Structure-activity analyzed by pattern recognition: the asymmetric case

The paper describes graph algorithms performance when using different types of data structures. To achieve that, we developed a multi-level graph processing system, which allows to create graph applications independently of any implementation details such as graph data structure or underlying computational architecture. We measure the performance of breadth-first search, max flow and random graph building algorithms when using compressed sparse row and adjacency matrix data structures. Experiments reveal different graph processing rates for different data structures, which indicates the need of using specific data structures for specific algorithms to achieve highest performance.

In this paper we investigate the problem of building a static data structure that represents a string s using space close to its compressed size, and allows fast access to individual characters of s. This type of data structures was investigated by the recent paper of Bille et al. [3]. Let n be the size of a context-free grammar that derives a unique string s of length L. (Note that L might be exponential in n.) Bille et al. showed a data structure that uses space O(n) and allows to query for the i-th character of s using running time O(logL). Their data structure works on a word RAM with a word size of logL bits.

Efficient indexing for semantic search

Computationally testable models in linguistics focus on declaring data structures and providing exemplar derivations. This paper outlines a comprehensive model of speech production which goes beyond derivations to show how actual instances of utterances can be formally characterised. Utterances contain a wealth of detail beyond the underlying utterance plan: some of this is a function of the mechanism itself (e.g. coarticulation) and some is the result of carefully supervised control. We develop the notion of managed or supervised speech production to enable the inclusion of EXPRESSIVE content in speech. Building on earlier work the Cognitive Phonetics Agent bridges the gap between the physical and cognitive processes in phonetics by controlling the way phonologically determined utterance plans are phonetically rendered in detail. The model is illustrated using different types of data structure which occur in speech, concentrating in particular on an XML characterisation of appropriate structures. We trace a simple utterance through from its phonological plan to a detailed intrinsic allophonic representation to show how stages in the model work.

We present a framework for compiling recurrence equations into native code. In our framework, users specify a system of recurrences, the types of data structures that store inputs and outputs, and scheduling commands for optimization. Our compiler then lowers these specifications into native code that respects the dependencies in the recurrence equations. Our compiler can generate code over both sparse and dense data structures, and determines if the recurrence system is solvable with the provided scheduling primitives. We evaluate the performance and correctness of the generated code on several recurrences, from domains as diverse as dense and sparse matrix solvers, dynamic programming, graph problems, and sparse tensor algebra. We demonstrate that the generated code has competitive performance to hand-optimized implementations in libraries. However, these handwritten libraries target specific recurrences, specific data structures, and specific optimizations. Our system, on the other hand, automatically generates implementations from recurrences, data formats, and schedules, giving our system more generality than library approaches.

In this paper, we present a new reversible decorrelation method of three-dimensional (3-D) medical images for progressive transmission. Progressive transmission of an image permits gradual improvement of image quality while being displayed. When the amount of image data is very large, as a 3-D medical image, the progressive transmission plays an important role in viewing or browsing the image. The data structure presented in this paper takes account of interframe correlation as well as intraframe correlation of the 3-D image. This type of data structure has been termed the 3-D hierarchy embedded differential image (3-D-HEDI) as was derived from the earlier HEDI structure. Experiments were conducted to verify the performance of 3-D HEDI in terms of the decorrelation efficiency as well as the progressive transmission efficiency. It is compared with those of conventional hierarchy interpolation (HINT), two-dimensional (2-D) HEDI and differential pulse code modulation (DPCM). Experimental results indicate that 3-D HEDI outperforms HINT, 2-D HEDI and DPCM in both decorrelation efficiency as well as the progressive transmission efficiency on 3-D medical images.

<p>The abstract mathematical structures known as coalgebras are of increasing interest in computer science for their use in modelling certain types of data structures and programs. Traditional algebraic methods describe objects in terms of their construction, whilst coalgebraic methods describe objects in terms of their decomposition, or observational behaviour. The latter techniques are particularly useful for modelling infinite data structures and providing semantics for object-oriented programming languages, such as Java. There have been many different logics developed for reasoning about coalgebras of particular functors, most involving modal logic. We define a modal logic for coalgebras of polynomial functors, extending Rößiger’s logic [33], whose proof theory was limited to using finite constant sets, by adding an operator from Goldblatt [11]. From the semantics we define a canonical coalgebra that provides a natural construction of a final coalgebra for the relevant functor. We then give an infinitary axiomatization and syntactic proof relation that is sound and complete for functors constructed from countable constant sets.</p>

The search process in the spatial database consumes much time for the user. The size of the database determines the efficacy and speed of information retrieval. The potency of spatial query in Geographic Information Systems (GIS) is of paramount importance. It depends heavily on the query processing algorithms. When the database size increases, the processes of data retrieval gets complicated. This paper employs two types of data structures; Rectangle-Tree (R-tree) and the Balanced-Tree (B-tree) which is called aRB-Tree. These two types have been formerly merged by researchers to facilitate the retrieval of data as quickly as possible while providing efficient results. The same idea has been adopted in this paper where the aRB (aggregate RB) accelerates executing complex queries and minimizes the time consumed in searching, editing, deleting and updating any record in the spatial database. In this paper a web application was developed to test the efficiency of using both data structures. This web application utilises a medical institution database that contains locations such as addresses of physicians, clinics, hospital, labs, and radiology centres. Results of aRB-Tree are more sufficient and accurate than using each index alone.

