Time-critical rendering of irregular grids

In recent years, as the importance of scene text detection and recognition in real life has gradually become prominent, its research methods have become mature, and more attention has been placed on improving accuracy. Due to the complex background in natural scenes, different font arrangements, lighting, etc., various methods are difficult to meet the requirements of the application. In this paper, the detection and recognition of curved text are studied, and the research method based on the ESIR framework[1] is improved, specifically in the following two aspects: (1) TPS transform fiducial points are predicted by the positioning network, and the positioning network uses CNN to return to the required fiducial points[2] The x and y coordinates. (2) Using ACE (aggregated cross entropy) method for sequence recognition, its loss function is realized faster and it occupies less memory than CTC and attention mechanism, and it performs well in the one-dimensional prediction of scene text recognition. In this paper, the irregular data set is sampled by reference points, and the parameter matrix T of TPS is calculated to transform the points in the picture, and cyclically transform until the image text is level, and then use the CNN-BLSTM model to perform one-dimensional prediction and output the recognition results. The selected data set is irregular text data set: for example, SVT-Perspective, CUTE80.

Abstract. In European phytosociology, national classifications of corresponding vegetation types show considerable differences even between neighbouring countries. Therefore, the European Vegetation Survey project urgently needs numerical classification methods for large data sets that are able to produce compatible classifications using data sets from different countries. We tested the ability of two methods, TWINSPAN and COCKTAIL, to produce similar classifications of wet meadows (Calthion, incl. Filipendulenion) for Germany (7909 relevés) and the Czech Republic (1287 relevés) in this respect.In TWINSPAN, the indicator ordination option was used for classification of two national data sets, and the extracted assignment criteria (indicator species) were applied crosswise from one to the other national data set. Although the data sets presumably contained similar community types, TWINSPAN revealed almost no correspondence between the groups derived from the proper classification of the national data set and the groups defined by the assignment criteria taken from the other national data set. The reason is probably the difference in structure between the national data sets, which is a typical, but hardly avoidable, feature of any pair of phytosociological data sets. As a result, the first axis of the correspondence analysis, and consequently the first TWINSPAN division, are associated with different environmental gradients; the difference in the first division is transferred and multiplied further down the hierarchy.COCKTAIL is a method which produces relevé groups on the basis of statistically formed species groups. The user determines the starting points for the formation of species groups, and groups already found in one data set can be tested for existence in the other data set. The correspondence between the national classifications produced by COCKTAIL was fairly good. For some relevé groups, the lack of correspondence to groups in the other national data set could be explained by the absence of the corresponding vegetation types in one of the countries, rather than by methodological problems.

The Parsimony Ratchet1 is presented as a new method for analysis of large data sets. The method can be easily implemented with existing phylogenetic software by generating batch command files. Such an approach has been implemented in the programs DADA (Nixon, 1998) and Winclada (Nixon, 1999). The Parsimony Ratchet has also been implemented in the most recent versions of NONA (Goloboff, 1998). These implementations of the ratchet use the following steps: (1) Generate a starting tree (e.g., a "Wagner" tree followed by some level of branch swapping or not). (2) Randomly select a subset of characters, each of which is given additional weight (e.g., add 1 to the weight of each selected character). (3) Perform branch swapping (e.g., "branch-breaking" or TBR) on the current tree using the reweighted matrix, keeping only one (or few) tree. (4) Set all weights for the characters to the "original" weights (typically, equal weights). (5) Perform branch swapping (e.g., branch-breaking or TBR) on the current tree (from step 3) holding one (or few) tree. (6) Return to step 2. Steps 2-6 are considered to be one iteration, and typically, 50-200 or more iterations are performed. The number of characters to be sampled for reweighting in step 2 is determined by the user; I have found that between 5 and 25% of the characters provide good results in most cases. The performance of the ratchet for large data sets is outstanding, and the results of analyses of the 500 taxon seed plant rbcL data set (Chase et al., 1993) are presented here. A separate analysis of a three-gene data set for 567 taxa will be presented elsewhere (Soltis et al., in preparation) demonstrating the same extraordinary power. With the 500-taxon data set, shortest trees are typically found within 22 h (four runs of 200 iterations) on a 200-MHz Pentium Pro. These analyses indicate efficiency increases of 20×-80× over "traditional methods" such as varying taxon order randomly and holding few trees, followed by more complete analyses of the best trees found, and thousands of times faster than nonstrategic searches with PAUP. Because the ratchet samples many tree islands with fewer trees from each island, it provides much more accurate estimates of the "true" consensus than collecting many trees from few islands. With the ratchet, Goloboff's NONA, and existing computer hardware, data sets that were previously intractable or required months or years of analysis with PAUP* can now be adequately analyzed in a few hours or days.

