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This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a robust network design model for the supply of blood during and after disasters. A practical optimization model is developed that can assist in blood facility location and allocation decisions for multiple post-disaster periods. The application of the proposed model is investigated in a case problem where real data is utilized to design a network for emergency supply of blood during potential disasters. Our analysis on the tradeoff between solution robustness and model robustness arrives at important practical insights. The performance of the proposed ‘robust optimization’ approach is also compared with that of an ‘expected value’ approach. [ABSTRACT FROM AUTHOR]

The novel ADER-DT time discretization is applied to two-dimensional transport in a quadrature-free, WENO- and FCT-limited, Finite-Volume context. Emphasis is placed on (1) the serial and parallel computational properties of ADER-DT and this framework and (2) the flexibility of ADER-DT and this framework in efficiently balancing accuracy with other constraints important to transport applications. This study demonstrates a range of choices for the user when approaching their specific application while maintaining good parallel properties. In this method, genuine multi-dimensionality, single-step and single-stage time stepping, strict positivity, and a flexible range of limiting are all achieved with only one parallel synchronization and data exchange per time step. In terms of parallel data transfers per simulated time interval, this improves upon multi-stage time stepping and post-hoc filtering techniques such as hyperdiffusion. This method is evaluated with standard transport test cases over a range of limiting options to demonstrate quantitatively and qualitatively what a user should expect when employing this method in their application. [ABSTRACT FROM AUTHOR]

Accurate boundary detection is a critical step of the image segmentation process. While most edge detectors rely on the presence of strong intensity gradients, this criteria can limit robustness in many real world cases. In this work we propose a scheme which makes use of a combination of both global and local segmentation methods to capture the boundary of target objects in low contrast images. This approach has several advantages: the globally convex segmentation scheme is immune from initial conditions and is easily adapted to the data. The addition of a segmentation scheme based on local curve evolution produces a solution which is shown to help preserve topology between the initial and target shape, a property lacking in globally convex segmentation schemes. Experimental results show that the proposed method achieves enhanced performance compared to classical data-driven segmentation schemes proposed in the literature. [ABSTRACT FROM AUTHOR]

The problem of robust sampled-data control for uncertain dynamic systems in the presence of missing data has been investigated. By using stochastic variables with a Bernoulli distributed white sequence to model missing data, along with time-varying norm-bounded uncertainties, and the input delay approach for sampled-data systems, two models for the considered sampled-data control system are proposed as the consecutive missing data scenario. Moreover, when data are missing, the control signals are held as the last received data. Sufficient conditions for the existence of desired robust sampled-data controllers are presented in the form of a linear matrix inequality. A numerical example is given to illustrate the validity of the proposed methods and to compare the results between the two proposed models of the sampled-data control system. [ABSTRACT FROM AUTHOR]

A novel robust validity index is proposed for subtractive clustering (SC) algorithm. Although the SC algorithm is a simple and fast data clustering method with robust properties against outliers and noise; it has two limitations. First, the cluster number generated by the SC algorithm is influenced by a given threshold. Second, the cluster centers obtained by SC are based on data that have the highest potential values but may not be the actual cluster centers. The validity index is a function as a measure of the fitness of a partition for a given data set. To solve the first problem, this study proposes a novel robust validity index that evaluates the fitness of a partition generated by SC algorithm in terms of three properties: compactness, separation and partition index. To solve the second problem, a modified algorithm based on distance relations between data and cluster centers is designed to ascertain the actual centers generated by the SC algorithm. Experiments confirm that the preferences of the proposed index outperform all others. [ABSTRACT FROM AUTHOR]

This paper describes a method for finding the topology of a data distribution online using a new growing graph network architecture. Many growing neural networks for finding the topology of data online, such as the Growing Neural Gas, depend on the order and number of input data. For this reason, conventional methods have certain drawbacks: weakness to noise, generating redundant nodes, requiring a great deal of input data, and so on. The proposed method is robust with respect to these issues since it has been developed from the viewpoint of a generative model. This paper presents both the theory and an algorithm in this paper. Moreover, the effectiveness of the proposed method is shown by experiments comparing the proposed method with various growing graph networks. [ABSTRACT FROM AUTHOR]

Tracking the same person across multiple cameras is an important task in human motion analysis systems. It is also desirable to re-identify the individuals who have been previously seen with a single camera. This paper addresses this problem by the re-identification of the same individual in two different datasets, which are both challenging situations from human motion analysis systems. In this paper, local descriptors are introduced for image description, and support vector machines are employed for high classification performance and so an efficient bag-of-features approach for image presentation. In this way, robustness against low resolution, occlusion, and pose, viewpoint and illumination changes is achieved in a very fast way. We obtain promising results from the evaluation with situations where a number of individuals vary continuously from a multicamera system. [ABSTRACT FROM AUTHOR]

