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Scalable Multi-view Regression Clustering for Large-scale Data

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhao, Xiaowei Fan, Jie Chang, Xiaojun Nie, Feiping Zhang, Qiang Guo, Jun Xidian University State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipment Shaanxi Xi’an710071 China Xidian University Center for Complex Systems School of Mechano-Electronic Engineering Shaanxi Xi’an710071 China Northwest University School of Information Science and Technology Shaanxi Xi’an710127 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Mohamed bin Zayed University of Artificial Intelligence Department of Computer Vision MBZUAI Abu Dhabi United Arab Emirates Xi’an710072 China

In recent years, unsupervised linear regression has attracted attention for its ability to directly capture the mapping relationship between samples and targets. However, existing algorithms can only utilize limited information from a single view, which often leads to unsatisfactory results. To address this problem, we propose a regression clustering model based on multi-view information fusion, called Scalable Multi-view Regression Clustering. This model consists of two parts: intra-view information fusion and inter-view information fusion. In the first part, to capture the local correlations among samples, we propose constructing view-specific bipartite graphs. Unlike traditional single-view and multi-view clustering algorithms, we treat the weights of the bipartite graph as additional features of the samples, thereby directly incorporating the local manifold structure of the samples at the feature level. Furthermore, since the original features of the samples also contain valuable information, we perform unsupervised linear regression separately on the samples represented by the original features and those represented by the bipartite graph weights in each view. The results are then integrated in a weighted manner. In the second part, we propose adaptively weighting the clustering results from each view to capture complementary information across views, thereby enhancing clustering performance. This strategy not only avoids the bipartite graph alignment issue in multi-view clustering but also enables clustering with linear time complexity, making it effective for handling large-scale data. An iterative optimization algorithm is developed to update all variables alternately. Experiments conducted on benchmark datasets demonstrate the superiority of our proposed model. © 2025 IEEE. All rights reserved.

关键词： Multiple linear regression

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A Semi-Supervised Learning Framework with Cross-Magnification Attention for Glioma Mitosis Classification

A Semi-Supervised Learning Framework with Cross-Magnificatio...

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IEEE International Symposium on Biomedical Imaging

作者： Manh Duong Nguyen Nguyen Dang Huy Pham Phi Le Nguyen Minh N. Do Institute for AI Innovation and Socital Impact (AI4LIFE) Hanoi University of Science and Technology Hanoi Vietnam Coordinated Science Laboratory University of Illinois at Urbana-Champaign Hanoi - Amsterdam High School for the Gifted Vietnam Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign VinUni-Illinois Smart Health Center VinUniversity

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Mitotic figure counting plays a critical role in glioma grading and prognostication, yet manual counting remains time-consuming and subject to variability. The Glioma-MDC 2025 Challenge, hosted at ISBI 2025, aims to address this challenge by advancing automated solutions. In response, we present SEMA, a semi-supervised learning framework that incorporates a cross-magnification attention mechanism. SEMA mimics the multi-magnification workflow used by pathologists, employing attention to model interactions between cells and neighboring regions, effectively replicating how pathologists analyze whole slide images. Additionally, SEMA leverages the abundance of unlabeled cells in raw tissue images from competition datasets through a semi-supervised learning approach, progressively enhancing its robustness and performance. Extensive experiments demonstrate that SEMA outperforms other baselines by up to 40% in FI score, with each component contributing significantly to its success. Notably, SEMA achieves a perfect F1 score, securing top performance on the public leaderboard.

关键词： Analytical models Attention mechanisms Foundation models MIMICs Manuals Semisupervised learning Robustness Cancer Biomedical imaging

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RFAE: A high-robust feature selector based on fractal autoencoder

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Expert Systems with Applications 2025年 285卷

作者： Ou, Jingfeng Li, Jiawei Xia, Zhiliang Dai, Shurui Guo, Yan Jiang, Limin Tang, Jijun College of Engineering Southern University of Science and Technology Shenzhen 518055 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518107 China Department of Public Health Sciences University of Miami Miami FL33136 United States Centre for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China

