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IEEE International Conference on big data and Cloud computing (BdCloud)

作者： Qingxian Wang Renjian Zhang Kangkang Ma Bo Chen Jiufang Chen Xiaoyu Shi School of Computer Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Intelligent Terminal Key Laboratory of SiChuan Province Yibin China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

In the recommendation system, user-item preferences are described by a High-Dimensional and Sparse (HiDS) matrix. Collaborative Filtering (CF)-based models have been widely adopted to solve unknown entries estimation. However, a CF-based model does not take the data distribution characteristic of an HiDS matrix into account, thereby its representation ability limited. To address this issue, this paper proposes to build a Siamese Generative Adversarial Predicting Network (SGAPN) to estimate the unknown entries in an HiDS matrix. Firstly, we build a model to learn the data distribution characteristic of an HiDS matrix. Secondly, based on the learned data distribution and observed entries, we build a model to estimate the unknown entries. Compared with the CF-based model, our model takes the data distribution of an HiDS matrix into account to address the unknown entries estimation. Experimental results over four HiDS matrices generated by industrial applications demonstrate that compared with several state-of-the-art models, the proposed model achieves competitive prediction accuracy.

关键词： Training Collaborative filtering Estimation Predictive models data models Sparse matrices

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Deep learning radiomics model related with genomics phenotypes for pathological complete response prediction in locally advanced rectal cancer under neoadjuvant chemoradiotherapy

Deep learning radiomics model related with genomics phenotyp...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Ye Wang Zhen Pan Lanyan Lin Huajun Cai Feng Ding Chaojun Tan Dingyuan Zheng Yajun Xie Guoxian Guan Department of Colorectal Surgery The First Affiliated Hospital of Fujian Medical University Fuzhou China Department of Geriatric Medicine Fujian Key Laboratory of Geriatrics Diseases Fujian Provincial Center for Geriatrics Fuzhou University Affiliated Provincial Hospital Shengli Clinical Medical College of Fujian Medical University Fuzhou China Fujian Huaye Information Technology Co. Ltd China School of Big Data \& Key Laboratory of Data Science and Intelligent Computing Fuzhou University of International Studies and Trade Fuzhou China Department of Colorectal Surgery The First Affiliated Hospital of Fujian Medical University National Regional Medical Center Binhai Campus of the First Affiliated Hospital Fuzhou China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Background: Developing a treatment strategy after neoadjuvant chemoradiotherapy (nCRT) based on accurate pathological complete response (pCR) predictions is crucial for locally advanced rectal cancer (LARC).Objective: To determine the effectiveness of a radiomics model using diffusion-weighted imaging (DWI) and T2-weighted in predicting post-nCRT pCR efficacy after ***: This study enrolled 237 LARC patients who had radical surgery under nCRT between February 2020 and August 2023. Tumor tissues from 54 patients were RNA sequenced. Radiomics features from preoperative MRI data were used to develop a model for deep learning via the LASSO algorithm. ROC curves were used to assess the model performance. Following Spearman's correlation, analyses of transcriptomic data were conducted to identify the genes correlated with the DPL ***: The DPL model, incorporating 7 features and 5 machine-learning algorithms, was used to create risk assessment models. The RF model excelled in predicting pCR, achieving an AUC of 0.91, accuracy of 0.88, and F1 score of 0.89. Furthermore, catabolic processes and pathways related to immunity were associated with selected DPL ***: The modified MRI-based radiomics model shows good predictive efficacy in predicting pCR after nCRT in LARC patients. Additionally, we investigated the genomic phenotypes associated with DPL.

关键词： Deep learning Support vector machines Pathology Analytical models Transcriptomics Predictive models data models Random forests Radiomics Cancer

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Unsupervised content-preserving transformation for optical microscopy

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Light(Science & Applications) 2021年第3期10卷 390-400页

作者： Xinyang Li Guoxun Zhang Hui Qiao Feng Bao Yue Deng Jiamin Wu Yangfan He Jingping Yun Xing Lin Hao Xie Haoqian Wang Qionghai Dai Department of Automation Tsinghua UniversityBeijing 100084China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China School of Astronautics Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Pathology Sun Yat-sen University Cancer CenterGuangzhou 510060China State Key Laboratory of Oncology in South China Guangzhou 510060China Collaborative Innovation Center for Cancer Medicine Guangzhou 510060China Beijing Innovation Center for Future Chips Tsinghua UniversityBeijing 100084China

The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation,which is gradually changing the landscape of optical imaging and biomedical ***,current implementations of deep learning usually operate in a supervised manner,and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general ***,we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy,even in some cases in which supervised models cannot be *** the introduction of a saliency constraint,the unsupervised model,named Unsupervised content-preserving Transformation for Optical Microscopy(UTOM);can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image *** shows promising performance in a wide range of biomedical image transformation tasks,including in silico histological staining,fluorescence image restoration,and virtual fluorescence *** evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and *** anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

关键词： transformation preserving image

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Computer-Automated Design of Self-Aligned Multi-Ball Pitch Artifact with High Precision

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SSRN 2024年

作者： Zhu, Hongjun Liu, Binghong Zhou, Sen Zhang, Xiaoyan Xin, Liu Chen, Kuo Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China Chongqing Academy of Metrology and Quality Inspection Chongqing401121 China School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland1010 New Zealand Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

This study presents a computational methodology for developing a multi-ball system that automatically aligns during assembly processes. The contributions of this work are threefold: Firstly, a comprehensive error analysis of the multi-ball system is performed. It is found that temperature and pressure-induced deformations should not be ignored when micrometer-level precision is required. Secondly, a novel process flow for designing the multi-ball pitch artifact is introduced, which enables optimization decision-making. Lastly, a series of experiments are conducted on standard bearing balls (grade G20). The resulting artifact meets the Class 1 standards. It is reasonable to assume that, by using balls of grade G3 precision, the multi-ball artifact can achieve accuracy at the sub-micrometer level. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Array optimization for MIMO radar based on harmony search mechanism

