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Astronomical data fusion tool based on PostgreSQL

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Research in Astronomy and Astrophysics 2016年第11期16卷 105-112页

作者： Bo Han Yan-Xia Zhang Shou-Bo Zhong Yong-Heng Zhao International school of software Wuhan University Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of Sciences

With the application of advanced astronomical technologies, equipments and methods all over the world, astronomical observations cover the range from radio, infrared, visible light, ultraviolet, X-ray and gamma-ray bands, and enter into the era of full wavelength astronomy. How to effectively integrate data from different ground- and space-based observation equipments, different observers, different bands and different observation times, requires data fusion technology. In this paper we introduce a cross-match tool that is developed in the Python language, is based on the PostgreSQL database and uses Q3C as the core index, facilitating the cross-match work of massive astronomical data. It provides four different cross- match functions, namely：（I） cross-match of the custom error range; （II） cross-match of catalog errors; （III） cross-match based on the elliptic error range; （IV） cross-match of the nearest neighbor algorithm. The resulting cross-matched set provides a good foundation for subsequent data mining and statistics based on multiwavelength data. The most advantageous aspect of this tool is a user-oriented tool applied locally by users. By means of this tool, users can easily create their own databases, manage their own data and cross- match databases according to their requirements. In addition, this tool is also able to transfer data from one database into another database. More importantly, it is easy to get started with the tool and it can be used by astronomers without writing any code.

关键词： astronomical databases: miscellaneous - catalogs - surveys

Integrating phenotypic features and tissue-specific information to prioritize disease genes

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Science China(Information Sciences) 2016年第7期59卷 5-14页

作者： Yue DENG Lin GAO Xingli GUO Bingbo WANG school of Computer Science and Technology Xidian University school of software Xidian University

Prioritization of candidate disease genes is crucial for improving medical care, and is one of the fundamental challenges in the post-genomic era. In recent years, different network-based methods for gene prioritization are proposed. Previous studies on gene prioritization show that tissue-specific protein-protein interaction(PPI) networks built by integrating PPIs with tissue-specific gene expression profiles can perform better than tissue-na¨?ve global PPI network. Based on the observations that diseases with similar phenotypes are likely to have common related genes, and genes associated with the same phenotype tend to interact with each other, we propose a method to prioritize disease genes based on a heterogeneous network built by integrating phenotypic features and tissue-specific information. In this heterogeneous network, the PPI network is built by integrating phenotypic features with a tissue-specific PPI network, and the disease network consists of the diseases that are associated with the same phenotype and tissue as the query disease. To determine the impacts of these two factors on gene prioritization, we test three typical network-based prioritization methods on heterogeneous networks consisting of combinations of different PPIs and disease networks built with or without phenotypic features and tissue-specific information. We also compare the proposed method with other tissuespecific networks. The results of case studies reveals that integrating phenotypic features with a tissue-specific PPI network improves the prioritization results. Moreover, the disease networks generated using our method not only show comparable performance with the widely used disease similarity dataset of 5080 human diseases,but are also effective for diseases that are not in the dataset.

关键词： gene prioritization tissue-specific network phenotype PPI network disease network

Abductive subconcept learning

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Science China(Information Sciences) 2023年第2期66卷 114-126页

作者： Zhongyi HAN Le-Wen CAI Wang-Zhou DAI Yu-Xuan HUANG Benzheng WEI Wei WANG Yilong YIN school of software Shandong University national Key Laboratory for Novel software Technology Nanjing University Department of Computing Imperial College London Center for Medical Artificial Intelligence Shandong University of Traditional Chinese Medicine

Bridging neural network learning and symbolic reasoning is crucial for strong AI. Few pioneering studies have made some progress on logical reasoning tasks that require partitioned inputs of instances(e.g.,sequential data), from which a final concept is formed based on the complex(perhaps logical) relationships between them. However, they cannot apply to low-level cognitive tasks that require unpartitioned inputs(e.g., raw images), such as object recognition and text classification. In this paper, we propose abductive subconcept learning(ASL) to bridge neural network learning and symbolic reasoning on unsegmented image classification tasks. ASL uses deep learning and abductive logical reasoning to jointly learn subconcept perception and secondary reasoning. Specifically, it first employs meta-interpretive learning(MIL) to induce first-order logical hypotheses capturing the relationships between the high-level subconcepts that account for the target concept. Then, it uses the groundings of the logical hypotheses as labels to train a deep learning model for identifying the subconcepts from unpartitioned data. ASL jointly trains the deep learning model and learns the MIL theory by minimizing the inconsistency between their grounded outputs. Experimental results show that ASL successfully integrates machine learning and logical reasoning with accurate and interpretable results in several object recognition tasks.

关键词： abductive learning subconcept learning logical reasoning subconcept set selection meta-interpretive learning

Energy-efficient virtual machine consolidation algorithm in cloud data centers

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Journal of Central South University 2017年第10期24卷 2331-2341页

作者： ZHOU Zhou HU Zhi-gang YU Jun-yang school of software Central South University school of Information Technology Deakin University

Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidation algorithm named PVDE(prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement(SLA) violations.

关键词： cloud computing energy consumption linear weighted method virtual machine consolidation virtual machine selection algorithm

Memory Request Priority Based Warp Scheduling for GPUs

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Chinese Journal of Electronics 2018年第5期27卷 985-994页

作者： ZHANG Jun HE Yanxiang SHEN Fanfan LI Qing'an TAN Hai school of software East China University of Technology Computer school Wuhan University

High performance of GPGPU comes from its super massive multithreading, which makes it more and more widely used especially in the field of throughputoriented. Data locality is one of the important factors affecting the performance of GPGPU. Although GPGPU can exploit intra/inter-warp locality by itself in part, there is still large improvement space for that. In our work, we analyze the characteristics of different applications and propose memory request based warp scheduling to better exploit inter-warp spatial locality. This method can make some warps with good inter-warp locality run faster, which is beneficial to improve the whole performance. Our experimental results show that our proposed method can achieve24.7% and 11.9% average performance improvement over LRR and MRPB respectively.

