Institution
University of Waterloo
Education•Waterloo, Ontario, Canada•
About: University of Waterloo is a education organization based out in Waterloo, Ontario, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 36093 authors who have published 93906 publications receiving 2948139 citations. The organization is also known as: UW & uwaterloo.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the state of the art and future challenges in the recent development of biomass and associated transformation technologies for clean production of biofuels are reviewed, and a discussion of the synergistic integration of various biochemical and bioprocessing technologies is provided.
391 citations
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TL;DR: This work demonstrates the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology and empirically demonstrates that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance.
Abstract: We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image quality assessment (BIQA). MEON consists of two sub-networks—a distortion identification network and a quality prediction network—sharing the early layers. Unlike traditional methods used for training multi-task networks, our training process is performed in two steps. In the first step, we train a distortion type identification sub-network, for which large-scale training samples are readily available. In the second step, starting from the pre-trained early layers and the outputs of the first sub-network, we train a quality prediction sub-network using a variant of the stochastic gradient descent method. Different from most deep neural networks, we choose biologically inspired generalized divisive normalization (GDN) instead of rectified linear unit as the activation function. We empirically demonstrate that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance. With modest model complexity, the proposed MEON index achieves state-of-the-art performance on four publicly available benchmarks. Moreover, we demonstrate the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology.
391 citations
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TL;DR: In this paper, the topic of photons interacting strongly when confined to a one-dimensional geometry is discussed from experimental and theoretical perspectives, and it is shown that it is possible to make them interact by altering environmental conditions, for instance, in the interior of certain materials or by squeezing them in confined geometries.
Abstract: Photons, the particles of light, are in most conditions very weakly interacting. Nevertheless, it is possible to make them interact by altering environmental conditions, for instance, in the interior of certain materials or by squeezing them in confined geometries. In this Colloquium the topic of photons interacting strongly when confined to a one-dimensional geometry is discussed from experimental and theoretical perspectives.
391 citations
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10 Apr 2011TL;DR: Mahout is presented, a low-overhead yet effective traffic management system that follows OpenFlow-like central controller approach for network management but augments the design with the authors' novel end host mechanism.
Abstract: Datacenters need high-bandwidth interconnection fabrics. Several researchers have proposed highly-redundant topologies with multiple paths between pairs of end hosts for datacenter networks. However, traffic management is necessary to effectively utilize the bisection bandwidth provided by these topologies. This requires timely detection of elephant flows—flows that carry large amount of data—and managing those flows. Previously proposed approaches incur high monitoring overheads, consume significant switch resources, and/or have long detection times. We propose, instead, to detect elephant flows at the end hosts. We do this by observing the end hosts's socket buffers, which provide better, more efficient visibility of flow behavior. We present Mahout, a low-overhead yet effective traffic management system that follows OpenFlow-like central controller approach for network management but augments the design with our novel end host mechanism. Once an elephant flow is detected, an end host signals the network controller using in-band signaling with low overheads. Through analytical evaluation and experiments, we demonstrate the benefits of Mahout over previous solutions.
391 citations
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TL;DR: A deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images and achieves state-of-the-art performance on both synthetic and authentic IQA databases is proposed.
Abstract: We propose a deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNNs), specializing in two distortion scenarios separately. For synthetic distortions, we first pre-train a CNN to classify the distortion type and the level of an input image, whose ground truth label is readily available at a large scale. For authentic distortions, we make use of a pre-train CNN (VGG-16) for the image classification task. The two feature sets are bilinearly pooled into one representation for a final quality prediction. We fine-tune the whole network on the target databases using a variant of stochastic gradient descent. The extensive experimental results show that the proposed model achieves state-of-the-art performance on both synthetic and authentic IQA databases. Furthermore, we verify the generalizability of our method on the large-scale Waterloo Exploration Database, and demonstrate its competitiveness using the group maximum differentiation competition methodology.
390 citations
Authors
Showing all 36498 results
Name | H-index | Papers | Citations |
---|---|---|---|
John J.V. McMurray | 178 | 1389 | 184502 |
David A. Weitz | 178 | 1038 | 114182 |
David Taylor | 131 | 2469 | 93220 |
Lei Zhang | 130 | 2312 | 86950 |
Will J. Percival | 129 | 473 | 87752 |
Trevor Hastie | 124 | 412 | 202592 |
Stephen Mann | 120 | 669 | 55008 |
Xuan Zhang | 119 | 1530 | 65398 |
Mark A. Tarnopolsky | 115 | 644 | 42501 |
Qiang Yang | 112 | 1117 | 71540 |
Wei Zhang | 112 | 1189 | 93641 |
Hans-Peter Seidel | 112 | 1213 | 51080 |
Theodore S. Rappaport | 112 | 490 | 68853 |
Robert C. Haddon | 112 | 577 | 52712 |
David Zhang | 111 | 1027 | 55118 |