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Gaia data Release 3: Analysis of RVS spectra using the General Stellar Parametriser from spectroscopy

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arXiv 2022年

作者： Recio-Blanco, Alejandra de Laverny, Patrick Palicio, P.A. Kordopatis, G. Álvarez, M.A. Schultheis, M. Contursi, G. Zhao, H. Torralba Elipe, G. Ordenovic, C. Manteiga, M. Dafonte, C. Oreshina-Slezak, I. Bijaoui, A. Frémat, Y. Seabroke, G. Pailler, F. Spitoni, E. Poggio, E. Creevey, O.L. Abreu Aramburu, A. Accart, S. Andrae, R. Bailer-Jones, C.A.L. Bellas-Velidis, I. Brouillet, N. Brugaletta, E. Burlacu, A. Carballo, R. Casamiquela, L. Chiavassa, A. Cooper, W.J. Dapergolas, A. Delchambre, L. Dharmawardena, T.E. Drimmel, R. Edvardsson, B. Fouesneau, M. Garabato, D. García-Lario, P. García-Torres, M. Gavel, A. Gomez, A. González-Santamaría, I. Hatzidimitriou, D. Heiter, U. Jean-Antoine Piccolo, A. Kontizas, M. Korn, A.J. Lanzafame, A.C. Lebreton, Y. Le Fustec, Y. Licata, E.L. Lindstrøm, H.E.P. Livanou, E. Lobel, A. Lorca, A. Magdaleno Romeo, A. Marocco, F. Marshall, D.J. Mary, N. Nicolas, C. Pallas-Quintela, L. Panem, C. Pichon, B. Riclet, F. Robin, C. Rybizki, J. Santoveña, R. Silvelo, A. Smart, R.L. Sarro, L.M. Sordo, R. Soubiran, C. Süveges, M. Ulla, A. Vallenari, A. Zorec, J. Utrilla, E. Bakker, J. Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Nice France CIGUS CITIC Department of Computer Science and Information Technologies University of A Coruña Campus de Elviña s/n A Coruña15071 Spain CIGUS CITIC Department of Nautical Sciences and Marine Engineering University of A Coruña Paseo de Ronda 51 A Coruña15071 Spain Royal Observatory of Belgium 3 avenue circulaire Brussels1180 Belgium Mullard Space Science Laboratory University College London Holmbury St Mary Dorking SurreyRH5 6NT United Kingdom 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 Madrid28692 Spain Max Planck Institute for Astronomy Königstuhl 17 Heidelberg 69117 Germany National Observatory of Athens I. Metaxa and Vas. Pavlou Palaia Penteli Athens15236 Greece Laboratoire d'astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Telespazio for CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse Cedex 931401 France Dpto. de Matemática Aplicada y Ciencias de la Computación Univ. de Cantabria ETS Ingenieros de Caminos Canales y Puertos Avda. de los Castros s/n Santander39005 Spain GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France Centre for Astrophysics Research University of Hertfordshire College Lane HatfieldAL10 9AB United Kingdom 10025 Italy Institut d'Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Theoretical Astrophysics Division of Astronomy and Space Physics Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Data Science and

Context. The chemo-physical parametrisation of stellar spectra is essential for understanding the nature and evolution of stars and of Galactic stellar populations. A worldwide observational effort from the ground has provided, in one century, an extremely heterogeneous collection of chemical abundances for about two million stars in total, with fragmentary sky coverage. Aims. This situation is revolutionised by the Gaia third data release (DR3), which contains the parametrisation of Radial Velocity Spectrometer (RVS) data performed by the General Stellar Parametriser-spectroscopy, GSP-Spec, module. Here we describe the parametrisation of the first 34 months of Gaia RVS observations. Methods. GSP-Spec estimates the chemo-physical parameters from combined RVS spectra of single stars, without additional inputs from astrometric, photometric, or spectro-photometric BP/RP data. The main analysis workflow described here, MatisseGauguin, is based on projection and optimisation methods and provides the stellar atmospheric parameters;the individual chemical abundances of N, Mg, Si, S, Ca, Ti, Cr, Fe i, Fe ii, Ni, Zr, Ce and Nd;the differential equivalent width of a cyanogen line;and the parameters of a diffuse interstellar band (DIB) feature. Another workflow, based on an artificial neural network (ANN) and referred to with the same acronym, provides a second set of atmospheric parameters that are useful for classification control. For both workflows, we implement a detailed quality flag chain considering different error sources. Results. With about 5.6 million stars, the Gaia DR3 GSP-Spec all-sky catalogue is the largest compilation of stellar chemo-physical parameters ever published and the first one from space data. Internal and external biases have been studied taking into account the implemented flags. In some cases, simple calibrations with low degree polynomials are suggested. The homogeneity and quality of the estimated parameters enables chemo-dynamical studies of G

关键词： Stars

ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

DYNAMICAL BEHAVIOR OF A STOCHASTIC HBV INFECTION MODEL WITH LOGISTIC HEPATOCYTE GROWTH

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Acta Mathematica Scientia 2017年第4期37卷 927-940页

