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While a testing sample may be first encoded linearly with labeled samples and then classified with KNN on the sum of the obtained weights of the samples in each class so as to avoid the consistent distribution assumption explicitly or implicitly used in most of the existing classification methods for training and testing samples, a novel self-paced and Bayes-decision-rule linear KNN prediction method SBLD-KNN in this study will be proposed to achieve three goals: (1) class-ware information will be explicitly reflected in a grouping effect regularization term so as to share the sparsity of a linear encoder and simultaneously have grouping effect of weights on each class; (2) the resultant predictor behaves like Bayes-decision-rule for minimum error; (3) self-paced regularized term is designed to adaptively truncate the weights of labeled samples for enhancing generalization. In order to do so, the corresponding objective function of SBLD-KNN is designed and then optimized by using the alternating optimization strategy, and its Bayes-decision-rule is theoretically analyzed. Our experimental results on benchmark datasets witness the effectiveness of SBLD-KNN, in contrast to the comparative methods, including SBLD-KNN's simplified version BD-KNN with weight's truncating rather than self-pacing. [ABSTRACT FROM AUTHOR]

When the maglev train levitation system runs for a long time, the components of the system will age and the control performance of the system will decline. Moreover, the suspension system of maglev train is a complex nonlinear system, and the system has unmodeled dynamics, so it is impossible to ensure the performance of the control system through the accurate identification of the suspension system model. In this paper, a data-driven control design framework of the maglev train suspension system based on Koopman operator is proposed. According to the measured data of the suspension system, the nonlinear maglev train suspension system is reconstructed in the linear framework by using Koopman characteristic function, and the optimal control of the suspension system is realized. Simulation and experiments verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Internet of things of cloud computing offers high-performance computing, storage and networking services, but there are still suffers from a high transmission and processing latency, poor scalability and other problems. Internet of things of edge computing can better meet the increasing requirements of electricity consumers for service quality, especially the increasingly stringent need for low delay. On the other hand, edge intelligent network technology can offers edge smart sensing while significantly improve the efficiency of task execution, but it will lead to a massive collaborative task scheduling optimization problem. In order to solve this problem, This paper studies an ubiquitous power internet of things (UPIoT) smart sensing network edge computing model and an improved multi node cluster cooperative scheduling optimization strategy. The cluster server is added to the edge aware computing network, and an improved low delay edge task collaborative scheduling algorithm (LLETCS) is designed by using the vertical cooperation and multi node cluster collaborative computing scheme between edge aware networks. Then the problem is transformed based on linear reconstruction technology, and a parallel optimization framework for solving the problem is proposed. The simulation results suggest that the proposed scheme can more effectively reduce the UPIoT edge computing latency, and improve the quality of service in UPIoT smart sensing networks.

Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA–miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA–miRNA interactions is still in its infancy. In this paper, we propose an accurate predictor, MILNP, for predicting plant lncRNA–miRNA interactions based on improved linear neighborhood similarity measurement and linear neighborhood propagation algorithm. Specifically, we propose a novel similarity measure based on linear neighborhood similarity from multiple similarity profiles of lncRNAs and miRNAs and derive more precise neighborhood ranges so as to escape the limits of the existing methods. We then simultaneously update the lncRNA–miRNA interactions predicted from both similarity matrices based on label propagation. We comprehensively evaluate MILNP on the latest plant lncRNA-miRNA interaction benchmark datasets. The results demonstrate the superior performance of MILNP than the most up-to-date methods. What's more, MILNP can be leveraged for isolated plant lncRNAs (or miRNAs). Case studies suggest that MILNP can identify novel plant lncRNA–miRNA interactions, which are confirmed by classical tools. The implementation is available on https://github.com/HerSwain/gra/tree/MILNP. [ABSTRACT FROM AUTHOR]

Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA–miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA–miRNA interactions is still in its infancy. In this paper, we propose an accurate predictor, MILNP, for predicting plant lncRNA–miRNA interactions based on improved linear neighborhood similarity measurement and linear neighborhood propagation algorithm. Specifically, we propose a novel similarity measure based on linear neighborhood similarity from multiple similarity profiles of lncRNAs and miRNAs and derive more precise neighborhood ranges so as to escape the limits of the existing methods. We then simultaneously update the lncRNA–miRNA interactions predicted from both similarity matrices based on label propagation. We comprehensively evaluate MILNP on the latest plant lncRNA-miRNA interaction benchmark datasets. The results demonstrate the superior performance of MILNP than the most up-to-date methods. What’s more, MILNP can be leveraged for isolated plant lncRNAs (or miRNAs). Case studies suggest that MILNP can identify novel plant lncRNA–miRNA interactions, which are confirmed by classical tools. The implementation is available on https://github.com/HerSwain/gra/tree/MILNP.

