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An Integrated Tone Mapping for High Dynamic Range Image Visualization

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An Integrated Tone Mapping for High Dynamic Range Image Visu...

作者： Lei Liang Jeng-Shyang Pan Yongjun Zhuang Department of Computer Science and Technology Shenzhen Graduate School Harbin Institute of Technology Qihan Research Qihan Technology Co. LTD Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Techology

There are two type tone mapping operators for high dynamic range(HDR) image visualization. HDR image mapped by perceptual operators have strong sense of reality, but will lose local details. Empirical operators can maximize local detail information of HDR image, but realism is not strong. A common tone mapping operator suitable for all applications is not available. This paper proposes a novel integrated tone mapping framework which can achieve conversion between empirical operators and perceptual operators. In this framework, the empirical operator is rendered based on improved saliency map, which simulates the visual attention mechanism of the human eye to the natural scene. The results of objective evaluation prove the effectiveness of the proposed solution.

关键词： image An Integrated Tone Mapping for High Dynamic Range Image Visualization HDR Mapping

A generalized method toward drug-target interaction prediction via low-rank matrix projection

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arXiv 2017年

作者： Pech, Ratha Hao, Dong Lee, Yan-Li Po, Maryna Zhou, Tao CompleX Lab University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Department of Chemistry and Biochemistry George Mason University Virginia22030 United States

Drug-target interaction (DTI) prediction plays a very important role in drug development and drug discovery. Biochemical experiments or in vitro methods are very expensive, laborious and time-consuming. Therefore, in silico approaches including docking simulation and machine learning have been proposed to solve this problem. In particular, machine learning approaches have attracted increasing attentions recently. However, in addition to the known drug-target interactions, most of the machine learning methods require extra characteristic information such as chemical structures, genome sequences, binding types and so on. Whenever such information is not available, they may perform poor. Very recently, the similarity-based link prediction methods were extended to bipartite networks, which can be applied to solve the DTI prediction problem by using topological information only. In this work, we propose a method based on low-rank matrix projection to solve the DTI prediction problem. On one hand, when there is no extra characteristic information of drugs or targets, the proposed method utilizes only the known interactions. On the other hand, the proposed method can also utilize the extra characteristic information when it is available and the performances will be remarkably improved. Moreover, the proposed method can predict the interactions associated with new drugs or targets of which we know nothing about their associated interactions, but only some characteristic information. We compare the proposed method with ten baseline methods, e.g., six similarity-based methods that utilize only the known interactions and four methods that utilize the extra characteristic information. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

Lifetime-Based Memory Management for Distributed data Processing Systems 42nd

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Lifetime-Based Memory Management for Distributed Data Proces...

42nd International Conference on Very Large data Bases, VLDB 2016

作者： Luy, Lu Shiy, Xuanhua Zhouz, Yongluan Zhangy, Xiong Jin, Hai Pei, Cheng He, Ligang Geng, Yuanzhen Services Computing Technology and System Lab Big Data Technology and System Lab Huazhong University of Science and Technology China University of Southern Denmark Denmark University of Warwick United Kingdom

In-memory caching of intermediate data and eager combining of data in shuffle buffers have been shown to be very effective in minimizing the re-computation and I/O cost in distributed data processing systems like Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap, which may quickly saturate the garbage collector, especially when handling a large dataset, and hence would limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user defined functions and data types, obtains the expected life time of the data objects, and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca, a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar life times into byte arrays and releases their space altogether when their lifetimes come to an end. An extensive experimental study using both synthetic and real datasets shows that, in comparing to Spark, Deca is able to 1) reduce the garbage collection time by up to 99.9%, 2) to achieve up to 22.7x speed up in terms of execution time in cases without data spilling and 41.6x speedup in cases with data spilling, and 3) to consume up to 46.6% less memory. © 2016 VLDB Endowment 21508097/16/08.