作者：刘群蒋达清史宁中 Tasawar HAYAT Ahmed ALSAEDI School of Mathematics and Statistics Key Laboratory of Applied Statistics of MOENortheast Normal UniversityChangchun 130024China School of Mathematics and Statistics Guangxi Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulinGuangxi 537000China Nonlinear Analysis and Applied Mathematics(NAAM)Research Group Department of MathematicsFaculty of ScienceKing Abdulaziz UniversityJeddah 121589Saudi Arabia College of Science China University of Petroleum(East China)Qingdao 266580China Department of Mathematics Quaid-i-Azam University 45320Islamabad 44000Pakistan

This paper is concerned with a stochastic HBV infection model with logistic growth. First, by constructing suitable stochastic Lyapunov functions, we establish sufficient conditions for the existence of ergodic stationary distribution of the solution to the HBV infection model. Then we obtain sufficient conditions for extinction of the disease. The stationary distribution shows that the disease can become persistent in vivo.

关键词： stochastic HBV infection model extinction stationary distribution Lyapunov function

Quantifying china's regional economic complexity

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arXiv 2017年

作者： Gao, Jian 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

China has experienced an outstanding economic expansion during the past decades, however, literature on nonmonetary metrics that reveal the status of China's regional economic development are still lacking. In this paper, we fill this gap by quantifying the economic complexity of China's provinces through analyzing 25 years' firm data. First, we estimate the regional economic complexity index (ECI), and show that the overall time evolution of provinces' ECI is relatively stable and slow. Then, after linking ECI to the economic development and the income inequality, we find that the explanatory power of ECI is positive for the former but negative for the latter. Next, we compare different measures of economic diversity and explore their relationships with monetary macroeconomic indicators. Results show that the ECI index and the non-linear iteration based Fitness index are comparative, and they both have stronger explanatory power than other benchmark measures. Further multivariate regressions suggest the robustness of our results after controlling other socioeconomic factors. Our work moves forward a step towards better understanding China's regional economic development and non-monetary macroeconomic indicators. Copyright © 2017, The Authors. All rights reserved.

关键词： Regional planning

Ultracool dwarfs in Gaia DR3

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arXiv 2022年

作者： Sarro, L.M. Berihuete, A. Smart, R.L. Reylé, C. Barrado, D. García-Torres, M. Cooper, W.J. Jones, H.R.A. Marocco, F. Creevey, O.L. Sordo, R. Bailer-Jones, C.A.L. Montegriffo, P. Carballo, R. Andrae, R. Fouesneau, M. Lanzafame, A.C. Pailler, F. Thévenin, F. Lobel, A. Delchambre, L. Korn, A.J. Recio-Blanco, A. Schultheis, M.S. De Angeli, F. Brouillet, N. Casamiquela, L. Contursi, G. de Laverny, P. García-Lario, P. Kordopatis, G. Lebreton, Y. Livanou, E. Lorca, A. Palicio, P.A. Slezak-Oreshina, I. Soubiran, C. Ulla, A. Zhao, H. Dpto. de Inteligencia Artificial UNED c/ Juan del Rosal 16 Madrid28040 Spain Pino Torinese10025 Italy Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange Bd de l’Observatoire CS 34229 Nice06304 Cedex 4 France INAF - Osservatorio astronomico di Padova Vicolo Osservatorio 5 Padova35122 Italy CNES Centre Spatial de Toulouse 18 avenue Edouard Belin Toulouse31401 Cedex 9 France 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 Max Planck Institute for Astronomy Königstuhl 17 Heidelberg69117 Germany INAF - Osservatorio Astrofisico di Catania via S. Sofia 78 Catania95123 Italy Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Institut d’Astrophysique et de Géophysique Université de Liège 19c Allée du 6 Août LiègeB-4000 Belgium Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna via Piero Gobetti 93/3 Bologna40129 Italy Laboratoire d’astrophysique de Bordeaux Univ. Bordeaux CNRS B18N allée Geoffroy Saint-Hilaire Pessac33615 France Dpto. de Matemática Aplicada y Ciencias de la Computación Univ. de Cantabria ETS Ingenieros de Caminos Canales y Puertos Avda. de los Castros s/n Santander39005 Spain GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France Centre for Astrophysics Research University of Hertfordshire College Lane HatfieldAL10 9AB United Kingdom Camino bajo del Castillo s/n Urbanizacion Villafranca del Castillo Villanueva de la Cañada Madrid28692 Spain Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Astrophysics Astronomy and Mechanics National and Kapodistrian University of Athens

