886 research outputs found

    Augmenting graphs to minimize the diameter

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    We study the problem of augmenting a weighted graph by inserting edges of bounded total cost while minimizing the diameter of the augmented graph. Our main result is an FPT 4-approximation algorithm for the problem.Comment: 15 pages, 3 figure

    Speeding up shortest path algorithms

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    Given an arbitrary, non-negatively weighted, directed graph G=(V,E)G=(V,E) we present an algorithm that computes all pairs shortest paths in time O(mn+mlgn+nTψ(m,n))\mathcal{O}(m^* n + m \lg n + nT_\psi(m^*, n)), where mm^* is the number of different edges contained in shortest paths and Tψ(m,n)T_\psi(m^*, n) is a running time of an algorithm to solve a single-source shortest path problem (SSSP). This is a substantial improvement over a trivial nn times application of ψ\psi that runs in O(nTψ(m,n))\mathcal{O}(nT_\psi(m,n)). In our algorithm we use ψ\psi as a black box and hence any improvement on ψ\psi results also in improvement of our algorithm. Furthermore, a combination of our method, Johnson's reweighting technique and topological sorting results in an O(mn+mlgn)\mathcal{O}(m^*n + m \lg n) all-pairs shortest path algorithm for arbitrarily-weighted directed acyclic graphs. In addition, we also point out a connection between the complexity of a certain sorting problem defined on shortest paths and SSSP.Comment: 10 page

    A simpler and more efficient algorithm for the next-to-shortest path problem

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    Given an undirected graph G=(V,E)G=(V,E) with positive edge lengths and two vertices ss and tt, the next-to-shortest path problem is to find an stst-path which length is minimum amongst all stst-paths strictly longer than the shortest path length. In this paper we show that the problem can be solved in linear time if the distances from ss and tt to all other vertices are given. Particularly our new algorithm runs in O(VlogV+E)O(|V|\log |V|+|E|) time for general graphs, which improves the previous result of O(V2)O(|V|^2) time for sparse graphs, and takes only linear time for unweighted graphs, planar graphs, and graphs with positive integer edge lengths.Comment: Partial result appeared in COCOA201

    Efficient Dynamic Approximate Distance Oracles for Vertex-Labeled Planar Graphs

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    Let GG be a graph where each vertex is associated with a label. A Vertex-Labeled Approximate Distance Oracle is a data structure that, given a vertex vv and a label λ\lambda, returns a (1+ε)(1+\varepsilon)-approximation of the distance from vv to the closest vertex with label λ\lambda in GG. Such an oracle is dynamic if it also supports label changes. In this paper we present three different dynamic approximate vertex-labeled distance oracles for planar graphs, all with polylogarithmic query and update times, and nearly linear space requirements

    Gender Equality and Human Rights

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    The UN Women Discussion Paper Series is a new initiative led by the Research and Data section. The Series features research commissioned as background papers for publications by leading researchers from different national and regional contexts. Each paper benefits from an anonymous external peer review process before being published in this Series. This paper has been produced for the UN Women flagship report Progress of the World’s Women 2015 by Sandra Fredman FBA QC, Hon Rhodes Professor of the Laws of the British Commonwealth and the USA, Oxford University and Beth Goldblatt, Associate Professor, University of Technology, Sydney (With valuable research assistance by Meghan Campbell, D Phil candidate, Oxford University

    Retroviral enhancer detection insertions in zebrafish combined with comparative genomics reveal genomic regulatory blocks - a fundamental feature of vertebrate genomes

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    A large-scale enhancer detection screen was performed in the zebrafish using a retroviral vector carrying a basal promoter and a fluorescent protein reporter cassette. Analysis of insertional hotspots uncovered areas around developmental regulatory genes in which an insertion results in the same global expression pattern, irrespective of exact position. These areas coincide with vertebrate chromosomal segments containing identical gene order; a phenomenon known as conserved synteny and thought to be a vestige of evolution. Genomic comparative studies have found large numbers of highly conserved noncoding elements (HCNEs) spanning these and other loci. HCNEs are thought to act as transcriptional enhancers based on the finding that many of those that have been tested direct tissue specific expression in transient or transgenic assays. Although gene order in hox and other gene clusters has long been known to be conserved because of shared regulatory sequences or overlapping transcriptional units, the chromosomal areas found through insertional hotspots contain only one or a few developmental regulatory genes as well as phylogenetically unrelated genes. We have termed these regions genomic regulatory blocks (GRBs), and show that they underlie the phenomenon of conserved synteny through all sequenced vertebrate genomes. After teleost whole genome duplication, a subset of GRBs were retained in two copies, underwent degenerative changes compared with tetrapod loci that exist as single copy, and that therefore can be viewed as representing the ancestral form. We discuss these findings in light of evolution of vertebrate chromosomal architecture and the identification of human disease mutations

