Distribution Of Data Points Research Articles

Parallel Skyline Queries

In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in Koutris and Suciu (2011), which requires that the data is perfectly load-balanced, and a variation of the model in Afrati and Ullman (2010), the GMP model, which demands weaker load balancing constraints. In addition to load balancing, we want to minimize the number of blocking steps, where all processors must wait and synchronize. We propose a 2-step algorithm in the MP model for any dimension of the dataset, as well a 1-step algorithm for the case of 2 and 3 dimensions. Finally, we present a 1-step algorithm in the GMP model for any number of dimensions and a 1-step algorithm in the MP model for uniform distributions of data points.

Construction method through forward and reverse ray tracing for a design of ultra-wide linear field-of-view off-axis freeform imaging systems

In this paper, a construction method through forward and reverse ray tracing for a design of ultra-wide linear field-of-view (FOV) off-axis freeform systems is proposed. The freeform initial system is numerically constructed by a point-by-point calculation. This method begins with a spherical system solved by the paraxial optical theory. In this method, the primary mirror is calculated through the reverse ray tracing; hence, the size and position of the aperture stop is not affected by the changes of mirrors. The fields in the reverse system are selected to cause certain beam overlaps on the primary mirror between adjacent fields. These overlaps offer a more uniform distribution of data points, improving the calculation of this mirror. As an example, an off-axis freeform system with an ultra-wide linear field-of-view of 70° is designed by this method. The effective focal length of the system is 75 mm, and the F-number is 5.8. The diffraction-limited performance of this freeform system in the visible band and the near infrared and near ultraviolet bands validate the construction method presented in this paper.

Determination of System Dimensionality from Observing Near-Normal Distributions

This paper identifies a previously undiscovered behavior of uniformly distributed data points or vectors in high dimensional ellipsoidal models. Such models give near normal distributions for each of its dimensions. Converse of this may also be true; that is, for a normal-like distribution of an observed variable, it is possible that the distribution is a result of uniform distribution of data points in a high dimensional ellipsoidal model, to which the observed variable belongs. Given the currently held notion of normal distributions, this new behavior raises many interesting questions. This paper also attempts to answer some of those questions. We cover both volume based (filled) and surface based (shell) ellipsoidal models. The phenomenon is demonstrated using statistical as well as mathematical approaches. We also show that the dimensionality of the latent model, that is, the number of hidden variables in a system, can be calculated from the observed distribution. We call the new distribution “Tanazur” and show through experiments that it is at least observed in one real world scenario, that of the motion of particles in an ideal gas. We show that the Maxwell-Boltzmann distribution of particle speeds can be explained on the basis of Tanazur distributions.

Fusion of multi-stream speech features for dialect classification

Current research in the area of voice recognition has entered a new stage. It does not only concentrate on the correct evaluation of linguistic information embodied in the speech signal, it also works towards identification of variations naturally present in speech. Undoubtedly, the focus is to enhance the accuracy and precision of the developed technique. Speaker’s accent due to his native dialect is one of the major source of variability. Prior knowledge of the spoken dialect will help in the creation of multi-model speech recognition system and can enhance its recognition performance. This paper focusses on applying some of the established dialect identification techniques to identify speaker’s spoken dialect among dialects of Hindi. Fusion of multiple streams obtained as a combination of phonetic and prosodic features is implemented to exploit the acoustic information. The work presented here also exploits the ability of AANN to capture the distribution of data points in a reduced number and further to classify them into groups. System performance for different level of fusion is recorded for Hindi dialect classification. It is observed that Duration as prosodic feature is an important cue for automatic dialect identification systems.

Genetic interactions matter more in less-optimal environments: a Focused Review of "Phenotype uniformity in combined-stress environments has a different genetic architecture than in single-stress treatments" (Makumburage and Stapleton, 2011).

An increase in the distribution of data points indicates the presence of genetic or environmental modifiers. Mapping of the genetic control of the spread of points, the uniformity, allows us to allocate genetic difference in point distribution to adjacent, cis effects or to independently segregating, trans genetic effects. Our genetic architecture-mapping experiment elucidated the “environmental context specificity” of modifiers, the number and effect size of positive and negative alleles important for uniformity in single and combined stress, and the extent of additivity in estimated allele effects in combined stress environments. We found no alleles for low uniformity in combined stress treatments in the maize mapping population we examined. The major advances in this research area since early 2011 have been in improved methods for modeling of distributions and means and detection of important loci. Double hierarchical general linear models and, more recently, a likelihood ratio formulation have been developed to better model and estimate the genetic and environmental effects in populations. These new methods have been applied to real data sets by the method authors and we now encourage additional development of the software and wider application of the methods. We also propose that simulations of genetic regulatory network models to examine differences in uniformity and systematic exploration of models using shared simulations across communities of researchers would be constructive avenues for developing further insight into the genetic mechanisms of variation control.