关键词： General purpose graphics processing unit(GPGPU) Memory request priority Performance Data locality Warp scheduling

Cross-modal learning using privileged information for long-tailed image classification

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Computational Visual Media 2024年第5期10卷 981-992页

作者： Xiangxian Li Yuze Zheng Haokai Ma Zhuang Qi Xiangxu Meng Lei Meng school of software Shandong UniversityJinan 250101China

The prevalence of long-tailed distributions in real-world data often results in classification models favoring the dominant classes,neglecting the less frequent *** approaches address the issues in long-tailed image classification by rebalancing data,optimizing weights,and augmenting ***,these methods often struggle to balance the performance between dominant and minority classes because of inadequate representation learning of the *** address these problems,we introduce descriptional words into images as cross-modal privileged information and propose a cross-modal enhanced method for long-tailed image classification,referred to as *** improves the learning of intraclass similarity of tail-class representations by cross-modal alignment and captures the difference between the head and tail classes in semantic space by cross-modal *** fusing the above information,CMLTNet achieved an overall performance that was better than those of benchmark long-tailed and cross-modal learning methods on the long-tailed cross-modal datasets,NUS-WIDE and *** effectiveness of the proposed modules was further studied through ablation *** a case study of feature distribution,the proposed model was better in learning representations of tail classes,and in the experiments on model attention,CMLTNet has the potential to help learn some rare concepts in the tail class through mapping to the semantic space.

关键词： long-tailed classification cross-modal learning representation learning privileged infor-mation

Selectivity Estimation for String Predicates Based on Modified Pruned Count-Suffix Tree

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Chinese Journal of Electronics 2015年第1期24卷 76-82页

作者： LI Dong ZHANG Qixu LIANG Xiaochong GUAN Jida XU Yang school of software Engineering South China University of Technology

The accuracy of predicates selectivity estimation is one of the important factors affecting query optimization performance. State-of-art selectivity estimation algorithms for string predicates based on Pruned countsuffix tree(PST) often suffer severe underestimating and overestimating problems, thus their average relative errors are not good. We analyze the main causes of the underestimating and overestimating prob-lems, propose a novel Restricted pruned count-suffix tree(RPST) and a new pruning strategy. Based on these, we present the EKVI algorithm and the EMO algorithm which are extended from the KVI algorithm and the MO algorithm respectively. The experiments compare the EKVI algorithm and the EMO algorithm with the traditional KVI algorithm and the MO algorithm, and the results show that the average relative errors of our selectivity estimation algorithms are significantly better than the traditional selectivity estimation algorithms. The EMO algorithm is the best among these algorithms from the overall view.

关键词： Query optimization Selectivity estimation String predicate Oruned count-suffix tree.

An adaptive and robust secret key extraction scheme from high noise wireless channel in IIoT

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Digital Communications and Networks 2023年第4期9卷 809-816页

作者： Lupeng Zhang Pingchuan Wang Yuming Zhang Zongzheng Chi Ning Tong Lei Wang Fengqi Li school of software Dalian Jiaotong UniversityDalian116028China school of software Dalian University of TechnologyDalian116024China Key Laboratory for Ubiquitous Network and Service software of Liaoning Province China

The Sixth-Generation(6G)enhances the Industrial Internet of Things(IIoT)communication efficiency and further raises security *** is crucial to construct a post-quantum security communication channel between any pair of industrial *** work shows that two legitimate devices can directly extract symmetrical secret keys in information-theoretic secure by measuring their common wireless ***,existing schemes may cause a high bit mismatch rate in IIoT with high *** addition,the physical layer key extraction scheme widely uses passive attack assumptions,which also contradicts the high-security requirements of *** analyzing and modeling IIoT systems and channels,we propose an Adaptive and Robust Secret Key Extraction scheme(ARSKE)from high noise wireless channels in 6G-enabled *** eliminate the non-reciprocity of the wireless channel,we design a smoothing method as the preprocessing ***,we propose a Robust Secure Reconciliation technique that can effectively resist active attacks by jointly designing the information reconciliation and privacy amplification *** real-world experiments are conducted to test the effectiveness and robustness of our scheme.

关键词： Security Key extraction Resource-constrained devices Privacy

Multi-scale joint feature network for micro-expression recognition

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Computational Visual Media 2021年第3期7卷 407-417页

作者： Xinyu Li Guangshun Wei Jie Wang Yuanfeng Zhou school of software Shandong UniversityJinan 250101China

Micro-expression recognition is a substantive cross-study of psychology and computer science,and it has a wide range of applications(e.g.,psychological and clinical diagnosis,emotional analysis,criminal investigation,etc.).However,the subtle and diverse changes in facial muscles make it difficult for existing methods to extract effective features,which limits the improvement of micro-expression recognition ***,we propose a multi-scale joint feature network based on optical flow images for micro-expression ***,we generate an optical flow image that reflects subtle facial motion *** optical flow image is then fed into the multi-scale joint network for feature extraction and *** proposed joint feature module(JFM)integrates features from different layers,which is beneficial for the capture of micro-expression features with different *** improve the recognition ability of the model,we also adopt a strategy for fusing the feature prediction results of the three JFMs with the backbone *** experimental results show that our method is superior to state-of-the-art methods on three benchmark datasets(SMIC,CASME II,and SAMM)and a combined dataset(3 DB).

关键词： micro-expression recognition multi-scale feature optical flow deep learning