Machine learning for Big Data analytics in plants.

Environmental monitoring programs provide large multivariate data sets that usually cover considerable spatial and temporal variabilities. The apparent complexity of these data sets requires sophisticated tools for their processing. Usually, fixed schemes are followed, including the application of numerical models, which are increasingly implemented in decision support systems. However, these schemes are too rigid with respect to detecting unexpected features, like the onset of subtle trends, non-linear relationships or patterns that are restricted to limited sub-samples of the total data set. In this study, an alternative approach is followed. It is based on an efficient non-linear visualization of the data. Visualization is the most powerful interface between computer and human brain. The idea is to apply an efficient and model-free tool, meaning without the necessity of prior assumptions about key properties of the data, such as dominant processes. In other words, processing of the data aimed at preserving a maximum amount of information and to leave it to the expert which features to analyze in more detail. A comprehensive data set from a 15-year monitoring program in the Lehstenbach watershed was used. The watershed is located in the Fichtelgebirge area, a mountainous region in South Germany, where land-use is forestry. Streamwater and groundwater have been monitored at 38 sampling sites, comprising 13 parameters. The data set was analyzed using a self-organizing map (SOM), combined with Sammon’s mapping. The 2D non-linear projection represented 89% of the variance of the data set. The visualization of the data set enabled an easy detection of outliers, assessing spatial versus temporal variance, and verifying a predefined classification of the sampling sites. Contamination of two of the observation wells was detected. Long-term trends of solute concentration in the catchment runoff could be differentiated from short-term dynamics, and a long-term shift in the dynamics was determined for different flow regimes individually. This analysis helped considerably to better understand the system’s behavior, to detect “hot spots” and to organize subsequent analyses of the data in a very efficient way.

With the advent of next-generation sequencing technologies, large data sets of several thousand loci from multiple conspecific individuals are available. Such data sets should make it possible to obtain accurate estimates of population genetic parameters, even for complex models of population history. In the analyses of large data sets, it is difficult to consider finite-sites mutation models (FSMs). Here, we use extensive simulations to demonstrate that the inclusion of FSMs is necessary to avoid severe biases in the estimation of the population mutation rate θ, population divergence times, and migration rates. We present a new version of Jaatha, an efficient composite-likelihood method for estimating demographic parameters from population genetic data and evaluate the usefulness of Jaatha in two biological examples. For the first application, we infer the speciation process of two wild tomato species, Solanum chilense and Solanum peruvianum. In our second application example, we demonstrate that Jaatha is readily applicable to NGS data by analyzing genome-wide data from two southern European populations of Arabidopsis thaliana. Jaatha is now freely available as an R package from the Comprehensive R Archive Network (CRAN).

SummaryAnalytical single-well models have been particularly useful in forecasting production rates and estimated ultimate recovery (EUR) for the massive number of wells in unconventional reservoirs. In this work, a physics-based decline-curve model accounting for linear flow and material balance in horizontal multistage-hydraulically-fractured wells is introduced. The main characteristics of pressure diffusion in the porous media and the fact that the reservoir is a limited resource are embedded in the functional form, such that there is a transition from transient to boundary-dominated flow and the EUR is always finite. Analogously to the frequently used Arps (1945) hyperbolic model, the new model has only three parameters, where two of them define the decline profile and the third one is a multiplier.This model is applied to a large data set in a work flow that incorporates heuristic knowledge into the history matching and uncertainty quantification by assigning weights to rate measurements. The heuristic rules aim to lessen the effects of nonreservoir-related variations in the production data (e.g., temporary shut-in caused by fracturing in a neighboring well) and emphasize the reservoir dynamics to perform reliable predictions. However, there are additional degrees of freedom in the way these rules define the values of the weights; therefore, a criterion is established that “calibrates” the uncertainty in the probabilistic models by adjusting the parameters in the heuristic rules. Uncertainty quantification and calibration are performed using a Bayesian approach with hindcasts. This methodology is implemented in an automated framework and applied to 992 gas wells from the Barnett Shale. A comparison with the Arps (1945) hyperbolic model, the Duong (2011) model, and stretched exponential model for this data set shows that the new model is the most conservative in terms of estimated reserves.

