Institution
York University
Education•Toronto, Ontario, Canada•
About: York University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Politics. The organization has 18899 authors who have published 43357 publications receiving 1568560 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a structural vector autoregression model is proposed to investigate the dynamic relationship between oil prices, exchange rates and emerging market stock prices, and the model also captures stylized facts regarding movements in oil prices.
506 citations
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TL;DR: This paper synthesizes systems theory and the resource-based view of the firm to build a unified conceptual model linking IT assets with firm-level benefits, suggesting that IT assets can play a strategic role when they are combined with organizational resources to create IT-enabled resources.
Abstract: This paper informs the literature on the business value of information technology by conceptualizing a path from IT assets-that is, commodity-like or off-the-shelf information technologies-to sustainable competitive advantage. This path suggests that IT assets can play a strategic role when they are combined with organizational resources to create IT-enabled resources. To the extent that relationships between IT assets and organizational resources are synergistic, the ensuing IT-enabled resources are capable of positively affecting firms' sustainable competitive advantage via their
improved strategic potential. This is an important contribution since IT-related organizational benefits have been hard to demonstrate despite attempts to study them through a variety of methods and theoretical lenses. This paper synthesizes systems theory and the resource-based view of the firm to build a unified conceptual model linking IT assets with firm-level benefits. Several propositions are derived from the model and their implications for IS research and practice are discussed.
506 citations
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TL;DR: In this article, detailed measurements of the electron performance of the ATLAS detector at the LHC were reported, using decays of the Z, W and J/psi particles.
Abstract: Detailed measurements of the electron performance of the ATLAS detector at the LHC are reported, using decays of the Z, W and J/psi particles. Data collected in 2010 at root s = 7 TeV are used, corresponding to an integrated luminosity of almost 40 pb(-1). The inter-alignment of the inner detector and the electromagnetic calorimeter, the determination of the electron energy scale and resolution, and the performance in terms of response uniformity and linearity are discussed. The electron identification, reconstruction and trigger efficiencies, as well as the charge misidentification probability, are also presented.
505 citations
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TL;DR: In this article, the authors proposed spatiotemporal residual networks (ResNets) for action recognition in videos, which is a combination of two-stream convolutional networks and residual connections between appearance and motion pathways.
Abstract: Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos. Recently, Residual Networks (ResNets) have arisen as a new technique to train extremely deep architectures. In this paper, we introduce spatiotemporal ResNets as a combination of these two approaches. Our novel architecture generalizes ResNets for the spatiotemporal domain by introducing residual connections in two ways. First, we inject residual connections between the appearance and motion pathways of a two-stream architecture to allow spatiotemporal interaction between the two streams. Second, we transform pretrained image ConvNets into spatiotemporal networks by equipping these with learnable convolutional filters that are initialized as temporal residual connections and operate on adjacent feature maps in time. This approach slowly increases the spatiotemporal receptive field as the depth of the model increases and naturally integrates image ConvNet design principles. The whole model is trained end-to-end to allow hierarchical learning of complex spatiotemporal features. We evaluate our novel spatiotemporal ResNet using two widely used action recognition benchmarks where it exceeds the previous state-of-the-art.
504 citations
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University of Arizona1, California Institute of Technology2, Washington University in St. Louis3, Optech4, University of Bristol5, University of Washington6, Space Science Institute7, Dalhousie University8, University of Ottawa9, Max Planck Society10, Aarhus University11, Canadian Space Agency12, University of Texas at Dallas13, Tufts University14, University of Alberta15, Texas A&M University16, University of Copenhagen17, Search for extraterrestrial intelligence18, Ames Research Center19, University of Colorado Boulder20, University of Michigan21, Imperial College London22, Delft University of Technology23, York University24
TL;DR: The analysis of the data from the Phoenix mission revealed an alkaline environment, in contrast to that found by the Mars Exploration Rovers, indicating that many different environments have existed on Mars.
Abstract: The Phoenix mission investigated patterned ground and weather in the northern arctic region of Mars for 5 months starting 25 May 2008 (solar longitude between 76.5° and 148°). A shallow ice table was uncovered by the robotic arm in the center and edge of a nearby polygon at depths of 5 to 18 centimeters. In late summer, snowfall and frost blanketed the surface at night; H2O ice and vapor constantly interacted with the soil. The soil was alkaline (pH = 7.7) and contained CaCO3, aqueous minerals, and salts up to several weight percent in the indurated surface soil. Their formation likely required the presence of water.
503 citations
Authors
Showing all 19301 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dan R. Littman | 157 | 426 | 107164 |
Martin J. Blaser | 147 | 820 | 104104 |
Aaron Dominguez | 147 | 1968 | 113224 |
Gregory R Snow | 147 | 1704 | 115677 |
Joseph E. LeDoux | 139 | 478 | 91500 |
Kenneth Bloom | 138 | 1958 | 110129 |
Osamu Jinnouchi | 135 | 885 | 86104 |
Steven A. Narod | 134 | 970 | 84638 |
David H. Barlow | 133 | 786 | 72730 |
Elliott Cheu | 133 | 1219 | 91305 |
Roger Moore | 132 | 1677 | 98402 |
Wendy Taylor | 131 | 1252 | 89457 |
Stephen P. Jackson | 131 | 372 | 76148 |
Flera Rizatdinova | 130 | 1242 | 89525 |
Sudhir Malik | 130 | 1669 | 98522 |