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Institution

University of Los Andes

EducationBogotá, Colombia
About: University of Los Andes is a education organization based out in Bogotá, Colombia. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 17616 authors who have published 25555 publications receiving 413463 citations.


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Journal ArticleDOI
Deanne N. Den Hartog1, Robert J. House2, Paul J. Hanges3, S. Antonio Ruiz-Quintanilla4, Peter W. Dorfman5, Ikhlas A. Abdalla6, Babajide Samuel Adetoun, Ram N. Aditya7, Hafid Agourram8, Adebowale Akande, Bolanle Elizabeth Akande, Staffan Åkerblom9, Carlos Altschul10, Eden Alvarez-Backus, Julian Andrews11, Maria Eugenia Arias, Mirian Sofyan Arif12, Neal M. Ashkanasy13, Arben Asllani14, Guiseppe Audia15, Gyula Bakacsi, Helena Bendova, David Beveridge16, Rabi S. Bhagat17, Alejandro Blacutt, Jiming Bao18, Domenico Bodega, Muzaffer Bodur19, Simon Booth20, Annie E. Booysen21, Dimitrios Bourantas22, Klas Brenk, Felix C. Brodbeck23, Dale Everton Carl24, Philippe Castel25, Chieh Chen Chang26, Sandy Chau, Frenda K.K. Cheung27, Jagdeep S. Chhokar28, Jimmy Chiu29, Peter Cosgriff30, Ali Dastmalchian31, Jose Augusto Dela Coleta, Marilia Ferreira Dela Coleta, Marc Deneire, Markus Dickson32, Gemma Donnelly-Cox33, Christopher P. Earley34, Mahmoud A. Elgamal35, Miriam Erez36, Sarah Falkus13, Mark Fearing30, Richard H. G. Field11, Carol Fimmen16, Michael Frese37, Ping Ping Fu38, Barbara Gorsler39, Mikhail V. Gratchev, Vipin Gupta40, Celia Gutiérrez41, Frans Marti Hartanto, Markus Hauser, Ingalill Holmberg9, Marina Holzer, Michael Hoppe, Jon P. Howell5, Elena Ibrieva42, John Ickis43, Zakaria Ismail44, Slawomir Jarmuz45, Mansour Javidan24, Jorge Correia Jesuino, Li Ji46, Kuen Yung Jone, Geoffrey Jones20, Revaz Jorbenadse47, Hayat Kabasakal19, Mary A. Keating33, Andrea Keller39, Jeffrey C. Kennedy30, Jay S. Kim48, Giorgi Kipiani, Matthias Kipping20, Edvard Konrad, Paul L. Koopman1, Fuh Yeong Kuan, Alexandre Kurc, Marie-Françoise Lacassagne25, Sang M. Lee42, Christopher Leeds, Francisco Leguizamón43, Martin Lindell, Jean Lobell, Fred Luthans42, Jerzy Maczynski49, Norma Binti Mansor, Gillian Martin33, Michael Martin42, Sandra Martinez5, Aly Messallam50, Cecilia McMillen51, Emiko Misumi, Jyuji Misumi, Moudi Al-Homoud35, Phyllisis M. Ngin52, Jeremiah O’Connell53, Enrique Ogliastri54, Nancy Papalexandris22, T. K. Peng55, Maria Marta Preziosa, José Prieto41, Boris Rakitsky, Gerhard Reber56, Nikolai Rogovsky57, Joydeep Roy-Bhattacharya, Amir Rozen36, Argio Sabadin, Majhoub Sahaba, Colombia Salon De Bustamante54, Carmen Santana-Melgoza58, Daniel A. Sauers30, Jette Schramm-Nielsen59, Majken Schultz59, Zuqi Shi18, Camilla Sigfrids, Kye Chung Song60, Erna Szabo56, Albert C. Y. Teo61, Henk Thierry62, Jann Hidayat Tjakranegara, Sylvana Trimi42, Anne S. Tsui63, Pavakanum Ubolwanna64, Marius W. Van Wyk21, Marie Vondrysova65, Jürgen Weibler66, Celeste P.M. Wilderom62, Rongxian Wu67, Rolf Wunderer68, Nik Rahiman Nik Yakob44, Yongkang Yang18, Zuoqiu Yin18, Michio Yoshida69, Jian Zhou18 
VU University Amsterdam1, University of Pennsylvania2, University of Maryland, Baltimore3, Cornell University4, New Mexico State University5, Qatar Airways6, Louisiana Tech University7, Université du Québec8, Stockholm School of Economics9, University of Buenos Aires10, University of Alberta11, University of Indonesia12, University of Queensland13, Bellevue University14, London Business School15, Western Illinois University16, University of Memphis17, Fudan University18, Boğaziçi University19, University of Reading20, University of South Africa21, Athens University of Economics and Business22, Ludwig Maximilian University of Munich23, University of Calgary24, University of Burgundy25, National Sun Yat-sen University26, Hong Kong Polytechnic University27, Indian Institute of Management Ahmedabad28, City University of Hong Kong29, Lincoln University (New Zealand)30, University of Lethbridge31, Wayne State University32, University College Dublin33, Indiana University34, Kuwait University35, Technion – Israel Institute of Technology36, University of Giessen37, The Chinese University of Hong Kong38, University of Zurich39, Fordham University40, Complutense University of Madrid41, University of Nebraska–Lincoln42, INCAE Business School43, National University of Malaysia44, Opole University45, Hong Kong Baptist University46, Tbilisi State University47, Ohio State University48, University of Wrocław49, Alexandria University50, University of San Francisco51, Melbourne Business School52, Bentley University53, University of Los Andes54, I-Shou University55, Johannes Kepler University of Linz56, International Labour Organization57, Smith College58, Copenhagen Business School59, Chungnam National University60, National University of Singapore61, Tilburg University62, Hong Kong University of Science and Technology63, Thammasat University64, Sewanee: The University of the South65, FernUniversität Hagen66, Soochow University (Suzhou)67, University of St. Gallen68, Kumamoto University69
TL;DR: In this paper, the authors focus on culturally endorsed implicit theories of leadership (CLTs) and show that attributes associated with charismatic/transformational leadership will be universally endorsed as contributing to outstanding leadership.
Abstract: This study focuses on culturally endorsed implicit theories of leadership (CLTs). Although cross-cultural research emphasizes that different cultural groups likely have different conceptions of what leadership should entail, a controversial position is argued here: namely that attributes associated with charismatic/transformational leadership will be universally endorsed as contributing to outstanding leadership. This hypothesis was tested in 62 cultures as part of the Global Leadership and Organizational Behavior Effectiveness (GLOBE) Research Program. Universally endorsed leader attributes, as well as attributes that are universally seen as impediments to outstanding leadership and culturally contingent attributes are presented here. The results support the hypothesis that specific aspects of charismatic/transformational leadership are strongly and universally endorsed across cultures.

