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Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

作者： Bharadwaj Amrutur Vasanth Rajaraman Srikrishna Acharya Rakshit Ramesh Ashish Joglekar Abhay Sharma Yogesh Simmhan Abhijit Lele Ashwin Mahesh Sathya Sankaran RBCCPS IISc Bangalore Dept. of Computation and Data Science IISc Bangalore Robert Bosch Engineering and Business Solutions Bangalore India Mapunity Inc. Bangalore India

Street light poles will be a key enabler for a smart city's hardware infrastructure, thanks to their ubiquity throughout the city as well as access to power. We propose an IoT test bed around light poles for the c... 详细信息

ISBN: (纸本)9781450349666

关键词： LoRa Smart City Test Bed Streetlight Poles Semantic Interoperability GPON Distributed Analytics IoT Middleware

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Informative data projections: A framework and two examples 24

Informative data projections: A framework and two examples

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24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016

作者： De Bie, Tijl Lijffijt, Jefrey Santos-Rodríguez, Raúl Kang, Bo Data Science Lab Ghent University Belgium Dept. of Engineering Mathematics University of Bristol United Kingdom

ISBN: (纸本)9782875870278

Projection Pursuit aims to facilitate visual exploration of high-dimensional data by identifying interesting low-dimensional projections. A major challenge in Projection Pursuit is the design of a projection index-a suitable quality measure to maximise. We introduce a strategy for tackling this problem based on quantifying the amount of information a projection conveys, given a user's prior beliefs about the data. The resulting projection index is a subjective quantity, explicitly dependent on the intended user. As an illustration, we developed this principle for two kinds of prior beliefs;the first leads to PCA, the second leads to a novel projection index, which we call t-PCA, that can be regarded as a robust PCA-variant. We demonstrate t-PCA's usefulness in comparative experiments against PCA and FastICA, a popular PP method.

关键词： Clustering algorithms

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DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

arXiv

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arXiv 2025年

作者： Huang, X. Baltasar, S. Ratier-Werbin, N. Storfer, C. Sheu, W. Agarwal, S. Tamargo-Arizmendi, M. Schlegel, D.J. Aguilar, J. Ahlen, S. Aldering, G. Banka, S. BenZvi, S. Bianchi, D. Bolton, A. Brooks, D. Cikota, A. Claybaugh, T. de la Macorra, A. Dey, A. Doel, P. Edelstein, J. Filipp, A. Forero-Romero, J.E. Gaztañaga, E. Gontcho, S.A. Gontcho Gu, A. Gutierrez, G. Honscheid, K. Jullo, E. Juneau, S. Kehoe, R. Kirkby, D. Kisner, T. Kremin, A. Kwon, K.J. Lambert, A. Landriau, M. Lang, D. Le Guillou, L. Liu, J. Meisner, A. Miquel, R. Moustakas, J. Myers, A.D. Perlmutter, S. Pérez-Ràfols, I. Prada, F. Rossi, G. Rubin, D. Sanchez, E. Schubnell, M. Shu, Y. Silver, E. Sprayberry, D. Suzuki, N. Tarlé, G. Weaver, B.A. Zou, H. Department of Physics & Astronomy University of San Francisco San FranciscoCA94117 United States Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics Complutense University of Madrid Madrid28040 Spain Department of Mathematics Complutense University of Madrid Madrid28040 Spain Institute for Astronomy University of Hawai’i HonoluluHI96822-1897 United States Department of Physics & Astronomy University of California Los Angeles Los AngelesCA90095 United States University of Chicago Department of Astronomy ChicagoIL60615 United States Department of Physics & Astronomy University of Pittsburgh PittsburghPA15260 United States Physics Dept. Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Electrical Engineering & Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics & Astronomy University of Rochester 206 Bausch and Lomb Hall P.O. Box 270171 RochesterNY14627-0171 United States Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 MilanoI-20133 Italy NSF’s National Optical-Infrared Astronomy Research Laboratory TucsonAZ85719 United States Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Gemini Observatory NSF’s NOIRLab Casilla 603 La Serena Chile Instituto de Física Universidad Nacional Autónoma de México Circuito de la Investigación Científica Ciudad Universitaria Cd. de MéxicoC. P. 04510 Mexico Space Sciences Laboratory University of California Berkeley 7 Gauss Way BerkeleyCA94720 United States Université de Montréal Physics Department 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Technical University Munich TUM School of Natural Sciences

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼ 3500 new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging. © 2025, CC BY.

