The structured flowchart niche

The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Current high-throughput technology such as microarrays is generating an overwhelming amount of data of biological systems at the molecular and cellular level. To adequately organize, maintain, analyze and interpret this deluge of information the adaptation of existing and the development of new computational methodologies and tools is required. The principal approach to analyzing and interpreting biological data is to abstract them into logical structures that support and incrementally promote the development of a more general conceptual framework for characterizing, explaining, and predicting processes in living systems. Cluster analysis refers to a computing methodology that discovers and describes meaningful patterns or structures in data. Generally, cluster algorithms are governed by a learning-by-observation process. A plethora of specific algorithms has been suggested in the literature. In the context of microarray gene expression profiling of tumors, this work describes a comparative study of five clustering methods.

The purpose of this research is to adapt and to evaluate five distinct models which are capable to measure risk preferences for an Indonesia context. Those models are: a) a risk attitude model by using a lottery technique; b) strength of preference by using mid-value splitting; c) an intrinsic risk attitude model, and two separate psychometric-based models. An empirical study is carried out (n = 30) with respondents of owners of clothing business in Indonesia. Goodness-of-fit tests for respective models indicate positive results with low errors (as indicated by MSE) and high explanatory power (R2). It suggests that the datasets generally confirm the underlying assumptions of each model. Using Spearman tests, convergent validities are observable for risk attitude vs. strength of preference, risk attitude vs. intrinsic risk attitude, risk attitude vs. psychometric. Results of this study extend the current body of literature pertaining evaluation of individual risk preference measurement.

A functional data structure is a data structure that is suitable for implementation in a functional programming language, or for coding in an ordinary language like C or Java using a functional style. Functional data structures are closely related to persistent data structures and immutable data structures—in fact, the three terms are often used interchangeably. Howevber, there are subtle differences. The term persistent data structures refers to the general class of data structures in which an update does not destroy the previous version of the data structure, but rather creates a new version that co-exists with the previous version. See Chapter 33 for more details about persistent data structures. The term immutable data structures emphasizes a particular implementation technique for achieving persistence, in which memory devoted to a particular version of the data structure, once initialized, is never altered. The term functional data structures emphasizes the language or coding style in which persistent data structures are implemented. Functional data structures are always immutable, except in a technical sense discussed in Section 41.4.

We present an algorithm for computing the closest point, transform to an explicitly described manifold on a rectilinear grid in low dimensional spaces. The closest point transform finds the closest point on a manifold and the Euclidean distance to a manifold for the points in a grid. We consider manifolds composed of simple geometric shapes, such as, a set of points, piecewise linear curves or triangle meshes. The algorithm solves the eikonal equation |∇u| = 1 with the method of characteristics. For many problems, the computational complexity of the algorithm is linear in both the number of grid points and the complexity of the manifold. Many query problems can be aided by using orthogonal range queries (ORQ). There are several standard data structures for performing ORQ's in 3-D, including kd-trees, octrees, and cell arrays. We develop additional data structures based on cell arrays. We study the characteristics of each data structure and compare their performance. We present a new algorithm for solving the single-source, non-negative weight, shortest-paths problem. Dijkstra's algorithm solves this problem with computational complexity O ((E + V)log V) where E is the number of edges and V is the number of vertices. The new algorithm, called Marching with a Correctness Criterion (MCC), has computational complexity O (E + RV), where R is the ratio of the largest to smallest edge weight. Sethian's Fast Marching Method (FMM) may be used to solve static Hamilton-Jacobi equations. It has computational complexity O (N log N), where N is the number of grid points. The FMM has been regarded as an optimal algorithm because it is closely related to Dijkstra's algorithm. The new shortest-paths algorithm discussed above can be used to develop an ordered, upwind, finite difference algorithm for solving static Hamilton-Jacobi equations. This algorithm requires difference schemes that difference not only in coordinate directions, but in diagonal directions as well. It has computational complexity O(RN), where R is the ratio of the largest to smallest propagation speed and N is the number of grid points.