A study of different wavelength spectral components of the gravity field derived from various terrestrial data sets

In the general scheme of gravity field modelling long-, medium- and short-wavelength constituents of the gravity field derived from e.g. geopotential model, terrestrial data and digital terrain model respectively, are routinely combined. In this study, spectral characteristics of terrestrial data sets are investigated. The estimation of spectral sensitivity of gravity related quantities such as gravity anomaly, vertical deflections and gravity gradients was accomplished through Fourier PSD and covariance analysis depending on the spatial distribution of data points. The information content of the estimated spectra were validated on global and local levels to access their further utilization. The spectra were compared to the 1D spectrum of the gravitational field derived from spherical harmonic coefficients using a high resolution global gravitational model as well as to an analytical approximation. Besides the frequency domain investigations the information content regarding the different wavelength structure comprised in terrestrial and EGM2008 model is investigated in the space domain based on covariance analysis. As a combined validation process the gravity degree variances were transformed to the necessary auto- and cross covariance functions to predict geoid height from gravity anomaly, which ensures an independent validation process of the computed spectrum. Based on the spectral characteristics of terrestrial measurement spectral weights for spectral combination were derived involving global gravity field model, gravity and gravity gradient data in gravity field modelling. To determine the geoid in the whole spectral band the specific integral kernels in the spectral domain should be modified using the suggested spectral weights.

Hydrogeochemical and isotopic characteristics of groundwater in the salt chemical industrial base of Guyuan City, northwestern China

The salt chemical industrial base of Guyuan is located in the south of Ningxia, northwestern China. Guyuan had been not only an important but also a challenging area for the state’s poverty alleviation and development work for a long time. In addition, the overexploitation of groundwater for agriculture activities in recent decades had led to the decline of the water table and the degradation of the water quality. In this study, environmental isotope data for different water sources are presented and discussed to identify the hydrochemical character of the aquifer systems and to investigate the geochemical processes controlling groundwater mineralization. Piper trilinear diagrams indicate that shallow groundwater in the western mountainous area is characterized by three water types (i.e., SO4–HCO3, HCO3–SO4, and SO4) while deep groundwater is characterized by two water types (i.e., SO4 and SO4–Cl). Furthermore, hydrochemical tracers show that the groundwater in the Qingshuihe plain is of the (1) HCO3–SO4 and HCO3 types in the southern piedmont recharge zone, (2) SO4 type in the southwestern recharge zone, (3) SO4–HCO3 and HCO3–SO4 types in the midwestern recharge zone, (4) SO4–HCO3 and SO4–Cl–HCO3 types in the northwestern recharge zone, and (5) SO4–HCO3, HCO3–SO4, and SO4 types in the runoff and discharge zone. Dissolution of soluble salts is the principal contributor to the water geochemistry in the western mountainous area within the study region. However, water geochemistry in the Qingshuihe plain is primarily controlled by mixing processes and by the recharge of highly mineralized surface water and groundwater in the southwestern and northwestern piedmont areas. In the conventional δD and δ 18O diagram, the distribution of data points indicates that the groundwater is of meteoric origin and has been affected by evaporation before or during underground transit. The deep groundwater in the western mountainous area is recharged under conditions that are different from currently prevailing conditions.

Dimensionality reduction using graph-embedded probability-based semi-supervised discriminant analysis

Probabilistic semi-supervised discriminant analysis (PSDA) is a recently proposed semi-supervised dimensionality reduction approach. It quantifies class membership probability to each unlabeled sample by using a well-designed soft assignment technique. Then discriminant analysis is performed over both labeled and unlabeled data which bears an analogy to the Fisher criterion. However, PSDA mainly focuses on discriminative information hidden in unlabeled data and ignores the local geometric information which is critical to reveal the intrinsic distribution of data points, especially for face image data. In this paper, we develop a graph-based semi-supervised learning method based on PSDA, termed as graph-embedded probability-based semi-supervised discriminant analysis (GPSDA) for dimensionality reduction. By introducing a similarity measurement of fuzzy sets to investigate the inexact class information of unlabeled data, an adjacency graph is modeled based on both neighborhood structure and category information, which is more relevant to classification compared with the unsupervised graph constructed in traditional graph-based semi-supervised dimensionality reduction technique. Since more information is learnt from unlabeled data, GPSDA is expected to enhance performance in classification task. We present experimental evidence on face and facial expression recognition suggesting that our algorithm is able to use unlabeled data effectively.

