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In this paper, we study the problem of extracting trends from time series data involving missing values. In particular, we investigate a general class of procedures that impute the missing data and then extract trends using seasonal-trend decomposition based on loess (STL), where loess stands for locally weighted smoothing, a popular tool for describing the regression relationship between two variables by a smooth curve. We refer to them as the imputation-STL procedures. Two results are obtained in this paper. First, we settle a theoretical issue, namely the connection between imputation error and the overall error from estimating the trend. Specifically, we derive the bounds for the overall error in terms of the imputation error. This subsequently facilitates the error analysis of any imputation-STL procedure and justifies its use in practice. Second, we investigate loess-STL, a particular imputation-STL procedure with the imputation also being performed using loess. Through both theoretical arguments and simulation results, we show that loess-STL has the capacity of handling a high proportion of missing data and providing reliable trend estimates if the underlying trend is smooth and the missing data are dispersed over the time series. In addition to mathematical derivations and simulation study, we apply our loess-STL procedure to profile radiosonde records of upper air temperature at 22 Antarctic research stations covering the past 50 years. For purpose of illustration, we present in this paper only the results for Novolazaravskaja station which has temperature records with more than 8.4% dispersed missing values at 8 pressure levels from October/1969 to March/2011. [ABSTRACT FROM AUTHOR]

Fuzzy time series is widely used in forecasting time series data in linguistic forms. Implementing the intuitionistic fuzzy sets (IFS) in fuzzy time series can better handle uncertainties and vagueness in the time series data. However, the time series data always fluctuate randomly and cause drastic changes. In this study, the 4253HT smoother is integrated with the intuitionistic fuzzy time series forecasting model to improve the forecasting accuracy. The proposed model is implemented in predicting the Malaysian crude palm oil prices. The data are firstly smoothed, and followed with the fuzzification process. Next are the transformation of fuzzy sets into IFS and the de-ifuzzification via equal distribution of hesitancy. The forecasted data are calculated based on the defuzzified values considering the new membership degrees of the IFS after de-i-fuzzification. The results show that the integrated model produces a better forecasting performance compared to the common intuitionistic fuzzy time series forecasting model. In the future, the integration of the data smoothing should be considered before the forecasting of data using fuzzy time series could be performed. [ABSTRACT FROM AUTHOR]

Semantic segmentation is the process to classify each pixel of an image. The current state-of-the-art semantic segmentation techniques use end-to-end trainable deep models. Generally, the training of these models is controlled by some external hyper-parameters rather to use the variation in data. In this paper, we investigate the impact of data smoothing on the training and generalization of deep semantic segmentation models. A mechanism is proposed to select the best level of smoothing to get better generalization of the deep semantic segmentation models. Furthermore, a smoothing layer is included in the deep semantic segmentation models to automatically adjust the level of smoothing. Extensive experiments are performed to validate the effectiveness of the proposed smoothing strategies. [ABSTRACT FROM AUTHOR]

In this work, a method for well log data classification is presented. The method relies on a coordinate transformation to restructure the data in an optimal way and a quasi-probabilistic interpolation technique capable of smoothing noisy data. The approach does not require case-specific design, is computationally efficient and provides a statistical characterization of the classification problem. Consequently, transition zones between facies can be modelled in a realistic fashion and intermediate rock types can be identified with ease. Apart from being capable of classifying unseen data with high accuracy, the technique can also be used as an informative quality and consistency assessment tool for manually classified data. The properties of the method are demonstrated on a realistic test case study. [ABSTRACT FROM AUTHOR]

In this research, smoothing methods and the Ordinary Least Squares (OLS) method for predicting producer milk prices in Turkey is implemented. Holt-Winters multiplicative (HWM) method and Holt-Winters additive (HWA) method are included in the exponential smoothing methods. Producer milk prices data belong to January 1st, 2010 to December 31st, 2019 period and gathered from the Turkish Statistical Institute. According to the given results, OLS method is fitted the data and has good root mean square error (RMSE) and determination coefficients (R²). [ABSTRACT FROM AUTHOR]

