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Gaussian graphical model-based heterogeneity analysis via penalized fusion

BIOMETRICS 2022年第2期78卷 524-535页

作者： Ren, Mingyang Zhang, Sanguo Zhang, Qingzhao Ma, Shuangge Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Yale Sch Publ Hlth Dept Biostat New Haven CT 06511 USA Xiamen Univ MOE Key Lab Econometr Wang Yanan Inst Studies Econ Dept StatSch Econ Xiamen 361005 Peoples R China Xiamen Univ Fujian Key Lab Stat Xiamen 361005 Peoples R China

Heterogeneity is a hallmark of cancer, diabetes, cardiovascular diseases, and many other complex diseases. This study has been partly motivated by the unsupervised heterogeneity analysis for complex diseases based on molecular and imaging data, for which, network-based analysis, by accommodating the interconnections among variables, can be more informative than that limited to mean, variance, and other simple distributional properties. In the literature, there has been very limited research on network-based heterogeneity analysis, and a common limitation shared by the existing techniques is that the number of subgroups needs to be specified a priori or in an ad hoc manner. In this article, we develop a penalized fusion approach for heterogeneity analysis based on the Gaussian graphical model. It applies penalization to the mean and precision matrix parameters to generate regularized and interpretable estimates. More importantly, a fusion penalty is imposed to "automatedly" determine the number of subgroups and generate more concise, reliable, and interpretable estimation. Consistency properties are rigorously established, and an effective computational algorithm is developed. The heterogeneity analysis of non-small-cell lung cancer based on single-cell gene expression data of the Wnt pathway and that of lung adenocarcinoma based on histopathological imaging data not only demonstrate the practical applicability of the proposed approach but also lead to interesting new findings.

关键词： Gaussian graphical model lung cancer penalized fusion unsupervised heterogeneity analysis

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Weakly Supervised Video Individual Counting

Weakly Supervised Video Individual Counting

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xinyan Liu Guorong Li Yuankai Qi Ziheng Yan Zhenjun Han Anton van den Hengel Ming-Hsuan Yang Qingming Huang University of Chinese Academy of Science Key Lab of Big Data Mining and Knowledge Management UCAS China Macquarie University Australian Institute for Machine Learning The University of Adelaide University of California at Merced Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. Existing methods learn representations based on trajectory labels for individuals, which are annotation-expensive. To provide a more realistic reflection of the underlying practical challenge, we introduce a weakly supervised VIC task, wherein trajectory labels are not provided. Instead, two types of labels are provided to indicate traffic entering the field of view (inflow) and leaving the field view (outflow). We also propose the first solution as a baseline that formulates the task as a weakly supervised contrastive learning problem under group-level matching. In doing so, we devise an end-to-end trainable soft contrastive loss to drive the network to distin-guish inflow, outflow, and the remaining. To facilitate future study in this direction, we generate annotations from the existing VIC datasets Sense Crowd and CroHD and also build a new dataset, UAVVIC. Extensive results show that our baseline weakly supervised method outperforms supervised methods, and thus, little information is lost in the transition to the more practically relevant weakly supervised task. The code and trained model can be found at CGNet.

关键词： Computer vision Costs Codes Annotations Contrastive learning Drives Reflection

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Multi-view Teacher-Student Network

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NEURAL NETWORKS 2022年第0期146卷 69-84页

作者： Tian, Yingjie Sun, Shiding Tang, Jingjing Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Southwestern Univ Finance & Econ Fac Business Adm Sch Business Adm Chengdu 611130 Peoples R China Southwestern Univ Finance & Econ Inst Big Data Chengdu 611130 Peoples R China

Multi-view learning aims to fully exploit the view-consistency and view-discrepancy for performance improvement. knowledge Distillation (KD), characterized by the so-called "Teacher-Student" (T-S) learning framework, can transfer information learned from one model to another. Inspired by knowledge distillation, we propose a Multi-view Teacher-Student Network (MTS-Net), which combines knowledge distillation and multi-view learning into a unified framework. We first redefine the teacher and student for the multi-view case. Then the MTS-Net is built by optimizing both the view classification loss and the knowledge distillation loss in an end-to-end training manner. We further extend MTS-Net to image recognition tasks and present a multi-view Teacher-Student framework with convolutional neural networks called MTSCNN. To the best of our knowledge, MTS-Net and MTSCNN bring a new insight to extend the Teacher-Student framework to tackle the multi-view learning problem. We theoretically verify the mechanism of MTS-Net and MTSCNN and comprehensive experiments demonstrate the effectiveness of the proposed methods. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Multi-view learning Information fusion Teacher-student Network knowledge distillation