Feature selection aims to consistently identify an optimal subset of features that effectively represents the entire dataset or enhances performance in downstream tasks. While deep learning-based approaches have made significant progress in feature selection, they continue to face key challenges, including instability in selected features, limited receptive fields in feature-selection layers due to architectural constraints, and suboptimal utilization of available sample information. To address these limitations, we propose the Robust Fractal Autoencoder (RFAE), an enhanced variant of the Fractal Autoencoder (FAE) designed to improve feature selection stability and adaptability. RFAE introduces three critical advancements: 1) Novel utilization of weight exponentiation to rectify the concern of FAE selecting a reduced number of features than designated. 2) Adoption of a dynamic and tailored strategy to optimize feature selection weights during the training process. 3) Introduction of a optional classification module, facilitating extension to supervised feature selection scenarios. We systematically evaluate RFAE against 14 established feature selection methods. Our experiments span 14 publicly available benchmark datasets, a large-scale GEO gene expression dataset, and a synthetic dataset with known ground-truth features. The results demonstrate that RFAE consistently selects features that achieve lower reconstruction errors while ensuring higher stability across repeated experiments, highlighting its robustness and effectiveness in feature selection tasks. © 2025

关键词： Feature Selection

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A Precise RFID Indoor Localization System with Sensor Network Assistance

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China Communications 2015年第4期12卷 13-22页

作者： ZHANG Dian LU Kezhong MAO Rui Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software EngineeringShenzhen University

Indoor localization is very critical for medical care applications, e.g., the patient localization or tracking inside the building of the hospital. Traditional Radio Frequency Identification(RFID) technologies are very popular in this area since their cost is very low. In such technologies, each tag acts as the transmitter and the Radio Signal Strength Indicator(RSSI) information is measured from the readers. However, RSSI information suffers severely from the multi- path phenomenon. As a result, if in a very large area, the localization accuracy will be affected seriously. In order to solve this problem, we introduce Wireless Sensor Networks(WSNs) with only a few nodes, each of which acts as both transmitter and receiver. In such networks, the change of signal strength(referred as dynamic of RSSI) is leveraged to select a cluster of reference tags as candidates. Then the fi nal target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Thus, the localization accuracy and scalability are able to be improved. We proposed two algorithms, SA-LANDMARC, and COCKTAIL. Experiments show that the localization accuracy of the two algorithms can reach 0.7m and 0.45 m, respectively. Compared to most traditional Radio Frequency(RF)-based approaches, the localization accuracy is improved at least 50%.

关键词： radio frequency RFID wirelesssensor networks hybrid support vectorregression

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The Joint Model of Multi-Intent Detection and Slot Filling Based on Bidirectional Interaction Structure

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Wuhan University Journal of Natural sciences 2025年第1期30卷 21-31页

作者： WANG Changjing ZENG Xianghui WANG Yuxin SUN Yuxin ZUO Zhengkang School of Digital Industry Jiangxi Normal UniversityShangrao 334000JiangxiChina School of Computer Information Engineering Jiangxi Normal UniversityNanchang 330022JiangxiChina Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal UniversityNanchang 330022JiangxiChina State International Science and Technology Cooperation Base of Networked Supporting Software Jiangxi Normal UniversityNanchang 330022JiangxiChina

Intent detection and slot filling are two important components of natural language *** their relevance,joint training is often performed to improve *** studies mostly use a joint model of multi-intent detection and slot-filling with unidirectional interaction,which improves the overall performance of the model by fusing the intent information in the slot-filling *** this basis,in order to further improve the overall performance of the model by exploiting the correlation between the two,this paper proposes a joint multi-intent detection and slot-filling model based on a bidirectional interaction structure,which fuses the intent encoding information in the encoding part of slot filling and fuses the slot decoding information in the decoding part of intent *** results on two public multi-intent joint training datasets,MixATIS and MixSNIPS,show that the bidirectional interaction structure proposed in this paper can effectively improve the performance of the joint *** addition,in order to verify the generalization of the bidirectional interaction structure between intent and slot,a joint model for single-intent scenarios is proposed on the basis of the model in this *** model also achieves excellent performance on two public single-intent joint training datasets,CAIS and SNIPS.

关键词： natural language understanding multi-intent detection slot filling bidirectional interaction joint training

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A Hybrid Neural Network for high-Throughput Attosecond Resolution Single-shot X-ray Pulse Characterization

arXiv

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arXiv 2025年

作者： Hirschman, Jack Mencer, Benjamin Obaid, Razib Shackelford, Amanda Coffee, Ryan Department of Applied Physics Stanford University 348 Via Pueblo StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Rd Menlo ParkCA94025 United States Department of Computer Science and Engineering University of California Santa Cruz 1156 High St Santa CruzCA95064 United States