Visual review of individual spectra in magnetic resonance spectroscopic imaging (MRSI) data benefits from the application of spectral smoothing; however, if this processing step is applied prior to spectral analysis this can impact the accuracy of the quantitation. This study aims to analyze the effect of spectral denoising and apodization smoothing on the quantitation of whole-brain MRSI data obtained at short TE. Short-TE MRSI data obtained at 3 T were analyzed with no spectral smoothing, following (i) Gaussian apodization with values of 1, 2, 4, 6, and 8 Hz, and (ii) denoising using principal component analysis (dnPCA) with 3 different values for the number of retained principal components. The mean lobar white matter estimates for four metabolites, signal-to-noise ratio (SNR), spectral linewidth, and confidence intervals were compared to data reconstructed using no smoothing. Additionally, a voxel-wise comparison for N -acetylaspartate quantitation with different smoothing schemes was performed. Significant pairwise differences were seen for all Gaussian smoothing methods as compared to no smoothing (p <0.001) in linewidth and metabolite estimates, whereas dnPCA methods showing no statistically significant differences in these measures. Confidence intervals decreased, and SNR increased with increasing levels of apodization smoothing or dnPCA denoising. Mild Gaussian apodization (≤2 Hz at 3 T) can be applied with minimal (1%) errors in quantitation; however, smoothing values greater than that can significantly affect metabolite quantification. In contrast, mild to moderate dnPCA based denoising provides quantitative results that are consistent with the analysis of unsmoothed data and this method is recommended for spectral denoising. • Analyze spectral denoising and apodization smoothing on the quantitation of MRSI • Gaussian apodization and denoising using principal component analysis (dnPCA) • Mild Gaussian apodization can be applied with minimal errors in quantitation. • dnPCA based denoising methods produce more consistent results. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel turbo equalizer based on the expectation propagation (EP) algorithm. Optimal equalization is computationally unfeasible when high-order modulations and/or large memory channels are used. In these scenarios, low-cost and suboptimal equalizers, such as those based on the linear minimum mean square error (LMMSE), are commonly used. The LMMSE-based equalizer can be efficiently implemented with a Kalman smoother (KS), i.e., a forward and backward Kalman filtering whose predictions are merged in a posterior smoothing step. Recently, it was shown that applying EP at the forward and backward stages of a KS equalizer could significantly improve its performance. In this paper, we investigate applying EP at the smoothing level instead. Also, we propose some further modifications to better exploit the information coming from the channel decoder in turbo equalization schemes. Overall, we remarkably reduce the computational complexity while highly improving the performance in terms of bit error rate. [ABSTRACT FROM AUTHOR]

Abstract We propose a method for modeling spatially dependent functional data, based on regression with differential regularization. The regularizing term enables to include problem-specific information about the spatio-temporal variation of the phenomenon under study, formalized in terms of a time-dependent partial differential equation. The method is implemented using a discretization based on finite elements in space and finite differences in time. This non-tensor product basis allows to handle efficiently data distributed over complex domains and where the shape of the domain influences the phenomenon's behavior. Moreover, the method can comply with specific conditions at the boundary of the domain of interest. Simulation studies compare the proposed model to available techniques for spatio-temporal data. The method is also illustrated via an application to the study of blood-flow velocity field in a carotid artery affected by atherosclerosis, starting from echo-color doppler and magnetic resonance imaging data. [ABSTRACT FROM AUTHOR]

Abstract In the literature on data depth applicable to random functions, it is usually assumed that the trajectories of all the random curves are continuous, known at each point of the domain, and observed exactly. These assumptions turn out to be unrealistic in practice, as the functions are often observed only on a finite grid of time points, and in the presence of measurement errors. In this work, we provide the necessary theoretical background enabling the extension of the statistical methodology based on data depth to measurable (not necessarily continuous) random functions observed within the latter framework. It is shown that even if the random functions are discontinuous, observed discretely, and contaminated with additive noise, many common depth functionals maintain the fine consistency properties valid in the ideal case of completely observed noiseless functions. For the integrated depth for functions, we provide uniform rates of convergence over the space of integrable functions. [ABSTRACT FROM AUTHOR]

We propose a flexible nonparametric regression method for ultrahigh-dimensional data. As a first step, we propose a fast screening method based on the favored smoothing bandwidth of the marginal local constant regression. Then, an iterative procedure is developed to recover both the important covariates and the regression function. Theoretically, we prove that the favored smoothing bandwidth based screening possesses the model selection consistency property. Simulation studies as well as real data analysis show the competitive performance of the new procedure. [ABSTRACT FROM AUTHOR]

