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arXiv 2020年

作者： Zhu, Haiping Shan, Hongming Zhang, Yuheng Che, Lingfu Xu, Xiaoyang Zhang, Junping Shi, Jianbo Wang, Fei-Yue The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-inspired Technology Shanghai201210 China The GRASP Laboratory University of Pennsylvania PhiladelphiaPA United States The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The University of Chinese Academy of Sciences Beijing100049 China

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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Profiling Adaptive Immunity: A Quantitative Framework for Immune Repertoire Dynamics and Clinical Diagnostics

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Fundamental Research 2025年

作者： Yexing Chen Haiwen Ni Yongjie Li Jin Ma Chen Huang Sixian Yang Xiangfei Xie Haitao Lv Min Li Peng Cao State Key Laboratory of Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture Nanjing University of Chinese Medicine Nanjing 210000 China Jiangsu Provincial Medical Innovation Center Affiliated Hospital of Integrated Traditional Chinese and Western Medicine Nanjing University of Chinese Medicine Nanjing 210000 China Department of Hematology Affiliated Hospital of Nanjing University of Chinese Medicine The First Clinical College of Nanjing University of Chinese Medicine Nanjing 210000 China Children’s Hospital of Soochow University Suzhou 215000 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230000 China Department of Computer Science College of Engineering and Applied Science University of Cincinnati Cincinnati 45221 USA Department of Oncology Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine Nanjing 210000 China

While immune repertoire(IR) dynamics encode critical disease signatures, existing models fail to capture the non-equilibrium processes that governing clonal selection and somatic hypermutation (SHM). We develop a biophysical framework that mathematically reconstructs IR evolution through energy landscape optimization, where 1) clonal emergence probabilities map to metastable states, 2) repertoire transitions obey non-equilibrium dynamics, and 3) inter-IR distances quantify the distribution transformation costs via optimal transport theory. This model enables macroscopic immune state detection from 10k cells by resolving critical fluctuations in sparse sampling regimes. Experimental validation across murine/human cohorts demonstrates precise unsupervised stratification of immune stages and disease states without prior clinical annotations. By bridging stochastic SHM kinetics with deterministic repertoire shifts, our approach establishes quantitative metrics for tracking immunological trajectories and pathological progression. This paradigm shift from static pattern recognition to dynamic process decoding empowers sensitive immunomonitoring and personalized therapeutic design through ultra-low-input clinical sampling.

关键词： Deductive reasoning Immune repertoire analysis Repertoire shift quantification Early disease screening Systemic immunity inference Kawasaki disease Colorectal cancer

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Correction to: A robust self-weighted SELO regression model

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International Journal of Machine Learning and Cybernetics 2020年第6期11卷 1357-1357页

作者： Su, Meihong Guo, Yaqing Men, Changqian Wang, Wenjian School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing (Shanxi University) Ministry of Education Taiyuan China

In the print published article, the reference 19 was published incorrectly and the correct reference is given below.

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Image Fusion via Domain and Feature Transfer

Image Fusion via Domain and Feature Transfer

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yicheng Yang Huabing Zhou Wei Zhang Changcai Yang Zhenghong Yu Computer Science and Engineering School Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China College of Computer and Information Sciences College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University FuZhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The infrared and visible images fusion technique is designed to simultaneously preserve the thermal radiation information of the infrared image and the detailed texture information of the visible image. Therefore, the fused image with rich detail information and a clear target area in order to find it more efficiently. Inspired by the powerful GANs technology in recent years, we proposed a novel method for fusing infrared and visible images that based on domain and feature transfer. First, it is designated as an optimization problem with a latent encoding which can be mapped into a pixel intensity consistent image on the latent image. We employ generative adversarial network, which can capture content characteristics of one image dataset and figure out how these characteristics could be translated into the domain of another image dataset, to transfer the appearance of an image from visible image to infrared one. And then we use the feature consistent constrains to enhance the features of the fused image. Moreover, by adding gradient constraints to preserve the details of visible image, therefore we combine the fused image with a similar gradient to the visible image. Qualitative and quantitative comparisons on public datasets demonstrate our proposed method is superior to the state-of-the-art technology. The fused image we have generated is more like a high-resolution enhanced infrared image, which is more efficient for discovering the target and tracking.

关键词： Image fusion Generative adversarial networks Feature extraction Gallium nitride Imaging Convolutional neural networks Manifolds

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Deep Adaptive Fusion Network for High Performance RGBT Tracking

Deep Adaptive Fusion Network for High Performance RGBT Track...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Yuan Gao Chenglong Li Yabin Zhu Jin Tang Tao He Futian Wang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Institute of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei China Anhui COWAROBOT CO. Ltd Wuhu China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Due to the complementarity of RGB and thermal data, RGBT tracking has received more and more attention in recent years because it can effectively solve the degradation of tracking performance in dark environments and bad weather conditions. How to effectively fuse the information from RGB and thermal modality is the key to give full play to their complementarities for effective RGBT tracking. In this paper, we propose a high performance RGBT tracking framework based on a novel deep adaptive fusion network, named DAFNet. Our DAFNet consists of a recursive fusion chain that could adaptively integrate all layer features in an end-to-end manner. Due to simple yet effective operations in DAFNet, our tracker is able to reach the near-real-time speed. Comparing with the state-of-the-art trackers on two public datasets, our DAFNet tracker achieves the outstanding performance and yields a new state-of-the-art in RGBT tracking.