关键词： Refuse collection

Topology analysis system for vehicular Ad Hoc network 17

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Topology analysis system for vehicular Ad Hoc network

17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016

作者： Dong, Baihong Deng, Jian Wu, Weigang Meng, Tianyu 1. School of Data and Computer Science Sun Yat-Sen Univ. China Guangdong Province Key Lab. of Big Data Analysis and Processing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781509050819

With the development of the technology, Vehicular Ad-hoc Network is developing rapidly and continuously. And many new algorithms in VANET were put forward. For example, many people apply the Named data Networks or Software Defined Networking6 to the VANET. However, few researches are looking into the analysis of the topology of the VANET, among most of which are based on simple situations and calculation of the track of vehicles. And therefore, these analysis are inadequate for analyzing the topology under complex situations. This paper is going to put forward a new method for analyzing the change of the topology of VANET, which is capable to quantitatively analyze the change under different and complex situations. In the real experiment, when compared with traditional analysis methods, this new method can analyze more precisely the impact of some possible wireless communication problem on the change of the topology, for example, the impact of hidden node on the vehicular wireless communication and whether the number or the density of vehicles makes the topology of VANET more stable. And this new method can provide a numerical result instead of purely qualitative analysis. © 2016 IEEE.

关键词： Topology

Gaia data Release 3: Apsis II - Stellar Parameters

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arXiv 2022年

作者： Fouesneau, Morgan Frémat, Y. Andrae, R. Korn, A.J. Soubiran, C. Kordopatis, G. Vallenari, A. Heiter, U. Creevey, O.L. Sarro, L.M. de Laverny, P. Lanzafame, A.C. Lobel, A. Sordo, R. Rybizki, J. Slezak, I. Álvarez, M.A. Drimmel, R. Garabato, D. Delchambre, L. Bailer-Jones, C.A.L. Hatzidimitriou, D. Lorca, A. Le Fustec, Y. Pailler, F. Mary, N. Robin, C. Utrilla, E. Aramburu, A. Abreu Bakker, J. Bellas-Velidis, I. Bijaoui, A. Blomme, R. Bouret, J.-C. Brouillet, N. Brugaletta, E. Burlacu, A. Carballo, R. Casamiquela, L. Chaoul, L. Chiavassa, A. Contursi, G. Cooper, W.J. Dafonte, C. Demouchy, C. Dharmawardena, T.E. García-Lario, P. García-Torres, M. Gomez, A. González-Santamaría, I. Piccolo, A. Jean-Antoine Kontizas, M. Lebreton, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Romeo, A. Magdaleno Manteiga, M. Marocco, F. Martayan, C. Marshall, D.J. Nicolas, C. Ordenovic, C. Palicio, P.A. Pallas-Quintela, L. Pichon, B. Poggio, E. Recio-Blanco, A. Riclet, F. Santoveña, R. Schultheis, M.S. Segol, M. Silvelo, A. Smart, R.L. Süveges, M. Thévenin, F. Elipe, G. Torralba Ulla, A. van Dillen, E. Zhao, H. Zorec, J. Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany Royal Observatory of Belgium Ringlaan 3 Brussels1180 Belgium Observational Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Bd de l'Observatoire CS 34229 Nice Cedex 406304 France INAF - Osservatorio Astronomico di Padova Vicolo Osservatorio 5 Padova35122 Italy Dpto. de Inteligencia Artificial UNED c/Juan del Rosal 16 Madrid28040 Spain INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Dipartimento di Fisica e Astronomia ""Ettore Majorana"" Università di Catania Via S. Sofia 64 Catania95123 Italy CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain INAF - Osservatorio Astrofisico di Torino via Osservatorio 20 TO Pino Torinese10025 Italy Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Department of Astrophysics Astronomy and Mechanics National and Kapodistrian University of Athens Panepistimiopolis Zografos Athens15783 Greece National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Thales Services for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid

Context. The third Gaia data release (Gaia DR3) contains, beyond the astrometry and photometry, dispersed light for hundreds of millions of sources from the Gaia prism spectra (BP and RP) and the spectrograph (RVS). This data release opens a new window on the chemo-dynamical properties of stars in our Galaxy, essential knowledge for understanding the structure, formation, and evolution of the Milky Way. Aims. To provide insight into the physical properties of Milky Way stars, we used these data to produce a uniformly-derived, all-sky catalog of stellar astrophysical parameters (APs): atmospheric properties (Teff, log g, [M/H], [α/Fe], activity index, emission lines, rotation), 13 chemical abundance estimates, evolution characteristics (radius, age, mass, bolometric luminosity), distance, and dust extinction. Methods. We developed the astrophysical parameters inference system (Apsis) pipeline to infer APs of Gaia objects by analyzing their astrometry, photometry, BP/RP, and RVS spectra. We validate our results against other works in the literature, including benchmark stars, interferometry, and asteroseismology. Here we assessed the stellar analysis performance from Apsis statistically. Results. We describe the quantities we obtained, including the underlying assumptions and the limitations of our results. We provide guidance and identify regimes in which our parameters should and should not be used. Conclusions. Despite some limitations, this is the most extensive catalog of uniformly-inferred stellar parameters to date. These comprise Teff, log g, and [M/H] (470 million using BP/RP, 6 million using RVS), radius (470 million), mass (140 million), age (120 million), chemical abundances (5 million), diffuse interstellar band analysis (1/2 million), activity indices (2 million), Hα equivalent widths (200 million), and further classification of spectral types (220 million) and emission-line stars (50 thousand). More precise and detailed astrophysical parameters based on

关键词： Stars

Application of Structural Equation Modelling for Oil Accumulation System Control in Oleaginous yeast

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Journal of Physics: Conference Series 2019年第1期1391卷

作者： S Aburatani Y Shida W Ogasawara H Yamazaki H Takaku Computational Bio Big Data Open Innovation Lab. (CBBD-OIL) National Institute of Advanced Industrial Science and Technology (AIST) AIST Tokyo Waterfront Main Bldg. 2-3-26 Aomi Koto-ku Tokyo 135-0064 Japan Department of Bioengineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata 940-2188 Japan Department of Applied Life Sciences Niigata University of Pharmacy and Applied Life Sciences 265-1 Higashijima Akiha-ku Niigata city Niigata 956-8603 Japan

Recently, we developed a new statistical method for revealing the regulatory systems in living cells. Our method is based on Structural Equation Modelling combined with factor analysis. In generally, Structural Equation Modelling is utilized to detect the model adaptability with the measured data, such as large-scale questionnaire data. In this study, we improved our developed iteration algorithm and gene selection procedure to infer the causalities between variables as a regulatory network from limited numerical data. Our improved gene selection method is based on cross correlation to summarize the time preceding information from gene expression profiles, which were systematically measured at 8 time points. Cross correlation is usually utilized as a measure of similarity between two waves by a time-lag application, and we defined the values of lags ranging from −2 to +4. By this improved method, we selected 14 genes as regulatory factors for the specific system in oleaginous yeast. In the inferred model, only 6 genes among the selected 14 genes were considered to affect the volume of oil accumulation in a closed and specific system. Our method will be useful to artificially control cell systems in the bioproduction and biotechnology fields.

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Metric All-k-Nearest-Neighbor Search

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Metric All-k-Nearest-Neighbor Search

作者： Chen, Lu Gao, Yunjun Chen, Gang Zhang, Haida College of Computer Science Zhejiang University 38 Zheda Road Hangzhou310027 China Key Lab of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China

An all-k-nearest-neighbor (AkNN) query finds from a given object set O, k nearest neighbors for each object in a specified query set Q. This operation is common in many applications such as GIS, data mining, and image analysis. Although it has received much attention in the Euclidean space, there is little prior work on the metric space. In this paper, we study the problem of AkNN retrieval in metric spaces, termed metric AkNN (MAkNN) search, and propose efficient algorithms for supporting MAk NN queries with arbitrary k value. Our methods utilize dynamic disk-based metric indexes (e.g., M-tree), employ a series of pruning rules, take advantage of grouping, reuse, pre-processing, and progressive pruning techniques, require no detailed representations of objects, and can be applied as long as the distance metric satisfies the triangle inequality. In addition, we extend our approaches to tackle metric self-AkNN (MSAkNN) search, a natural variation of MAkNN queries, where the query set Q is identical to the object set O. Extensive experiments using both real and synthetic data sets demonstrate, compared with state-of-the-art euclidean Ak NN, MAkNN, and MSAkNN algorithms, the performance of our proposed algorithms and the effectiveness of our presented techniques. © 2015 IEEE.