We propose a robust estimation procedure for the analysis of longitudinal data including a hidden process to account for unobserved heterogeneity between subjects in a dynamic fashion. We show how to perform estimation by an expectation–maximization-type algorithm in the hidden Markov regression literature. We show that the proposed robust approaches work comparably to the maximum-likelihood estimator when there are no outliers and the error is normal and outperform it when there are outliers or the error is heavy tailed. A real data application is used to illustrate our proposal. We also provide details on a simple criterion to choose the number of hidden states. [ABSTRACT FROM PUBLISHER]

Grain boundaries and other mesoscale interfaces play an important role in controlling macroscale mechanical properties of various hierarchical materials needed for advanced technologies. Recent enhancements in both instrumentation and data analyses protocols have demonstrated the tremendous potential of nanoindentation as a high-throughput technique for robust and quantitative assessment of the mechanical role of these mesoscale interfaces. These new protocols are geared to address some of the most critical gaps impeding the efforts aimed at the accelerated and cost-effective design and development of new and improved materials. This review summarizes recent advances and identifies specific research directions that are likely to produce the maximum benefit and impact in these pursuits. [ABSTRACT FROM AUTHOR]

The acquisition of data stored on cloud services has become increasingly important to digital forensic investigations. Apple, Inc. continues to expand the capabilities of its cloud service, iCloud. As such, it is critical to determine an effective means for forensic acquisition of data from this service and its effect on the original file data and metadata. This research examined files acquired from the iCloud service via the native Mac OS X system synchronization with the service. The goal was to determine the operating system locations of iCloud-synched files. Once located, the secondary goal was to determine if the file hash values match those of the original files and whether file metadata, particularly timestamps, are altered. [ABSTRACT FROM AUTHOR]

Objective: Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. Methods and materials: The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Results: Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Conclusion: Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data. [ABSTRACT FROM AUTHOR]

Abstract: Data assimilation optimally merges model forecasts with observations taking into account both model and observational uncertainty. This paper presents a new data assimilation framework that enables the many Open Model Interface (OpenMI) 2.0 .NET compliant hydrological models already available, access to a robust data assimilation library. OpenMI is an open standard that allows models to exchange data during runtime, thus transforming a complex numerical model to a ‘plug and play’ like component. OpenDA is an open interface standard for a set of tools, filters, and numerical techniques to quickly implement data assimilation. The OpenDA–OpenMI framework is presented and tested on a synthetic case that highlights the potential of this new framework. MIKE SHE, a distributed and integrated hydrological model is used to assimilate hydraulic head in a catchment in Denmark. The simulated head over the entire domain were significantly improved by using an ensemble based Kalman filter. [Copyright &y& Elsevier]

The Galaxy And Mass Assembly (GAMA) survey has obtained spectra of over 230 000 targets using the Anglo-Australian Telescope. To homogenize the redshift measurements and improve the reliability, a fully automatic redshift code was developed (autoz). The measurements were made using a cross-correlation method for both the absorption- and the emission-line spectra. Large deviations in the high-pass-filtered spectra are partially clipped in order to be robust against uncorrected artefacts and to reduce the weight given to single-line matches. A single figure of merit (FOM) was developed that puts all template matches on to a similar confidence scale. The redshift confidence as a function of the FOM was fitted with a tanh function using a maximum likelihood method applied to repeat observations of targets. The method could be adapted to provide robust automatic redshifts for other large galaxy redshift surveys. For the GAMA survey, there was a substantial improvement in the reliability of assigned redshifts and in the lowering of redshift uncertainties with a median velocity uncertainty of 33 km s−1. [ABSTRACT FROM AUTHOR]

Cell-free protein expression is a promising tool for improving protein-specific expression techniques. Despite their advantages, insect cell-free expression systems are not as well established as Escherichia coli cell-free systems. In most studies, characterization and optimization strategies are based on manual 'one-factor-at-a-time' investigations that are expensive and time consuming. In this paper, two insect cell-free expression systems ( Sf9 and High Five™) were reproducibly ( CV = 2.9%) implemented on a robotic platform with integrated analytics. All experiments were planned by statistical design of experiments using central composite designs and analyzed by multivariate data analysis. Quadratic response surface models were fitted to the experimental data and model predictivity was validated successfully for both insect cell types. The characterization of the complete in vitro translation process included quantification and visualization of the parameter influences on the expression yield and the robustness of the systems. The results were compared to previous studies, which confirmed the applicability of the new method. In the future, yields from insect cell-free expression can be enhanced using a comprehensive system characterization based on optimally designed high-throughput screenings on robotic systems. [ABSTRACT FROM AUTHOR]