    Dynamic Range Majority Data Structures

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    Given a set PP of coloured points on the real line, we study the problem of answering range α\alpha-majority (or "heavy hitter") queries on PP. More specifically, for a query range QQ, we want to return each colour that is assigned to more than an α\alpha-fraction of the points contained in QQ. We present a new data structure for answering range α\alpha-majority queries on a dynamic set of points, where α(0,1)\alpha \in (0,1). Our data structure uses O(n) space, supports queries in O((lgn)/α)O((\lg n) / \alpha) time, and updates in O((lgn)/α)O((\lg n) / \alpha) amortized time. If the coordinates of the points are integers, then the query time can be improved to O(lgn/(αlglgn)+(lg(1/α))/α))O(\lg n / (\alpha \lg \lg n) + (\lg(1/\alpha))/\alpha)). For constant values of α\alpha, this improved query time matches an existing lower bound, for any data structure with polylogarithmic update time. We also generalize our data structure to handle sets of points in d-dimensions, for d2d \ge 2, as well as dynamic arrays, in which each entry is a colour.Comment: 16 pages, Preliminary version appeared in ISAAC 201

    Fast Locality-Sensitive Hashing Frameworks for Approximate Near Neighbor Search

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    The Indyk-Motwani Locality-Sensitive Hashing (LSH) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution H\mathcal{H} over locality-sensitive hash functions that partition space. For a collection of nn points, after preprocessing, the query time is dominated by O(nρlogn)O(n^{\rho} \log n) evaluations of hash functions from H\mathcal{H} and O(nρ)O(n^{\rho}) hash table lookups and distance computations where ρ(0,1)\rho \in (0,1) is determined by the locality-sensitivity properties of H\mathcal{H}. It follows from a recent result by Dahlgaard et al. (FOCS 2017) that the number of locality-sensitive hash functions can be reduced to O(log2n)O(\log^2 n), leaving the query time to be dominated by O(nρ)O(n^{\rho}) distance computations and O(nρlogn)O(n^{\rho} \log n) additional word-RAM operations. We state this result as a general framework and provide a simpler analysis showing that the number of lookups and distance computations closely match the Indyk-Motwani framework, making it a viable replacement in practice. Using ideas from another locality-sensitive hashing framework by Andoni and Indyk (SODA 2006) we are able to reduce the number of additional word-RAM operations to O(nρ)O(n^\rho).Comment: 15 pages, 3 figure

    Cross-Document Pattern Matching

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    We study a new variant of the string matching problem called cross-document string matching, which is the problem of indexing a collection of documents to support an efficient search for a pattern in a selected document, where the pattern itself is a substring of another document. Several variants of this problem are considered, and efficient linear-space solutions are proposed with query time bounds that either do not depend at all on the pattern size or depend on it in a very limited way (doubly logarithmic). As a side result, we propose an improved solution to the weighted level ancestor problem

    Dynamic Set Intersection

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    Consider the problem of maintaining a family FF of dynamic sets subject to insertions, deletions, and set-intersection reporting queries: given S,SFS,S'\in F, report every member of SSS\cap S' in any order. We show that in the word RAM model, where ww is the word size, given a cap dd on the maximum size of any set, we can support set intersection queries in O(dw/log2w)O(\frac{d}{w/\log^2 w}) expected time, and updates in O(logw)O(\log w) expected time. Using this algorithm we can list all tt triangles of a graph G=(V,E)G=(V,E) in O(m+mαw/log2w+t)O(m+\frac{m\alpha}{w/\log^2 w} +t) expected time, where m=Em=|E| and α\alpha is the arboricity of GG. This improves a 30-year old triangle enumeration algorithm of Chiba and Nishizeki running in O(mα)O(m \alpha) time. We provide an incremental data structure on FF that supports intersection {\em witness} queries, where we only need to find {\em one} eSSe\in S\cap S'. Both queries and insertions take O\paren{\sqrt \frac{N}{w/\log^2 w}} expected time, where N=SFSN=\sum_{S\in F} |S|. Finally, we provide time/space tradeoffs for the fully dynamic set intersection reporting problem. Using MM words of space, each update costs O(MlogN)O(\sqrt {M \log N}) expected time, each reporting query costs O(NlogNMop+1)O(\frac{N\sqrt{\log N}}{\sqrt M}\sqrt{op+1}) expected time where opop is the size of the output, and each witness query costs O(NlogNM+logN)O(\frac{N\sqrt{\log N}}{\sqrt M} + \log N) expected time.Comment: Accepted to WADS 201
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