HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search

Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity

A burst search for gravitational waves from binary black holes

Compact binary coalescence (CBC) is one of the most promising sources of gravitational waves. These sources are usually searched for with matched filters which require accurate calculation of the GW waveforms and generation of large template banks. We present a complementary search technique based on algorithms used in un-modeled searches. Initially designed for detection of un-modeled bursts, which can span a very large set of waveform morphologies, the search algorithm presented here is constrained for targeted detection of the smaller subset of CBC signals. The constraint is based on the assumption of elliptical polarisation for signals received at the detector. We expect that the algorithm is sensitive to CBC signals in a wide range of masses, mass ratios, and spin parameters. In preparation for the analysis of data from the fifth LIGO-Virgo science run (S5), we performed preliminary studies of the algorithm on test data. We present the sensitivity of the search to different types of simulated CBC waveforms. Also, we discuss how to extend the results of the test run into a search over all of the current LIGO-Virgo data set.Comment: 12 pages, 4 figures, 2 tables, submitted for publication in CQG in the special issue for the conference proceedings of GWDAW13; corrected some typos, addressed some minor reviewer comments one section restructured and references updated and correcte

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

X-ENS: Semantic Enrichment of Web Search Results at Real-Time

While more and more semantic data are published on the Web, an important question is how typical web users can access and exploit this body of knowledge. Although, existing interaction paradigms in semantic search hide the complexity behind an easy-to-use interface, they have not managed to cover common search needs. In this paper, we present X-ENS (eXplore ENtities in Search), a web search application that enhances the classical, keyword-based, web searching with semantic information, as a means to combine the pros of both Semantic Web standards and common Web Searching. X-ENS identifies entities of interest in the snippets of the top search results which can be further exploited in a faceted interaction scheme, and thereby can help the user to limit the - often very large - search space to those hits that contain a particular piece of information. Moreover, X-ENS permits the exploration of the identified entities by exploiting semantic repositories

Racial profiling or racist policing? bounds tests in aggregate data

State-wide reports on police traffic stops and searches summarize very large populations, making them potentially powerful tools for identifying racial bias, particularly when statistics on search outcomes are included. But when the reported statistics conflate searches involving different levels of police discretion, standard tests for racial bias are not applicable. This paper develops a model of police search decisions that allows for non-discretionary searches and derives tests for racial bias in data that mixes different search types. Our tests reject unbiased policing as an explanation of the disparate impact of motor-vehicle searches on minorities in MissouriHouseholds ; Public policy