LEARNING DECISION REGIONS BASED ON ADAPTIVE ELLIPSOIDS

A fuzzy classifier using multiple ellipsoids to approximate decision regions for classification is designed in this paper. To learn the sizes and orientations of ellipsoids, an algorithm called evolutionary ellipsoidal classification algorithm (EECA) that integrates the genetic algorithm (GA) with the Gustafson-Kessel algorithm (GKA) is proposed. Within EECA the GA is employed to learn the size of every ellipsoid. With the size of every ellipsoid encoded and intelligently estimated in the GA chromosome, GKA is utilized to learn the corresponding ellipsoid. GKA is able to adapt the distance norm to the underlying distribution of the prototype data points for an assigned ellipsoid size. A process called directed initialization is proposed to improve EECA's learning efficiency. Because EECA learns the data point distribution in every cluster by adjusting an ellipsoid with suitable size and orientation, the information contained in the ellipsoid is further utilized to improve the cluster validity. A cluster validity measure based on the ratio of summation for each intra-cluster scatter with respect to the inter-cluster separation is defined in this paper. The proposed cluster validity measure takes advantage of EECA's learning capability and serves as an effective index for determining the adequate number of ellipsoids required for classification.

A size-insensitive integrity-based fuzzy c-means method for data clustering

Fuzzy c-means (FCM) is one of the most popular techniques for data clustering. Since FCM tends to balance the number of data points in each cluster, centers of smaller clusters are forced to drift to larger adjacent clusters. For datasets with unbalanced clusters, the partition results of FCM are usually unsatisfactory. Cluster size insensitive FCM (csiFCM) dealt with “cluster-size sensitivity” problem by dynamically adjusting the condition value for the membership of each data point based on cluster size after the defuzzification step in each iterative cycle. However, the performance of csiFCM is sensitive to both the initial positions of cluster centers and the “distance” between adjacent clusters. In this paper, we present a cluster size insensitive integrity-based FCM method called siibFCM to improve the deficiency of csiFCM. The siibFCM method can determine the membership contribution of every data point to each individual cluster by considering cluster's integrity, which is a combination of compactness and purity. “Compactness” represents the distribution of data points within a cluster while “purity” represents how far a cluster is away from its adjacent cluster. We tested our siibFCM method and compared with the traditional FCM and csiFCM methods extensively by using artificially generated datasets with different shapes and data distributions, synthetic images, real images, and Escherichia coli dataset. Experimental results showed that the performance of siibFCM is superior to both traditional FCM and csiFCM in terms of the tolerance for “distance” between adjacent clusters and the flexibility of selecting initial cluster centers when dealing with datasets with unbalanced clusters.

A New Imbalanced Learning and Dictions Tree Method for Breast Cancer Diagnosis

In the most of standard learning algorithms it is presumed or at least expected that the distribution of data points in different classes of at-hand dataset are balanced; it means that there are almost the identical number of data points in each class. It is also implicitly presumed that the misclassification cost of each data point is a fixed value regardless of its class. The standard algorithms will fail to learn if the at-hand dataset is imbalanced. An imbalance dataset is the one that the distribution of data points among their classes is not identical; it means that the numbers of data points in different classes are considerably different. A well-known domain in that it is highly likely for each exemplary dataset to be imbalanced is patient detection. In such systems there are many clients while a few of them are patient and the all others are healthy. So it is very common and likely to face an imbalanced dataset in such a system that is to detect a patient from various clients. In a breast cancer detection that is a special case of the mentioned systems, it is tried to discriminate the patient clients from healthy clients. It should be noted that the imbalanced shape of a dataset can be either relative or non-relative. The imbalanced shape of a dataset is relative where the mean number of samples is high in the minority class, but it is very less rather than the number of samples in the majority class. The imbalanced shape of a dataset is non-relative where the mean number of samples is low in the minority class. This paper presents an algorithm which is well-suited for and applicable to the field of non-relative imbalanced datasets. It is efficient in terms of both of the speed and the efficacy of learning. The experimental results show that the performance of the proposed algorithm outperforms some of the best methods in the literature.

