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Cross-media analysis and reasoning: advances and directions

Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 44-57页

作者： Yu-xin PENG Wen-wu ZHU Yao ZHAO Chang-sheng XU Qing-ming HUANG Han-qing LU Qing-hua ZHENG Tie-jun HUANG Wen GAO Institute of Computer Science and Technology Peking University Department of Computer Science and Technology Tsinghua University Institute of Information Science Beijing Jiaotong University National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences Department of Computer Science and Technology Xi'an Jiaotong University School of Electronics Engineering and Computer Science Peking University

Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows：（1） theory and model for cross-media uniform representation; （2） cross-media correlation understanding and deep mining; （3） cross-media knowledge graph construction and learning methodologies; （4） cross-media knowledge evolution and reasoning; （5） cross-media description and generation; （6） cross-media intelligent engines; and （7） cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.

关键词： Cross-media analysis Cross-media reasoning Cross-media applications

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Adaptive dynamic loading and unloading mechanism applied to development environment for image processing algorithm

Adaptive dynamic loading and unloading mechanism applied to ...

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International Conference on Computer Science and Service System (CSSS)

作者： Haifeng Lu Tianxu Zhang Wei Yan Zhao Cheng State Key Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Technology Hubei Daily Media Group Wuhan China

An advanced development environment contributes to research process greatly. This paper presents a novel adaptive dynamic loading and unloading mechanism applied to development environment for image processing algorithm and two implementation methods of the mechanism. Based on the inherent characteristic of variable arguments of functions in programming language and explicit linking technique for dynamic link library (DLL), the mechanism is to integrate the image processing algorithm into the DLL file which is independent from the development environment, and to make the development environment identify the normative algorithm DLL and load functions of image processing algorithm successfully when needed. The proposed mechanism has been implemented in a development environment written by C/C++ language. The results show that the development environment based on the novel mechanism can improve work efficiency by making researchers only focus on the development of image processing algorithm rather than the other inefficient and repetitive work. At the same time the standardability, reusability and confidentiality of image processing algorithm can be promoted greatly. This mechanism is also applicable to other similar systems.

关键词： Image processing Heuristic algorithms Algorithm design and analysis Loading Computational modeling Computer languages Mathematical model

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Simultaneously Infer Cell Pseudotime,Velocity Field and Gene Interaction from Multi-Branch scRNA-seq Data with scPN

arXiv

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arXiv 2024年

作者： Zhou, Zhen Li, Jiachen Xin, Hongyi Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Modeling cellular dynamics from single-cell RNA sequencing (scRNA-seq) data is critical for understanding cell development and underlying gene regulatory relationships. Many current methods rely on single-cell velocity to obtain pseudotime, which can lead to inconsistencies between pseudotime and velocity. It is challenging to simultaneously infer cell pseudotime and gene interaction networks, especially in multi-branch differentiation scenarios. We present single-cell Piecewise Network (scPN), a novel high-dimensional dynamical modeling approach that iteratively extracts temporal patterns and inter-gene relationships from scRNA-seq data. To tackle multi-branch differentiation challenges, scPN models gene regulatory dynamics using piecewise gene-gene interaction networks, offering an interpretable framework for deciphering complex gene regulation patterns over time. Results on synthetic data and multiple scRNA-seq datasets demonstrate the superior performance of scPN in reconstructing cellular dynamics and identifying key transcription factors involved in development compared to existing methods. To the best of our knowledge, scPN is the first attempt at modeling that can recover pseudotime, velocity fields, and gene interactions all at once on multi-branch datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Transcription

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OUGENE： a disease associated over-expressed and under-expressed gene database

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Science Bulletin 2016年第10期61卷 752-754页

