76 research outputs found
On the Power of Adaptivity in Sparse Recovery
The goal of (stable) sparse recovery is to recover a -sparse approximation
of a vector from linear measurements of . Specifically, the goal is
to recover such that ||x-x*||_p <= C min_{k-sparse x'} ||x-x'||_q for some
constant and norm parameters and . It is known that, for or
, this task can be accomplished using non-adaptive
measurements [CRT06] and that this bound is tight [DIPW10,FPRU10,PW11].
In this paper we show that if one is allowed to perform measurements that are
adaptive, then the number of measurements can be considerably reduced.
Specifically, for and we show - A scheme with measurements that uses
rounds. This is a significant improvement over the best possible non-adaptive
bound. - A scheme with measurements
that uses /two/ rounds. This improves over the best possible non-adaptive
bound. To the best of our knowledge, these are the first results of this type.
As an independent application, we show how to solve the problem of finding a
duplicate in a data stream of items drawn from using
bits of space and passes, improving over the best
possible space complexity achievable using a single pass.Comment: 18 pages; appearing at FOCS 201
Mining the âInternet Graveyardâ: Rethinking the Historiansâ Toolkit
âMining the Internet Graveyardâ argues that the advent of massive quantity of born-digital historical sources necessitates a rethinking of the historiansâ toolkit. The contours of a third wave of computational history are outlined, a trend marked by ever-increasing amounts of digitized information (especially web based), falling digital storage costs, a move to the cloud, and a corresponding increase in computational power to process these sources. Following this, the article uses a case study of an early born-digital archive at Library and Archives Canada â Canadaâs Digital Collections project (CDC) â to bring some of these problems into view. An array of off-the-shelf data analysis solutions, coupled with code written in Mathematica, helps us bring context and retrieve information from a digital collection on a previously inaccessible scale. The article concludes with an illustration of the various computational tools available, as well as a call for greater digital literacy in history curricula and professional development.Social Sciences and Humanities Research Council || 430-2013-061
- âŠ