1,725,148 research outputs found

    Haptic interaction with deformable objects using real-time dynamic simulation

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    Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1995.Includes bibliographical references (p. 81-83).by Nitish Swarup.M.S

    Design and control of a two-axis gimbal system for use in active vision

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    Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1993.Includes bibliographical references (leaves 43-44).by Nitish Swarup.B.S

    NItish katoch - Blank pages

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    This is a blank page for test.This is a blank page for the test

    IPA Analysis Course Spring 2020

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    Hello, I am uploading the PPT for the IPA pathway and network analysis for you to download. Thanks Nitish</p

    Press Release (1961-10-16) India's Nitish Laharry will speak before a UMD audience

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    University of Minnesota, Duluth. News Service. (1961). Press Release (1961-10-16) India's Nitish Laharry will speak before a UMD audience. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/193567

    Bihar, Nitish Kumar, and the prohibition debate

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    Curtailing basic liberties on a presumption that individuals tend to abuse them is self-defeating

    sj-docx-1-joh-10.1177_00207314211066748 - Supplemental material for The Political Economy of Public Health Inequalities and Inequities in India: Complexities, Challenges, and Strategies for Inclusive Public Health Care Policy

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    Supplemental material, sj-docx-1-joh-10.1177_00207314211066748 for The Political Economy of Public Health Inequalities and Inequities in India: Complexities, Challenges, and Strategies for Inclusive Public Health Care Policy by Nitish Gogoi and S.S. Sumesh in International Journal of Health Services</p

    sj-docx-3-joh-10.1177_00207314211066748 - Supplemental material for The Political Economy of Public Health Inequalities and Inequities in India: Complexities, Challenges, and Strategies for Inclusive Public Health Care Policy

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    Supplemental material, sj-docx-3-joh-10.1177_00207314211066748 for The Political Economy of Public Health Inequalities and Inequities in India: Complexities, Challenges, and Strategies for Inclusive Public Health Care Policy by Nitish Gogoi and S.S. Sumesh in International Journal of Health Services</p

    Single-Sink Network Design with Vertex Connectivity Requirements

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    We study single-sink network design problems in undirected graphs with vertex connectivity requirements. The input to these problems is an edge-weighted undirected graph G=(V,E)G=(V,E), a sink/root vertex rr, a set of terminals TVT \subseteq V, and integer kk. The goal is to connect each terminal tTt \in T to rr via kk \emph{vertex-disjoint} paths. In the {\em connectivity} problem, the objective is to find a min-cost subgraph of GG that contains the desired paths. There is a 22-approximation for this problem when k2k \le 2 \cite{FleischerJW} but for k3k \ge 3, the first non-trivial approximation was obtained in the recent work of Chakraborty, Chuzhoy and Khanna \cite{ChakCK08}; they describe and analyze an algorithm with an approximation ratio of O(kO(k2)log4n)O(k^{O(k^2)}\log^4 n) where n=Vn=|V|. In this paper, inspired by the results and ideas in \cite{ChakCK08}, we show an O(kO(k)logT)O(k^{O(k)}\log |T|)-approximation bound for a simple greedy algorithm. Our analysis is based on the dual of a natural linear program and is of independent technical interest. We use the insights from this analysis to obtain an O(kO(k)logT)O(k^{O(k)}\log |T|)-approximation for the more general single-sink {\em rent-or-buy} network design problem with vertex connectivity requirements. We further extend the ideas to obtain a poly-logarithmic approximation for the single-sink {\em buy-at-bulk} problem when k=2k=2 and the number of cable-types is a fixed constant; we believe that this should extend to any fixed kk. We also show that for the non-uniform buy-at-bulk problem, for each fixed kk, a small variant of a simple algorithm suggested by Charikar and Kargiazova \cite{CharikarK05} for the case of k=1k=1 gives an 2O(logT)2^{O(\sqrt{\log |T|})} approximation for larger kk. These results show that for each of these problems, simple and natural algorithms that have been developed for k=1k=1 have good performance for small k>1k > 1
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