作者： Xiaoyong Pan Hong-Bin Shen Section for Animal Genetics Bioinformatics and Breeding Center for Non-Coding RNA in Technology and Health University of Copenhagen 1870 Frederiksberg Denmark Department of Disease Systems Biology Novo NordiskFoundation Center for Protein Research University ofCopenhagen 1870 Frederiksberg Denmark Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Gene over-expression or under-expression is closely associated with human diseases, which contributes to phenotypic variations and diversity. To our best knowledge, there is no single open specific resource available to provide the association information between gene over- or under-expression and various diseases. In this study, we presented a comprehensive disease-associated over- and under-expressed gene database （OUGene） based on our proposed text mining pipeline and several open curated databases. It contains total 41,269 unique associa- tions between 7,238 over- or under-expressed genes and 1,480 diseases, which are supported by 81,974 evidence sentences from 56,442 articles. The OUGene is compre- hensive and covers most important therapeutic areas. Meanwhile a new scoring system is designed to rank the associations based on benchmarking against hand-curated data. OUGene provides an easy-of-use web interface for researchers to analyze these data and visualize the associ- ated networks, which can give insights to the complex relationships between over- and under-expressed genes and diseases at a system level. It is available at ***. ***/bioinf/OUGene/.

关键词： Text mining Gene over- and under-expression Disease Database

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Mountain landslide monitoring based on wireless sensor network

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Information Technology Journal 2013年第15期12卷 3357-3362页

作者： Li, Xiaoling Yuan, Jimin School of Computer Science and Technology Chengdu University 610106 Chengdu China University Key Laboratory of Pattern Recognition Intelligent Information Processing 610106 Chengdu China Department Computer Engineering Chengdu Aeronautic Vocational and Technical College Chengdu 610000 China

The study provides a design scheme for the mountain landslide monitoring System based on wireless sensor networks. Different sensors are mainly used to collect data about the water depth in the soil and the sloping angel of the hillside in real-time. After data analysis and processing by monitoring center, the warning information is provided for related departments to take effective measures rapidly to protect people's lives and properties. © 2013 Asian Network for Scientific Information.

关键词： Landforms

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Epileptic EEG Signal Classification with ANFIS Based on Harmony Search Method

Epileptic EEG Signal Classification with ANFIS Based on Harm...

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International Conference on Computational Intelligence and Security

作者： Jing Wang X.Z. Gao Jarno M.A. Tanskanen Ping Guo Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Tampereen yliopisto - Hervannan kampus Tampere Pirkanmaa FI

In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained using a back propagation algorithm in combination with a least squares method. However, the training method sometimes may lead to local optima. We here propose a new strategy of hybrid training algorithm based on the fusion of the ANFIS and Harmony Search (HS), HS-ANFIS, which is adopted to tune all the parameters of the ANFIS. The validity of our method is verified by numerical experiments.

关键词： Electroencephalography Epilepsy Feature extraction Training pattern classification Adaptive systems Expert systems

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The first nontrivial three color upper domination Ramsey number is 13

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Journal of Combinatorial Mathematics and Combinatorial Computing 2012年 83卷 161-165页

作者： Su, Changming Lang, Fangnian Shao, Zehui School of Information Science and Technology Chengdu University Chengdu 610106 China University Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province China School of Electronic Engineering University of Electronic Science and Technology of China Chengdu 610054 China

The upper domination Ramsey number u(3, 3, 3) is the smallest integer n such that every 3-coloring of the edges of complete graph Kn contains a monochromatic graph G with T(G) ≥ 3, where T(G) is the maximum order over all the minimal dominating sets of the complement of G. In this note, with the help of computers, we determine that U(3, 3, 3) = 13, which improves the results that 13 ≤ U(3, 3, 3) ≤ 14 provided by Michael A. Henning and Ortrud R. Oellermann.