We call smoothing a self-crossing point of an oriented link diagram self-smoothing. By self-smoothing repeatedly, we obtain an oriented link diagram without self-crossing points. In this paper, we show that every knot has an oriented diagram which becomes a two-component oriented link diagram without self-crossing points by a single self-smoothing. [ABSTRACT FROM AUTHOR]

In the human visual system, visual saliency perception is a rapid pre-attention processing mechanism, which can benefit myriad visual tasks, such as segmentation, localization, and detection. While most research is devoted to saliency detection on 2D images and 3D meshes, little work has been performed for efficient saliency detection on 3D point clouds. In this paper, we present a novel point clouds saliency detection method by employing principal component analysis (PCA) in a sigma-set feature space. In this method, we first construct local shape descriptors based on covariance matrices for saliency detection, considering that covariance matrices can naturally model nonlinear correlations of different low-level compact and rotational-invariant features. Secondly, we transform these covariance matrices to vector descriptors in Euclidean vector space by applying the sigma-point technique, which keeps the inner statistics of regions of 3D point clouds. Based on our informative descriptors, PCA is employed in the descriptor space for identifying saliency patterns in a point cloud. Our method shows its advantages of being structure-sensitive, capturing geometry information and computationally efficient. Experimental results demonstrate that our method achieves good performance without using any topological information. [ABSTRACT FROM AUTHOR]

This paper presents a recursive algorithm where a time delay is considered in the estimation of the forces applied to a structure and the corresponding system states. In particular when the measured response is not collocated with the estimated forces, essential information on the estimated forces and/or system states is contained in the response at L consecutive time steps following the time step where the estimation is performed. The main focus in this paper is on the reduction in estimation uncertainty that can be achieved by so-called smoothing, i.e. by considering a time delay in the estimation. When the calculation of the gain matrices is included in the recursive estimation, the calculation time of the algorithm largely increases with the time delay. It is shown that a prior calculation of the steady-state gain matrices allows for a significant reduction of the calculation time. The presented algorithm is first verified using numerical simulations. Next, a validation is performed using data obtained from a field test on a footbridge. [ABSTRACT FROM AUTHOR]

We present an offline, iterated particle filter to facilitate statistical inference in general state space hidden Markov models. Given amodel and a sequence of observations, the associated marginal likelihood L is central to likelihood-based inference for unknown statistical parameters. We define a class of "twisted" models: each member is specified by a sequence of positive functions ψ and has an associated ψ-auxiliary particle filter that provides unbiased estimates of L. We identify a sequence ψ* that is optimal in the sense that the ψ*-auxiliary particle filter's estimate of L has zero variance. In practical applications, ψ* is unknown so the ψ*-auxiliary particle filter cannot straightforwardly be implemented. We use an iterative scheme to approximateψ* and demonstrate empirically that the resulting iterated auxiliary particle filter significantly outperforms the bootstrap particle filter in challenging settings. Applications include parameter estimation using a particle Markov chain Monte Carlo algorithm. [ABSTRACT FROM AUTHOR]

We present approximate algorithms for performing smoothing in a class of high-dimensional state-space models via sequential Monte Carlo methods (particle filters). In high dimensions, a prohibitively large number of Monte Carlo samples (particles), growing exponentially in the dimension of the state space, are usually required to obtain a useful smoother. Employing blocking approximations, we exploit the spatial ergodicity properties of the model to circumvent this curse of dimensionality. We thus obtain approximate smoothers that can be computed recursively in time and parallel in space. First, we show that the bias of our blocked smoother is bounded uniformly in the time horizon and in the model dimension. We then approximate the blocked smoother with particles and derive the asymptotic variance of idealized versions of our blocked particle smoother to show that variance is no longer adversely effected by the dimension of the model. Finally, we employ our method to successfully perform maximum-likelihood estimation via stochastic gradient-ascent and stochastic expectation–maximization algorithms in a 100-dimensional state-space model. [ABSTRACT FROM PUBLISHER]

ABSTRACT Certain degree of smoothness of velocity models is required for most ray-based migration and tomography. Applying conventional smoothing in model parameters results in offset-dependent travel-time errors for reflected events, which can be large even for small contrasts in model parameters between the layers. This causes the shift in both the depth and residual moveout of the migrated images. To overcome this problem in transversely isotropic medium with a vertical symmetry axis, the preserved travel-time smoothing method was proposed earlier. We extend this method for orthorhombic media with and without azimuthal variation between the layers. We illustrate this method for a single interface between two orthorhombic layers and show that the smoothing-driven errors in travel time are very small for practical application. [ABSTRACT FROM AUTHOR]