Nowadays, multimedia, especially video, is used widely in many domains. But camera lens are easily stained because they are always outside, such as in traffic and monitoring. Admittedly, the video from these cameras is out of action because it cannot provide clear frames. In this paper, we provide a detecting method from video with background restructure. This method is presented to detect stains in video. Firstly, the first video frame is used as a pseudo-background because original cameras are clear with no stains. Then, background is constructed by the pseudo- background and the continuous frames which are from the video. Moreover, with training in the frames, we restructure background by dropped moving objects in it. Finally, after background restructured, we detect stains in camera lenses by the restructured background, and provide positions of stained area. Experimental results show its robustness and practicability. [ABSTRACT FROM AUTHOR]

Motivation: Gene set analysis is a popular method for large-scale genomic studies. Because genes that have common biological features are analyzed jointly, gene set analysis often achieves better power and generates more biologically informative results. With the advancement of technologies, genomic studies with multi-platform data have become increasingly common. Several strategies have been proposed that integrate genomic data from multiple platforms to perform gene set analysis. To evaluate the performances of existing integrative gene set methods under various scenarios, we conduct a comparative simulation analysis based on The Cancer Genome Atlas breast cancer dataset.Results: We find that existing methods for gene set analysis are less effective when sample heterogeneity exists. To address this issue, we develop three methods for multi-platform genomic data with heterogeneity: two non-parametric methods, multi-platform Mann–Whitney statistics and multi-platform outlier robust T-statistics, and a parametric method, multi-platform likelihood ratio statistics. Using simulations, we show that the proposed multi-platform Mann–Whitney statistics method has higher power for heterogeneous samples and comparable performance for homogeneous samples when compared with the existing methods. Our real data applications to two datasets of The Cancer Genome Atlas also suggest that the proposed methods are able to identify novel pathways that are missed by other strategies.Availability and implementation: http://www4.stat.ncsu.edu/∼jytzeng/Software/Multiplatform_gene_set_analysis/Contact: john.hu@omicsoft.com, jhu7@ncsu.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Highlights: [•] A weighted multivariate fuzzy clustering method for interval-valued data is introduced. [•] Weights measure importance of each variable to compute membership degree by cluster. [•] This method is robust to ambiguous cluster membership assignment. [•] An application with interval-valued scientific production data is described. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model. [Copyright &y& Elsevier]

Abstract: An efficient procedure to process the textile simulation data and generate realistic finite element meshes of woven composites is proposed. The textile topology data in point cloud format is used to identify individual yarns, interpolate their cross-sectional contours, and generate smooth yarn surfaces. A robust algorithm to repair possible interpenetrations between yarn surfaces is developed and implemented in MATLAB. A 3D finite element mesh of the unit cell of composite material is generated based on the obtained yarn surfaces. The anisotropic material properties of the constituents are assigned with proper orientations. The procedure is successfully applied to generate four finite element models with 1–10million degrees of freedom. The models are used to predict effective elastic properties of an orthogonal 3D woven composite. The sensitivity of results to the level of finite element discretization is investigated. [Copyright &y& Elsevier]

A multivariate change point control chart based on data depth (CPDP) is considered for detecting shifts in either the mean vector, the covariance matrix, or both of the processes for Phase I. The proposed chart is preferable from a robustness point of view, has attractive detection performance, and can be especially useful in Phase I analysis setting, where there is limited information about the underlying process. Comparison results and an illustrative example show that our CPDP chart has great potential for Phase I analysis of multivariate individual observations. The application of CPDP chart is illustrated in a real data example. [ABSTRACT FROM PUBLISHER]

Abstract: The introduction of arterial spin labelling (ASL) techniques in magnetic resonance imaging (MRI) has made feasible a non-invasive measurement of the cerebral blood flow (CBF). However, to date, the low signal-to-noise ratio of ASL gives us no option but to repeat the acquisition to accumulate enough data in order to get a reliable signal. The perfusion signal is then usually extracted by averaging across the repetitions. But the sample mean is very sensitive to outliers. A single incorrect observation can therefore be the source of strong detrimental effects on the perfusion-weighted image estimated with the sample mean. We propose to estimate robust ASL CBF maps with M-estimators to overcome the deleterious effects of outliers. The behavior of this method is compared to z-score thresholding as recommended in Tan et al. (Journal of Magnetic Resonance Imaging 2009;29(5):1134–9.). Validation on simulated and real data is provided. Quantitative validation is undertaken by measuring the correlation with the most widespread technique to measure perfusion with MRI: dynamic susceptibility weighted contrast imaging. [Copyright &y& Elsevier]