关键词： Dark energy

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Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining

Ocean surface current prediction based on HF radar observati...

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IEEE International Conference on Big data

作者： Kulsawasd Jitkajornwanich Peerapon Vateekul Upa Gupta Teeranai Kormongkolkul Arnon Jirakittayakorn Siam Lawawirojwong Siwapon Srisonphan Dept. of Computer Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Dept. of Computer Engineering Chulalongkorn University Bangkok Thailand Chulalongkorn University Big Data Analytics & IoT Center (CUBIC) Dept. of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand GISTDA Academy Geo-Informatics & Space Technology Development Agency (PO) Bangkok Thailand Dept. of Electrical Engineering Kasetsart University Bangkok Thailand

HF (high frequency) coastal radar system is used to capture the surface current behavior - in terms of velocity and direction - in the ocean near the coast. 18 HF coastal radar stations were implemented along the Gulf of Thailand in order to monitor for disasters (e.g., Tsunami) as well as relevant risks. The HF systems are also to serve other life-critical applications, such as water quality control and monitoring, chemical spill backtracking, and marine navigation. However, not all the applications can benefit from this near-real-time HF data; some applications in different domains require forecast values. The examples include search-and-rescue system and hazardous materials spill trajectory prediction. Therefore, in this paper, we propose a predictive model for future current data based on historical HF coastal radar data sets, utilizing association rule mining combined with an object dispersion concept. So, the full potential of HF radar systems can be exploited. The spatial and temporal dimensions are taken into account when designing our predictive system, which consists of two phases: ocean surface current track formulation and spatio-temporal association rule mining. The experiments are performed on a two-year HF radar dataset (2014-2015) using Google Cloud Platform. The resulting forecast current values: velocity and direction are then compared with testing datasets (using 10-fold cross validation) of the actual recorded values and evaluated based on percentage accuracy and RMSE, respectively.

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Discriminative localization in CNNs for weakly-supervised segmentation of pulmonary nodules

arXiv

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arXiv 2017年

作者： Feng, Xinyang Yang, Jie Laine, Andrew F. Angelini, Elsa D. Dept. of Biomedical Engineering Columbia University New YorkNY United States NIHR Imperial BRC ITMAT Data Science Group Imperial College London United Kingdom

Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method. Copyright © 2017, The Authors. All rights reserved.

关键词： Image segmentation

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology (vol 13, 634, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-13页