In this paper, we consider the filtering and smoothing recursions in nonparametric finite state space hidden Markov models (HMMs) when the parameters of the model are unknown and replaced by estimators. We provide an explicit and time uniform control of the filtering and smoothing errors in total variation norm as a function of the parameter estimation errors. We prove that the risk for the filtering and smoothing errors may be uniformly upper bounded by the \mathrm L^1 -risk of the estimators. It has been proved very recently that statistical inference for finite state space nonparametric HMMs is possible. We study how the recent spectral methods developed in the parametric setting may be extended to the nonparametric framework and we give explicit upper bounds for the \mathrm L^2 -risk of the nonparametric spectral estimators. In the case where the observation space is compact, this provides explicit rates for the filtering and smoothing errors in total variation norm. The performance of the spectral method is assessed with simulated data for both the estimation of the (nonparametric) conditional distribution of the observations and the estimation of the marginal smoothing distributions. [ABSTRACT FROM AUTHOR]

The regularity problem of heat conduction equation corresponding to modeling of solidification based on the latent heat evolution approach is considered in the present study. It is shown that the corresponding PDE is actually semi-smooth, an issue that has not been taken into account explicitly in the related literature. A general smoothing (regularization) strategy is introduced to solve this problem. More specifically, the smoothed version of the effective heat capacity method is presented in this work. The presented approach is applied to model the quasi steady-state heat transfer problem in the continuous casting process. Numerical experiments demonstrate the success of the presented regularization strategy. [ABSTRACT FROM AUTHOR]

We consider kernel smoothed Grenander-type estimators for a monotone hazard rate and a monotone density in the presence of randomly right censored data. We show that they converge at rate n2/5 and that the limit distribution at a fixed point is Gaussian with explicitly given mean and variance. It is well known that standard kernel smoothing leads to inconsistency problems at the boundary points. It turns out that, also by using a boundary correction, we can only establish uniform consistency on intervals that stay away from the end point of the support (although we can go arbitrarily close to the right boundary). [ABSTRACT FROM AUTHOR]

Quantile regression models, as an important tool in practice, can describe effects of risk factors on the entire conditional distribution of the response variable with its estimates robust to outliers. However, there is few discussion on quantile regression for longitudinal data with both missing responses and measurement errors, which are commonly seen in practice. We develop a weighted and bias-corrected quantile loss function for the quantile regression with longitudinal data, which allows both missingness and measurement errors. Additionally, we establish the asymptotic properties of the proposed estimator. Simulation studies demonstrate the expected performance in correcting the bias resulted from missingness and measurement errors. Finally, we investigate the Lifestyle Education for Activity and Nutrition study and confirm the effective of intervention in producing weight loss after nine month at the high quantile. [ABSTRACT FROM AUTHOR]

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but – for this case study – multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods. • Data preprocessing is an essential step in a modelling framework for wastewater-based epidemiology. • Equal spacing of the scatted data is essential but downsampling is sufficient. • Data smoothing is key for regression and forecast. • For long term WBE data series, multivariate regression is essential. • Short term signal prediction (7 days) is reasonable accurate. [ABSTRACT FROM AUTHOR]

A method of parameterising and smoothing the unknown underlying distributions using Bernstein type polynomials with positive coefficients is proposed, verified and investigated. Any distribution with bounded and smooth enough density can be approximated by the proposed model which turns out to be a mixture of the beta distributions, beta,, for some optimal degreem. A simple change-point estimating method for choosing the optimal degreemof the approximate model is presented. The proposed method gives a maximum likelihood density estimate which is consistent indistance at a nearly parametric rateunder some conditions. Simulation study shows that one can benefit from both the smoothness and the efficiency by using the proposed method which can also be used to estimate some population parameters such as the mean. The proposed methods are applied to three data sets of different types. [ABSTRACT FROM AUTHOR]

