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RETRACTED ARTICLE: Scenario analysis for low carbon development in Nigeria

Cogent Engineering 2016年第1期3卷 Article: 1218818页

作者： Emodi, Nnaemeka Vincent Birku, Reta Entele Marc Nwosu Ogbuagu Chinenye Comfort Emodi Adaeze Saratu Augusta Emodi Okoro, Felix Chimezirim College of Business Law and Governance James Cook University P.O. Box 6811 Cairns 4870 Queensland Australia Department of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul 151-744 South Korea Department of Chemistry College of Physical and Applied Sciences Michael Okpara University of Agriculture Umudike Nigeria Department of Computer Science College of Physical and Applied Science Michael Okpara University of Agriculture Umudike Nigeria Faculty of Law Abia State University Uturu Nigeria Department of Vocational Teacher Education University of Nigeria Nsukka Road Nsukka 410001 Nigeria

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An empirical evaluation of costs and benefits of simplifying Bayesian networks by removing weak arcs 27

An empirical evaluation of costs and benefits of simplifying...

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27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014

作者： Ratnapinda, Parot Druzdzel, Marek J. Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Faculty of Computer Science Bialystok University of Technology Wiejska 45A Bialystok15-351 Poland

We report the results of an empirical evaluation of structural simplification of Bayesian networks by removing weak arcs. We conduct a series of experiments on six networks built from real data sets selected from the UC Irvine Machine Learning Repository. We systematically remove arcs from the weakest to the strongest, relying on four measures of arc strength, and measure the classification accuracy of the resulting simplified models. Our results show that removing up to roughly 20 percent of the weakest arcs in a network has minimal effect on its classification accuracy. At the same time, structural simplification of networks leads to significant reduction of both the amount of memory taken by the clique tree and the amount of computation needed to perform inference. Copyright © 2014, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Bayesian networks

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MASS CALIBRATION OF OPTICALLY SELECTED DES CLUSTERS USING A MEASUREMENT OF CMB-CLUSTER LENSING WITH SPTpol DATA

arXiv

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arXiv 2018年

作者： Raghunathan, S. Patil, S. Baxter, E. Benson, B.A. Bleem, L.E. Chou, T.L. Crawford, T.M. Holder, G.P. McClintock, T. Reichardt, C.L. Rozo, E. Varga, T.N. Abbott, T.M.C. Ade, P.A.R. Allam, S. Anderson, A.J. Annis, J. Austermann, J.E. Avila, S. Beall, J.A. Bechtol, K. Bender, A.N. Bernstein, G. Bertin, E. Bianchini, F. Brooks, D. Burke, D.L. Carlstrom, J.E. Carretero, J. Chang, C.L. Chiang, H.C. Cho, H.-M. Citron, R. Crites, A.T. Cunha, C.E. da Costa, L.N. Davis, C. Desai, S. Diehl, H.T. Dietrich, J.P. Dobbs, M.A. Doel, P. Eifler, T.F. Everett, W. Evrard, A.E. Flaugher, B. Fosalba, P. Frieman, J. Gallicchio, J. García-Bellido, J. Gaztanaga, E. George, E.M. Gilbert, A. Gruen, D. Gruendl, R.A. Gschwend, J. Gupta, N. Gutierrez, G. de Haan, T. Halverson, N.W. Harrington, N. Hartley, W.G. Henning, J.W. Hilton, G.C. Hollowood, D.L. Holzapfel, W.L. Honscheid, K. Hou, Z. Hoyle, B. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. James, D.J. Jeltema, T. Kim, A.G. Carrasco Kind, M. Knox, L. Kovacs, A. Kuehn, K. Kuropatkin, N. Lee, A.T. Li, T.S. Lima, M. Maia, M.A.G. Marshall, J.L. McMahon, J.J. Melchior, P. Menanteau, F. Meyer, S.S. Miller, C.J. Miquel, R. Mocanu, L. Montgomery, J. Nadolski, A. Natoli, T. Nibarger, J.P. Novosad, V. Padin, S. Plazas, A.A. Pryke, C. Rapetti, D. Romer, A.K. Carnero Rosell, A. Ruhl, J.E. Saliwanchik, B.R. Sanchez, E. Sayre, J.T. Scarpine, V. Schaffer, K.K. Schubnell, M. Serrano, S. Sevilla-Noarbe, I. Smecher, G. Smith, R.C. Soares-Santos, M. Sobreira, F. Stark, A.A. Story, K.T. Suchyta, E. Swanson, M.E.C. Tarle, G. Thomas, D. Tucker, C. Vanderlinde, K. de Vicente, J. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Zhang, Y. School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Physics and Astronomy University of California Los AngelesCA90095 United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics McGill University 3600 Rue University MontrealQCH3A 2T8 Canada Canadian Institute for Advanced Research CIFAR Program in Cosmology and Gravity TorontoONM5G 1Z8 Canada Department of Physics University of Arizona TucsonAZ85721 United States Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse Garching85748 Germany Universitäts-Sternwarte Fakultät für Physik LudwigMaximilians Universität München Scheinerstr. 1 München81679 Germany Cerro Tololo Inter-American Observatory National Optical Astronomy Observatory Casilla 603 La Serena Chile Cardiff University CardiffCF10 3XQ United Kingdom Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Institute of Cosmology & Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom LSST 933 North Cherry Avenue TucsonAZ85721 United States CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Department of Physics & Astronomy University College London Gower

