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Includes bibliographies and indexes

xi,284p. : 25 cm

In our quest to constrain the dynamical and structural properties of Local Group spirals from high-quality interferometric data, we have performed a neutral hydrogen survey in the direction of Messier 33. Here we present a few preliminary results from the survey and show the benefits of fitting the HI spectra by multiple peaks on constraining the structure of the Messier 33 disk. In particular we report on the discovery of new inner spiral-like and outer annular structures overlaying with the well-known main HI disk of Messier 33. Possible origins of the additional outer annular structure are presented.

From the bitsavers.org collectio n, a scanned-in computer-related document. components :: motorola :: appNotes :: AN-1752 Data Structures for 8-bit Microcontrollers

From the bitsavers.org collectio n, a scanned-in computer-related document. honeywell :: series60level6 :: comm :: ethernetSoftware :: 01 LAN Subsystem L6 Data Structures

Data Structures in JavaScript - Master the Fundamentals

Data Structures in JavaScript - Master the Fundamentals

To facilitate flexible and efficient structural bioinformatics analyses, new functionality for three-dimensional structure processing and analysis has been introduced into PyCogent -- a popular feature-rich framework for sequence-based bioinformatics, but one which has lacked equally powerful tools for handling stuctural/coordinate-based data. Extensible Python modules have been developed, which provide object-oriented abstractions (based on a hierarchical representation of macromolecules), efficient data structures (e.g. kD-trees), fast implementations of common algorithms (e.g. surface-area calculations), read/write support for Protein Data Bank-related file formats and wrappers for external command-line applications (e.g. Stride). Integration of this code into PyCogent is symbiotic, allowing sequence-based work to benefit from structure-derived data and, reciprocally, enabling structural studies to leverage PyCogent's versatile tools for phylogenetic and evolutionary analyses.

We present a frame-invariant method for detecting coherent structures from Lagrangian flow trajectories that can be sparse in number, as is the case in many fluid mechanics applications of practical interest. The method, based on principles used in graph coloring and spectral graph drawing algorithms, examines a measure of the kinematic dissimilarity of all pairs of fluid trajectories, either measured experimentally, e.g. using particle tracking velocimetry; or numerically, by advecting fluid particles in the Eulerian velocity field. Coherence is assigned to groups of particles whose kinematics remain similar throughout the time interval for which trajectory data is available, regardless of their physical proximity to one another. Through the use of several analytical and experimental validation cases, this algorithm is shown to robustly detect coherent structures using significantly less flow data than is required by existing spectral graph theory methods.

Karp et al. (1988) described Deferred Data Structures for Multisets as "lazy" data structures which partially sort data to support online rank and select queries, with the minimum amount of work in the worst case over instances of size $n$ and number of queries $q$ fixed (i.e., the query size). Barbay et al. (2016) refined this approach to take advantage of the gaps between the positions hit by the queries (i.e., the structure in the queries). We develop new techniques in order to further refine this approach and to take advantage all at once of the structure (i.e., the multiplicities of the elements), the local order (i.e., the number and sizes of runs) and the global order (i.e., the number and positions of existing pivots) in the input; and of the structure and order in the sequence of queries. Our main result is a synergistic deferred data structure which performs much better on large classes of instances, while performing always asymptotically as good as previous solutions. As intermediate results, we describe two new synergistic sorting algorithms, which take advantage of the structure and order (local and global) in the input, improving upon previous results which take advantage only of the structure (Munro and Spira 1979) or of the local order (Takaoka 1997) in the input; and one new multiselection algorithm which takes advantage of not only the order and structure in the input, but also of the structure in the queries. We described two compressed data structures to represent a multiset taking advantage of both the local order and structure, while supporting the operators rank and select on the multiset.