1,227 citations

Posted ContentDOI
Spyridon Bakas1, Mauricio Reyes, Andras Jakab2, Stefan Bauer3  +435 moreInstitutions (111)
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

1,165 citations

Posted Content
TL;DR: Using hypercolumns as pixel descriptors, this work defines the hypercolumn at a pixel as the vector of activations of all CNN units above that pixel, and shows results on three fine-grained localization tasks: simultaneous detection and segmentation, and keypoint localization.
Abstract: Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation. However, the information in this layer may be too coarse to allow precise localization. On the contrary, earlier layers may be precise in localization but will not capture semantics. To get the best of both worlds, we define the hypercolumn at a pixel as the vector of activations of all CNN units above that pixel. Using hypercolumns as pixel descriptors, we show results on three fine-grained localization tasks: simultaneous detection and segmentation[22], where we improve state-of-the-art from 49.7[22] mean AP^r to 60.0, keypoint localization, where we get a 3.3 point boost over[20] and part labeling, where we show a 6.6 point gain over a strong baseline.

1,090 citations

Journal ArticleDOI
TL;DR: This survey classifies routing problems from the perspective of information quality and evolution and presents a comprehensive review of applications and solution methods for dynamic vehicle routing problems.

1,066 citations

Posted Content
TL;DR: A new geocentric embedding is proposed for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity to facilitate the use of perception in fields like robotics.
Abstract: In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an average precision of 37.3%, which is a 56% relative improvement over existing methods. We then focus on the task of instance segmentation where we label pixels belonging to object instances found by our detector. For this task, we propose a decision forest approach that classifies pixels in the detection window as foreground or background using a family of unary and binary tests that query shape and geocentric pose features. Finally, we use the output from our object detectors in an existing superpixel classification framework for semantic scene segmentation and achieve a 24% relative improvement over current state-of-the-art for the object categories that we study. We believe advances such as those represented in this paper will facilitate the use of perception in fields like robotics.

1,059 citations


Authors

Showing all 17748 results

NameH-indexPapersCitations
Alexander Belyaev1421895100796
Sarah Catherine Eno1411645105935
Mitchell Wayne1391810108776
Kaushik De1391625102058
Pierluigi Paolucci1381965105050
Randy Ruchti1371832107846
Gabor Istvan Veres135134996104
Raymond Brock135146897859
Harrison Prosper1341587100607
J. Ellison133139292416
Gyorgy Vesztergombi133144494821
Andrew Brandt132124694676
Scott Snyder131131793376
Shuai Liu129109580823
C. A. Carrillo Montoya128103378628
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202334
2022205
20211,504
20201,645
20191,563
20181,599