimize the resource efficiency of bioassays considering the time constraints. Specifically, we investigate the existing forms of time constraints and propose a corresponding flexible modeling approach based on reaction-control which can achieve downward compatibility for physical design. Additionally, we also analyze the impact of storage and wash on time constraints. The experimental results show that our method achieves efficient execution and low devices cost while satisfying time constraints.

关键词： Costs Biochips Time factors Microfluidics Physical design

来源：评论

学校读者我要写书评

暂无评论

Galaxy: A Resource-Efficient Collaborative Edge ai System for In-situ Transformer Inference

Galaxy: A Resource-Efficient Collaborative Edge AI System fo...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Shengyuan Ye Jiangsu Du Liekang Zeng Wenzhong Ou Xiaowen Chu Yutong Lu Xu Chen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China IoT Thrust and Research Center for Digital World With Intelligent Things HKUST (Guangzhou) Guangzhou China Data Science and Analytics Thrust HKUST (Guangzhou) Guangzhou China Department of Computer Science and Engineering HKUST Hong Kong SAR China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload the inference workloads to the remote cloud server, which would induce substantial pressure on the backbone network as well as raise users’ privacy concerns. To address that, in-situ inference has been recently recognized for edge intelligence, but it still confronts significant challenges stemming from the conflict between intensive workloads and limited on-device computing resources. In this paper, we leverage our observation that many edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources and propose Galaxy, a collaborative edge ai system that breaks the resource walls across heterogeneous edge devices for efficient Transformer inference acceleration. Galaxy introduces a novel hybrid model parallelism to orchestrate collaborative inference, along with a heterogeneity-aware parallelism planning for fully exploiting the resource potential. Furthermore, Galaxy devises a tile-based fine-grained overlapping of communication and computation to mitigate the impact of tensor synchronizations on inference latency under bandwidth-constrained edge environments. Extensive evaluation based on prototype implementation demonstrates that Galaxy remarkably outperforms state-of-the-art approaches under various edge environment setups, achieving up to 2.5× end-to-end latency reduction.

关键词： Tensors Computational modeling Collaboration Prototypes Edge ai Smart homes Parallel processing

来源：评论

学校读者我要写书评

暂无评论

Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet

引用

computers in Biology and Medicine 2025年 185卷 109494-109494页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Yi, Ding Atwereboannah, Abena A. Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Bakanina Kissanga, Grace-Mercure Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Sichuan Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Life Science University of Electronic Science and Technology of China Chengdu China

Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of computer-aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy,

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

Incremental Learning with Pseudo Labels for Enhanced Glomeru...

引用

International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Xinkai Liu Boyuan Peng Xin Zhu Tsuyoshi Iwasaki Atsuya Sato Junichiro James Kazama Jie Huang Graduate Department of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan Department of Nephrology and Hypertension Fukushima Medical University Fukushima Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Aizu-Wakamatsu Fukushima Japan

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM's segmentation accuracy for renal pathology.

关键词： Deep learning Pathology Analytical models Incremental learning Accuracy Semantic segmentation Transfer learning Contrastive learning Clinical trials Cams

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：