Array optimization for MIMO radar based on harmony search me...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Ling Jiang Yi Jiang Guimin Shi Zhongjie Xiao School of Mathematics and Computer Wuyi University Wuyishan China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan China Digital Fujian Tourism Big Data Institute Wuyishan China Big Data Development Administration of Anji County Huzhou China

ISBN: (纸本)9781728194202

In order to overcome the premarure risk of differential evolution algorithm, harmony search mechanism is introduced to optimize the pattern synthesis of multiple input and multiple output radar. Firstly, the memory of harmony search receives the optimizing results of differential evolution algorithm. And then it disturbs the local best to achieve better global optimization results. The advantage of novel method than standard differential evolution is tested with benchmark function while it can also maintain the diversity of population. What's more, several experiments are conducted to show that the optimal peak side lobe level and convergence performance have been achieved better through the proposed algorithm for multiple input and multiple output radar.

关键词： MIMO radar Sociology Education Radar Statistics Information technology Optimization

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification 28

A Two-phase Prototypical Network Model for Incremental Few-s...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Ren, Haopeng Cai, Yi Chen, Xiaofeng Wang, Guohua Li, Qing School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781952148279

Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in an open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Deep learning

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Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks

Graph Regularized Nonnegative Latent Factor Analysis Model f...

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IEEE International Conference on Networking, Sensing and Control

作者： Yue Zhou ZhiGang Liu Ye Yuan School of Computer Science and Technology Choncgqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China and with the Chongqing School University of Chinese Academy of Sciences Chongqing China College of Computer and Information Science Southwest University Chongqing China

ISBN: (纸本)9781665472449

With the development of blockchain technology, a cryptocurrency based on blockchain technology is becoming more and more popular. The huge cryptocurrency transaction network has therefore received widespread attention. The link prediction learning structure of the network is supportive to understand the mechanism of networks, so it also has been widely studied in the cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in past studies. In this study, therefore, we use a graph-regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, nonnegative, multiplicative, and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose a graph regularized nonnegative latent factor analysis (GrNLFA) model. Eventually, the experimental results on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and computational efficiency.

关键词： Analytical models Tensors Computational modeling Heuristic algorithms Optimization methods Predictive models Prediction algorithms

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Comprehensive Characteristic Decomposition of Parametric Polynomial Systems 21

Comprehensive Characteristic Decomposition of Parametric Pol...

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46th International Symposium on Symbolic and Algebraic Computation, ISSAC 2021

作者： Dong, Rina Lu, Dong Mou, Chenqi Wang, Dongming Chongqing Key Laboratory of Automated Reasoning and Cognition Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Mathematical Sciences Beihang University Beijing100191 China LMIB-School of Mathematical Sciences Beihang University Beijing100191 China Centre National de la Recherche Scientifique Paris cedex 1675794 France

ISBN: (纸本)9781450383820

This paper presents an algorithm that decomposes an arbitrary set F of multivariate polynomials involving parameters into finitely many sets Gi of (lexicographical) Gröbner bases G ij such that associated with each Gi there is a system Ai of parametric constraints, all the Ai's partition the parameter space, all the Gij's in each Gi remain Gröbner bases under specialization of the parameters satisfying the constraints in Ai, and for each i the Gröbner bases Gij together with their corresponding W-characteristic sets form a normal characteristic decomposition of F. The sets of Gröbner bases computed by the algorithm provide a comprehensive characteristic decomposition of F that is structure-invariant under the associated constraints and possesses many algebraic and geometric properties, including most of the notable properties on comprehensive Gröbner systems and comprehensive triangular decomposition. Some of these properties are highlighted in the paper and advantages of the proposed algorithm are discussed briefly from the aspects of methodological simplicity and computational performance using illustrative examples and preliminary experiments. © 2021 ACM.

关键词： Polynomials

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Reconstructing Missing Modalities in Multi-Modal Endoscopic Ultrasound Via Cross-Modal Feature Replacement Representation

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SSRN 2024年

作者： Zheng, Cenyang Gong, Xun Fan, Lin Li, Jiao School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Department of Gastroenterology The Third People’s Hospital of Chendu Affiliated Hospital of Southwest Jiaotong University Chengdu610031 China

Endoscopic ultrasound (EUS) is the primary diagnostic imaging technique for gastrointestinal stromal tumors. However, accurately identifying these tumors through EUS imaging alone remains challenging, even for experienced clinicians. They often rely on additional data, such as white light endoscopy (WLE) and EUS text reports, to aid in the diagnosis. Unfortunately, in practice, these supplementary modalities are sometimes overlooked during patient evaluation. To address these issues, we propose the Cross-modal Feature Reconstruction Network (CFRN), which integrates multimodal data modeling for medical diagnostic processes and performs specific learning for different modalities with missing data. The network fuses the multi-modal features based on the clinical medical process, realizes the feature-level missing modality reconstruction, and learns the refined features of different modal true cases by decoding the mode-specific missing. We trained and tested the model on two publicly available datasets, Audiovision-MNIST, MM-IMDb, and data sets collected from real clinical scenarios at three hospitals. Our results show that CFRN outperforms state-of-the-art methods, achieving higher prediction accuracy than using EUS alone. By simultaneously incorporating multimodal or reconstructed modal data for disease diagnosis, our single-modal EUS model proves to be more suitable for clinical scenarios. © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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