2 Search Results for "Kamath, Akshay"

Document
Introduction
DOI: 10.4230/LITES.8.1.0
Introduction to the Special Issue on Embedded Systems for Computer Vision

Authors: Samarjit Chakraborty and Qing Rao

Published in: LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision. Leibniz Transactions on Embedded Systems, Volume 8, Issue 1

Abstract
We provide a broad overview of some of the current research directions at the intersection of embedded systems and computer vision, in addition to introducing the papers appearing in this special issue. Work at this intersection is steadily growing in importance, especially in the context of autonomous and cyber-physical systems design. Vision-based perception is almost a mandatory component in any autonomous system, but also adds myriad challenges like, how to efficiently implement vision processing algorithms on resource-constrained embedded architectures, and how to verify the functional and timing correctness of these algorithms. Computer vision is also crucial in implementing various smart functionality like security, e.g., using facial recognition, or monitoring events or traffic patterns. Some of these applications are reviewed in this introductory article. The remaining articles featured in this special issue dive into more depth on a few of them.
Cite as

LITES, Volume 8, Issue 1: Special Issue on Embedded Systems for Computer Vision, pp. 0:i-0:viii, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)

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@Article{chakraborty_et_al:LITES.8.1.0,
  author =	{Chakraborty, Samarjit and Rao, Qing},
  title =	{{Introduction to the Special Issue on Embedded Systems for Computer Vision}},
  booktitle =	{LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision},
  pages =	{00:1--00:8},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2022},
  volume =	{8},
  number =	{1},
  editor =	{Chakraborty, Samarjit and Rao, Qing},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES.8.1.0},
  doi =		{10.4230/LITES.8.1.0},
  annote =	{Keywords: Embedded systems, Computer vision, Cyber-physical systems, Computer architecture}
}
Document
DOI: 10.4230/LIPIcs.CCC.2021.37
A Simple Proof of a New Set Disjointness with Applications to Data Streams

Authors: Akshay Kamath, Eric Price, and David P. Woodruff

Published in: LIPIcs, Volume 200, 36th Computational Complexity Conference (CCC 2021)

Abstract
The multiplayer promise set disjointness is one of the most widely used problems from communication complexity in applications. In this problem there are k players with subsets S¹, …, S^k, each drawn from {1, 2, …, n}, and we are promised that either the sets are (1) pairwise disjoint, or (2) there is a unique element j occurring in all the sets, which are otherwise pairwise disjoint. The total communication of solving this problem with constant probability in the blackboard model is Ω(n/k). We observe for most applications, it instead suffices to look at what we call the "mostly" set disjointness problem, which changes case (2) to say there is a unique element j occurring in at least half of the sets, and the sets are otherwise disjoint. This change gives us a much simpler proof of an Ω(n/k) randomized total communication lower bound, avoiding Hellinger distance and Poincare inequalities. Our proof also gives strong lower bounds for high probability protocols, which are much larger than what is possible for the set disjointness problem. Using this we show several new results for data streams: 1) for 𝓁₂-Heavy Hitters, any O(1)-pass streaming algorithm in the insertion-only model for detecting if an ε-𝓁₂-heavy hitter exists requires min(1/(ε²)log((ε²n)/δ), 1/(ε)n^{1/2}) bits of memory, which is optimal up to a log n factor. For deterministic algorithms and constant ε, this gives an Ω(n^{1/2}) lower bound, improving the prior Ω(log n) lower bound. We also obtain lower bounds for Zipfian distributions. 2) for 𝓁_p-Estimation, p > 2, we show an O(1)-pass Ω(n^{1-2/p} log(1/δ)) bit lower bound for outputting an O(1)- approximation with probability 1-δ, in the insertion-only model. This is optimal, and the best previous lower bound was Ω(n^{1-2/p} + log(1/δ)). 3) for low rank approximation of a sparse matrix in ℝ^{d× n}, if we see the rows of a matrix one at a time in the row-order model, each row having O(1) non-zero entries, any deterministic algorithm requires Ω(√d) memory to output an O(1)-approximate rank-1 approximation. Finally, we consider strict and general turnstile streaming models, and show separations between sketching lower bounds and non-sketching upper bounds for the heavy hitters problem.
Cite as

Akshay Kamath, Eric Price, and David P. Woodruff. A Simple Proof of a New Set Disjointness with Applications to Data Streams. In 36th Computational Complexity Conference (CCC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 200, pp. 37:1-37:24, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)

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@InProceedings{kamath_et_al:LIPIcs.CCC.2021.37,
  author =	{Kamath, Akshay and Price, Eric and Woodruff, David P.},
  title =	{{A Simple Proof of a New Set Disjointness with Applications to Data Streams}},
  booktitle =	{36th Computational Complexity Conference (CCC 2021)},
  pages =	{37:1--37:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-193-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{200},
  editor =	{Kabanets, Valentine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CCC.2021.37},
  URN =		{urn:nbn:de:0030-drops-143119},
  doi =		{10.4230/LIPIcs.CCC.2021.37},
  annote =	{Keywords: Streaming algorithms, heavy hitters, communication complexity, information complexity}
}
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