The performance of the state estimation (SE) depends on the accuracy of the received measured data, as well as the parameter data of the power system. In this paper, a new algorithm is proposed for the simultaneous identification and correction of measurement and branch parameter errors (series and shunt admittances) in the power system SE problem. The proposed method uses Lagrange multipliers for the identification and correction of branch parameter errors without the need for a-priori specification of suspect parameter vectors. Erroneous measurements and branch parameter values can be corrected using the proposed method to estimate the measurement and branch parameter errors. Finally, IEEE 14-, 30-, and 57-bus test systems are used to show the validity and robustness of the proposed algorithm. Single, multiple, and simultaneous errors in the conventional measurements and branch parameters are considered for different case studies. [ABSTRACT FROM AUTHOR]

Background: Caesarean sections (CS) rates continue to increase worldwide without a clear understanding of the main drivers and consequences. The lack of a standardized internationally-accepted classification system to monitor and compare CS rates is one of the barriers to a better understanding of this trend. The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. This classification has become very popular over the last years in many countries. We conducted a systematic review to synthesize the experience of users on the implementation of this classification and proposed adaptations. Methods: Four electronic databases were searched. A three-step thematic synthesis approach and a qualitative metasummary method were used. Results: 232 unique reports were identified, 97 were selected for full-text evaluation and 73 were included. These publications reported on the use of Robson's classification in over 33 million women from 31 countries. According to users, the main strengths of the classification are its simplicity, robustness, reliability and flexibility. However, missing data, misclassification of women and lack of definition or consensus on core variables of the classification are challenges. To improve the classification for local use and to decrease heterogeneity within groups, several subdivisions in each of the 10 groups have been proposed. Group 5 (women with previous CS) received the largest number of suggestions. Conclusions: The use of the Robson classification is increasing rapidly and spontaneously worldwide. Despite some limitations, this classification is easy to implement and interpret. Several suggested modifications could be useful to help facilities and countries as they work towards its implementation. [ABSTRACT FROM AUTHOR]

The robust statistical model of a Gaussian core contaminated by outlying data that underlies robust estimation of the magnetotelluric (MT) response function has been re-examined. The residuals from robust estimators are systematically long tailed compared to a distribution based on the Gaussian, and hence are inconsistent with the robust model. Instead, MT data are pervasively described by the alpha stable distribution family whose variance and sometimes mean are undefined. A maximum likelihood estimator (MLE) that exploits the stable nature of MT data is formulated, and its two-stage implementation in which stable parameters are first fit to the data and then the MT responses are solved for is described. The MLE is shown to be inherently robust, but differs from the conventional robust estimator because it is based on a model derived from the data, while robust estimators are ad hoc, being based on the robust model that is inconsistent with actual data. Propriety versus impropriety of the complex MT response was investigated, and a likelihood ratio test for propriety and its null distribution was established. The Cramér-Rao lower bounds for the covariance matrix of proper and improper MT responses were specified.The MLE was applied to exemplar long period and broad-band data sets from South Africa. Both are shown to be significantly stably distributed using the Kolmogorov–Smirnov goodness of fit and Ansari-Bradley non-parametric dispersion tests. Impropriety of the MT responses at both sites is pervasive, hence the improper Cramér-Rao bound was used to estimate the MLE covariance. The MLE is shown to be nearly unbiased and well described by a Gaussian distribution based on bootstrap simulation. The MLE was compared to a conventional robust estimator, establishing that the standard errors of the former are systematically smaller than for the latter and that the standardized differences between them exhibit excursions that are both too frequent and too large to be described by a Gaussian model. This is ascribed to pervasive bias of the robust estimator that is to some degree obscured by their systematically large confidence bounds. Finally, a series of topics for further investigation is proposed. [ABSTRACT FROM PUBLISHER]