A major cause of supply chain deficiencies is the bullwhip effect, which implies that demand variability amplifies as one moves upstream in supply chains. Smoothing inventory decision rules have been recognized as the most powerful approach to counteract the bullwhip effect. Although several studies have evaluated these smoothing rules with respect to several demand processes, focusing mainly on the smoothing order-up-to (OUT) replenishment rule, less attention has been devoted to investigate their effectiveness in seasonal supply chains. This research addresses this gap by investigating the impact of the smoothing OUT on the seasonal supply chain performances. A simulation study has been conducted to evaluate and compare the smoothing OUT with the traditional OUT (no smoothing), both integrated with the Holt-Winters (HW) forecasting method, in a four-echelon supply chain experiences seasonal demand modified by random variation. The results show that the smoothing OUT replenishment rule is superior to the traditional OUT, in terms of the bullwhip effect, inventory variance ratio and average fill rate, especially when the seasonal cycle is small. In addition, the sensitivity analysis reveals that employing the smoothing replenishment rules reduces the impact of the demand parameters and the poor selection of the forecasting parameters on the ordering and inventory stability. Therefore, seasonal supply chain managers are strongly recommended to adopt the smoothing replenishment rules. Further managerial implications have been derived from the results. [ABSTRACT FROM AUTHOR]

While there has been a lot of attention paid recently to big data, in which data is written to massive repositories for later analysis, there also is a rapidly increasing amount of data available in the form of data streams or events. Data streams typically represent very recent measurements or current system states. Events represent things that happen, often in the context of computer processing. When processing data streams or events, we often need to make decisions in real time. Complex event processing (CEP) is an important area of computer science that provides powerful tools for processing events and analyzing data streams. CEP deals with events that can be comprised of other events and can model complex phenomena like a user's interactions with a web site or a stock market crash. In the current literature, CEP is almost entirely deterministic, that is, it does not account for randomness or rely on statistical methods. However, statistics and machine learning have a critical role to play in the use of data streams and events. Also, understanding how CEP works is critical to analyzing data based on complex events. When processing data streams, a distinction must be made between analysis, the human activity in which we try to gain understanding of an underlying process, and decision making, in which we apply knowledge to data to decide what action to take. Useful statistical techniques for data streams include smoothing, generalized additive models, change point detection, and classification methods. [ABSTRACT FROM AUTHOR]

Optional Pólya tree (OPT) is a flexible nonparametric Bayesian prior for density estimation. Despite its merits, the computation for OPT inference is challenging. In this article, we present time complexity analysis for OPT inference and propose two algorithmic improvements. The first improvement, named limited-lookahead optional Pólya tree (LL-OPT), aims at accelerating the computation for OPT inference. The second improvement modifies the output of OPT or LL-OPT and produces a continuous piecewise linear density estimate. We demonstrate the performance of these two improvements using simulated and real date examples. [ABSTRACT FROM AUTHOR]

We investigate the practice of regularization (also termed damping) in inverse problems, meaning the use of prior information to supplement observations, in order to suppress instabilities in the solution caused by noisy and incomplete data. Our focus is on forms of regularization that create smooth solutions, for smoothness is often considered a desirable-or at least acceptable-attribute of inverse theory solutions (and especially tomographic images). We consider the general inverse problem, in its continuum limit. By deconstruction into the part controlled by the regularization and the part controlled by the data kernel, we show the general solution depends on a smoothed version of the back-projected data as well as a smoothed version of the generalized inverse. Crucially, the smoothing function that controls both is the solution to the simple data smoothing problem. We then consider how the choice of regularization shapes the smoothing function, in particular exploring the dichotomy between expressing prior information either as a constraint equation (such as a spatial derivative of the solution being small) or as a covariance matrix (such as spatial correlation falling off at a specified rate). By analyzing the data smoothing problem in its continuum limit, we derive analytic solutions for different choices of regularization. We consider four separate cases: (1) the first derivative of the solution is close to zero, (2) the prior covariance is a two-sided declining exponential, (3) the second derivative of the solution is close to zero, and (4) the solution is close to its localized average. First-derivative regularization is put forward as having several attractive properties and few, if any, drawbacks. [ABSTRACT FROM AUTHOR]