作者： Bjornsdottir, Gyda Stefansdottir, Lilja Thorleifsson, Gudmar Sulem, Patrick Norland, Kristjan Ferkingstad, Egil Oddsson, Asmundur Zink, Florian Lund, Sigrun H. Nawaz, Muhammad S. Walters, G. Bragi Skuladottir, Astros Th. Gudjonsson, Sigurjon A. Einarsson, Gudmundur Halldorsson, Gisli H. Bjarnadottir, Valgerdur Sveinbjornsson, Gardar Helgadottir, Anna Styrkarsdottir, Unnur Gudmundsson, Larus J. Pedersen, Ole B. Hansen, Thomas Folkmann Werge, Thomas Banasik, Karina Troelsen, Anders Skou, Soren T. Thorner, Lise Wegner Erikstrup, Christian Nielsen, Kaspar Rene Mikkelsen, Susan Jonsdottir, Ingileif Bjornsson, Aron Olafsson, Ingvar H. Ulfarsson, Elfar Blondal, Josep Vikingsson, Arnor Brunak, Soren Ostrowski, Sisse R. Ullum, Henrik Thorsteinsdottir, Unnur Stefansson, Hreinn Gudbjartsson, Daniel F. Thorgeirsson, Thorgeir E. Stefansson, Kari deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) University of Iceland Faculty of Medicine School of Health Sciences Reykjavik Iceland (GRID:grid.14013.37) (ISNI:0000 0004 0640 0021) deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) University of Iceland School of Engineering and Natural Sciences Reykjavik Iceland (GRID:grid.14013.37) (ISNI:0000 0004 0640 0021) Landspitali University Hospital Reykjavik Iceland (GRID:grid.410540.4) (ISNI:0000 0000 9894 0842) Zealand University Hospital Department of Clinical Immunology Køge Denmark (GRID:grid.512923.e) (ISNI:0000 0004 7402 8188) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Rigshospitalet-Glostrup Danish Headache Center Dept. Neurology Glostrup Denmark (GRID:grid.475435.4) University of Copenhagen Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Copenhagen University Hospital Institute of Biological Psychiatry Mental Health Services Copenhagen Denmark (GRID:grid.4973.9) (ISNI:0000 0004 0646 7373) University of Copenhagen Lundbeck Foundation for GeoGenetics GLOBE Institute Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001

Back pain is a common and debilitating disorder with largely unknown underlying biology. Here we report a genome-wide association study of back pain using diagnoses assigned in clinical practice; dorsalgia (119,100 cases, 909,847 controls) and intervertebral disc disorder (IDD) (58,854 cases, 922,958 controls). We identify 41 variants at 33 loci. The most significant association (ORIDD = 0.92, P = 1.6 × 10−39; ORdorsalgia = 0.92, P = 7.2 × 10−15) is with a 3’UTR variant (rs1871452-T) in CHST3, encoding a sulfotransferase enzyme expressed in intervertebral discs. The largest effects on IDD are conferred by rare (MAF = 0.07 − 0.32%) loss-of-function (LoF) variants in SLC13A1, encoding a sodium-sulfate co-transporter (LoF burden OR = 1.44, P = 3.1 × 10−11); variants that also associate with reduced serum sulfate. Genes implicated by this study are involved in cartilage and bone biology, as well as neurological and inflammatory *** is known about the biology of back pain, a leading cause of disability. Here the authors report 30 new back pain loci, implicating genes involved in cartilage/bone biology, as well as neurological and inflammatory processes.

关键词： Bone Genome-wide association studies Pain

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Testing the application of warranting theory to online third party marketplaces: The effects of information uniqueness and product type 16

Testing the application of warranting theory to online third...

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7th International Conference on Social Media and Society, SMSociety 2016

作者： Sanders, W. Scott Nutakki, Gopi Chand Nasraoui, Olfa Department of Communication University of Louisville United States Knowledge Discovery and Web Mining Lab Dept. of Computer Engineering and Computer Science Speed School of Engineering University of Louisville United States

ISBN: (纸本)9781450339384

Online third party marketplaces link buyers and sellers by providing a neutral platform for exchange. However, this requires buyers to assess the quality of goods without being able to handle or sample them. Recent research has proposed extending the warranting principle, an emerging theory of online interpersonal impression formation, to the judgements consumer make about products online. The warranting principle holds that information that is more difficult to manufacture or manipulate, such as an individualized photo of an auction item, should be more informative to a consumers judgement of the product than information which is easy to falsify or manipulate. While this theoretical extension assumes that all online goods are capable of being assessed in the same way, some goods (i.e. experience goods) are more difficult to assess in online marketplaces because they must be experienced before their true quality and characteristics can be known. The current study examines 2,401 completed auctions from eBay revealing that the relationship between warranting cues and price discounts is moderated by the type of good being sold. The theoretical contributions and limitations of the study are discussed. © Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic commerce