关键词： Nearest neighbor search

Advancing image understanding in poor visibility environments: A collective benchmark study

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arXiv 2019年

作者： Yang, Wenhan Yuan, Ye Ren, Wenqi Liu, Jiaying Scheirer, Walter J. Wang, Zhangyang Zhang, Taiheng Zhong, Qiaoyong Xie, Di Pu, Shiliang Zheng, Yuqiang Qu, Yanyun Xie, Yuhong Chen, Liang Li, Zhonghao Hong, Chen Jiang, Hao Yang, Siyuan Liu, Yan Qu, Xiaochao Wan, Pengfei Zheng, Shuai Zhong, Minhui Su, Taiyi He, Lingzhi Guo, Yandong Zhao, Yao Zhu, Zhenfeng Liang, Jinxiu Wang, Jingwen Chen, Tianyi Quan, Yuhui Xu, Yong Liu, Bo Liu, Xin Sun, Qi Lin, Tingyu Li, Xiaochuan Lu, Feng Gu, Lin Zhou, Shengdi Cao, Cong Zhang, Shifeng Chi, Cheng Zhuang, Chubin Lei, Zhen Li, Stan Z. Wang, Shizheng Liu, Ruizhe Yi, Dong Zuo, Zheming Chi, Jianning Wang, Huan Wang, Kai Liu, Yixiu Gao, Xingyu Chen, Zhenyu Guo, Chang Li, Yongzhou Zhong, Huicai Huang, Jing Guo, Heng Yang, Jianfei Liao, Wenjuan Yang, Jiangang Zhou, Liguo Feng, Mingyue Qin, Likun Wangxuan Institute of Computer Technology Peking University Beijing100080 China Department of Computer Science and Engineering Texas A&M University TX77843 United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mechanical Engineering Zhejiang University Hangzhou310027 China Hikvision Research Institute Hangzhou310051 China Mtlab Meitu Inc. Beijing100080 China Insitute of Information Science Beijing Jiaotong University Beijing100044 China Department of Computer Science and Technology Tongji University Shanghai201804 China XPENGMOTORS Beijing China School of Computer Science and Engineering at South China University of Technology Guangzhou510006 China Tencent AI Lab Shenzhen518000 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Northeastern University Shenyang110819 China Nanyang Technological University Singapore639798 Big Data Center State Grid Corporation of China Beijing China Winsense Inc. Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Informatics Technical University of Munich Garching85748 Germany Institute of Microelectronics Chinese Academy of Sciences Beijing100190 China China Electric Power Research Institute Beijing100031 China Westlake University Hangzhou310024 China Department of Computer Science Durham University Durham United Kingdom Australian National University ActonACT0200 Australia Sunway-AI Co. Ltd Zhuhai China Chinese Academy of Sciences R&D Center for Internet of Things Wuxi214200 China

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG2+challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Orderness predicts academic performance: Behavioral analysis on campus lifestyle

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arXiv 2017年

作者： Cao, Yi Gao, Jian Lian, Defu Rong, Zhihai Shi, Jiatu Wang, Qing Wu, Yifan Yao, Huaxiu Zhou, Tao CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and Technology Center for Information in Medicine University of Electronic Science and Technology of China Chengdu611731 China

Quantitative understanding of relationships between students' behavioral patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that mainly based on questionnaire surveys, in this paper, we collect behavioral records from 18,960 undergraduate students' smart cards and propose a novel metric, called orderness, which measures the regularity of campus daily life (e.g., meals and showers) of each student. Empirical analysis demonstrates that academic performance (GPA) is strongly correlated with orderness. Furthermore, we show that orderness is an important feature to predict academic performance, which remarkably improves the prediction accuracy even at the presence of students' diligence. Based on these analyses, education administrators could better guide students' campus lives and implement effective interventions in an early stage when necessary. Copyright © 2017, The Authors. All rights reserved.

关键词： Students