Karp et al. (1988) described Deferred Data Structures for Multisets as "lazy" data structures which partially sort data to support online rank and select queries, with the minimum amount of work in the worst case over instances of size $n$ and number of queries $q$ fixed (i.e., the query size). Barbay et al. (2016) refined this approach to take advantage of the gaps between the positions hit by the queries (i.e., the structure in the queries). We develop new techniques in order to further refine this approach and to take advantage all at once of the structure (i.e., the multiplicities of the elements), the local order (i.e., the number and sizes of runs) and the global order (i.e., the number and positions of existing pivots) in the input; and of the structure and order in the sequence of queries. Our main result is a synergistic deferred data structure which performs much better on large classes of instances, while performing always asymptotically as good as previous solutions. As intermediate results, we describe two new synergistic sorting algorithms, which take advantage of the structure and order (local and global) in the input, improving upon previous results which take advantage only of the structure (Munro and Spira 1979) or of the local order (Takaoka 1997) in the input; and one new multiselection algorithm which takes advantage of not only the order and structure in the input, but also of the structure in the queries. We described two compressed data structures to represent a multiset taking advantage of both the local order and structure, while supporting the operators rank and select on the multiset.

Karp et al. (1988) described Deferred Data Structures for Multisets as "lazy" data structures which partially sort data to support online rank and select queries, with the minimum amount of work in the worst case over instances of size $n$ and number of queries $q$ fixed (i.e., the query size). Barbay et al. (2016) refined this approach to take advantage of the gaps between the positions hit by the queries (i.e., the structure in the queries). We develop new techniques in order to further refine this approach and to take advantage all at once of the structure (i.e., the multiplicities of the elements), the local order (i.e., the number and sizes of runs) and the global order (i.e., the number and positions of existing pivots) in the input; and of the structure and order in the sequence of queries. Our main result is a synergistic deferred data structure which performs much better on large classes of instances, while performing always asymptotically as good as previous solutions. As intermediate results, we describe two new synergistic sorting algorithms, which take advantage of the structure and order (local and global) in the input, improving upon previous results which take advantage only of the structure (Munro and Spira 1979) or of the local order (Takaoka 1997) in the input; and one new multiselection algorithm which takes advantage of not only the order and structure in the input, but also of the structure in the queries. We described two compressed data structures to represent a multiset taking advantage of both the local order and structure, while supporting the operators rank and select on the multiset.

Community detection (CD) algorithms are applied to Hi-C data to discover new communities of loci in the 3D conformation of human and mouse DNA. We find that CD has some distinct advantages over pre-existing methods: (1) it is capable of finding a variable number of communities, (2) it can detect communities of DNA loci either adjacent or distant in the 1D sequence, and (3) it allows us to obtain a principled value of k, the number of communities present. Forcing k = 2, our method recovers earlier findings of Lieberman-Aiden, et al. (2009), but letting k be a parameter, our method obtains as optimal value k = 6, discovering new candidate communities. In addition to discovering large communities that partition entire chromosomes, we also show that CD can detect small-scale topologically associating domains (TADs) such as those found in Dixon, et al. (2012). CD thus provides a natural and flexible statistical framework for understanding the folding structure of DNA at multiple scales in Hi-C data.

Community detection (CD) algorithms are applied to Hi-C data to discover new communities of loci in the 3D conformation of human and mouse DNA. We find that CD has some distinct advantages over pre-existing methods: (1) it is capable of finding a variable number of communities, (2) it can detect communities of DNA loci either adjacent or distant in the 1D sequence, and (3) it allows us to obtain a principled value of k, the number of communities present. Forcing k = 2, our method recovers earlier findings of Lieberman-Aiden, et al. (2009), but letting k be a parameter, our method obtains as optimal value k = 6, discovering new candidate communities. In addition to discovering large communities that partition entire chromosomes, we also show that CD can detect small-scale topologically associating domains (TADs) such as those found in Dixon, et al. (2012). CD thus provides a natural and flexible statistical framework for understanding the folding structure of DNA at multiple scales in Hi-C data.

Community detection (CD) algorithms are applied to Hi-C data to discover new communities of loci in the 3D conformation of human and mouse DNA. We find that CD has some distinct advantages over pre-existing methods: (1) it is capable of finding a variable number of communities, (2) it can detect communities of DNA loci either adjacent or distant in the 1D sequence, and (3) it allows us to obtain a principled value of k, the number of communities present. Forcing k = 2, our method recovers earlier findings of Lieberman-Aiden, et al. (2009), but letting k be a parameter, our method obtains as optimal value k = 6, discovering new candidate communities. In addition to discovering large communities that partition entire chromosomes, we also show that CD can detect small-scale topologically associating domains (TADs) such as those found in Dixon, et al. (2012). CD thus provides a natural and flexible statistical framework for understanding the folding structure of DNA at multiple scales in Hi-C data.