In this work, we find and test a new approximated formula (based on the thin plate approximation), for recovering small, unknown damages on the inaccessible surface of a thin conducting (aluminium) plate. We solve this inverse problem from a controlled heat flux and a sequence of temperature maps on the accessible front boundary of our sample. We heat the front boundary by means of a sinusoidal flux. In the meanwhile, we take a sequence of temperature maps of the same side by means of an infrared camera. This procedure is called active infrared thermography. The solution of the heat equation on the accessible boundary of the damaged sample simulates the collection of data. We use domain derivative to linearize the boundary value problem for heat equation. Then, Fourier analysis on the periodic component of solutions leads us to an elliptic BVP. Finally, we apply perturbation theory in order to find out an approximation of the damage. Numerical tests obtained with synthetic data are encouraging. The solution of the heat equation on the accessible boundary of the damaged sample simulates the collection of data by means of the infrared camera. We use domain derivative to linearize the BVP for heat equation. Then, Fourier analysis on the periodic component of solutions leads us to an elliptic BVP. Finally, we apply the perturbation theory in(ais the thickness of our sample) in order to find out an approximation of the damage. Numerical tests obtained with synthetic data are encouraging. [ABSTRACT FROM AUTHOR]

We propose a new version of functional data model for analyzing familial related individuals, where the within-subject correlation depends smoothly on a covariate such as age and the between-subject correlation follows family-wise genetic association. Our motivating example concerns measurements of weight as a function of age in sibling cows from independent families. Observations are sparsely sampled from trajectories of a phenotype contaminated with measurement error, where the phenotypic trajectory consists of a genetic component and an environmental component. By combining information across individuals, the genetic and environmental covariances are estimated via smoothing techniques. We study the genetic and environmental effects using principal component analysis, taking into account the genetic correlation to enhance the subject-level signal extraction. We show via the real data and simulations that incorporating the correlation structure improves predictions of individual phenotypic trajectories. [ABSTRACT FROM PUBLISHER]

The functional linear array model (FLAM) is a unified model class for functional regression models including function-on-scalar, scalar-on-function and function-on-function regression. Mean, median, quantile as well as generalized additive regression models for functional or scalar responses are contained as special cases in this general framework. Our implementation features a broad variety of covariate effects, such as, linear, smooth and interaction effects of grouping variables, scalar and functional covariates. Computational efficiency is achieved by representing the model as a generalized linear array model. While the array structure requires a common grid for functional responses, missing values are allowed. Estimation is conducted using a boosting algorithm, which allows for numerous covariates and automatic, data-driven model selection. To illustrate the flexibility of the model class we use three applications on curing of resin for car production, heat values of fossil fuels and Canadian climate data (the last one in the electronic supplement). These require function-on-scalar, scalar-on-function and function-on-function regression models, respectively, as well as additional capabilities such as robust regression, spatial functional regression, model selection and accommodation of missings. An implementation of our methods is provided in the R add-on package FDboost. [ABSTRACT FROM AUTHOR]

Recommender systems assist the e-commerce providers for services computing in aggregating user profiles and making suggestions tailored to user interests from large-scale data. This is mainly achieved by two primary schemes, i.e., memory-based collaborative filtering and model-based collaborative filtering. The former scheme predicts user interests over the entire large-scale data records and thus are less scalable. The latter scheme is often unsatisfactory in recommendation accuracy. In this paper, we propose Large-scale E-commerce Recommendation Using Smoothing and Fusion (CFSF) for e-commerce providers. CFSF is divided into an offline phase and an online phase. During the offline phase, CFSF creates a global item similarity matrix (GIS) and user clusters, where user ratings within each cluster is smoothed. In the online phase, when a recommendation needs to be made, CFSF dynamically constructs a locally-reduced item-user matrix for the active user item by selecting the top M similar items from GIS and top the K like-minded users from user clusters. Our empirical study shows that CFSF outperforms existing CF approaches in terms of recommendation accuracy and scalability. [ABSTRACT FROM AUTHOR]

Mixed-model assembly lines are needed for the assembly of products with a variety of models at comparatively lower costs. The design of such lines requires the work to be done at stations well balanced, satisfying the constraints such as time, space and location for optimal productivity and efficiency. This paper presents a heuristic algorithm for the mixed-model assembly line balancing problem to minimize the number of stations for a given cycle time. The proposed algorithm further reduces time discrepancies among stations due to differences in times for common operations of different models by using a smoothing approach which is based on the tabu search algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a nonparametric time-domain estimation method for linear slowly time-varying systems. The proposed method uses a 2-D impulse response function. That is modeled by a generalized B-spline smoothing technique. This means that double smoothing is applied: once over the different realizations (referring to the system behavior) and once over the global time (referring to the system memory). With respect to the system dynamics using the proposed method, it is possible to reduce the model order and to decrease the effect of the disturbing noise. [ABSTRACT FROM PUBLISHER]

