3,054 research outputs found
Pivot Selection for Median String Problem
The Median String Problem is W[1]-Hard under the Levenshtein distance, thus,
approximation heuristics are used. Perturbation-based heuristics have been
proved to be very competitive as regards the ratio approximation
accuracy/convergence speed. However, the computational burden increase with the
size of the set. In this paper, we explore the idea of reducing the size of the
problem by selecting a subset of representative elements, i.e. pivots, that are
used to compute the approximate median instead of the whole set. We aim to
reduce the computation time through a reduction of the problem size while
achieving similar approximation accuracy. We explain how we find those pivots
and how to compute the median string from them. Results on commonly used test
data suggest that our approach can reduce the computational requirements
(measured in computed edit distances) by \% with approximation accuracy as
good as the state of the art heuristic.
This work has been supported in part by CONICYT-PCHA/Doctorado
Nacional/ through a Ph.D. Scholarship; Universidad Cat\'{o}lica
de la Sant\'{i}sima Concepci\'{o}n through the research project DIN-01/2016;
European Union's Horizon 2020 under the Marie Sk\l odowska-Curie grant
agreement ; Millennium Institute for Foundational Research on Data
(IMFD); FONDECYT-CONICYT grant number ; and for O. Pedreira, Xunta de
Galicia/FEDER-UE refs. CSI ED431G/01 and GRC: ED431C 2017/58
Indexability, concentration, and VC theory
Degrading performance of indexing schemes for exact similarity search in high
dimensions has long since been linked to histograms of distributions of
distances and other 1-Lipschitz functions getting concentrated. We discuss this
observation in the framework of the phenomenon of concentration of measure on
the structures of high dimension and the Vapnik-Chervonenkis theory of
statistical learning.Comment: 17 pages, final submission to J. Discrete Algorithms (an expanded,
improved and corrected version of the SISAP'2010 invited paper, this e-print,
v3
Engineering Parallel String Sorting
We discuss how string sorting algorithms can be parallelized on modern
multi-core shared memory machines. As a synthesis of the best sequential string
sorting algorithms and successful parallel sorting algorithms for atomic
objects, we first propose string sample sort. The algorithm makes effective use
of the memory hierarchy, uses additional word level parallelism, and largely
avoids branch mispredictions. Then we focus on NUMA architectures, and develop
parallel multiway LCP-merge and -mergesort to reduce the number of random
memory accesses to remote nodes. Additionally, we parallelize variants of
multikey quicksort and radix sort that are also useful in certain situations.
Comprehensive experiments on five current multi-core platforms are then
reported and discussed. The experiments show that our implementations scale
very well on real-world inputs and modern machines.Comment: 46 pages, extension of "Parallel String Sample Sort" arXiv:1305.115
Parallel String Sample Sort
We discuss how string sorting algorithms can be parallelized on modern
multi-core shared memory machines. As a synthesis of the best sequential string
sorting algorithms and successful parallel sorting algorithms for atomic
objects, we propose string sample sort. The algorithm makes effective use of
the memory hierarchy, uses additional word level parallelism, and largely
avoids branch mispredictions. Additionally, we parallelize variants of multikey
quicksort and radix sort that are also useful in certain situations.Comment: 34 pages, 7 figures and 12 table
SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities
Algorithmic complexity vulnerabilities occur when the worst-case time/space
complexity of an application is significantly higher than the respective
average case for particular user-controlled inputs. When such conditions are
met, an attacker can launch Denial-of-Service attacks against a vulnerable
application by providing inputs that trigger the worst-case behavior. Such
attacks have been known to have serious effects on production systems, take
down entire websites, or lead to bypasses of Web Application Firewalls.
Unfortunately, existing detection mechanisms for algorithmic complexity
vulnerabilities are domain-specific and often require significant manual
effort. In this paper, we design, implement, and evaluate SlowFuzz, a
domain-independent framework for automatically finding algorithmic complexity
vulnerabilities. SlowFuzz automatically finds inputs that trigger worst-case
algorithmic behavior in the tested binary. SlowFuzz uses resource-usage-guided
evolutionary search techniques to automatically find inputs that maximize
computational resource utilization for a given application.Comment: ACM CCS '17, October 30-November 3, 2017, Dallas, TX, US
Impact of the initialization in tree-based fast similarity search techniques
Many fast similarity search techniques relies on the use of pivots (specially selected points in the data set). Using these points, specific structures (indexes) are built speeding up the search when queering. Usually, pivot selection techniques are incremental, being the first one randomly chosen. This article explores several techniques to choose the first pivot in a tree-based fast similarity search technique. We provide experimental results showing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity. Moreover, most pivot tree-based indexes emphasizes in building balanced trees. We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones.The authors thank the Spanish CICyT for partial support of this work through projects TIN2009-14205-C04-C1, the Ist Programme of the European Community, under the Pascal Network of Excellence, (Ist–
2006-216886), and the program Consolider Ingenio 2010 (Csd2007-00018)
Simple data analysis for biologists
This document provides a simple introduction to research methods and analysis tools for biologists or environmental scientists, with particular emphasis on fish biology in devleoping countries
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