Includes bibliographical references and index

Includes bibliographical references and index

Includes bibliographical references and index

From the bitsavers.org collection, a scanned-in computer-related document. brownUniversity :: BUGS :: MIDAS data structures Sep1977

From the bitsavers.org collection, a scanned-in computer-related document. brownUniversity :: BUGS :: MIDAS data structures Sep1977

xxiii, 885 p., 2 leaves of folded plates : 24 cm

We introduce detection techniques of targets behind walls and inside enclosed structures. These techniques rely on the intensity histogram distinctions between targets and clutter and perform iterative thresholding to achieve desirable probabilities of detection and false alarm. We propose wall clutter mitigation techniques based on wall strong specular reflection as compared to target diffuse multipath characteristics. Multipath exploitation techniques are developed to eliminate ghosts and increase target to clutter ratio. Fast data acquisition is performed by reducing frequencies and thinning the synthesized aperture. Compressive sensing methods are applied to provide situation awareness of sparse scenes using significantly reduced data.

We introduce detection techniques of targets behind walls and inside enclosed structures. These techniques rely on the intensity histogram distinctions between targets and clutter and perform iterative thresholding to achieve desirable probabilities of detection and false alarm. We propose wall clutter mitigation techniques based on wall strong specular reflection as compared to target diffuse multipath characteristics. Multipath exploitation techniques are developed to eliminate ghosts and increase target to clutter ratio. Fast data acquisition is performed by reducing frequencies and thinning the synthesized aperture. Compressive sensing methods are applied to provide situation awareness of sparse scenes using significantly reduced data.

We introduce detection techniques of targets behind walls and inside enclosed structures. These techniques rely on the intensity histogram distinctions between targets and clutter and perform iterative thresholding to achieve desirable probabilities of detection and false alarm. We propose wall clutter mitigation techniques based on wall strong specular reflection as compared to target diffuse multipath characteristics. Multipath exploitation techniques are developed to eliminate ghosts and increase target to clutter ratio. Fast data acquisition is performed by reducing frequencies and thinning the synthesized aperture. Compressive sensing methods are applied to provide situation awareness of sparse scenes using significantly reduced data.

We introduce detection techniques of targets behind walls and inside enclosed structures. These techniques rely on the intensity histogram distinctions between targets and clutter and perform iterative thresholding to achieve desirable probabilities of detection and false alarm. We propose wall clutter mitigation techniques based on wall strong specular reflection as compared to target diffuse multipath characteristics. Multipath exploitation techniques are developed to eliminate ghosts and increase target to clutter ratio. Fast data acquisition is performed by reducing frequencies and thinning the synthesized aperture. Compressive sensing methods are applied to provide situation awareness of sparse scenes using significantly reduced data.

We introduce detection techniques of targets behind walls and inside enclosed structures. These techniques rely on the intensity histogram distinctions between targets and clutter and perform iterative thresholding to achieve desirable probabilities of detection and false alarm. We propose wall clutter mitigation techniques based on wall strong specular reflection as compared to target diffuse multipath characteristics. Multipath exploitation techniques are developed to eliminate ghosts and increase target to clutter ratio. Fast data acquisition is performed by reducing frequencies and thinning the synthesized aperture. Compressive sensing methods are applied to provide situation awareness of sparse scenes using significantly reduced data.

Includes bibliographical references and index

Includes bibliographical references and index

Includes bibliographical references and index

Algorithms and Data Structures Wiki dumped with WikiTeam tools.

Tries are among the most versatile and widely used data structures on words. They are pertinent to the (internal) structure of (stored) words and several splitting procedures used in diverse contexts ranging from document taxonomy to IP addresses lookup, from data compression (i.e., Lempel-Ziv'77 scheme) to dynamic hashing, from partial-match queries to speech recognition, from leader election algorithms to distributed hashing tables and graph compression. While the performance of tries under a realistic probabilistic model is of significant importance, its analysis, even for simplest memoryless sources, has proved difficult. Rigorous findings about inherently complex parameters were rarely analyzed (with a few notable exceptions) under more realistic models of string generations. In this paper we meet these challenges: By a novel use of the contraction method combined with analytic techniques we prove a central limit theorem for the external path length of a trie under a general Markov source. In particular, our results apply to the Lempel-Ziv'77 code. We envision that the methods described here will have further applications to other trie parameters and data structures.

