Institution
University of Los Andes
Education•Bogotá, 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.
Topics: Population, Large Hadron Collider, Standard Model, Lepton, Higgs boson
Papers published on a yearly basis
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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
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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
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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
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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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Alexander Belyaev | 142 | 1895 | 100796 |
Sarah Catherine Eno | 141 | 1645 | 105935 |
Mitchell Wayne | 139 | 1810 | 108776 |
Kaushik De | 139 | 1625 | 102058 |
Pierluigi Paolucci | 138 | 1965 | 105050 |
Randy Ruchti | 137 | 1832 | 107846 |
Gabor Istvan Veres | 135 | 1349 | 96104 |
Raymond Brock | 135 | 1468 | 97859 |
Harrison Prosper | 134 | 1587 | 100607 |
J. Ellison | 133 | 1392 | 92416 |
Gyorgy Vesztergombi | 133 | 1444 | 94821 |
Andrew Brandt | 132 | 1246 | 94676 |
Scott Snyder | 131 | 1317 | 93376 |
Shuai Liu | 129 | 1095 | 80823 |
C. A. Carrillo Montoya | 128 | 1033 | 78628 |