The present paper deals with numerical simulations of complete phase change process inside porous media, based on the two-phase mixture model, using finite volume method for discretisation. The investigation proposes a successful remedy in order to eliminate the occurrence of “jump” in predicted properties, by introducing an efficient smoothing algorithm for the effective diffusion coefficient. A thorough parametric study also indicates that the adoption of proposed remedy does not alter the true or expected solution. All tested cases, covering applicable ranges of parametric variations, could be physically interpreted. The methodology is, therefore, recommended for future simulations of complete phase change processes within porous media. [ABSTRACT FROM AUTHOR]

We present a parallelized navigation architecture that is capable of running in real-time and incorporating long-term loop closure constraints while producing the optimal Bayesian solution. This architecture splits the inference problem into a low-latency update that incorporates new measurements using just the most recent states (filter), and a high-latency update that is capable of closing long loops and smooths using all past states (smoother). This architecture employs the probabilistic graphical models of factor graphs, which allows the low-latency inference and high-latency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the full joint density is employed that allows the different inference operations to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous nature of this algorithm, updates to the state estimates from the high-latency smoother will naturally be delayed until the smoother calculations have completed. This architecture has been tested within a simulated aerial environment and on real data collected from an autonomous ground vehicle. In all cases, the concurrent architecture is shown to recover the full batch solution, even while updated state estimates are produced in real-time. [ABSTRACT FROM AUTHOR]

The ROC surface is the major criterion for assessing the accuracy of diagnosis test statistics s(X) in regard to their capacity of discriminating between K 3 statistical populations. It provides additionally a widely used visual tool in the cases K=2 and K=3. It is the main purpose of this paper to investigate how to bootstrap a natural empirical estimator of the ROC surface in order to build accurate confidence regions in the ROC space. We first introduce a resampling procedure based on smooth versions of the empirical distributions involved to construct non Gaussian confidence regions. Simulation results are then displayed to show that such a "smoothed bootstrap" technique is preferable to a "naive" bootstrap approach in this situation. The accuracy of the method proposed is also illustrated using a psychometric dataset. An asymptotic analysis providing a rigorous theoretical basis for the method proposed is finally carried out in a functional framework. [ABSTRACT FROM AUTHOR]

We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter ε > 0. We provide theoretical results which quantify, in terms of ε, the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is, wherenis the number of time steps over which smoothing is performed. For numerical implementation, we adopt the forward-only sequential Monte Carlo (SMC) scheme of [14] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Removal of shear locking. [•] First time combination of CUF and cell-based SFEM with MITC technique. [•] Excellent results for composite plates. [ABSTRACT FROM AUTHOR]

Moving average as a trend generator or smoother for data on a trajectory has been investigated to produce a smooth trend, such that a smooth value can be generated corresponding to every raw data value with the same time or space coordinate. In achieving this goal, computed moving average values are first assigned coordinates at equal intervals such that they cover the same space occupied by raw data. This process is called Stretched Moving Averages. This is followed by a linear interpolation process between stretched moving average values to generate the Stretched Interpolated Moving Averages that have the same coordinates as the raw data values. Accuracy of smoothing obtained depends on the number of data values used in computing the moving averages. This is highlighted by computing the root mean square deviation values, and comparing with the same deviations obtained from local linear regression smoother. [ABSTRACT FROM AUTHOR]