We use cosmic microwave background (CMB) temperature maps from the 500 deg2 SPTpol survey to measure the stacked lensing convergence of galaxy clusters from the Dark Energy Survey (DES) Year-3 redMaPPer (RM) cluster catalog. The lensing signal is extracted through a modified quadratic estimator designed to be unbiased by the thermal Sunyaev-Zel’dovich (tSZ) effect. The modified estimator uses a tSZ-free map, constructed from the SPTpol 95 and 150 GHz datasets, to estimate the background CMB gradient. For lensing reconstruction, we employ two versions of the RM catalog: a flux-limited sample containing 4003 clusters and a volume-limited sample with 1741 clusters. We detect lensing at a significance of 8.7σ(6.7σ) with the flux(volume)-limited sample. By modeling the reconstructed convergence using the Navarro-Frenk-White profile, we find the average lensing masses to be M200m = (1.62+0−0..3225 [stat.] ± 0.04 [sys.]) and (1.28+0−0..1418 [stat.] ± 0.03 [sys.]) ×1014 M☉ for the volume- and flux-limited samples respectively. The systematic error budget is much smaller than the statistical uncertainty and is dominated by the uncertainties in the RM cluster centroids. We use the volume-limited sample to calibrate the normalization of the mass-richness scaling relation, and find a result consistent with the galaxy weak-lensing measurements from DES (McClintock et al. 2018). Copyright © 2018, The Authors. All rights reserved.

关键词： Galaxies

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Author Correction: A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis

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Nature communications 2019年第1期10卷 2956页