Improved fuzzy C-means clustering algorithm based on distance correction

Based on Euclidean distance,the classic Fuzzy C-Means(FCM) clustering algorithm has the limitation of equal partition trend for data sets.And the clustering accuracy is lower when the distribution of data points is not spherical.To solve these problems,a distance correction factor based on dot density was introduced.Then a distance matrix with this factor was built for measuring the differences between data points.Finally,the new matrix was applied to modify the classic FCM algorithm.Two sets of experiments using artificial data and UCI data were operated,and the results show that the proposed algorithm is suitable for non-spherical data sets and outperforms the classic FCM algorithm in clustering accuracy.

LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream

Clustering evolving data streams is important to be performed in a limited time with a reasonable quality. The existing micro clustering based methods do not consider the distribution of data points inside the micro cluster. We propose LeaDen-Stream (Leader Density-based clustering algorithm over evolving data Stream), a density-based clustering algorithm using leader clustering. The algorithm is based on a two-phase clustering. The online phase selects the proper mini-micro or micro-cluster leaders based on the distribution of data points in the micro clusters. Then, the leader centers are sent to the offline phase to form final clusters. In LeaDen-Stream, by carefully choosing between two kinds of micro leaders, we decrease time complexity of the clustering while maintaining the cluster quality. A pruning strategy is also used to filter out real data from noise by introducing dense and sparse mini-micro and micro-cluster leaders. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.

Spatial Interpolation of Tidal Data Using a Multiple-Order Harmonic Equation for Unstructured Grids

A general spatial interpolation method for tidal properties has been developed by solving a partial differential equation with a combination of different orders of harmonic operators using a mixed finite element method. Numerically, the equation is solved implicitly without iteration on an unstructured triangular mesh grid. The paper demonstrates the performance of the method for tidal property fields with different characteristics, boundary complexity, number of input data points, and data point distribution. The method has been successfully applied under several different tidal environments, including an idealized distribution in a square basin, coamplitude and cophase lines in the Taylor semi-infiite rotating channel, and tide coamplitude and cophase lines in the Bohai Sea and Chesapeake Bay. Compared to Laplace’s equation that NOAA/NOS currently uses for interpolation in hydrographic and oceanographic applications, the multiple-order harmonic equation method eliminates the problem of singularities at data points, and produces interpolation results with better accuracy and precision.

Statistical modelling of a new global potential vegetation distribution

The potential natural vegetation (PNV) distribution is required for several studies in environmental sciences. Most of the available databases are quite subjective or depend on vegetation models. We have built a new high-resolution world-wide PNV map using a objective statistical methodology based on multinomial logistic models. Our method appears as a fast and robust alternative in vegetation modelling, independent of any vegetation model. In comparison with other databases, our method provides a realistic PNV distribution in agreement with respect to BIOME 6000 data. Among several advantages, the use of probabilities allows us to estimate the uncertainty, bringing some confidence in the modelled PNV, or to highlight the regions needing some data to improve the PNV modelling. Despite our PNV map being highly dependent on the distribution of data points, it is easily updatable as soon as additional data are available and provides very useful additional information for further applications.

Connectivity threshold of Bluetooth graphs

We study the connectivity properties of random Bluetooth graphs that model certain \ad hoc wireless networks. The graphs are obtained as \irrigation subgraphs of the well-known random geometric graph model. There are two parameters that control the model: the radius r that determines the \visible neighbors of each node and the number of edges c that each node is allowed to send to these. The randomness comes from the underlying distribution of data points in space and from the choices of each vertex. We prove that no connectivity can take place with high probability for a range of parameters r;c and completely characterize the connectivity threshold (in c) for values of r close the critical value for connectivity in the underlying random geometric graph.

