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This paper describes the design of an automatic parallelization framework. The kernel supplied at its front end was suggested as an instrument for parallel potential assessment. It was used to measure the maximum achievable speedups in the major set of the CHStone benchmark suite programs. In such framework, we suggested the liberation of parallelism incrementally. We proposed a data dependency heuristic-based transformation method to make true dependences dissociation. We generated an internal representation (IR2), where the Banerjee test conditions are met. Two among three of Banerjee test conditions came to be committed. In shared memory many/multicore platforms, the third condition could be satisfied by privatization. We would be able to choose the safe and the opportune pairwise (mapping-privatization) scheme among a number of threads mapping scenarios that become available in the IR2 structure. Instrumentation on a subset of CHStone benchmark was carried out as a validity proof of our proposal, and the results confirmed that our framework kernel is robust. [ABSTRACT FROM AUTHOR]

We proposed a robust mean change-point estimation algorithm in linear regression with the assumption that the errors follow the Laplace distribution. By representing the Laplace distribution as an appropriate scale mixture of normal distribution, we developed the expectation maximization (EM) algorithm to estimate the position of mean change-point. We investigated the performance of the algorithm through different simulations, finding that our methods is robust to the distributions of errors and is effective to estimate the position of mean change-point. Finally, we applied our method to the classical Holbert data and detected a changepoint. [ABSTRACT FROM AUTHOR]

Abstract: Clustering is the process of grouping objects that are similar, where similarity between objects is usually measured by a distance metric. The groups formed by a clustering method are referred as clusters. Clustering is a widely used activity with multiple applications ranging from biology to economics. Each clustering technique has some advantages and disadvantages. Some clustering algorithms may even require input parameters which strongly affect the result. In most cases, it is not possible to choose the best distance metric, the best clustering method, and the best input argument values for an input data set. Therefore, multiple clusterings can be obtained by several distance metrics, several clustering methods, and several input argument values. And, multiple clusterings can be combined into a new and better quality final clustering. We propose a family of combining multiple clustering algorithms that are memory efficient, scalable, robust, and intuitive. Our new algorithms offer tremendous speed gain and low memory requirements by working at cluster level, while producing very good quality final clusters. Extensive experimental evaluations on some very challenging artificially generated and real data sets from a diverse set of domains establish the usefulness of our methods. [Copyright &y& Elsevier]

Coordinate measurement machines (CMMs) have been widely used in inspecting mechanical parts with higher accuracy. Both the distribution and the number of the sampled points on measurand have an important effect on the efficiency and quality of the measurement of CMMs. In this paper, an adaptive sampling method is proposed for inspection planning on CMM for free-form surface. The points are iteratively sampled from a form error model, which is constructed by superimposing appropriate form errors on the nominal data. Moreover, a modified algorithm is introduced to determine the deviation of two point sets for the improvement of the inspecting accuracy, and the inspection uncertainty is also analyzed. A comparison is performed between the proposed method and the two well-known sampling methods, which are the equi-parametric method and the patch mean Gaussian curvature-based method, both simulated and experiment results show the effectiveness and robustness of this method. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) fluxes over the temperate Eura-sian region have been estimated robustly for the period 2006-2008 using the time-dependent inversion com-ponent of the TRANSCOM protocol. Data from six CO2 measuring stations in temperate Eurasia, includ-ing the Indian station at Hanle along with 75 others from different parts of the world have been used. A careful analysis of a posteriori errors and model reso-lution shows that temperate Eurasia is a major resid-ual sink of CO2 with a value of 1.5 Gigatonnes of carbon/year. [ABSTRACT FROM AUTHOR]