关键词： Graph theory

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ENHANCING KERNEL FLEXIBILITY VIA LEARNING ASYMMETRIC LOCALLY-ADAPTIVE KERNELS

arXiv

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arXiv 2023年

作者： He, Fan He, Mingzhen Shi, Lei Huang, Xiaolin Suykens, Johan A.K. STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Belgium Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University China

The lack of sufficient flexibility is the key bottleneck of kernel-based learning that relies on manually designed, pre-given, and non-trainable kernels. To enhance kernel flexibility, this paper introduces the concept of Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial Basis Function (RBF) kernel, giving rise to the LAB RBF kernel. The parameters in LAB RBF kernels are data-dependent, and its number can increase with the dataset, allowing for better adaptation to diverse data patterns and enhancing the flexibility of the learned function. This newfound flexibility also brings challenges, particularly with regards to asymmetry and the need for an efficient learning algorithm. To address these challenges, this paper for the first time establishes an asymmetric kernel ridge regression framework and introduces an iterative kernel learning algorithm. This novel approach not only reduces the demand for extensive support data but also significantly improves generalization by training bandwidths on the available training data. Experimental results on real datasets underscore the remarkable performance of the proposed algorithm, showcasing its superior capability in handling large-scale datasets compared to Nyström approximation-based algorithms. Moreover, it demonstrates a significant improvement in regression accuracy over existing kernel-based learning methods and even surpasses residual neural networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Bandwidth

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Object based video watermarking scheme using inertia ellipse and shape adaptive DCT

Object based video watermarking scheme using inertia ellipse...

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IEEE Workshop on Multimedia Signal processing

作者： Guo Jie Shi Peng-fei Fang Tao Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China The State Key Laboratory of Information Security Chinese Academy and Sciences Beijing China Chinese Academy of Seiencer The State Key Laboratory of Information Security Beijing China

ISBN: (纸本)0780377133

Video object (VO) is an important concept in MPEG-4. For objects can be easily manipulated without visible distortion, the copyright protection of video objects becomes an important issue. This paper presents a watermarking scheme for video objects. Different from other methods, the proposed scheme employed inertia ellipse to achieve fast synchronization recovery in case the object was manipulated. Shape adaptive DCT and visual mask were combined to embed the watermark into the arbitrarily shaped object, which was designed to achieve the trade-off between the invisibility and the robustness. Experiments show that our algorithm is robust to object manipulations such as rotations, translations, scaling and lossy compression. Our scheme can be easily incorporated into the object-based coding framework of MPEG-4.

关键词： Watermarking Shape Discrete cosine transforms MPEG 4 Standard Robustness Image coding Streaming media Discrete wavelet transforms Image processing Image recognition

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Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features

Predicting functional impact of single amino acid polymorphi...

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5th IEEE International Conference on Systems Biology, ISB 2011

作者： Wang, Mingjun Shen, Hong-Bin Akutsu, Tatsuya Song, Jiangning State Engineering Laboratory for Industrial Enzymes Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto 611-0011 Japan Department of Biochemistry and Molecular Biology Faculty of Medicine Monash University Melbourne VIC 3800 Australia

ISBN: (纸本)9781457716669

Single amino acid polymorphisms (SAPs) are the most abundant form of known genetic variations associated with human diseases. It is of great interest to study the sequence-structure-function relationship underlying SAPs. In this work, we collected the human variant data from three databases and divided them into three categories, i.e. cancer somatic mutations (CSM), Mendelian disease-related variant (SVD) and neutral polymorphisms (SVP). We built support vector machine (SVM) classifiers to predict these three classes of SAPs, using the optimal features selected by a random forest algorithm. Consequently, 280 sequence-derived and structural features were initially extracted from the curated datasets from which 18 optimal candidate features were further selected by random forest. Furthermore, we performed a stepwise feature selection to select characteristic sequence and structural features that are important for predicting each SAPs class. As a result, our predictors achieved a prediction accuracy (ACC) of 84.97, 96.93, 86.98 and 88.24%, for the three classes, CSM, SVD and SVP, respectively. Performance comparison with other previously developed tools such as SIFT, SNAP and Polyphen2 indicates that our method provides a favorable performance with higher Sensitivity scores and Matthew's correlation coefficients (MCC). These results indicate that the prediction performance of SAPs classifiers can be effectively improved by feature selection. Moreover, division of SAPs into three respective categories and construction of accurate SVM-based classifiers for each class provides a practically useful way for investigating the difference between Mendelian disease-related variants and cancer somatic mutations. © 2011 IEEE.

关键词： Amino acids

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