1,757,587 research outputs found
Mitu Aggarwal oral history summary
No full textA summary of an oral history interview of Mitu Aggarwal
Data for: Design of a Compact Proton Beam Energy Modulator for Imaging
No full textRaw data for Design of Compact Proton Beam Energy Modulator for Imaging by Aggarwal et al
How do variations in Urban Heat Islands in space and time influence household water use? The case of Phoenix, Arizona
No full textabstract: This paper explores how urbanization, through its role in the evolution of Urban Heat Island (UHI), affects residential water consumption. Using longitudinal data and drawing on a mesoscale atmospheric model, we examine how variations in surface temperature at the census tract level have affected water use in single family residences in Phoenix, Arizona. Results show that each Fahrenheit rise in nighttime temperature increases water consumption by 1.4%. This temperature effect is found to vary significantly with lot size and pool size. The study provides insights into the links between urban form and water use, through the dynamics of UHI.Corresponding Author:
Rimjhim M. Aggarwal
Arizona State University
[email protected]
Kailash Aggarwal, 1995
No full textKailash Aggarwal, Lecturer, School of Civil Engineering and Building retires after twenty-two years service. Swinburne Staff News 2 February 1995
Just Take the Average! An Embarrassingly Simple 2^n-Time Algorithm for SVP (and CVP)
No full textWe show a 2^{n+o(n)}-time (and space) algorithm for the Shortest Vector Problem on lattices (SVP) that works by repeatedly running an embarrassingly simple "pair and average" sieving-like procedure on a list of lattice vectors. This matches the running time (and space) of the current fastest known algorithm, due to Aggarwal, Dadush, Regev, and Stephens-Davidowitz (ADRS, in STOC, 2015), with a far simpler algorithm. Our algorithm is in fact a modification of the ADRS algorithm, with a certain careful rejection sampling step removed.
The correctness of our algorithm follows from a more general "meta-theorem," showing that such rejection sampling steps are unnecessary for a certain class of algorithms and use cases. In particular, this also applies to the related 2^{n + o(n)}-time algorithm for the Closest Vector Problem (CVP), due to Aggarwal, Dadush, and Stephens-Davidowitz (ADS, in FOCS, 2015), yielding a similar embarrassingly simple algorithm for gamma-approximate CVP for any gamma = 1+2^{-o(n/log n)}. (We can also remove the rejection sampling procedure from the 2^{n+o(n)}-time ADS algorithm for exact CVP, but the resulting algorithm is still quite complicated.
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