• A novel smoothing data association idea in the LM-IPDA algorithm. • The significant detection and tracking performance of UAV are analyzed by experiment. • Backward multi-tracks are used to estimate a forward track in past scan for smoothing. • False-track discrimination is obtained using smoothing target existence probability. • The algorithm reduces RMS errors, and tracks multi-vehicles in clutter efficiently. The unidentified number of unmanned aerial vehicles (UAVs) can execute aggressive maneuvers in the restricted and the cluttered environment. Therefore, it is difficult to detect and track the uncertain motion of the UAV target in such complex environment. In addition, multi-target tracking (MTT) algorithms such as joint data association approach faces various computational complexities that could exceeds the available computation resources. This paper develops a novel smoothing data association idea in a linear multi-target (LM) tracking based on integrated probabilistic data association (sLM-IPDA) algorithm that acts like a single target tracker in the MTT situation. The significant detection and tracking performance of a UAV are validated without a-prior information of the UAV's initial position. The forward and backward tracks are initialized separately using sensor measurements received in each scan. The sLM-IPDA estimates the backward multi-tracks simultaneously associating backward tracks in a subsequent predicted forward track for fusion. Thus, a forward track state estimate is obtain using the smoothing (fusion) measurements. This significantly improves estimation accuracy for large number of cross-over targets in heavy clutter. Numerical assessments of the sLM-IPDA are verified using both simulation and experiment to demonstrate the application of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Tree-based methods similar to CART have recently been utilized for problems in which the main goal is to estimate some set of interest. It is often the case that the boundary of the true set is smooth in some sense, however tree-based estimates will not be smooth, as they will be a union of ‘boxes’. We propose a general methodology for smoothing such sets that allows for varying levels of smoothness on the boundary automatically. The method is similar to the idea underlying support vector machines, which is applying a computationally simple technique to data after a non-linear mapping to produce smooth estimates in the original space. In particular, we consider the problem of level-set estimation for regression functions and the dyadic tree-based method of Willett and Nowak [Minimax optimal level-set estimation, IEEE Trans. Image Process. 16 (2007), pp. 2965–2979]. [ABSTRACT FROM AUTHOR]

Abstract: Smoothing of noisy data has always been a topic of interest in many areas where computer simulations have been performed, including natural sciences as well as economics and social sciences. In this paper we present an approximation method of explicit curves or surfaces from exact and noisy data by fairness cubic or bicubic splines. A variational problem of explicit curves or surfaces is obtained by minimizing a quadratic functional in a space of cubic or bicubic splines from noisy data. We show the existence and uniqueness of this problem as long as a convergence result especially for noisy data is carefully established. We analyze some numerical and graphical examples using fictional noisy data in order to prove the validity of our method. [Copyright &y& Elsevier]

. Systematic sampling is frequently used in surveys, because of its ease of implementation and its design efficiency. An important drawback of systematic sampling, however, is that no direct estimator of the design variance is available. We describe a new estimator of the model-based expectation of the design variance, under a non-parametric model for the population. The non-parametric model is sufficiently flexible that it can be expected to hold at least approximately in many situations with continuous auxiliary variables observed at the population level. We prove the model consistency of the estimator for both the anticipated variance and the design variance under a non-parametric model with a univariate covariate. The broad applicability of the approach is demonstrated on a dataset from a forestry survey. [ABSTRACT FROM AUTHOR]

Several methods exist for investigation of the relationship between records and weather data. These can be broadly classified into models that attempt to incorporate information about underlying biological processes, such as those based on the concept of thermal time, and linear regression methods. The latter are less driven by the biology but have the advantages of ease of use and flexibility. Regression can be used where there is no obvious mechanistic model or to suggest the form of a mechanistic or empirical model where there are several to choose from. Stepwise regression is commonly used in phenology. However, it requires aggregation of the weather records, resulting in loss of information. Penalised signal regression (PSR) was recently introduced to overcome this weakness. Here, we introduce a further method to the phenology context called fusion, which is a sparse version of PSR. In this paper, we compare the performance of these three regression methods based on simulations from two types of mechanistic models, the spring warming and sequential models. Given a suitable choice of temperature days as regression covariates, PSR and fusion performed better than stepwise regression for the spring warming model and PSR performed best for the sequential model. However, if a large number of redundant temperature days were included as covariates, the performance of PSR fell off whilst fusion was quite robust to this change. For this reason, it is best to use PSR and fusion methods in tandem, and to vary the number of covariates included. [ABSTRACT FROM AUTHOR]

A method based on point cloud smoothing approaches for detecting noise and outliers is introduced. This method firstly estimates thresholds according to points’ shifts after smoothing, secondly identifies outliers and noise whose shifts are more than the thresholds and lastly removes them and repeats the whole process. The main difference from other methods is that it tries to screen out outliers from point clouds rather than makes points smoother by correcting their coordinates. The precondition to assure it work is that outliers will be shifted more, which requires smoothing operators possess the ability of low-pass filter. Since this method does not rely on any local parametric representation, it can deal with noisy data in the case of ambiguous orientation and complex geometry. The proposed method is rather simple and can be easily implemented. Some cases are provided to illustrate its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]