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NetNMSP: Nonoverlapping maximal sequential pattern mining

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APPLIED INTELLIGENCE 2022年第9期52卷 9861-9884页

作者： Li, Yan Zhang, Shuai Guo, Lei Liu, Jing Wu, Youxi Wu, Xindong Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Hebei Key Lab Big Data Comp Tianjin 300401 Peoples R China Hefei Univ Technol Res Inst Big Knowledge Hefei 230009 Peoples R China Mininglamp Software Syst Beijing 100084 Peoples R China

Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.

关键词： Sequential pattern mining Maximal pattern mining Nonoverlapping pattern mining Gap constraint Backtracking strategy COVID-19 MERS-CoV

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Overview of Essential Components in deep learning reference-based super resolution methods 11

Overview of Essential Components in deep learning reference-...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Xue, Jiayu Liu, Junjie Shi, Yong School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Reference-based super resolution (RefSR) aims to recover the lost details in a low-resolution image and generate a high-resolution result, guided by a high-resolution reference image with similar contents or textures. In contrast to the traditional single-image super-resolution, which focuses on the intrinsic properties of the single low-resolution image, the challenge of RefSR lies in matching and aggregating highly-related but misaligned reference images with low-resolution images. Several effective but complex designs have been proposed to address this challenge, which poses difficulties in implementing RefSR in real-world applications. In order to better understand the working mechanism of RefSR and design a more efficient and lightweight architecture, we provide a review about the essential components of the existing deep learning-based RefSR. We decompose and classify the common pipeline into four submodules according to the functionalities. Then, we summarize and describe the implementation details of the commonly-adopted approaches in each submodule. Finally, we discuss the challenges and promising research directions. © 2024 The Authors.

关键词： Deep learning

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Towards Compact Broad Learning System by Combined Sparse Regularization

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2022年第1期21卷 169-194页

作者： Miao, Jianyu Yang, Tiejun Jin, Jun-Wei Sun, Lijun Niu, Lingfeng Shi, Yong Minist Educ Key Lab Grain Informat Proc & Control HAUT Zhengzhou 450001 Peoples R China Henan Key Lab Grain Photoelect Detect & Control H Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Artificial Intelligence & Big Data Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Southwest Minzu Univ Sch Elect & Informat Engn Chengdu 610041 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

Broad Learning System (BLS) has been proven to be one of the most important techniques for classification and regression in machine learning and data mining. BLS directly collects all the features from feature and enhancement nodes as input of the output layer, which neglects vast amounts of redundant information. It usually leads to be inefficient and overfitting. To resolve this issue, we propose sparse regularization-based compact broad learning system (CBLS) framework, which can simultaneously remove redundant nodes and weights. To be more specific, we use group sparse regularization based on l(2,1) norm to promote the competition between different nodes and then remove redundant nodes, and a class of nonconvex sparsity regularization to promote the competition between different weights and then remove redundant weights. To optimize the resulting problem of the proposed CBLS, we exploit an efficient alternative optimization algorithm based on proximal gradient method together with computational complexity. Finally, extensive experiments on the classification task are conducted on public benchmark datasets to verify the effectiveness and superiority of the proposed CBLS.