关键词： Feature extraction Target tracking Robustness Fuses Convolution Adaptive systems

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TSABCNN: Two-stage attention-based convolutional neural network for frame identification 17th

TSABCNN: Two-stage attention-based convolutional neural netw...

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17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Zhao, Hongyan Li, Ru Duan, Fei Wu, Zepeng Guo, Shaoru School of Computer and Information Technology Shanxi University Taiyuan030006 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan030024 China Key Laboratory of Ministry of Education for Computation Intelligence and Chinese Information Processing Shanxi University Taiyuan030006 China

ISBN: (纸本)9783030017156

As an essential sub-task of frame-semantic parsing, Frame Identification (FI) is a fundamentally important research topic in shallow semantic parsing. However, most existing work is based on sophisticated, hand-crafted features which might not be compatible with FI procedure. Besides that, they usually heavily rely on available natural language processing (NLP) toolkits and various lexical resources. Thus existing methods with hand-crafted features may not achieve satisfactory performance. In this paper, we propose a two-stage attention-based convolutional neural network (TSABCNN) to alleviate this problem and capture the most important context features for FI task. In order to dynamically adjust the weight of each feature, we build two levels of attention over instances at input layer and pooling layer respectively. Furthermore, the proposed model is an end-to-end learning framework which does not need any complicated NLP toolkits and feature engineering, and can be applied to any language. Experiments results on FrameNet and Chinese FrameNet (CFN) show the effectiveness of the proposed approach for the FI task. © Springer Nature Switzerland AG 2018.

关键词： Semantics

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Cloud computing data security protection strategy 4th

Cloud computing data security protection strategy

引用

4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Wei, Yu Zhang, Yongsheng School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9783030000059

Cloud computing is a computing model that has emerged in recent years. It is of great significance for promoting massive information data calculations. We store large amounts of data in the cloud and use algorithms to make huge computations on large-scale data centers on the Internet. Big data cloud computing resources have a huge amount of information, which can provide convenient and fast computing while being a new type of storage. Due to the large amount of data that needs to be processed by cloud computing, many information processing methods are involved, and customer privacy data is difficult to be effectively protected in the cloud computing environment. Therefore, the protection of information security in the cloud computing environment becomes more important. Based on the analysis of the data security problems in the big data cloud computing environment, this paper analyzes the security protection of big data cloud computing data, which is of great significance for promoting the establishment of a complete data security system, and can also be further promoted the useful of cloud computing. © Springer Nature Switzerland AG 2018.

关键词： Data privacy

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

引用

arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Otem&Utem: Over- and under-translation evaluation metric for NMT

arXiv

引用

arXiv 2018年

作者： Yang, Jing Zhang, Biao Qin, Yue Zhang, Xiangwen Lin, Qian Su, Jinsong Xiamen University Xiamen China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from over- and under-translation issues [Tu et al., 2016], of which studies have become research hotspots in NMT. At present, these studies mainly apply the dominant automatic evaluation metrics, such as BLEU, to evaluate the overall translation quality with respect to both adequacy and fluency. However, they are unable to accurately measure the ability of NMT systems in dealing with the above-mentioned issues. In this paper, we propose two quantitative metrics, the Otem and Utem, to automatically evaluate the system performance in terms of over- and under-translation respectively. Both metrics are based on the proportion of mismatched n-grams between gold reference and system translation. We evaluate both metrics by comparing their scores with human evaluations, where the values of Pearson Correlation Coefficient reveal their strong correlation. Moreover, in-depth analyses on various translation systems indicate some inconsistency between BLEU and our proposed metrics, highlighting the necessity and significance of our metrics. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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A deep neural information fusion architecture for textual network embeddings

arXiv

引用

arXiv 2019年

作者： Xu, Zenan Su, Qinliang Quan, Xiaojun Zhang, Weijia School of Data and Computer Science Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Electronics and Information Technology Sun Yat-Sen University

Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations. Traditionally, the structural and textual embeddings were learned by models that rarely take the mutual influences between them into account. In this paper, a deep neural architecture is proposed to effectively fuse the two kinds of informations into one representation. The novelties of the proposed architecture are manifested in the aspects of a newly defined objective function, the complementary information fusion method for structural and textual features, and the mutual gate mechanism for textual feature extraction. Experimental results show that the proposed model outperforms the comparing methods on all three datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

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