Talk by Joshua Vogelstein of Johns Hopkins University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Determining whether certain properties are related to other properties is fundamental to scientific discovery. As data collection rates accelerate, it is becoming increasingly difficult yet ever more important to determine whether one property of data (e.g., cloud density) is related to another (e.g., grass wetness). Only if two properties are related are further investigations into the geometry of the relationship warranted. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes in real data scenarios, and do not provide insight into the geometry underlying the structure of the relationship. We juxtapose hypothesis testing, manifold learning, and harmonic analysis to obtain Multiscale Generalized Correlation (MGC). Our key insight is that one can adaptively restrict the analysis to the "jointly local" observations - that is, one can estimate the scale with the most informative neighbors for determining the existence and geometry of a relationship. We prove that to achieve a given true positive rate, MGC typically requires far fewer samples than existing methods for all investigated dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC uniquely provides a simple and elegant characterization of the potentially complex latent geometry underlying the relationship. We used MGC to detect the presence and reveal the geometry of the relationships between mental and brain properties, to perform a proteomics screening, and to develop an imaging biomarker for disease, while avoiding the false positive inflation problems that have plagued conventional parametric approaches. Our open source implementation of MGC is easy to use, parameter-free, and applicable to previously vexing statistical questions that are ubiquitous in science, government, finance, and other disciplines.

Slides for this talk . Talk by Rina Panigrahy of Google.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: What is a reasonable architecture for an algorithmic view of the mind? Is it akin to a single giant deep network or is it more like several small modules connected by some graph? How is memory captured — is it some lookup table? Take a simple event like meeting someone over coffee — how would your mind remember who the person was, what was discussed? Such information needs to be organized and indexed in a way so that it can be quickly accessed in future if say I met the same person again. We propose that information related to such events and inputs is stored as a sketch (a compact representation that approximately reconstructs the input) — the sketching mechanism is based on random subspace embedding and is able to approximately reconstruct the original input and its basic statistics up to some level of accuracy. The sketching mechanism implicitly enables different high level object oriented abstractions such as classes, attributes, references, type-information, modules into a knowledge graph without explicitly incorporating such ideas into the mechanism operations. We will see how ideas based on sketching can lead to an initial version of a very simplified architecture for an algorithmic view of the mind. We will see how a simplified implementation of Neural Memory based storing sketches using Locality sensitive hashing can be used to almost double the capacity of BERT with a small amount of Neural Memory while adding

Slides for this talk . Talk by Rina Panigrahy of Google.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: What is a reasonable architecture for an algorithmic view of the mind? Is it akin to a single giant deep network or is it more like several small modules connected by some graph? How is memory captured — is it some lookup table? Take a simple event like meeting someone over coffee — how would your mind remember who the person was, what was discussed? Such information needs to be organized and indexed in a way so that it can be quickly accessed in future if say I met the same person again. We propose that information related to such events and inputs is stored as a sketch (a compact representation that approximately reconstructs the input) — the sketching mechanism is based on random subspace embedding and is able to approximately reconstruct the original input and its basic statistics up to some level of accuracy. The sketching mechanism implicitly enables different high level object oriented abstractions such as classes, attributes, references, type-information, modules into a knowledge graph without explicitly incorporating such ideas into the mechanism operations. We will see how ideas based on sketching can lead to an initial version of a very simplified architecture for an algorithmic view of the mind. We will see how a simplified implementation of Neural Memory based storing sketches using Locality sensitive hashing can be used to almost double the capacity of BERT with a small amount of Neural Memory while adding