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Major Issues of Post-earthquake Housing Construction during Resettlement

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Journal of Geological Resource and engineering 2017年第3期5卷 105-111页

作者： Raju Sarkar Karan Narang Sonam Yangdhen Dept. of Civil Engineering and Architecture College of Science and Technology Rinchending P.O. Box 450 Bhutan Department of Civil Engineering Delhi Technological University Bawana Road Delhi 110042 lndia Bechtel Corporation Knowledge Park 244-24 Udyog Vihar Phase-IV Gurgaon Haryana 122015 India Engineering Adaptation and Risk Reduction Division Department of Engineering Services Ministry of Works and HumanSettlement Thimphu 11001 Bhutan

Natural disasters provide an acute image of how man-made technologies are a cause of conflict when it comes to nature. It is man versus wild in its true means. The nature lets us grow and increase our settlements. We encroach on other animals＇ territories and it is only when the environment＇s patience runs out that it retaliates in forms of natural disasters. These disasters affect numerous lives and kill a lot ofhnmans. This is the main reason why we require more stable structures and preventive measures to battle the wrath of the nature. Rescue and search operations are conducted by many different government and private agencies including NGOs. These operations aim at providing the required relief and supplies after the disaster. Injured people need to be treated. People in dangerous zones need to be evacuated. Help is needed in many forms. Out of the many natural disasters, this paper will focus on the occurrence of earthquakes. Severe earthquakes destroy buildings and structures like roads, bridges etc. and wreak havoc in the community. Earthquakes largely damage all human constructions, including houses. This is the reason why a reconstruction program for dwellings and housings is of utmost importance. A home is only secondary to basic needs such as food and water. A well-planned strategy is important when it comes to launching a post-earthquake reconstruction program. The strategy should be reasonable and should consider the best interests of everyone affected; self-help and imported fabrication should play no part in the decision-making procedures. Creating awareness, physically demonstrating options and delivering are the three steps for the success of reconstruction programs. The extent of affected area and the magnitude of earthquakes are variable in nature and it is on these two aspects that the reconstruction strategy is devised. Technical aspects are focused towards the development of the affected areas and conceptual design of surrounding neighborhoods

关键词： Post-earthquake reconstruction resettlement holistic planning housing construction.

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An efficient distributed learning algorithm based on effective local functional approximations

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2018年第1期19卷

作者： Dhruv Mahajan Nikunj Agrawal S. Sathiya Keerthi Sundararajan Sellamanickam Léon Bottou Facebook AI Menlo Park CA Indian Institute of Technology Dept. of Computer Science & Engineering Kanpur India Office Data Science Group Microsoft Mountain View CA Microsoft Research Bangalore India Facebook AI Research New York NY

Scalable machine learning over big data is an important problem that is receiving a lot of attention in recent years. On popular distributed environments such as Hadoop running on a cluster of commodity machines, communication costs are substantial and algorithms need to be designed suitably considering those costs. In this paper we give a novel approach to the distributed training of linear classifiers (involving smooth losses and L2 regularization) that is designed to reduce the total communication costs. At each iteration, the nodes minimize locally formed approximate objective functions; then the resulting minimizers are combined to form a descent direction to move. Our approach gives a lot of freedom in the formation of the approximate objective function as well as in the choice of methods to solve them. The method is shown to have O(log(1/ε)) time convergence. The method can be viewed as an iterative parameter mixing method. A special instantiation yields a parallel stochastic gradient descent method with strong convergence. When communication times between nodes are large, our method is much faster than the Terascale method (Agarwal et al., 2011), which is a state of the art distributed solver based on the statistical query model (Chu et al., 2006) that computes function and gradient values in a distributed fashion. We also evaluate against other recent distributed methods and demonstrate superior performance of our method.

关键词： L2 regularization distributed learning example partitioning

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