作者： Lohith Madireddy Kicheol Kim Xiaoming Jia Adam Santaniello Stacy J. Caillier Bruce A. C. Cree Roland Henry Stephen L. Hauser Jorge R. Oksenberg Sergio E. Baranzini Nikolaos A. Patsopoulos Philip De Jager Chris Cotsapas Matthew R. Lincoln David A. Hafler Steffan D. Bos Hanne F. Harbo Tone Berge Elisabeth G. Celius Ashley Beecham Jacob McCauley Margaret A. Pericak-Vance Till F. M. Andlauer Christiane Gasperi Bernhard Hemmer Lisa F. Barcellos Luisa Bernardinelli Filippo Martinelli-Boneschi David R. Booth Graeme J. Stewart Farren Briggs Jonathan Haines Manuel Comabella Giancarlo Comi Federica Esposito Sandra D’Alfonso Katrina Dedham Pierre Duquette Efthimios Dardiotis Bertrand Fontaine An Goris Bénédicte Dubois Pierre-Antoine Gourraud Georgios Hadjigeorgiou Clive Hawkins Seema Kalra Rogier Hintzen Dana Horakova Noriko Isobe Jun-ichi Kira Michael Khalil Ingrid Kockum Tomas Olsson Christina M. Lill Felix Luessi Frauke Zipp Roland Martin Annette Oturai Aarno Palotie Janna Saarela Adrian Ivinson Bruce V. Taylor Alastair Compston Stephen Sawcer Weill Institute for Neurosciences Department of Neurology University of California San Francisco San Francisco CA 94158 USA Bakar Institute for Computational Health Science. University of California San Francisco San Francisco CA 94158 USA Systems Biology and Computer Science Program Ann Romney Center for Neurological Diseases Department of Neurology and Division of Genetics Department of Medicine Brigham & Women’s Hospital Harvard Medical School Boston MA 02115 USA Broad Institute of Harvard University and Massachusetts Institute of Technology Cambridge MA 02142 USA Department of Neurology Center for Translational and Computational Neuroimmunology and Multiple Sclerosis Center Columbia University Medical Center New York NY 10032 USA Departments of Neurology Yale School of Medicine 300 George St New Haven CT 06511 USA Department of Genetics Yale School of Medicine 300 George St New Haven CT 06511 USA Institute of Clinical Medicine University of Oslo Oslo 0318 Norway Department of Neurology Oslo University Hospital Oslo 0424 Norway Department of Mechanical Electronics and Chemical Engineering Oslo Metropolitan University Oslo 0167 Norway Institute of Health and Society University of Oslo Oslo 0318 Norway John P. Hussman Institute for Human Genomics University of Miami Miller School of Medicine Miami FL 33136 USA Dr. John T. Macdonald Foundation Department of Human Genetics University of Miami Miller School of Medicine Miami FL 33136 USA Department of Neurology Klinikum rechts der Isar School of Medicine Technical University of Munich 81675 Munich Germany Munich Cluster for Systems Neurology (SyNergy) 81377 Munich Germany Division of Epidemiology School of Public HealthUniversity of California 324 Stanley Hall MC#3220 Berkeley CA 94720 USA Section of Biostatistics Neurophyisiology and Psychiatry Unit of medical and genomic statistics Universita of Pavia Pavia 27100 Italy Department of Biomedical Sciences for Health Univers

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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Comparative study of leaf image recognition with a novel learning-based approach

Comparative study of leaf image recognition with a novel lea...

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science and Information Conference (SAI)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chih-Yang Lin Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.

关键词： Image recognition Dictionaries Training Feature extraction Shape Vectors Image coding

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On the Secrecy Capacity of the MISO Wiretap Channel under Imperfect Channel Estimation

On the Secrecy Capacity of the MISO Wiretap Channel under Im...

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IEEE Global Communications Conference

作者： Zouheir Rezki Basel Alomair Mohamed-Slim Alouini Electrical Engineering Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology The National Center for Cybersecurity Technology (C4C) King Abdulaziz City for Science and Technology Electrical Engineering Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology

ISBN: (纸本)9781479935130

We consider a wiretap channel consisting of a source with multiple antennas, a legitimate receiver and an eavesdropper with a single antenna each. The channels between the source and the receivers undergo fast fading. We assume that the transmitter, in addition to the statistics of both channels, is only aware of a noisy version of the CSI to the legitimate receiver referred to as main channel. The legitimate receiver is aware of both its instantaneous channel gain and the transmitter's estimate of the main channel. On the other hand, the eavesdropper's receiver, in addition to its instantaneous channel realization, is aware of the actual main CSI and the transmitter's estimate as well. While the capacity of this channel is still open even with perfect CSI at the transmitter, we provide in this paper upper and lower bounds on the secrecy capacity. The upper bound is tighter than the one corresponding to perfect main CSI and the gap between the two upper bounds is characterized in function of the channel estimation error variance, at high-SNR. Furthermore, we show that our upper and lower bounds coincide in the case of no main CSI providing a trivial secrecy capacity.