Abstract In this paper, a class of compact higher-order gas-kinetic schemes (GKS) with spectral-like resolution will be presented. Based on the high-order gas evolution model, both the flux function and conservative flow variables in GKS can be evaluated explicitly from the time-accurate gas distribution function at a cell interface. As a result, inside each control volume both the cell-averaged flow variables and their cell-averaged gradients can be updated within each time step. The flow variable update and slope update are coming from the same physical solution at the cell interface. This strategy needs time accurate solution at a cell interface, which cannot be achieved by the Riemann problem based flow solvers, even though they can also provide the interface flux functions and interface flow variables. Instead, in order to update the slopes in the Riemann-solver based schemes, such as HWENO, there are additional governing equations for slopes or equivalent degrees of freedom inside each cell. In GKS, only a single time accurate gas evolution model is needed at the cell interface for updating cell averaged flow variables through interface fluxes and updating the cell averaged slopes through the interface flow variables. Based on both cell averaged values and their slopes, compact 6th-order and 8th-order linear and nonlinear reconstructions can be developed. As analyzed in this paper, the local linear compact reconstruction without limiter can achieve a spectral-like resolution at large wavenumber than the well-established compact scheme of Lele with globally coupled flow variables and their derivatives. For nonlinear gas dynamic evolution, in order to avoid spurious oscillation in discontinuous region, the above compact linear reconstruction from the symmetric stencil can be divided into sub-stencils and apply a biased nonlinear WENO-Z reconstruction. Consequently discontinuous solutions can be captured through the 6th-order and 8th-order compact WENO-type nonlinear reconstruction. In GKS, the time evolution solution of the gas distribution function at a cell interface is based on an integral solution of the kinetic model equation, which covers a physical process from an initial non-equilibrium state to a final equilibrium one. Since the initial non-equilibrium state is obtained based on the nonlinear WENO-Z reconstruction, and the equilibrium state is basically constructed from the linear symmetric reconstruction, the GKS evolution models unifies the nonlinear and linear reconstructions in a gas relaxation process in the determination of a time-dependent gas distribution function. This property gives GKS great advantages in capturing both discontinuous shock waves and the linear aero-acoustic waves in a single computation due to its dynamical adaptation of non-equilibrium and equilibrium states in different flow regions. This dynamically adaptive model helps to solve a long lasting problem in the development of high-order schemes about the choices of the linear and nonlinear reconstructions. Compared with discontinuous Galerkin (DG) scheme, the current compact GKS uses the same local and compact stencil, achieves the 6th-order and 8th-order accuracy, uses a much larger time step with CFL number ≥ 0.3, has the robustness as a 2nd-order scheme, and gets accurate solutions in both shock and smooth regions without introducing trouble cell and additional limiting process. The nonlinear reconstruction in the compact GKS is solely based on the WENO-Z technique. At the same time, the current scheme solves the Navier-Stokes equations automatically due to combined inviscid and viscous flux terms from a single time evolution gas distribution function at a cell interface. Due to the use of multi-stage multi-derivative (MSMD) time-stepping technique, for achieving a 4th-order time accuracy, the current scheme uses only two stages instead of four in the traditional Runge-Kutta method. As a result, the current GKS becomes much more efficient than the corresponding same order DG method. A variety of numerical tests are presented to validate the compact 6th and 8th-order GKS. The current scheme presents a state-of-art numerical solutions under a wide range of flow conditions, i.e., strong shock discontinuity, shear instability, aero-acoustic wave propagation, and NS solutions. It promotes the development of high-order scheme to a new level of maturity. The success of the current scheme crucially depends on the high-order gas evolution model, which cannot be achieved by any other approach once the 1st-order Riemann flux function is still used in the development of high-order numerical algorithms.