Hydrogeochemical and isotopic characteristics of groundwater in Mbanga, Njombe and Penja (Banana Plain) – Cameroon

Hydrochemical and environmental isotopic (2H, 18O and 3H) data were used to investigate the hydrogeochemical and isotopic characteristics of groundwater within Mbanga, Njombe and Penja (Banana Plain). Hydrogeochemically, the groundwaters are mainly Ca–HCO3, Ca–Mg–HCO3, Ca–Na–HCO3, and Ca–Na–NO3–Cl–HCO3 water types. The groundwater chemistry may be adequately explained by the incongruent dissolution of silicate and alumino-silicate minerals, impact of anthropogenic activities and cation exchange between the groundwaters and clay minerals. The isotopic contents of groundwaters ranged from −4.2‰ to −2.1‰ for δ18O, from −23.4‰ to −10.6‰ for δD and from 0.6 to 1.4 TU for tritium. In the conventional δD–δ18O diagram, the distribution of data points indicates that the groundwaters are of meteoric origin and have not been affected by evaporation. Environmental isotopes (18O, 2H and 3H) indicate mixing between recent and old groundwaters; the latter were recharged under more humid climatic conditions than that at present or from higher elevations. Groundwater in Mbanga, Njombe and Penja are of meteoric origin, young and are still in the early stage of geochemical evolution.

Longitudinal threshold model analysis of clinical mastitis using linear splines

This study evaluated random regression threshold models using linear splines (RRLS) versus Legendre polynomials (RRLP) as basis functions for the genetic analysis of clinical mastitis (CM) in first-lactation Finnish Ayrshire cows. Estimates of genetic parameters from RRLS models with 4 and 6-knots were compared to estimates from RRLP model fitting Legendre polynomials of third-order in modelling the genetic trajectory of CM over lactation. Co-variance components were estimated using Bayesian threshold models and MCMC procedures. Posterior means of heritability of CM before calving to one month after calving was from 0.04 to 0.14 and 0.04 to 0.05 from RRLP and RRLS models, respectively. From calving to 300dim after calving, heritability ranged from 0.03 to 0.09 and 0.04 to 0.06, respectively. Estimates from RRLP were particularly high and erratic at both ends of the lactation trajectory whilst estimates from RRLS had no marked end-of range problems. However, with RRLS models concavity in heritability curves and angularity in genetic variance estimates at position of knots were observed. This could partly be due to the sparse and uneven distribution of data points resulting in weak genetic correlations between knots. During lactation, genetic correlations from RRLP ranged from 0.27 to 0.98. Whereas correlations between adjacent knots from RRLS were in part weak and ranged from 0.01 to 0.52 except between the first two knots (rg=0.97). Rank correlations between estimated breeding values from both models were relatively high and ranged from 0.96 to 0.98. However, compared to RRLS 6-knots and RRLP models, RRLS 4-knots model had the lowest Deviance Information Criterion indicating that it is best fit and less parameterised model.

Local Density-based Hierarchical Clustering for Overlapping Distribution using Minimum Spanning Tree

this paper, we propose a clustering algorithm to find clusters of different sizes, shapes and densities. Density and Hierarchical based approaches are adopted in the algorithm using Minimum Spanning Tree, resulting in a new algorithm - Local Density-based Hierarchical Clustering Algorithm for overlapping data distribution using Minimum Spanning Tree (LDHCODMST). The algorithm is divided into two stages. In the first stage, local density is estimated at each data point. In the second stage, hierarchical approach is used by merging clusters according to the computed cluster distance based on overlap in distribution of data points. The proposed algorithm improves the effectiveness of clustering result in which data are distributed in different shapes and different density, and that it can get a better clustering efficiency.

Evaluating the impact of data quantity, distribution and algorithm selection on the accuracy of 3D subsurface models using synthetic grid models of varying complexity

Testing the accuracy of 3D modelling algorithms used for geological applications is extremely difficult as model results cannot be easily validated. This paper presents a new approach to evaluate the effectiveness of common interpolation algorithms used in 3D subsurface modelling, utilizing four synthetic grids to represent subsurface environments of varying geological complexity. The four grids are modelled with Inverse Distance Weighting and Ordinary Kriging, using data extracted from the synthetic grids in different spatial distribution patterns (regular, random, clustered and sparse), and with different numbers of data points (100, 256, 676 and 1,600). Utilizing synthetic grids for this evaluation allows quantitative statistical assessment of the accuracy of both interpolation algorithms in a variety of sampling conditions. Data distribution proved to be an important factor; as in many geological situations, relatively small numbers of randomly distributed data points can generate more accurate 3D models than larger amounts of clustered data. This study provides insight for optimizing the quantity and distribution of data required to accurately and cost-effectively interpolate subsurface units of varying complexity.