Institution
University of Texas at Austin
Education•Austin, Texas, United States•
About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.
Topics: Population, Poison control, Galaxy, Stars, Finite element method
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors estimate the amount of spillovers from R&D expenditures on a geographic basis, using a new data set which encompasses most of the world's innovative activity between 1970 and 1995, and find that technology is to a substantial degree local, not global, as the benefits from spillovers are declining with distance.
Abstract: Income convergence across countries turns on whether technological knowledge spillovers are global or local. I estimate the amount of spillovers from R&D expenditures on a geographic basis, using a new data set which encompasses most of the world's innovative activity between 1970 and 1995. I find that technology is to a substantial degree local, not global, as the benefits from spillovers are declining with distance. The distance at which the amount of spillovers is halved is about 1,200 kilometers. I also find that over time, technological knowledge has become considerably more global. Moreover, language skills are important for spillover diffusion. (JEL F0, O1, O3)
921 citations
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27 May 2013TL;DR: This paper reviews recent progress in the area, including design of approximate arithmetic blocks, pertinent error and quality measures, and algorithm-level techniques for approximate computing.
Abstract: Approximate computing has recently emerged as a promising approach to energy-efficient design of digital systems. Approximate computing relies on the ability of many systems and applications to tolerate some loss of quality or optimality in the computed result. By relaxing the need for fully precise or completely deterministic operations, approximate computing techniques allow substantially improved energy efficiency. This paper reviews recent progress in the area, including design of approximate arithmetic blocks, pertinent error and quality measures, and algorithm-level techniques for approximate computing.
921 citations
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04 Jul 2004TL;DR: Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.
Abstract: Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.
921 citations
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TL;DR: In this article, a low-complexity hybrid analog/digital precoding for downlink multiuser mmWave systems is proposed, which involves a combination of analog and digital processing that is inspired by the power consumption of complete radio frequency and mixed signal hardware.
Abstract: Antenna arrays will be an important ingredient in millimeter-wave (mmWave) cellular systems. A natural application of antenna arrays is simultaneous transmission to multiple users. Unfortunately, the hardware constraints in mmWave systems make it difficult to apply conventional lower frequency multiuser MIMO precoding techniques at mmWave. This paper develops low-complexity hybrid analog/digital precoding for downlink multiuser mmWave systems. Hybrid precoding involves a combination of analog and digital processing that is inspired by the power consumption of complete radio frequency and mixed signal hardware. The proposed algorithm configures hybrid precoders at the transmitter and analog combiners at multiple receivers with a small training and feedback overhead. The performance of the proposed algorithm is analyzed in the large dimensional regime and in single-path channels. When the analog and digital precoding vectors are selected from quantized codebooks, the rate loss due to the joint quantization is characterized, and insights are given into the performance of hybrid precoding compared with analog-only beamforming solutions. Analytical and simulation results show that the proposed techniques offer higher sum rates compared with analog-only beamforming solutions, and approach the performance of the unconstrained digital beamforming with relatively small codebooks.
919 citations
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01 Oct 2017TL;DR: This paper proposes distance weighted sampling, which selects more informative and stable examples than traditional approaches, and shows that a simple margin based loss is sufficient to outperform all other loss functions.
Abstract: Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.
918 citations
Authors
Showing all 95138 results
Name | H-index | Papers | Citations |
---|---|---|---|
George M. Whitesides | 240 | 1739 | 269833 |
Eugene Braunwald | 230 | 1711 | 264576 |
Yi Chen | 217 | 4342 | 293080 |
Robert J. Lefkowitz | 214 | 860 | 147995 |
Joseph L. Goldstein | 207 | 556 | 149527 |
Eric N. Olson | 206 | 814 | 144586 |
Hagop M. Kantarjian | 204 | 3708 | 210208 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Francis S. Collins | 196 | 743 | 250787 |
Gordon B. Mills | 187 | 1273 | 186451 |
Scott M. Grundy | 187 | 841 | 231821 |
Michael S. Brown | 185 | 422 | 123723 |
Eric Boerwinkle | 183 | 1321 | 170971 |
Aaron R. Folsom | 181 | 1118 | 134044 |
Jiaguo Yu | 178 | 730 | 113300 |