Context. Previous Gaia data Releases offered the opportunity to uncover ultracool dwarfs (UCDs) through astrometric, rather than purely photometric selection. The most recent, third data release offers in addition the opportunity to use low-resolution spectra to refine and widen the selection. Aims. In this work we use the Gaia DR3 set of ultracool dwarf candidates and complement the Gaia spectrophotometry with additional photometry in order to characterise its global properties. This includes the inference of the distances, their locus in the Gaia colour-absolute magnitude diagram and the (biased through selection) luminosity function in the faint end of the Main Sequence. We study the overall changes in the Gaia RP spectra as a function of spectral type. We study the UCDs in binary systems, attempt to identify low-mass members of nearby young associations, star forming regions and clusters, and analyse their variability properties. Methods. We use a forward model and the Bayesian inference framework to produce posterior probabilities for the distribution parameters and a calibration of the colour index as a function of the absolute magnitude in the form of a Gaussian Process. Additionally we apply the HMAC unsupervised classification algorithm for the detection and characterisation of overdensities in the space of celestial coordinates, projected velocities and parallaxes. Results. We detect 57 young, kinematically homogeneous groups some of which are identified as well known star forming regions, associations and clusters of different ages. We find that the primary members of 880 binary systems with a UCD belong mainly to the thin and thick disk components of the Milky Way. We identify 1109 variable UCDs using the variability tables in the Gaia archive, 728 of which belong to the star forming regions defined by HMAC. We define two groups of variable UCDs with extreme bright or faint outliers. Conclusions. The set of sources identified as UCDs in the Gaia archive

关键词： Photometry

Measuring the popularity of job skills in recruitment market: A multi-criteria approach

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arXiv 2017年

作者： Xu, Tong Zhu, Hengshu Zhu, Chen Li, Pan Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Rutgers Business School Rutgers University

To cope with the accelerating pace of technological changes, talents are urged to add and refresh their skills for staying in active and gainful employment. This raises a natural question: what are the right skills to learn? Indeed, it is a nontrivial task to measure the popularity of job skills due to the diversified criteria of jobs and the complicated connections within job skills. To that end, in this paper, we propose a data driven approach for modeling the popularity of job skills based on the analysis of large-scale recruitment data. Specifically, we first build a job skill network by exploring a large corpus of job postings. Then, we develop a novel Skill Popularity based Topic Model (SPTM) for modeling the generation of the skill network. In particular, SPTM can integrate different criteria of jobs (e.g., salary levels, company size) as well as the latent connections within skills, thus we can effectively rank the job skills based on their multi-faceted popularity. Extensive experiments on real-world recruitment data validate the effectiveness of SPTM for measuring the popularity of job skills, and also reveal some interesting rules, such as the popular job skills which lead to high-paid *** Codes 68T30 Copyright © 2017, The Authors. All rights reserved.

关键词： Compensation (personnel)

Biomedical image analysis competitions: The state of current participation practice

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

Maximizing the collective learning effects in regional economic development

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arXiv 2017年

作者： Gao, Jian Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China MIT Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States

Collective learning in economic development has been revealed by recent empirical studies, however, investigations on how to benefit most from its effects remain still lacking. In this paper, we explore the maximization of the collective learning effects using a simple propagation model to study the diversification of industries on real networks built on Brazilian labor data. For the inter-regional learning, we find an optimal strategy that makes a balance between core and periphery industries in the initial activation, considering the core-periphery structure of the industry space--a network representation of the relatedness between industries. For the inter-regional learning, we find an optimal strategy that makes a balance between nearby and distant regions in establishing new spatial connections, considering the spatial structure of the integrated adjacent network that connects all regions. Our findings suggest that the near to by random strategies are likely to make the best use of the collective learning effects in advancing regional economic development practices. Copyright © 2017, The Authors. All rights reserved.

关键词： Optimal systems

Reports of the workshops of the 32nd AAAI conference on artificial intelligence

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Reports of the workshops of the 32nd AAAI conference on arti...

作者： Bouchard, Bruno Bouchard, Kevin Brown, Noam Chhaya, Niyati Farchi, Eitan Gaboury, Sebastien Geib, Christopher Gyrard, Amelie Jaidka, Kokil Keren, Sarah Khardon, Roni Kordjamshidi, Parisa Martinez, David Mattei, Nicholas Michalowski, Martin Mirsky, Reuth Osborn, Joseph Sahin, Cem Shehory, Onn Shaban-Nejad, Arash Sheth, Amit Shimshoni, Ilan Shrobe, Howie Sinha, Arunesh Sinha, Atanu R. Srivastava, Biplav Streilein, William Theocharous, Georgios Venable, K. Brent Wagner, Neal Zamansky, Anna Université du Québec ChicoutimiQC Canada Computer Science Carnegie Mellon University United States Big Data Experience Lab Adobe Research Bangalore India IBM Research Haifa Research Lab. Israel Smart Information Flow Technologies United States Kno.e.sis Wright State University United States University of Pennsylvania United States Harvard University United States Computer Science Tufts University United States Tulane University IHMC United States MIT Lincoln Laboratory United States IBM Research TJ Watson Research Center United States University of Minnesota School of Nursing United States ISE Department Ben Gurion University United States Computer Science Department Pomona College United States Bar Ilan University Israel University of Tennessee Health Science Center United States Information Systems Department University of Haifa Israel Massachusetts Institute of Technology United States University of Southern California United States AI Department IBM Research Yorktown HeightsNY United States Adobe Research San JoseCA United States