It has been suggested that torque, the tendency to draw circles in the clockwise direction, is related to left-handedness and is a marker for psychopathology. Results of recent studies conflict with this hypothesis but are weakened by imprecise definition and unreliable assessment of torque. Measures of torque, lateral preference, and cognitive ability were administered to 181 children in kindergarten and first grade. As reported previously, rate of torque decreased with age and was greater among males. Children with complete clockwise or counter-clockwise circling at both assessments differed in pattern but not in overall level of cognitive ability. No differences were found in strength, direction, or concordance of hand and foot preference. Results of this and other studies are seen as inconsistent with proposals that torque is symptomatic of psychopathology.

We present a framework for automatic grading of programming exercises using property-based testing, a form of model-based black-box testing. Models are developed to assess both the functional behaviour of programs and their algorithmic complexity. From the functional correctness model a large number of test cases are derived automatically. Executing them on the body of exercises gives rise to a (partial) ranking of programs, so that a program A is ranked higher than program B if it fails a strict subset of the test cases failed by B. The model for algorithmic complexity is used to compute worst-case complexity bounds. The framework moreover considers code structural metrics, such as McCabe's cyclomatic complexity, giving rise to a composite program grade that includes both functional, non-functional, and code structural aspects. The framework is evaluated in a course teaching algorithms and data structures using Java.

The purpose of the study is to analyze the domestic and foreign experience of using mobilization elements in tax policy and prove that mobilization elements of tax policy, focusing on building a fair tax system, during inventory and index assessment of the effectiveness of mobilization measures of tax policy and tax preferences under sanctions contribute to the sustainable long–term development of Russia. The article analyzes theoretical approaches to the mobilization economy in sanctions conditions, defines its role and characteristic features. The essence, types and practices of the use of mobilization elements in tax policy are revealed, the author’s scientific interpretation of the concept of «mobilization elements of tax policy» is given. The system of mobilization elements of tax policy used in fiscal practice, which increase and decrease the tax burden, is presented; a strategy for their practical application is set; an index of effectiveness of mobilization elements of tax policy is recommended for calculation and monitoring. In order to assess their effectiveness, the index of tax preferences of the region has been set, which allows to assess the impact of tax benefits and preferences offered by the regions for the sustainable development of entrepreneurial activity. In the current sanctions conditions, the use of mobilization elements of tax policy aimed at building a fair tax system of the country, with an index assessment of the effectiveness of mobilization measures of tax policy and tax preferences subject to inventory, contributes to the growth of the number of subjects, an increase in value created and sustainable long-term development.

X-ray absorption fine structure has been studied at the Cu K-edge in copper complexes having benzimidazole as primary ligand and chloride, bromide, nitrate, sulphate as secondary ligands. The spectra have been recorded on beamline BL-8 of Indus-2 synchrotron at RRCAT, Indore. From the X-ray absorption near edge structure (XANES) data, various X-ray absorption parameters, viz., shift of the edge, shift of the principal absorption maximum and edge-width have been obtained. The experimentally observed values of chemical shifts indicate that copper is in oxidation state +2 in the samples. The chemical shifts have also been used to determine effective nuclear charge on the central metal ion in the complexes. The first shell bond distances have been determined from the extended X-ray absorption fine structure (EXAFS) data by three different graphical methods, namely, Levy’s, Lytle’s and LSS methods. Fourier transforms of the normalized spectra have also been used to obtain bond lengths. The bond lengths determined from LSS method and that determined from the Fourier transformation method are in good agreement with each other. The present results obtained using synchrotron have been compared with those reported earlier using laboratory X-ray spectroscopic set-up employing X-ray tube.

An efficient image segmentation method based on local fractal dimension was proposed by analyzing the fractal characteristics of clouds to solve the problems existed in image segmentation algorithms in high complexity,low efficiency and difficult to calculate the fractal dimension of a small area quickly.Firstly,an efficient algorithm to calculate the fractal dimension of a small area was proposed.By utilizing a tree array as data structure and taking the advantage of calculated information,the result could be obtained in the time of O(logN).Then,through calculating the fractal dimension of each horizontal line in the infrared cloud image,the fractal dimension that exceeds a certain threshold area was identified as the cloud area.Finally,the segmentation result was gotten through combining all the high dimensions of each horizontal line.The result demonstrates that this method solves the problems of complexity and inefficiency of the traditional methods based on fractal dimension,and the computing time remains less than 0.1 s for a large image of 640 pixel×480 pixel.The algorithm can effectively separate the clouds from the other artificial objects,changed background light,and the local noise to obtain good segmentation results.