High-performance architectures rely upon powerful optimizing and parallelizing compilers to maximize performance. Such compilers need accurate program analysis to enable their performance-enhancing transformations. In the domain of program analysis for parallelization, pointer analysis is a difficult and increasingly common problem. When faced with dynamic, pointer-based data structures, existing solutions are either too limited in the types of data structures they can analyze, or require too much effort on the part of the programmer. In this paper we present a powerful description language for expressing the aliasing properties of dynamic date structures. Such descriptions provide the compiler with better information during alias analysis, and require only minimal effort from the programmer. Ultimately, this enables a more accurate program analysis, and an increased application of performance-enhancing transformations. >

This book discusses the development and implementation of data structures and algorithms using C#. The data structures we use in this book are found in the .NET Framework class library System.Collections. In this chapter, we develop the concept of a collection by first discussing the implementation of our own Collection class (using the array as the basis of our implementation) and then by covering the Collection classes in the .NET Framework. An important addition to C# 2.0 is generics. Generics allow the C# programmer to write one version of a function, either independently or within a class, without having to overload the function many times to allow for different data types. C# 2.0 provides a special library, System.Collections.Generic, that implements generics for several of the System.Collections data structures. This chapter will introduce the reader to generic programming. Finally, this chapter introduces a custom-built class, the Timing class, which we will use in several chapters to measure the performance of a data structure and/or algorithm. This class will take the place of Big O analysis, not because Big O analysis isn't important, but because this book takes a more practical approach to the study of data structures and algorithms. COLLECTIONS DEFINED A collection is a structured data type that stores data and provides operations for adding data to the collection, removing data from the collection, updating data in the collection, as well as operations for setting and returning the values of different attributes of the collection.

Grammars are a powerful technique for modeling and extracting the structure of documents. One large challenge, however, is computational complexity. The computational cost of grammatical parsing is related to both the complexity of the input and the ambiguity of the grammar. For programming languages, where the terminals appear in a linear sequence and the grammar is unambiguous, parsing is O(N). For natural languages, which are linear yet have an ambiguous grammar, parsing is O(N/sup 3/). For documents, where the terminals are arranged in two dimensions and the grammar is ambiguous, parsing time can be exponential in the number of terminals. In this paper we introduce (and unify) several types of geometrical data structures which can be used to significantly accelerate parsing time. Each data structure embodies a different geometrical constraint on the set of possible valid parses. These data structures are very general, in that they can be used by any type of grammatical model, and a wide variety of document understanding tasks, to limit the set of hypotheses examined and tested. Assuming a clean design for the parsing software, the same parsing framework can be tested with various geometric constraints to determine the most effective combination.

Cross-classified multilevel modelling is an extension of standard multilevel modelling for non-hierarchical data that have cross-classified structures. Traditional multilevel models involve hierarchical data structures whereby lower level units such as students are nested within higher level units such as schools and where these higher level units may in turn be nested within further groupings or clusters such as school districts, regions, and countries. With hierarchical data structures, there is an exact nesting of each lower level unit in one and only one higher level unit. For example, each student attends one school, each school is located within one school district, and so on. However, social reality is more complicated than this, and so social and behavioural data often do not follow pure or strict hierarchies. Two types of non-hierarchical data structures which often appear in practice are cross-classified and multiple membership structures. In this article, we describe cross-classified data structures and cross-classified hierarchical linear modelling which can be used to analyse them.

The data structures that are written in a typical data structures course cannot or would not be used in common applications in industry for two reasons. First, they do not work according to their specifications if they are used in a multi-threaded environment. If two threads are deleting adjacent elements of a linked list at the same time, for example, then one of the deletes may not have any effect. Second, they do not fire change events and thus frustrate common design practices of connecting software components. As a result, it is more difficult for the application programmer to separate the data, the structures, the logic, the user interface, and the application code from each other. Here we discuss some issues involved in including these two topics, threads and components, in a course on data structures. Students who learn these techniques will have less to learn when they begin to use and write data structures in industry.

Bloom filters are a type of data structure that is used to test whether or not an element is a member of a set. They are known for being space-efficient and are commonly employed in various applications, such as network routers, web browsers, and databases. These filters work by allowing a fixed probability of incorrectly identifying an element as being a member of the set, known as the false positive rate (FPR). However, traditional bloom filters suffer from a high FPR and extensive memory usage, which can lead to incorrect query results and a slow performance. Thus, this study indicates that a content-based strategy could be a practical solution for these challenges. Specifically, our approach requires less bloom filter storage, consequently decreasing the probability of false positives. The effectiveness of several hash functions on our strategy’s performance was also evaluated. Experimental evaluations demonstrated that the proposed strategy could potentially decrease false positives by a substantial margin of up to 79.83%. The use of size-based content bits significantly contributes to the decrease in the number of false positives as well. However, as the volume of content bits rises, the impact on time is not considerably noticeable. Moreover, the evidence suggests that the application of a singular approach leads to a more than 50% decrease in false positives.

This paper discusses a variety of conditional autoregressive (CAR) models for mapping disease rates, beyond the usual first-order intrinsic CAR model. We illustrate the utility and scope of such models for handling different types of data structures. To encourage their routine use for map production at statistical and health agencies, a simple algorithm for fitting such models is presented. This is derived from penalized quasi-likelihood (PQL) inference which uses an analogue of best-linear unbiased estimation for the regional risk ratios and restricted maximum likelihood for the variance components. We offer the practitioner here the use of the parametric bootstrap for inference. It is more reliable than standard maximum likelihood asymptotics for inference purposes since relevant hypotheses for the mapping of rates lie on the boundary of the parameter space. We illustrate the parametric bootstrap test of the practically relevant and important simplifying hypothesis that there is no spatial autocorrelation. Although the parametric bootstrap requires computational effort, it is straightforward to implement and offers a wealth of information relating to the estimators and their properties. The proposed methodology is illustrated by analysing infant mortality in the province of British Columbia in Canada.