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Institution

University of Texas at Austin

EducationAustin, 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.


Papers
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Journal ArticleDOI
TL;DR: This review explores recent work directed towards more targeted treatment of cancer, whether through more specific anti-cancer agents or through methods of delivery, including delivery by avoiding the reticuloendothelial system, utilizing the enhanced permeability and retention effect and tumor-specific targeting.

2,389 citations

Journal ArticleDOI
TL;DR: In this article, a new finite element method is presented that features the ability to include in the finite element space knowledge about the partial differential equation being solved, which can therefore be more efficient than the usual finite element methods.
Abstract: A new finite element method is presented that features the ability to include in the finite element space knowledge about the partial differential equation being solved This new method can therefore be more efficient than the usual finite element methods An additional feature of the partition-of-unity method is that finite element spaces of any desired regularity can be constructed very easily This paper includes a convergence proof of this method and illustrates its efficiency by an application to the Helmholtz equation for high wave numbers The basic estimates for a posteriori error estimation for this new method are also proved © 1997 by John Wiley & Sons, Ltd

2,387 citations

Journal ArticleDOI
TL;DR: This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.
Abstract: Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

2,380 citations

Journal ArticleDOI
TL;DR: In this article, the authors focus on the photodriven conversion of liquid water to gaseous hydrogen and oxygen, a process similar to that of biological photosynthesis, using sunlight to drive a thermodynamically uphill reaction of an abundant material to produce fuel.
Abstract: The maintenance of life on earth, our food, oxygen, and fossil fuels depend upon the conversion of solar energy into chemical energy by biological photosynthesis carried out by green plants and photosynthetic bacteria. In this process sunlight and available abundant raw materials (water, carbon dioxide) are converted to oxygen and the reduced organic species that serve as food and fuel. A long-standing challenge has been the development of a practical artificial photosynthetic system that can roughly mimic the biological one, not by duplicating the self-organization and reproduction of the biological system nor the aesthetic beauty of trees and plants, but rather by being able to use sunlight to drive a thermodynamically uphill reaction of an abundant materials to produce a fuel. In this Account we focus on “water splitting”, the photodriven conversion of liquid water to gaseous hydrogen and oxygen:

2,377 citations

Journal ArticleDOI
TL;DR: This paper defines the various components comprising a GRASP and demonstrates, step by step, how to develop such heuristics for combinatorial optimization problems.
Abstract: Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.

2,370 citations


Authors

Showing all 95138 results

NameH-indexPapersCitations
George M. Whitesides2401739269833
Eugene Braunwald2301711264576
Yi Chen2174342293080
Robert J. Lefkowitz214860147995
Joseph L. Goldstein207556149527
Eric N. Olson206814144586
Hagop M. Kantarjian2043708210208
Rakesh K. Jain2001467177727
Francis S. Collins196743250787
Gordon B. Mills1871273186451
Scott M. Grundy187841231821
Michael S. Brown185422123723
Eric Boerwinkle1831321170971
Aaron R. Folsom1811118134044
Jiaguo Yu178730113300
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023304
20221,209
202110,137
202010,331
20199,727
20188,973