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New high-spin structure and possible chirality in In109

Physical Review C 2018年第1期98卷 014304-014304页

作者： M. Wang Y. Y. Wang L. H. Zhu B. H. Sun G. L. Zhang L. C. He W. W. Qu F. Wang T. F. Wang Y. Y. Chen C. Xiong J. Zhang J. M. Zhang Y. Zheng C. Y. He G. S. Li J. L. Wang X. G. Wu S. H. Yao C. B. Li H. W. Li S. P. Hu J. J. Liu School of Physics and Nuclear Energy Engineering Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing 100083 China State Key Laboratory of Radiation Medicine and Protection School of Radiation Medicine and Protection Soochow University Suzhou 215123 China China Institute of Atomic Energy Beijing 102413 China College of Physics Jilin University Changchun 130013 China College of Physics and Energy Shenzhen University Shenzhen 518060 China

The high-spin structure of In109 has been investigated with the Mo100(N14, 5n)In109 reaction at a beam energy of 78 MeV using the in-beam γ spectroscopic method. The level scheme of In109 has been modified considerably and extended by 46 new γ rays to the highest excited state at 8.980 MeV and Jπ=(45/2+). The new level scheme consists of eight bands, six of which are identified as dipole bands. The configurations have been tentatively assigned with the help of the systematics of neighboring odd-A indium isotopes and the experimental aligned angular momenta. The dipole bands are then compared with the titled axis cranking calculation in the framework of covariant density function theory. The results of theoretical calculations based on the configurations, which involve one proton hole at the g9/2 orbital and two or four unpaired neutrons at the g7/2, d5/2, and h11/2 orbitals, show that the shape of In109 undergoes an evolution on both β and γ deformations, and possible chirality is suggested in In109.

关键词： Collective levels Electromagnetic transitions Energy levels & level densities Nuclear density functional theory Nuclear structure & decays 90 ≤ A ≤ 149

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Random asynchronous iterations in distributed coordination algorithms

arXiv

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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Overdue Prediction of Bank Loans Based on Deep Neural Network

Overdue Prediction of Bank Loans Based on Deep Neural Networ...

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2017 International Symposium on Computer Science and Artificial Intelligence（ISCSAI 2017）

作者： Li Xin Sun Guozi Li Huakang Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Collaborative Innovation Center for Economics crime investigation and prevention technology State Key Laboratory of Mathematical Engineering and Advanced Computing

With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data *** paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm inc rease over 6% prediction than kNN, Bayes algorithm.

关键词： Overdue Prediction Bank Loans DNN

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Metric based on multi-order spaces for cross-modal retrieval

Metric based on multi-order spaces for cross-modal retrieval

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang Zhang Bingpeng Ma Guorong Li Qingming Huang Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Computer and Control Engineering University of Chinese Academy of Sciences China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS China

ISBN: (纸本)9781509060689

This paper proposes a novel method for cross-modal retrieval. Different from vector (text)-to-vector (image) framework of the traditional cross-modal methods, we adopt a vector (text)-to-matrix (image) framework. We assume that compared with vectors, matrices can directly represent images and characterize the structure of feature space. Furthermore, we propose a Metric based on Multi-order spaces (MMs). Multi-order statistic features are used to represent images for enriching the semantic information, and metrics among the multi-spaces are jointly learned to measure the similarity between two different modalities. Specifically, there are three steps for MMs. First, we jointly use the bags of visual features (zero-order), mean (first-order) and covariance (second-order) to characterize each image. Second, considering that covariance matrices and vectors lie on a Riemannian manifold and an Euclidean space respectively, we embed multi-order spaces into their corresponding Hilbert spaces to reduce the heterogeneity among the original spaces. Finally, the similarity between two different modalities can be measured by learning multiple transformations from the different Hilbert spaces to a common subspace. The performance of the proposed method over the state-of-the-art has been demonstrated through the experiments on two public datasets.

关键词： Hilbert space Manifolds Correlation Covariance matrices Feature extraction Linear programming

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A gene expression map of shoot domains reveals new regulatory mechanisms

A gene expression map of shoot domains reveals new regulator...

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中国植物学会第十六次全国会员代表大会暨85周年学术年会

作者： Caihuan Tian Ying Wang Haopeng Yu Jun He Jin Wang Bihai Shi Qingwei Du Nicholas J.Provart Elliot M.Meyerowitz Yuling Jiao State Key Laboratory of Plant Genomics Institute of Genetics and Developmental BiologyChinese Academy of Sciences College of Life Sciences University of Chinese Academy of Sciences West China Biomedical Big Data Center West China Hospital/West China School of Medicineand Medical Big Data CenterSichuan University Horticultural Plant Biology and Metabolomics Center Institute of Science and TechnologyFujian Agricultural and Forestry University Zhongshan School of Medicine Sun Yat-sen University Laboratoire Reproduction et Developpement des Plantes Univ LyonENS de Lyon Department of Cell and Systems Biology Centre for the Analysis of Genome Evolution and FunctionUniversity of Toronto Howard Hughes Medical Institute and Division of Biology and Biological Engineering California Institute of Technology

Gene regulatory networks control development via cell type-specific gene *** seed plants,self-renewing stem cells located in the shoot apical meristem(SAM)produce leaves from the SAM peripheral *** develop polarity patterns following initiation to form a planar *** we provide a comparison of translating RNAs among SAM and leaf *** used RNA sequencing to quantify gene

关键词： A gene expression map of shoot domains reveals new regulatory mechanisms

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Inverse extreme learning machine for learning with label proportions

Inverse extreme learning machine for learning with label pro...