The central challenge in robotic mapping is obtaining reliable data associations (or “loop closures”): state-of-the-art inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a long-lived or multi-robot system will still encounter errors, leading to system failure. We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, rather than characterizing loop closures as being “right” or “wrong”, we propose characterizing the error of those loop closures in a more expressive manner that can account for their non-Gaussian behavior. Our approach leads to an fully integrated Bayesian framework for dealing with error-prone data. Unlike earlier multiple-hypothesis approaches, our approach avoids exponential memory complexity and is fast enough for real-time performance. We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the “front-end” loop-validation systems can be unnecessary. We demonstrate our system both on standard benchmarks and on the real-world data sets that motivated this work. [ABSTRACT FROM AUTHOR]

The L-median is a robust estimator of multivariate location with good statistical properties. Several algorithms for computing the L-median are available. Problem specific algorithms can be used, but also general optimization routines. The aim is to compare different algorithms with respect to their precision and runtime. This is possible because all considered algorithms have been implemented in a standardized manner in the open source environment R. In most situations, the algorithm based on the optimization routine NLM (non-linear minimization) clearly outperforms other approaches. Its low computation time makes applications for large and high-dimensional data feasible. [ABSTRACT FROM AUTHOR]

Abstract: The advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of prediction, but the slowest in terms of time. It is very unfortunate that as fascinating and classic an area of statistics as model selection, with important practical applications, has received very little attention in terms of algorithmic design and engineering. In this paper, in order to partially fill this gap, we make the following contributions: (A) the first general algorithmic paradigm for stability-based methods for model selection; (B) reductions showing that all of the known methods in this class are an instance of the proposed paradigm; (C) a novel algorithmic paradigm for the class of stability-based methods for cluster validity, i.e., methods assessing how statistically significant is a given clustering solution; (D) a general algorithmic paradigm that describes heuristic and very effective speed-ups known in the literature for stability-based model selection methods. Since the performance evaluation of model selection algorithms is mainly experimental, we offer, for completeness and without even attempting to be exhaustive, a representative synopsis of known experimental benchmarking results that highlight the ability of stability-based methods for model selection and the computational resources that they require for the task. As a whole, the contributions of this paper generalize in several respects reference methodologies in statistics and show that algorithmic approaches can yield deep methodological insights into this area, in addition to practical computational procedures. [Copyright &y& Elsevier]

Co-training is a multiview semi-supervised learning algorithm to learn from both labeled and unlabeled data, which iteratively adopts a classifier trained on one view to teach the other view using some confident predictions given on unlabeled examples. However, as it does not examine the reliability of the labels provided by classifiers on either view, co-training might be problematic. Even very few inaccurately labeled examples can deteriorate the performance of learned classifiers to a large extent. In this paper, a new method named robust co-training is proposed, which integrates canonical correlation analysis (CCA) to inspect the predictions of co-training on those unlabeled training examples. CCA is applied to obtain a low-dimensional and closely correlated representation of the original multiview data. Based on this representation the similarities between an unlabeled example and the original labeled examples are determined. Only those examples whose predicted labels are consistent with the outcome of CCA examination are eligible to augment the original labeled data. The performance of robust co-training is evaluated on several different classification problems where encouraging experimental results are observed. [ABSTRACT FROM AUTHOR]

Motivation: Binary datasets represent a compact and simple way to store data about the relationships between a group of objects and their possible properties. In the last few years, different biclustering algorithms have been specially developed to be applied to binary datasets. Several approaches based on matrix factorization, suffix trees or divide-and-conquer techniques have been proposed to extract useful biclusters from binary data, and these approaches provide information about the distribution of patterns and intrinsic correlations.Results: A novel approach to extracting biclusters from binary datasets, BiBit, is introduced here. The results obtained from different experiments with synthetic data reveal the excellent performance and the robustness of BiBit to density and size of input data. Also, BiBit is applied to a central nervous system embryonic tumor gene expression dataset to test the quality of the results. A novel gene expression preprocessing methodology, based on expression level layers, and the selective search performed by BiBit, based on a very fast bit-pattern processing technique, provide very satisfactory results in quality and computational cost. The power of biclustering in finding genes involved simultaneously in different cancer processes is also shown. Finally, a comparison with Bimax, one of the most cited binary biclustering algorithms, shows that BiBit is faster while providing essentially the same results.Availability: The source and binary codes, the datasets used in the experiments and the results can be found at: http://www.upo.es/eps/bigs/BiBit.htmlContact: dsrodbae@upo.esSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