Vectoral and alignment-free approaches to biological sequence representation have been explored in bioinformatics to efficiently handle big data. Even so, most current methods involve sequence comparisons via alignment-based heuristics and fail when applied to the analysis of large data sets. Here, we present “Spaced Words Projection (SWeeP)”, a method for representing biological sequences using relatively small vectors while preserving intersequence comparability. SWeeP uses spaced-words by scanning the sequences and generating indices to create a higher-dimensional vector that is later projected onto a smaller randomly oriented orthonormal base. We constructed phylogenetic trees for all organisms with mitochondrial and bacterial protein data in the NCBI database. SWeeP quickly built complete and accurate trees for these organisms with low computational cost. We compared SWeeP to other alignment-free methods and Sweep was 10 to 100 times quicker than the other techniques. A tool to build SWeeP vectors is available at https://sourceforge.net/projects/spacedwordsprojection/.

Deep phylogenetic incongruence in the angiosperm clade Rosidae

Astronomy datasets are generally terabytes in size and contain hundreds of millions of objects separated into millions of files-factors which makes many analyses impractical to perform on small computers. The key question we answer in this paper is: How can we leverage Grid resources to make the analysis of large astronomy datasets a reality for the astronomy community? To address this question, we have developed a Web Services-based system, AstroPortal, that uses grid computing to federate large computing and storage resources for dynamic analysis of large datasets. Building on the GT4, we have built a prototype and implemented a first analysis, stacking, that sums multiple regions of the sky, a function that can help both identify variable sources and detect faint objects. AstroPortal gives the astronomy community a new tool to advance their research and to open new doors to opportunities never before possible on such a large scale.

Is power everything? What can we learn from large data sets

Identification of (composite) key attributes is of fundamental importance for many different data management tasks such as data modeling, data integration, anomaly detection, query formulation, query optimization, and indexing. However, information about keys is often missing or incomplete in many real-world database scenarios. Surprisingly, the fundamental problem of automatic key discovery has received little attention in the existing literature. Existing solutions ignore composite keys, due to the complexity associated with their discovery. Even for simple keys, current algorithms take a brute-force approach; the resulting exponential CPU and memory requirements limit the applicability of these methods to small datasets. In this paper, we describe GORDIAN, a scalable algorithm for automatic discovery of keys in large datasets, including composite keys. GORDIAN can provide exact results very efficiently for both real-world and synthetic datasets. GORDIAN can be used to find (composite) key attributes in any collection of entities, e.g., key column-groups in relational data, or key leaf-node sets in a collection of XML documents with a common schema. We show empirically that GORDIAN can be combined with sampling to efficiently obtain high quality sets of approximate keys even in very large datasets.

Image processing and machine learning communities have long addressed the problems involved in the analysis of large high-dimensional data sets. To deal with high-dimensional data efficiently, learning core properties of given data set is important. The manifold learning methods such as ISOMap try to identify a low-dimensional manifold from a set of unorganized samples. ISOMap method is an extension of the classical multidimensional scaling method for dimension reduction, which find a linear subspace in which dissimilarity between data points is preserved. In order to measure dissimilarity, ISOMap uses the geodesic distances on the manifold instead of Euclidean distance. In this paper, we propose a modification of ISOMap using class information, which is often given in company with input data in many applications such as pattern classification. Since the conventional ISOMap does not use class information in approximating true geodesic distance between each pair of data points, it is difficult to construct a data structure related to class-membership that may give important information for given task such as data visualization and classification. The proposed method utilizes class-membership for measuring distance of data pair so as to find a low-dimensional manifold preserving the distance between classes as well as the distance between data points. Through computational experiments on artificial data sets and real facial data sets, we confirm that the proposed method gives better performance than the conventional ISOMap.