关键词： Broad learning system (BLS) group sparsity nonconvex regularization proximal gradient method

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Improved Diversity-Promoting Collaborative Metric Learning for Recommendation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 9004-9022页

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur SKLOIS Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Alibaba Grp Secur Dept Hangzhou 311121 Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen 518107 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Key Lab Big Data Min & Knowledge Management BDKM Beijing 101408 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing practices exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to introduce a set of multiple representations for each user in the system where users' preference toward an item is aggregated by taking the minimum item-user distance among their embedding set. Specifically, we instantiate two effective assignment strategies to explore a proper quantity of vectors for each user. Meanwhile, a Diversity Control Regularization Scheme (DCRS) is developed to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could induce a smaller generalization error than traditional CML. Furthermore, we notice that CML-based approaches usually require negative sampling to reduce the heavy computational burden caused by the pairwise objective therein. In this paper, we reveal the fundamental limitation of the widely adopted hard-aware sampling from the One-Way Partial AUC (OPAUC) perspective and then develop an effective sampling alternative for the CML-based paradigm. Finally, comprehensive experiments over a range of benchmark datasets speak to the efficacy of DPCML.

关键词： Collaborative metric learning (CML) machine learning partial AUC optimization recommendation system

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Mega research infrastructure as a driver for high-quality development and innovation: Promoting scientific cooperation and interdisciplinarity☆

PROJECT LEADERSHIP AND SOCIETY

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PROJECT LEADERSHIP AND SOCIETY 2025年 6卷

作者： Xing, Ying Wu, Yuejia Xiao, Xiao Wang, Daojuan Zhang, Ling-ling Univ Chinese Acad Sci Sino Danish Coll Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Humanities Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Aalborg Univ Business Sch DK-9200 Aalborg Denmark Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China MOE Social Sci Lab Digital Econ Forecasts & Policy Beijing 100190 Peoples R China

Mega research infrastructures, as megaprojects, play a crucial role in promoting high-quality development, particularly in relation to innovation. They contribute to the development of disciplinary layouts and scientific collaborations. Additionally, during the operational phase of a infrastructure, maximizing efficiency and ensuring sustainable development become key aspects of project management. The operation of mega research infrastructures involves increasing levels of interdisciplinarity, interagency collaboration, and cross-regional cooperation. Therefore, it is essential to further clarify the characteristics of scientific collaboration within these facilities and understand the current status and evolving trends of disciplinary and collaborative networks. This work serves as a fundamental and necessary endeavor. In this study, we employ social network analysis methods to investigate the Shanghai Synchrotron Radiation Facility as a case study. We analyze the network structure and evolution of its collaborative and disciplinary networks from 2009 to 2022. The findings reveal the enormous potential and value of mega research infrastructures in promoting scientific research cooperation and development. They emphasize the need for increased attention and support for instrument scientists by facility managers, as well as the importance of expanding collaboration areas and formats to enrich the range of disciplines involved. The contribution of this study lies in its systematic analysis of the evolutionary process of collaborative and disciplinary networks within mega research infrastructures. It reveals the scientific value of these facilities in driving scientific research cooperation and interdisciplinary integration, providing a scientific basis for project management and policy formulation in the operational phase of mega research infrastructures. The research will align the strategic development direction of mega research infrastructures with the requirement

关键词： Social network analysis Collaborative network Disciplinary network Mega research infrastructure Innovation High-quality development

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Two-Stage Polishing Network for Camouflaged Object Detection 11th

Two-Stage Polishing Network for Camouflaged Object Detectio...

引用

11th International Conference on Image and Graphics, ICIG 2021

作者： Jiang, Xuan Wu, Zhe Zhang, Yajie Su, Li Huang, Qingming University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9783030873547

Due to the needs of medical and military fields, Camouflaged Object Detection (COD) becomes one of important branches of object detection. It has gradually gained people’s attention in recent years. How to correctly locate camouflaged objects accurately segment them are the main problems in this field. The COD task is far different from SOD because of the more complex background with similar colors and textures. Therefore it is more challenging. At present, there are still few existing methods, and lacks targeted method for the edge detection problem. In this paper, we propose a novel Two-Stage Polishing Network (TSPNet). This network consists of Front Feature Fusion Module (FFFM) and Polishing Module (PM). FFFM adopts Cross-modal Feature Aggregation and Global and Local Feature Aggregation to capture global context information and detail local information simultaneously. Meanwhile, PM uses edge truth map as supervision imformation to further study the object edge features. The experiments was conducted on three available datasets and the result shows that the proposed framework outperforms state-of-the-arts. Besides, TSPNet is compact with 50% model size saving than existed COD models. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification

arXiv

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arXiv 2024年

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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