关键词： Secrecy capacity Imperfect channel estimation On-off signaling Multiple antennas Artificial noise Beamforming

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Fabrication of artificial gemstones from glasses: From waste to jewelry

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AIP Conference Proceedings 2014年第1期1617卷 120-125页

作者： N. Srisittipokakun Y. Ruangtaweep M. Horprathum J. Kaewkhao Center of Excellence in Glass Technology and Materials Science (CEGM) Nakhon Pathom Rajabhat University 73000 Thailand and Science Program Faculty of Science and Technology Nakhon Pathom Rajabhat University 73000 Thailand Optical Thin-Film Laboratory National Electronics and Computer Technology Center Pathumthani 12120 Thailand

In this review, several aspects of artificial gemstones from glasses have been addressed from the advantages, the fabrication process, the coloration, their properties and finally the use of RHA as the glass former for the simulant gemstones. The silica sources for preparation of glasses were locally obtained from sand and biomass ashes in Thailand. The refractive index, density and hardness values of the glass gemstones reported in these researches had been meet the standard of EU-regulation for crystal. The glass gemstones were fabricated in a variety of colors with some special features such as color changing when exposed under different light sources. Barium was used instead of lead to increase the density and refractive index of the glasses. The developments of high refractive index lead-free glasses are also leave non-toxically impact to our environment.

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Learning sparse representation for leaf image recognition

Learning sparse representation for leaf image recognition

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chao-Yung Hsu Chia-Yen Chen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Institute of Information Science Taipei Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. For each test leaf image, we calculate the correlation between the image and each learned dictionary of leaf species to achieve the identification of the leaf image. As a result, efficient leaf recognition can be achieved on public leaf dataset based on the proposed framework leading to a more compact and richer representation of leaf images compared to traditional clustering approaches. Moreover, our method is also adapted to newly added leaf species without retraining classifiers and suitable to be highly parallelized as well as integrated with any leaf image descriptors/features.

关键词： Image recognition Dictionaries Training Feature extraction Shape Educational institutions computer vision

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Multi-camera based human localization for room utilization monitoring system

Multi-camera based human localization for room utilization m...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Issariyapol Siriwat Thanaruk Theeramankong Ithipan Methasate Hiroaki Kunieda ICTES Program Thammasat University Thailand National Electronics and Computer Technology Center Thailand School of Science and Engineering Tokyo Institute of Technology Japan

ISBN: (纸本)9781479958238

Room utilization monitoring system is an interesting application towards optimized facility usage. Due to its cost effectiveness and robustness, a camera-based system for room utilization is focused. This paper presents a multiple camera based process for room utilization monitoring system. The system is composed with three parts of processes that are single-camera processing, multi-camera processing and room event detection. In single camera processing, we have presented the object detection and tracking and transform the detected position on the image to the room map using Homography. The results of single camera processing are the location on the room map of detected object which are the input for multiple camera data fusion. We have purposed three different methods on multiple camera data fusion that are Uniform Bias Weighting, Best Camera Selection and Error Bias Weighting. And then we use the best detected object location on the map for room event detection. The experiment verified the performance of the different data fusion method on multiple cameras and showed that Error Bias weighting provide the best result.

关键词： Component Room utilization Multiple camera data fusion Object detection and tracking multiple camera Object detection Process cameras room utilization monitoring systems Data fusion data integration maps Event detection Cameras THREE-PART

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Feature extraction for identification of sugarcane rust disease

Feature extraction for identification of sugarcane rust dise...

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International Conference on Information, Communication technology and System (ICTS)

作者： Ratih Kartika Dewi R. V. Hari Ginardi Information Technology and Computer Science Program Brawijaya University Malang Indonesia Faculty of Information Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

This research propose an image pattern classification to identify rust disease in sugarcane leaf with a combination of texture and color feature extraction. The purpose of this research is to find appropriate features that can identify sugarcane rust disease. Firstly, normal and diseased images are collected and pre-processed. Then, features of shape, color and texture are extracted from these images. After that, these images are classified by support vector machine classifier. A combination of several features are used to evaluate the appropriate features to find distinctive features for identification of rust disease. When a single feature is used, shape feature has the lowest accuracy of 51% and texture feature has the highest accuracy of 96.5%. A combination of texture and color feature extraction results a highest classification accuracy of 97.5%. A combination of texture and color feature extraction with polynomial kernel results in 98.5 % classification accuracy.

关键词： Image color analysis Feature extraction Diseases Shape Accuracy Support vector machines Kernel

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