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| jackey
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09-20-2009 10:16 PM ET (US)
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| Paul Smith
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07-21-2009 10:00 PM ET (US)
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| Geihcoik
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07-15-2009 08:15 PM ET (US)
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qpmXLu
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07-14-2009 10:51 PM ET (US)
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Deleted by topic administrator 07-16-2009 02:09 AM
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| Paul Smith
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06-24-2009 04:46 AM ET (US)
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| christian louboutin
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06-19-2009 10:08 PM ET (US)
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robots
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06-12-2009 07:19 AM ET (US)
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| Gary Winnick
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04-02-2009 01:32 AM ET (US)
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In recognition to his major achievements he was awarded with the doctorate from C.W Post University, Alma Mater in 2004. He was awarded by Jacoby International award of 2001 too that was delivered by Dashew International Center of UCLA. Gary Winnick and Hank Freid was also awarded with the humanitarian laureate award which was given by Simon Wealth Center.
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| Welcome
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03-26-2009 02:32 AM ET (US)
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Webmaster Link Exchange - Free Web Link Exchange Directory. Where Webmasters go to trade links with other webmasters
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| wiliam6
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02-12-2009 04:16 AM ET (US)
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The original UGG Boots. " UGG" is not a brand name but an age old generic term for this style of Australian-made UGG Boots. UGG
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06-29-2008 12:47 PM ET (US)
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Deleted by topic administrator 06-29-2008 07:03 PM
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| Andy D
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03-12-2007 09:56 PM ET (US)
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Hi class, just wanted to mention that Lawrence was correct in pointing out that the distortion for embedding NEG metrics into L2 achieved in Lee's paper was log n ^{3/4}, not log n ^{4/3}, which would've been inferior to the Bourgain embedding.
Also, remember--the proof technique we focused on, based on finding large well-separated sets in NEG metrics, only leads to an embedding preserving a single distance scale [tau, 2tau]. To get a low-distortion embedding one has to follow up with the technical machinery of the Gluing Lemma, which wasn't covered.
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Daniel Hsu
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03-07-2007 01:29 PM ET (US)
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Embedding l2 into l1
It seems that random projections should yield constant distortion embeddings from l2 into l1. As a crude estimate, if x is a unit vector in l2^D and A is a d-by-D matrix of Gaussian random variables, then in expectation, Ax/d has norm about 0.8. Large deviation bounds or concentration of measure (and d=O(log n)) can then do the rest as in the JL lemma.
Does this seem plausible? Using Dvorestsky's theorem would probably be of the same flavor, but might give a lower-dimension embedding.
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| Konstantin
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03-06-2007 10:16 PM ET (US)
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Fjola,
I didn't mean any embedding of planar graphs into planes.
Simply, the "fact" that plane can be partitioned using only two BFS suggests that the same can be done for planar graphs.
The techniques you presented seem to suffice for this.
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| Fjola
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03-06-2007 04:51 PM ET (US)
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Konstantin,
I have a question about your "splitting the plane into cells": Are you assuming there is an obvious embedding from the planar graph metric into the euclidean plane first? When you talk of cells of size "delta x delta" I understand that you have the points of the original metric somehow laid out on a rectangular grid, then partition the points into squares on that grid. But you don't mean to look at the diameter of components of that partition in the original "shortest path" metric. Is that a correct understanding?
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| Konstantin
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03-01-2007 11:27 PM ET (US)
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Edited by author 03-01-2007 11:30 PM
PLANAR GRAPH PARTITIONS: (Rao et al.)
I was wondering why _three_ BFS are needed in order to obtain delta-partition of a given planar graph. Apparently, plane can be split into cells of size delta x delta by only two types of lines: horizontal and vertical (they correspond in some sense to two BFS over our plane). Then why two BFS don't suffice for planar graphs?
Now it seems to me that two BFS _do_ suffice to delta-partition any planar graph. A reason for that is that from the inner BFS we can get K3,2 (not only K3,1), then we can extend it to K3,3 using the outer BFS.
Any opinions as to whether this is correct?
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