Data Structures and Algorithms implementation in Go Data Structures and Algorithms Clean and simple implementation in Go Implementation There are several data structures and algorithms implemented in this project. The list will be replenished with time. Data structures Circular Buffer Linked List Doubly Linked List Stack Queue Searching algorithms Linear Search Binary Search Jump Search Interpolation Search Exponential Search Ternary Search Sorting algorithms Selection Sort Insertion Sort Bubble Sort Comb Sort Usage The library is not intended for direct use by importing. We strongly recommend copying the necessary implementations and adjusting to your case. You can download the source using go get command: go get github.com/floyernick/Data-Structures-and-Algorithms Don't forget about proverb: A little copying is better than a little dependency. Contribute We would be happy to receive your propositions of improvement ;) License This project is licensed under the MIT License . Authors Mykyta Paliienko - GitHub profile To restore the repository download the bundle wget https://archive.org/download/github.com-floyernick-Data-Structures-and-Algorithms_-_2018-04-01_17-10-51/floyernick-Data-Structures-and-Algorithms_-_2018-04-01_17-10-51.bundle and run: git clone floyernick-Data-Structures-and-Algorithms_-_2018-04-01_17-10-51.bundle Source: https://github.com/floyernick/Data-Structures-and-Algorithms Uploader: floyernick Upload date: 2018-04-01

Data Structures and Algorithms in C++ Second Edition

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xxii, 696 pages : 25 cm

xxii, 696 pages : 25 cm

xxii, 696 pages : 25 cm

xxii, 696 pages : 25 cm

xxii, 696 pages : 25 cm

This report is concerned with the development of data generating programs and analytical models to facilitate the structural analysis of antenna mast and tower structures subjected to static and dynamic loadings. The programs generate the data cards necessary to define the structure geometry and material properties of typical masts and towers for the NASA Structural Analysis Program (NASTRAN). The automatic data generating program for masts can be used to model any mast of constant cross section and can handle any number of cable tie points and anchor points; deform cards to duplicate cable pretension are also generated as well as the force cards needed for representing a uniform wind type pressure. The automatic data generator for towers generates a series of tower stages and considers four (4) variations of tower geometry, three (3) possible base conditions and any number of sides greater than two (2). This program was designed for towers with regular generation properties and equal side lengths, but a separate program enables some variations in side length to be considered. In order to provide an economical but efficient NASTRAN model for analyzing antenna towers, three models, with varying degrees of exactness were investigated using several NASTRAN rigid formats for analysis. The capabilities and limitations of these models, with respect to the rigid formats considered, was studied. (Author)

In this paper we study standard program components applicable to a wide variety of design tasks; we choose for this study the specific problem domain of data structures for general search problems. Within this domain transformations for converting solutions of simple searching problems to solutions of more complex problems have been developed. We discuss one of those transformations, specify precisely the transformation and its conditions of applicability, and prove its correctness; we accomplish this by casting it in terms of abstract data types -- specifically by using the Alphard form mechanism. We also demonstrate that the costs of the structures derived by this transformation are only slightly greater than the costs of the original solutions. The transformation we describe has already been used to develop a number of new algorithms, and it represents a new level of generality in software engineering tools.

An analysis of one and multidegree of freedom systems with classical damping is presented. Definition and minimization of error functions for each system are discussed. Systems with classical and nonclassical normal modes are studied, and results for first order perturbation are given. An alternative method of matching power spectral densities is provided, and numerical results are reviewed.

A mode of a multiset $S$ is an element $a \in S$ of maximum multiplicity; that is, $a$ occurs at least as frequently as any other element in $S$. Given a list $A[1:n]$ of $n$ items, we consider the problem of constructing a data structure that efficiently answers range mode queries on $A$. Each query consists of an input pair of indices $(i, j)$ for which a mode of $A[i:j]$ must be returned. We present an $O(n^{2-2\epsilon})$-space static data structure that supports range mode queries in $O(n^\epsilon)$ time in the worst case, for any fixed $\epsilon \in [0,1/2]$. When $\epsilon = 1/2$, this corresponds to the first linear-space data structure to guarantee $O(\sqrt{n})$ query time. We then describe three additional linear-space data structures that provide $O(k)$, $O(m)$, and $O(|j-i|)$ query time, respectively, where $k$ denotes the number of distinct elements in $A$ and $m$ denotes the frequency of the mode of $A$. Finally, we examine generalizing our data structures to higher dimensions.