One of the remaining problems in iris recognition is the implementation of its efficient hardware systems. The difficulty arises from the fact that the methods forming part of this type of authentication process are fairly complex. Furthermore, next generation algorithms for iris recognition will become even more complex to enhance the reliability and functionality of currently used solutions. An example of such an intensive processing task is iris image quality assessment and segmentation using video signal obtained at-a-distance and on-the-move. Thus, processing robustness and predictable analysis time in such dedicated architectures may be particularly important. This paper describes a hardware system designed to analyze iris biometric data developed as a part of the work conducted by the authors on the complete iris identification system (1:N). This specialized architecture is mainly composed of digital signal processors and field-programmable gate arrays. Several issues concerning efficient biometric sample processing are discussed. Algorithms for iris recognition previously developed by the authors on a PC platform were adapted to the described architecture. The obtained results are presented, and the developed system is compared with chosen commercially available iris authentication systems. [ABSTRACT FROM AUTHOR]

Abstract: Terrestrial lidar (TLiDAR) has been used increasingly over recent years to assess tree architecture and to extract metrics of forest canopies. Analysis of TLiDAR data remains a difficult task mainly due to the effects of object occlusion and wind on the quality of the retrieved results. We propose to link TLiDAR and tree structure attributes by means of an architectural model. The proposed methodology uses TLiDAR scans combined with allometric relationships to define the total amount of foliage in the crown and to build the tree branching structure. It uses the range (distance) and intensity information of the TLiDAR scans (i) to extract the stem and main branches of the tree, (ii) to reconstruct the fine branching structure at locations where the presence of foliage is very likely, and (iii) to use the availability of light as a criterion to add foliage in the center of the crown where TLiDAR information is sparse or absent due to occlusion effects. An optimization algorithm guides the model towards a realistic tree structure that fits the information gathered from TLiDAR scans and field inventory. The robustness and validity of the proposed model is assessed on five trees belonging to four different conifer species from natural forest environments. This approach addresses the data limitation of TLiDAR scans and aims to extract forest architectural metrics at different structural levels. [Copyright &y& Elsevier]

Abstract: There are many data clustering techniques available to extract meaningful information from real world data, but the obtained clustering results of the available techniques, running time for the performance of clustering techniques in clustering real world data are highly important. This work is strongly felt that fuzzy clustering technique is suitable one to find meaningful information and appropriate groups into real world datasets. In fuzzy clustering the objective function controls the groups or clusters and computation parts of clustering. Hence researchers in fuzzy clustering algorithm aim is to minimize the objective function that usually has number of computation parts, like calculation of cluster prototypes, degree of membership for objects, computation part for updating and stopping algorithms. This paper introduces some new effective fuzzy objective functions with effective fuzzy parameters that can help to minimize the running time and to obtain strong meaningful information or clusters into the real world datasets. Further this paper tries to introduce new way for predicting membership, centres by minimizing the proposed new fuzzy objective functions. And experimental results of proposed algorithms are given to illustrate the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel technique for estimating normals on unorganized point clouds. Methods from robust statistics are used to detect the best local tangent plane for each point. Therefore the algorithm is capable to deal with points located in high curvature regions or near/on complex sharp features, while being highly robust with respect to noise and outliers. In particular, the presented method reliably recovers sharp features but does not require tedious manual parameter tuning as done by current methods. The key ingredients of our approach are a robust noise-scale estimator and a kernel density estimation (KDE) based objective function. In contrast to previous approaches the noise-scale estimation is not affected by sharp features and achieves high accuracy even in the presence of outliers. In addition, our normal estimation procedure allows detection and elimination of outliers. We confirm the validity and reliability of our approach on synthetic and measured data and demonstrate applications to point cloud denoising. [Copyright &y& Elsevier]