As scientific facilities transition toward high-throughput, data-intensive experiments, there is a growing need for real-time computing at the edge to support autonomous decision-making and minimize data transmission bottlenecks. department of Energy (DOE) initiatives emphasize the development of heterogeneous computing architectures that integrate machine learning (ML) models with specialized hardware such as FPGAs, GPUs, and embedded systems to meet the demands of next-generation experiments. These advances are critical for facilities such as X-ray free-electron lasers (XFELs), where high-repetition-rate experiments produce terabyte-scale datasets, requiring real-time analysis and control. To address this challenge, we introduce DCIFR, a deep learning framework optimized for edge processing and high-speed X-ray diagnostics at SLAC’s Linac Coherent Light Source (LCLS). DCIFR leverages a hybrid neural architecture, combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and residual neural networks (ResNets) to denoise sinograms, classify X-ray sub-spikes, and extract phase separation with attosecond precision. The model achieves a phase extraction accuracy of 0.04652 radians (29.6 attoseconds at 1.2 µm streaking wavelength) with inference latencies of 168.3 µs and throughput exceeding 10kHz. Designed for deployment on heterogeneous architectures, DCIFR reduces computational overhead and supports future AI-driven feedback loops for ultrafast experiments. This work demonstrates the potential of edge AI and hardware acceleration to drive scalable, autonomous data analysis in next-generation DOE scientific facilities. © 2025, CC BY.

关键词： Free electron lasers

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A generalized model of TiOx-based memristive devices and its application for image processing

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Chinese Physics B 2017年第9期26卷 70-81页

作者：张江伟汤振森许诺王耀孙红辉王之元方粮 State Key Laboratory of High Performance Computing National University of Defense Technology School of Computer National University of Defense Technology Department of Electrical and Computer Engineering University of Pittsburgh Department of Material Science and Engineering College of EngineeringSeoul National University School of Computer National University of Defense Technology

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.

关键词： memristor modeling memristor-based network gray sketching edge detection

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Electric Fields Induced by Water Movement in Proximity to HVDC Submarine Cables

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IEEE Journal of Oceanic engineering 2025年

作者： Rzempoluch, Joanna Goddard, Kevin Chaudhary, Sunny Callender, George Olsen, Robert G. Dix, Justin Lewin, Paul Renew, David University of Southampton Tony Davies High Voltage Laboratory SouthamptonSO17 1BJ United Kingdom Washington State University School of Electrical Engineering and Computer Science PullmanWA99164-2752 United States University of Southampton Department of Ocean and Earth Science SouthamptonSO14 3ZH United Kingdom EMF Scientific Ltd. Ashtead99164-2752 United Kingdom

With the raised investment in interconnectors and offshore wind farms located further from the shore, there has been an increased need for the licensing and environmental assessment of high voltage direct current (HVDC) submarine cables. The motionally induced electric fields due to HVDC submarine cables have not been previously modeled in the literature. In this article, a methodology for modeling this phenomenon is outlined. Factors, such as geographical location of the cable system, electrical conductivities of the media, reference frames, and water velocity profile, are included in the analysis. The results are compared with an approximation adopted in other publications. The resulting electric fields and their spatial distributions are presented and discussed for different cable systems, cable orientations and reference frames. The dependency of the local electric fields on the geographical location is shown. The investigation demonstrates the sensitivity of electric fields to variations in seabed and water conductivities. Specifically, the most extreme combination yielded an 85% increase compared to the case with equal conductivities, which emphasises the substantial impact of media conductivities on local electric field prediction. The methodology outlined in this paper can provide a basis for future empirical validation, and inform biological experiments and licensing processes. © 1976-2012 IEEE.

关键词： Electric conductivity of solids

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Programming for scientific computing on peta-scale heterogeneous parallel systems

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Journal of Central South University 2013年第5期20卷 1189-1203页

作者：杨灿群吴强唐滔王锋薛京灵 State Key Laboratory of High Performance Computing(National University of Defense Technology) School of Computer Science and Engineering University of New South Wales

Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains （e.g., physics and biology） rather than MPI and OpenME This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-IA, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems.

关键词： heterogeneous parallel system programming framework scientific computing GPU computing molecular dynamic

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A parallel computing framework for big data

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Frontiers of computer science 2017年第4期11卷 608-621页

作者： Guoliang CHEN Rui MAO Kezhong LU Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen 518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China

Abstract Big data has received great attention in research and application. However, most of the current efforts focus on system and application to handle the challenges of ＂volume＂ and ＂velocity＂, and not much has been done on the theoreti- cal foundation and to handle the challenge of ＂variety＂. Based on metric-space indexing and computationalcomplexity the- ory, we propose a parallel computing framework for big data. This framework consists of three components, i.e., universal representation of big data by abstracting various data types into metric space, partitioning of big data based on pair-wise distances in metric space, and parallel computing of big data with the NC-class computing theory.

关键词： NC-computing metric space data partitioning,parallel computing

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