Computing and presenting emergent crowd simulations in real time is a computationally intensive task. This intensity is mostly due to the complexity of the traversal algorithm needed for the interactions of all elements against each other on the basis of a proximity query. By using special data structures such as grids, and due to the parallel nature of graphics hardware, relevant previou work reduced this complexity considerably, making it possible to achieve interactive frame rates. However, existing proposals tend to be heavily bound by the maximum density of such grids, which is usually high, leading to arguably inefficient algorithms. In this article we propose the use of a fine- grained grid and accompanying data manipulation, to lead to scalable algorithmic complexity. We also implement a representative flocking boids case study, from which we ran benchmarks with more than one million simulated and rendered boids at nearly 30fps. We remark that related previous work achieved no more than 15,000 boids with interactive frame rates.

A genetic approach for efficient outlier detection in projected space

A methodology for biosignal data preprocessing is presented. Experiments were mainly carried out with voice signals for automatically detecting pathologies. The proposed methodology was structured on 3 elements: outlier detection, normality verification and distribution transformation. It improved classification performance if basic assumptions about data structure were met. This entailed a more accurate detection of voice pathologies and it reduced the computational complexity of classification algorithms. Classification performance improved by 15%.

Range-join is an operation for finding overlaps in interval-form genomic data. Range-join is widely used in various genome analysis processes such as annotation, filtering and comparison of variants in whole-genome and exome analysis pipelines. The quadratic complexity of current algorithms with sheer data volume has surged the design challenges. Existing tools have limitations on algorithm efficiency, parallelism, scalability and memory consumption. This paper proposes BIndex, a novel bin-based indexing algorithm and its distributed implementation to attain high throughput range-join processing. BIndex features near-constant search complexity while the inherently parallel data structure facilitates exploitation of parallel computing architectures. Balanced partitioning of dataset further enables scalability on distributed frameworks. The implementation on Message Passing Interface shows upto 933.5x speedup in comparison to state-of-the-art tools. Parallel nature of BIndex further enables GPU-based acceleration with 3.72x speedup than CPU implementations. The add-in modules for Apache Spark provides upto 4.65x speedup than the previously best available tool. BIndex supports wide variety of input and output formats prevalent in bioinformatics community and the algorithm is easily extendable to streaming data in recent Big Data solutions. Furthermore, the index data structure is memory-efficient and consumes upto two orders-of-magnitude lesser RAM, while having no adverse effect on speedup.

We propose a data structure that decreases complexity of unsupervised competitive learning algorithms which are based on the growing cells structures approach. The idea is based on a novel way of ordering the cells in a tree like data structure in a way that random access during training is replaced by tree traversals. Overall time complexity is reduced from O(n2) to O(n log n) which opens new application fields to the growing cells structures approach.

Loops are critical elements in boundary representation (B-rep) models because they link all edges corresponding to a face. Loops can be used in feature recognition for identifying depressions or protrusions. In real 3D CAD models, however, features typically lie across multiple faces, which is beyond the data structure of current B-rep models. This study presents a virtual loop concept to account for all loop types used in CAD models, and develops algorithms for recognizing them. In accordance with the complexity of the recognition algorithm, this study defines three types of loop: single, virtual, and multivirtual. A single loop is the current loop recorded in the B-rep model. A virtual loop lies across faces that are at least G1 continuous. Finally, a multivirtual loop lies across faces that are either G0 or G1 continuous. The proposed loop structure provides a more complete data structure for recognizing various types of features in feature-recognition modules. Several realistic CAD models are presented to confirm the feasibility of the proposed loop-recognition and feature-recognition methods.