Nonnegative Matrix Factorization (NMF) has been one of the most widely used clustering techniques for exploratory data analysis. However, since each data point enters the objective function with squared residue error, a few outliers with large errors easily dominate the objective function. In this article, we propose a Robust Manifold Nonnegative Matrix Factorization (RMNMF)method using 2,1-norm and integrating NMF and spectral clustering under the same clustering framework. We also point out the solution uniqueness issue for the existing NMF methods and propose an additional orthonormal constraint to address this problem. With the new constraint, the conventional auxiliary function approach no longer works. We tackle this difficult optimization problem via a novel Augmented Lagrangian Method (ALM)-based algorithm and convert the original constrained optimization problem on one variable into a multivariate constrained problem. The new objective function then can be decomposed into several subproblems that each has a closedform solution. More importantly, we reveal the connection of our method with robust K-means and spectral clustering, and we demonstrate its theoretical significance. Extensive experiments have been conducted on nine benchmark datasets, and all empirical results show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a robust algorithm is proposed for reconstructing 2D curve from unorganized point data with a high level of noise and outliers. By constructing the quadtree of the input point data, we extract the “grid-like” boundaries of the quadtree, and smooth the boundaries using a modified Laplacian method. The skeleton of the smoothed boundaries is computed and thereby the initial curve is generated by circular neighboring projection. Subsequently, a normal-based processing method is applied to the initial curve to smooth jagged features at low curvatures areas, and recover sharp features at high curvature areas. As a result, the curve is reconstructed accurately with small details and sharp features well preserved. A variety of experimental results demonstrate the effectiveness and robustness of our method. [Copyright &y& Elsevier]

Abstract: This paper presents a data SONAR classification approach that is based on multiway analysis. The passive SONAR system receives the acoustic signals emitted by ships and tries to categorize them as a function of the similarities between ships of the same class. The identification of ship class through the analysis of its emitted signal is a non-trivial task because the signals received from SONAR sensors frequently contain values that represent combinations of different properties of the real world. Systems for modeling the real acoustic signal produced by a ship must be able to remove irrelevant components to obtain the signal's true value. This work uses multiway analysis for both dimensionality reduction and signal denoising to generate a model of acoustic signature that is compact and robust to background sounds. The parallel decomposition method CANDECOMP/PARAFAC is used to eliminate irrelevant information in the class-ship mapping process. The classification model was calibrated and cross-validated on a real dataset. The results showed the effectiveness of the proposed methodology. [Copyright &y& Elsevier]

Flushing is a pavement defect that has a negative effect on the structural integrity and performance of a pavement surface. The aim of this study was to develop a model to predict the occurrence and progression of flushing on sprayed seal pavements. Data analysis and model development were performed using data sourced from New Zealand's Long-Term Pavement Performance programme. The developed model consists of two phases to model (1) the probability of flushing initiating on a pavement and (2) the progression of flushing. The probability of flushing initiation was modelled using a logistic model format, and the progression of flushing was modelled using a linear model format. Testing of the developed model revealed that the logistic model predicted the probability of flushing initiation at 76% accuracy, whereas the linear model had statistically robust predictions of flushing. The developed model is recommended for use in sprayed seal pavement management processes. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) has been shown recently to be tractable under the separability assumption, under which all the columns of the input data matrix belong to the convex cone generated by only a few of these columns. Bittorf, Recht, R e and Tropp (`Factoring nonnegative matrices with linear programs', NIPS 2012) proposed a linear programming (LP) model, referred to as Hottopixx, which is robust under any small perturbation of the input matrix. However, Hottopixx has two important drawbacks: (i) the input matrix has to be normalized, and (ii) the factorization rank has to be known in advance. In this paper, we generalize Hottopixx in order to resolve these two drawbacks, that is, we propose a new LP model which does not require normalization and detects the factorization rank automatically. Moreover, the new LP model is more exible, significantly more tolerant to noise, and can easily be adapted to handle outliers and other noise models. Finally, we show on several synthetic data sets that it outperforms Hottopixx while competing favorably with two state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Mobile data traffic has been steadily rising in the past years. This has generated a significant interest in the deployment of incentive mechanisms to reduce peak-time congestion. Typically, the design of these mechanisms requires information about user demand and sensitivity to prices. Such information is naturally imperfect. In this paper, we propose a fixed-budget rebate mechanism that gives each user a reward proportional to his percentage contribution to the aggregate reduction in peak-time demand. For comparison, we also study a time-of-day pricing mechanism that gives each user a fixed reward per unit reduction of his peak-time demand. To evaluate the two mechanisms, we introduce a game-theoretic model that captures the public good nature of decongestion. For each mechanism, we demonstrate that the socially optimal level of decongestion is achievable for a specific choice of the mechanism's parameter. We then investigate how imperfect information about user demand affects the mechanisms' effectiveness. From our results, the fixed-budget rebate pricing is more robust when the users' sensitivity to congestion is “sufficiently” convex. This feature of the fixed-budget rebate mechanism is attractive for many situations of interest and is driven by its closed-loop property, i.e., the unit reward decreases as the peak-time demand decreases . [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a new spectral envelop method to analyze oscillatory traffic patterns. [•] The method can be carried out in long-term or short-term time scales. [•] The method is robust against noises, and can uncover complicated oscillatory behaviors. [•] The method is validated with case studies based on both simulated and real data. [•] The proposed method is expected to be a useful traffic data process and analysis tool. [Copyright &y& Elsevier]

Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique that produces similar outcomes with all types of classifiers. This is because all feature selection techniques have individual statistical biases, whereas classifiers exploit different statistical properties of data for evaluation. In numerous situations, this can put researchers into dilemma with regard to which feature selection method and classifiers to choose from a vast range of choices. In this article, we propose a technique that aggregates the consensus properties of various feature selection methods in order to develop a more optimal solution. The ensemble nature of our technique makes it more robust across various classifiers. In other words, it is stable toward achieving similar and, ideally, higher classification accuracy across a wide variety of classifiers. We quantify this concept of robustness with a measure known as the robustness index (RI). We perform an extensive empirical evaluation of our technique on eight datasets with different dimensions, including arrythmia, lung cancer, Madelon, mfeat-fourier, Internet ads, leukemia-3c, embryonal tumor, and a real-world dataset, vis., acute myeloid leukemia (AML). We demonstrate not only that our algorithm is more robust, but also that, compared with other techniques, our algorithm improves the classification accuracy by approximately 3–4% in a dataset with fewer than 500 features and by more than 5% in a dataset with more than 500 features, across a wide range of classifiers. [ABSTRACT FROM AUTHOR]

Abstract: Finding correspondences between two related feature point sets is a basic task in computer vision and pattern recognition. In this paper, we present a novel method for point pattern matching via spectral graph analysis. In particular, we aim to render the spectral matching algorithm more robust for positional jitter and outlier. A local structural descriptor, namely the spectral context, is proposed to describe the attribute domain of point sets, which is fundamentally different from the previous methods. Furthermore, the approximate distance order is defined and employed as the metric for geometric consistency of neighboring points in this work. By combining these two novel ingredients, we formulate feature point set matching as an optimization problem with one-to-one constraints. The correspondences are then obtained by maximizing the given objective function via the technique of probabilistic relaxation. Comparative experiments conducted on both synthetic and real data demonstrate the effectiveness of the proposed method, especially in the presence of positional jitter and outliers. [Copyright &y& Elsevier]

Presentation of case study Sectoral Operational Programme - Increase of Economic Competitiveness (SOP-IEC), in the context of proposals formulation for streamlining the system of non-reimbursable European funds absorption, comes to give a very practical nature of the implementation methodology of specific Knowledge Management toolkit. As the data collected by the Authority shall contribute to the elaboration of analysis, the link between theoretical and actual plan becomes very close, and upon created models it could intervene in the future workflows, in such a way that system's bottlenecks to be removed and collaborative climate for aligning the requirements for funding with the real needs of beneficiaries, is contributing decisively to the increasing performance in the field of funds absorption allocated for Romania. [ABSTRACT FROM AUTHOR]

Abstract: Scientific workflows are used to model applications of high throughput computation and complex large scale data analysis. In recent years, Cloud computing is fast evolving as the target platform for such applications among researchers. Furthermore, new pricing models have been pioneered by Cloud providers that allow users to provision resources and to use them in an efficient manner with significant cost reductions. In this paper, we propose a scheduling algorithm that schedules tasks on Cloud resources using two different pricing models (spot and on-demand instances) to reduce the cost of execution whilst meeting the workflow deadline. The proposed algorithm is fault tolerant against the premature termination of spot instances and also robust against performance variations of Cloud resources. Experimental results demonstrate that our heuristic reduces up to 70% execution cost as against using only on-demand instances. [Copyright &y& Elsevier]

Several algorithms for processing reverse skyline queries have been proposed in recent literature. However, these algorithms are based on pre-processing approaches, and hence involve complex procedures and waste storage space due to inefficient use of storage. In addition, they are not robust to frequently changing data as, they have to re-compute and update the pre-computed results. To overcome these issues, this paper proposes a novel algorithm to efficiently process reverse skyline queries using an approach based on two pruning methods: the search-area pruning method and the candidate-objects pruning method. Utilizing these pruning methods, the algorithm is able to process reverse skyline queries efficiently even in situations where data is changing frequently. The proposed algorithm also effectively reduces the inefficient use of storage under existing approaches for storing pre-computed results. We conducted extensive experiments to show that the proposed algorithm shows better performance compared to existing approaches regardless of the dimension, distribution, or size of the data. [ABSTRACT FROM AUTHOR]

