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Book ChapterDOI

Introduction to Algorithms

01 Jan 2014-pp 1-21
TL;DR: This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation.
Abstract: Algorithms are important tools for solving problems computationally. All computation involves algorithms, and the efficiency of an algorithm largely determines its usefulness. This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation. A brief history of recent nature-inspired algorithms for optimization is outlined in this chapter.
Citations
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Journal ArticleDOI
01 Apr 2012-Fly
TL;DR: It appears that the 5′ and 3′ UTRs are reservoirs for genetic variations that changes the termini of proteins during evolution of the Drosophila genus.
Abstract: We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w1118; iso-2; iso-3 strain and the reference y1; cn1 bw1 sp1 strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in...

8,017 citations

Journal ArticleDOI
TL;DR: This work describes Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions, including those for sequence analysis, differential expression analysis and visualization.
Abstract: We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization.

3,005 citations


Cites methods from "Introduction to Algorithms"

  • ...The findOverlaps function uses an efficient interval tree algorithm [4] to detect overlaps between two IRanges objects, as well as the more complex range-based data structures introduced later....

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  • ...[4] chr13 [106142141, 106142541] + | 174818 ,NA....

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Proceedings ArticleDOI
01 Jan 2014
TL;DR: DREBIN is proposed, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone and outperforms several related approaches and detects 94% of the malware with few false alarms.
Abstract: Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain unprotected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an application as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for explaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explanations provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, rendering it suitable for checking downloaded applications directly on the device.

1,905 citations


Cites background or methods from "Introduction to Algorithms"

  • ...It thus suffices to only store the features extracted from an application for sparsely representing the vector φ(x), for example, using hash tables [6] or Bloom filters [3]....

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  • ...This approach can be efficiently realized by maintaining the k largest weights ws in a heap during the computation of the function f(x) [6]....

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01 Jan 2014
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
Abstract: Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks’ dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering. © 2005 Elsevier B.V. All rights reserved.

1,758 citations

01 Jan 2015
TL;DR: The abstract should follow the structure of the article (relevance, degree of exploration of the problem, the goal, the main results, conclusion) and characterize the theoretical and practical significance of the study results.
Abstract: Summary) The abstract should follow the structure of the article (relevance, degree of exploration of the problem, the goal, the main results, conclusion) and characterize the theoretical and practical significance of the study results. The abstract should not contain wording echoing the title, cumbersome grammatical structures and abbreviations. The text should be written in scientific style. The volume of abstracts (summaries) depends on the content of the article, but should not be less than 250 words. All abbreviations must be disclosed in the summary (in spite of the fact that they will be disclosed in the main text of the article), references to the numbers of publications from reference list should not be made. The sentences of the abstract should constitute an integral text, which can be made by use of the words “consequently”, “for example”, “as a result”. Avoid the use of unnecessary introductory phrases (eg, “the author of the article considers...”, “The article presents...” and so on.)

1,229 citations

References
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Journal ArticleDOI
13 May 1983-Science
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

41,772 citations

Book
Vladimir Vapnik1
01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

40,147 citations

Book
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)

32,573 citations

Journal ArticleDOI
Rainer Storn1, Kenneth Price
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

24,053 citations

Journal ArticleDOI
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described

18,439 citations