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IEEE International Conference on big data

作者： Limeng Cui Jiawei Zhang Zhensong Chen Yong Shi Philip S. Yu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Computer Science Florida State University FL USA Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China Department of Computer Science University of Illinois at Chicago IL USA

In large-scale learning problem, the scalability of learning algorithms is usually the key factor affecting the algorithm practical performance, which is determined by both the time complexity of the learning algorithms and the amount of supervision information (i.e., labeled data). Learning with label proportions (LLP) is a new kind of machine learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, LLP can estimate a classifier from groups of weakly labeled data, where only the positive/negative class proportions of each group are known. Due to its weak requirements for the input data, LLP presents a variety of real-world applications in almost all the fields involving anonymous data, like computer vision, fraud detection and spam filtering. However, even through the required labeled data is of a very small amount, LLP still suffers from the long execution time a lot due to the high time complexity of the learning algorithm itself. In this paper, we propose a very fast learning method based on inversing output scaling process and extreme learning machine, namely Inverse Extreme Learning Machine (IELM), to address the above issues. IELM can speed up the training process by order of magnitudes for large datasets, while achieving highly competitive classification accuracy with the existing methods at the same time. Extensive experiments demonstrate the significant speedup of the proposed method. We also demonstrate the feasibility of IELM with a case study in real-world setting: modeling image attributes based on ImageNet Object Attributes dataset.

关键词： Training Calibration Support vector machines Supervised learning Visualization Optimization

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Study on the Effect of Web Color Scheme on User Behavior

Study on the Effect of Web Color Scheme on User Behavior

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Shujuan Tian Xun Luo Dan Lu Yi Chen School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer and Information Engineering Beijing Technology and Business University Beijing China

ISBN: (纸本)9781538626375;9781538626368

Internet users spend most of their time reading and using web pages. Because of the reason there is a high demand for the user browsing experience to be pleasant. Experiments show that when people look at objects, the first impression that causes visual response is the color of the object. Therefore, it is of great significance to explore how web color schemes affect user behavior. This paper presents a framework that can automatically recolor the web pages based on reference palettes. Moreover, it analyzes and explores the effect of web color schemes on user behavior through a series of user studies. The preliminary experimental results show that the color scheme has a statistically significant effect on user preferences. The other factors include gender and hearing capability.

关键词： Image color analysis Web pages Visualization Virtual reality Auditory system Tools

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Wireless Ad Hoc Network Simulation Based on Virtual Reality Technology

Wireless Ad Hoc Network Simulation Based on Virtual Reality ...

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Yuanyuan Li Xun Luo Edwin Lobo Andrea Pilco Yi Chen School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer and Information Engineering Beijing Technology and Business University Beijing China

ISBN: (纸本)9781538626375;9781538626368

This paper proposes wireless ad hoc network simulation based on virtual reality technology. Functional components provided by a game engine such as scene management, animation, rendering, scripting, and physics engine can provide suitable interfaces for ad hoc network simulation. Using these components, simulation of ad hoc networks can be easily implemented. The ad-hoc network simulation technology can be carried out in virtual reality by game engine, which is illustrated by advantages of the game engine and the simulation process in this paper. The workability and accuracy of the simulation scheme are demonstrated, with the metrics of comparing the experimental data with other simulation techniques commonly used.

关键词： Solid modeling Games Engines Virtual reality Computational modeling Mobile ad hoc networks

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AN EXTENDED MODEL FOR PROJECT PORTFOLIO SELECTION WITH PROJECT DIVISIBILITY AND INTERDEPENDENCY

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Journal of Systems Science and Systems engineering 2016年第1期25卷 119-138页

作者： Xingmei Li Shu-Cherng Fang Xiaoling Guo Zhibin Deng Jianxun Qi School of Economics and Management North China Electric Power University Beijing 102206 China Industrial and Systems Engineering Department North Carolina State University Raleigh NC 27695 USA College of Engineering andlnformation Technology University of Chinese Academy of Sciences Beijing100049 China School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

In this paper, we develop an extended model for the project portfolio selection problem over a planning horizon with multiple time periods. The model incorporates the factors of project divisibility and interdependency at the same time for real-life applications. The project divisibility is considered as a strategy, not an unfortunate event as in the literature, in choosing the best execution schedule for the projects, and the classical concept of＂project interdependencies＂ among fully executed projects is then extended to the portions of executed projects. Additional constraints of reinvestment consideration, setup cost, cardinality restriction, precedence relationship and scheduling are also included in the model. For efficient computations, an equivalent mixed integer linear programming representation of the proposed model is derived. Numerical examples under four scenarios are presented to highlight the characteristics of the proposed model. In particular, the positive effects of project divisibility are shown for the first time.

关键词： Project management project portfolio selection problem project divisibility project interdependency

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Publisher Correction: Equivalence and its invalidation between non-Markovian and Markovian spreading dynamics on complex networks

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Nature communications 2019年第1期10卷 4677页

作者： Mi Feng Shi-Min Cai Ming Tang Ying-Cheng Lai School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai 200241 China. Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China. Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China. School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai 200241 China. tangminghan007@***. Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China. tangminghan007@***. School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85287 USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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