Jenner, R. A. (2004). The scientific status of metazoan cladistics: why current research practice must change. —Zoologica Scripta,33, 293–310.Metazoan phylogenetics is bustling with activity. The use of comprehensive morphological data sets in recent phylogenetic analyses of the Metazoa indicates that morphological evidence continues to play a key role in the reconstruction of metazoan deep history. In this paper I review the scientific status of morphological metazoan cladistics from the perspective of cladistic research cycles. Each research cycle consists of three main steps: (1) the compilation of a data matrix (2) the simultaneous evaluation of all possible cladograms in a character congruence test, and (3) the assessment of the relationship between evidence and hypothesis after finding the optimal tree. I identify a striking discrepancy between the sophistication of the analysis of given data sets (Step 2), and their compilation and the interpretation of the results (Steps 1 and 3). The latter two steps deserve far greater attention than is current practice. Uncritical and nonexplicit character selection, character coding, and character scoring seriously compromise Step 1. Careful comparative morphological study prior to data matrix construction is necessary to remedy this problem in future cladistic analyses. Step 2 is the locus of most recent advances in metazoan cladistics through the increasing availability of computing power, and the development of increasingly efficient phylogenetic software that allows analysis of large data sets. Failure to identify problems and errors generated in Step 1 of the research cycle is testament to the general failure of Step 3. Consequently, recent progress in metazoan cladistics is primarily analytical, while the only empirical anchor of the discipline receives surprisingly little attention. Not surprisingly, the first generation of modern metazoan phylogeneticists used computers principally as a relatively quick and easy means to generate abundant phylogenies from morphological data. The next phase should build on this foundation by critically testing these alternative hypotheses by a thorough qualitative reassessment and elaboration of morphological data matrices, and a more critical approach to data selection. A rigorous research program for metazoan cladistics can only be established when the cladistic research cycle is properly completed, and when subsequent research cycles are effectively linked to previous efforts.

Background Availability of numerous oil deposits with a wide variety of the conditions of occurrence, reservoir properties and physical-chemical properties of fluids makes important the need of grouping of development objects and identification of relatively uniform groups by certain criteria. Grouping allows to define similarities and differences between deposits in determining development objects, and to substantiate systems of development. Analysis of large data sets on objects characterized by the measured or estimated parameters is labor-intensive, requiring a lot of time and effort, so there is a question of dimension reduction, i.e. of reducing the original data set to a smaller number of parameters, and grouping of objects into relatively uniform groups. Here the parameters can be selected from the original ones or obtained by calculation and conversion (with the least loss of information about the objects under study). An effective tool of analysis that can help in this matter is the method of principal component analysis. Aims and Objectives Apply the method of principal component analysis to reduce the number of parameters which define objects of study, so that to find effective ways to develop new oil and gas deposits, and fast and correct solution of practical problems through the use of gained summary experience and assessment of similarities and differences of oil and gas reservoirs by combinations of geological-field attributes. Methods The problem of ranking oil deposits in Western Siberia, is solved by the method of principal component analysis, which is a linear transformation of features in a new set of «independent» random values, which are arranged in descending order of their variances, and the main components are determined by the eigenvectors of the correlation matrix obtained based on input parameters characterizing the objects under investigation. Having selected the components that comprise the bulk of the variances, one can calculate them for different types of objects and classify by clusterable points. Grouping of objects of research was carried out using built-in functions of computer statistics programs (a system of statistical analysis Statistica, analytical applications for Microsoft Excel XLStat). The same calculation was performed on the basis of the theory of mathematical statistics. The calculation results coincided. Results The method of principal component analysis allowed selection from the original data set comprising 50 research objects characterized by 12 parameters of six relatively uniform groups using only 4 factors (principal components) which, however, contain much more information than the individual parameters. As a result of the carried out analysis data were systematized and the dimension of the initial population (transition from multidimensional space to the four-dimensional) was reduced, with no significant loss of information about research objects. Conclusion Based on the proposed algorithm, and relatively uniform groups obtained there is a possibility for inclusion of new deposits into one of these groups and selection of the nearest to it analogous object, that will simplify the design of development system and identify ways and means of improving system’s effectiveness.