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Institution

York University

EducationToronto, 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
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
Georges Aad, B. Abbott1, Jalal Abdallah2, A. A. Abdelalim3  +3013 moreInstitutions (174)
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

Posted Content
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

Journal ArticleDOI
03 Jul 2009-Science
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

NameH-indexPapersCitations
Dan R. Littman157426107164
Martin J. Blaser147820104104
Aaron Dominguez1471968113224
Gregory R Snow1471704115677
Joseph E. LeDoux13947891500
Kenneth Bloom1381958110129
Osamu Jinnouchi13588586104
Steven A. Narod13497084638
David H. Barlow13378672730
Elliott Cheu133121991305
Roger Moore132167798402
Wendy Taylor131125289457
Stephen P. Jackson13137276148
Flera Rizatdinova130124289525
Sudhir Malik130166998522
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023180
2022528
20212,676
20202,857
20192,426
20182,137