This paper presents a robust image watermarking scheme based on a sample spline approach by extracting distinctive invariant features from images that can be used to perform reliable matching between different objects. The features are invariant to basic image transformations like rotation and scaling. The features are highly distinctive, means that every single feature can be correctly matched with high probability against the features of image. This paper also describes an approach called object recognition by using these features. The recognition can be done by matching individual features to a database of features from known objects using a fast nearest-neighbour algorithm. We use the low frequency components of image blocks for data hiding In our watermarking algorithm, to obtain high robustness. We use four samples of the approximation coefficients of the image blocks to construct a spline curve in the 2-D space. The slopes of this spline curve are employed for watermarking purpose. We embed the watermarking code by constructing a spline curve according to watermarking bits. To get a maximum likelihood decoder, we use the distribution of the slope of the embedding spline curve for Gaussian samples. [ABSTRACT FROM AUTHOR]

Abstract: This paper uses a 2D system setting in the form of repetitive process stability theory to design an iterative learning control law that is robust against model uncertainty. In iterative learning control the same finite duration operation, known as a trial over the trial length, is performed over and over again with resetting to the starting location once each is complete, or a stoppage at the end of the current trial before the next one begins. The basic idea of this form of control is to use information from the previous trial, or a finite number thereof, to compute the control input for the next trial. At any instant on the current trial, data from the complete previous trial is available and hence noncausal information in the trial length indeterminate can be used. This paper also shows how the new 2D system based design algorithms provide a setting for the effective deployment of such information. [Copyright &y& Elsevier]

Abstract: This paper presents an automatic approach to reconstruct human motion using noisy depth data from multiple views. Although multi-view constraints are provided by this setup, it still exhibits great challenges to robustly reconstruct dynamic human performances due to inherent complexity and self-occlusion of human motion. In the insight that the semantics of human motion will supply strong prior in motion reconstruction, we therefore propose a SCAPE-based motion reconstruction algorithm. As the building blocks of this main algorithm, we (1) re-train a SCAPE model based on an expanded human pose database containing human poses collected from different databases to enlarge the tracking space, (2) develop a correspondence estimation method based on articulated ICP to improve the robustness of SCAPE tracking. We conduct experiments to demonstrate the effectiveness of our method, and show that our system is able to capture and reconstruct accurate human motion. [Copyright &y& Elsevier]

Highlights: [•] A novel action descriptor is proposed by using novel feature extraction techniques. [•] A novel fish action dataset is introduced to test effectiveness for animal actions. [•] The proposed system has highest accuracy with lowest processing time. [•] Robust to viewpoint, shadow, occlusion, corrupted-image and low frame rate. [•] Unlike other methods, proposed system computes action cycle length automatically. [Copyright &y& Elsevier]

Water distribution system (WDS) models may improve system control when applied using real-time data, and in doing so, help meet consumer and regulatory demands. Such real-time modeling often overlooks the multiple sources of system uncertainty that cascade into model forecasts and affect the identification of robust operational solutions. This paper considers key uncertainties in WDS modeling and reviews promising approaches for uncertainty quantification and reduction in the modeling cascade from calibration, through data assimilation, to model forecasting. An uncertainty framework exemplifying how such methods may be applied to propagate uncertainty through the real-time control process is outlined. Innovative methods to constrain uncertainty when the time-horizon and data availability limit such thorough analysis are also discussed, alongside challenges that need to be addressed to incorporate uncertain information into the control decision. Further work evaluating the value of these methods in light of computational resources, and the nature of model errors in real WDS, is required. Such work is necessary to demonstrate the benefits of considering model and data uncertainty, leading to robust control decisions. [ABSTRACT FROM AUTHOR]

A global smoothing procedure is developed using B-spline function approximation for estimating the unknown functions of a functional coefficient regression model with spatial data. A general formulation is used to treat mean regression, median regression, quantile regression and robust mean regression in one setting. The global convergence rates of the estimators of unknown coefficient functions are established. Various applications of the main results, including estimating conditional quantile coefficient functions and robustifying the mean regression coefficient functions are given. Finite sample properties of our procedures are studied through Monte Carlo simulations. A housing data example is used to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Abstract: The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed. [Copyright &y& Elsevier]

For the existing motion capture (MoCap) data processing methods, manual interventions are always inevitable, most of which are derived from the data tracking process. This paper addresses the problem of tracking non-rigid 3D facial motions from sequences of raw MoCap data in the presence of noise, outliers and long time missing. We present a novel dynamic spatiotemporal framework to automatically solve the problem. First, based on a 3D facial topological structure, a sophisticated non-rigid motion interpreter (SNRMI) is put forward; together with a dynamic searching scheme, it cannot only track the non-missing data to the maximum extent but recover missing data (it can accurately recover more than five adjacent markers under long time (about 5 seconds) missing) accurately. To rule out wrong tracks of the markers labeled in open structures (such as mouth, eyes), a semantic-based heuristic checking method was raised. Second, since the existing methods have not taken the noise propagation problem into account, a forward processing framework is presented to solve the problem. Another contribution is the proposed method could track facial non-rigid motions automatically and forward, and is believed to greatly reduce even eliminate the requirements of human interventions during the facial MoCap data processing. Experimental results proved the effectiveness, robustness and accuracy of our system. [ABSTRACT FROM AUTHOR]

Abstract: Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation uncertainty caused by wind tunnel effects. We then propose a robust super-resolution approach via sparsity constraint for acoustic imaging in strong background noises. The sparsity parameter is adaptively derived from the sparse distribution of source powers. The proposed approach can jointly reconstruct source powers and positions, as well as the background noise power. Our approach is compared with the conventional beamforming, deconvolution and sparse regularization methods by simulated, wind tunnel data and hybrid data respectively. It is feasible to apply the proposed approach for effectively mapping monopole sources in wind tunnel tests. [Copyright &y& Elsevier]

Highlights: [•] The familiarity effect (FE) is analysed in a texture segmentation study. [•] Spatial and temporal conditions are systematically varied. [•] Data are presented as a function of target eccentricity. [•] Results show a rather robust FE. Limiting conditions are high density and short presentation time. [•] As tentative explanation an influence of top-down processes on bottom-up processes is assumed. [Copyright &y& Elsevier]

Abstract: Our research focusses on improving the quality and accuracy of the common operating picture of a tactical scenario through the efficient allocation of bandwidth in the tactical data networks among self-interested actors, who may resort to strategic behaviour dictated by self-interest. We propose a two-stage bandwidth allocation mechanism based on modified strictly-proper scoring rules, whereby multiple agents can provide track data estimates of limited precisions and the centre does not have to rely on knowledge of the true state of the world when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the data uncertainty in the operating environment. We apply our robust optimization – based scoring rules mechanism to an agent-based model framework of the tactical defence scenario, and analyse the results obtained. [Copyright &y& Elsevier]

Abstract: This paper describes the activity of a workshop on Data-Driven Tradespace Exploration and Analysis: A Key Technical Thrust of Engineered Resilient Systems (ERS). The workshop was attended by 40 academic, government, and industry researchers and practitioners involved in tradespace exploration for a variety of engineering domains. The one-and-one-half day workshop sought to develop near and far term tradespace technology research recommendations for the ERS Priority Steering Council (PSC) Lead. To determine promising research areas, workshop attendees were asked to describe desired tradespace capabilities, the associated current approach and its deficiencies, and gaps between the two states. These research areas were summarized in statements of need, supporting rationale, and investment timeframe. Resilience in the context of ERS is more than robustness; resilience implies that when the system is placed into an environment in which it was not originally intended to operate, after some degradation in performance, the system can be adapted or reconfigured to perform at its intended levels. To support design for resilience, more alternatives must be generated earlier, considered longer, explored over multiple, dynamic alternative futures, and searched exhaustively. The workshop described in this paper was organized to discuss current methods, process, and tools for performing these tradespace analysis related tasks and to better understand existing tradespace capabilities and their suitability for ERS. [Copyright &y& Elsevier]

Abstract: The data of real-world optimization problems are usually uncertain, that is especially true for early stages of system design. Data uncertainty can significantly affect the quality of the nominal solution. Robust Optimization (RO) methodology uses chance and robust constraints to generate a robust solution immunized against the effect of data uncertainty. RO methodology can be applied to any generic optimization problem where one can separate uncertain numerical data from the problem's structure. Since 2000, the RO area is witnessing a burst of research activity in both theory and applications. However, RO could lead to over-conservative requirements, resulting in typical-case bad solutions or even empty solution spaces. This drawback of the classical RO methodology can be overcome by distinguishing between real decision variables and so-called state variables. While the first type should satisfy the chance or robust constraints and their value cannot depend on a specific realization of the uncertain data, the state variables are adjustable (i.e., their value can depend on the specific realization of the uncertain data), since most of the constraints defining state variables merely “calculate” their exact value, and hence are always satisfied. In this paper we summarize how adjustable RO approach can be applied to a general uncertain linear optimization problem. Then, using an allocation example we demonstrate how this approach can be integrated in the design optimization process and its impact on the optimal system design. [Copyright &y& Elsevier]