This paper investigates the application of trend quantifiers of project time-cost analysis as a tool for decision-making support in the project management. Practical project management-related problems are solved under information shortages. It means that methods of statistical analysis cannot be easily used as they are based on the law of large numbers of observations. Numbers are information intensive quantifiers. The least information intensive quantifier is a trend; its values are increasing, constant, decreasing. If a derivative cannot be quantified by a trend, then nothing is known and therefore nothing can be analyzed/predicted. For this reason, the trend model M was created. The model M is based on a degraded set of differential equations or heuristics. A trend analysis of the model M is an evaluation of the relevant discrete set of solutions/scenarios S. A trend reconstruction is an evaluation of the model M if a (sub) set of scenarios S is given. The paper studies linear reconstruction, i.e. the model M is a set of linear differential equations. The trend reconstruction is partially reverse process to trend analysis. A case study has 7 variables (e.g. Project duration, Direct personnel costs, Indirect personal costs etc.) and the reconstructed set of linear differential equations has 7 equations. The set of 243 scenarios is obtained if this reconstructed set of trend linear equations is solved. Any future or past behavior of the model M can be described by a sequence of obtained scenarios. [ABSTRACT FROM AUTHOR]

With the development of feature extraction technique, one sample always can be represented by multiple features which are located in different high-dimensional spaces. Because multiple features can reflect one same sample from various perspectives, there must be compatible and complementary information among the multiple views. Therefore, it's natural to learn information from multiple views to obtain better performance. However, most multi-view dimension reduction methods cannot handle multiple features from nonlinear space with high dimensions. To address this problem, we propose a novel multi-view dimension reduction method named multi-view reconstructive preserving embedding (MRPE) in this paper. MRPE reconstructs each sample by utilizing its k nearest neighbors. The similarities between each sample and its neighbors are mapped into lower-dimensional space in order to preserve the underlying neighborhood structure of the original manifold. MRPE fully exploits correlations between each sample and its neighbors from multiple views by linear reconstruction. Furthermore, MRPE constructs an optimization problem and derives an iterative procedure to obtain the low-dimensional embedding. Various evaluations based on the applications of document classification, face recognition and image retrieval demonstrate the effectiveness of our proposed approach on multi-view dimension reduction. [ABSTRACT FROM AUTHOR]

A population quantity of interest in statistical shape analysis is the location of landmarks, which are points that aid in reconstructing and representing shapes of objects. We provide an automated, model-based approach to inferring landmarks given a sample of shape data. The model is formulated based on a linear reconstruction of the shape, passing through the specified points, and a Bayesian inferential approach is described for estimating unknown landmark locations. The question of how many landmarks to select is addressed in two different ways: (1) by defining a criterion-based approach and (2) joint estimation of the number of landmarks along with their locations. Efficient methods for posterior sampling are also discussed. We motivate our approach using several simulated examples, as well as data obtained from applications in computer vision, biology, and medical imaging. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Improved and enhanced oil recovery methods require sophisticated simulation tools to predict the injected flow pass together with the chemical reactions inside it. One approach is application of higher-order numerical schemes to avoid excessive numerical diffusion that is very typical for transport processes. In this work, we provide a first step towards higher-order schemes applicable on general polyhedral and corner-point grids typically used in reservoir simulation. We compare three possible approaches of linear reconstruction and slope limiting techniques on a variety of different meshes in two and three spatial dimensions and discuss advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Analysis of dual sports performance typically involves observational techniques to gather data samples during actual competition. These techniques are limited by the amount of data that can be collected and the need to define the observable variables in advance. Today's advanced technologies have considerably overcome these limitations, enabling high-volume data collection for post-recording analysis. The present study was based on the three-dimensional kinematic data recorded by the automated ball-tracking Hawk-Eye system between the 2003 and 2008 seasons in elite tennis tournaments, which provided a database of 262 596 points. The analysis consisted of an examination of the relationships between the various characteristics of the serve summed up by the resulting ball trajectory and winning-point probabilities. The influence of factors such as serve speed, serve location, court surface, gender differences, and spin intensity on the winning-point rate was assessed to gain insight into efficient serve tendencies in world-class tennis. The implications for practitioners are highlighted and directions for future research in tennis performance analysis based on automatic ball tracking are proposed. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016 [ABSTRACT FROM AUTHOR]

Linear reconstruction based on local cell-averaged values is the most commonly adopted technique to achieve a second-order accuracy when one uses the finite volume scheme on unstructured grids. For solutions with discontinuities appearing in such as conservation laws, a certain limiter has to be applied to the predicted gradient to prevent numerical oscillations. We propose in this paper a new formulation for linear reconstruction on unstructured grids, which integrates the prediction of the gradient and the limiter together. By solving on each cell a tiny linear programming problem without any parameters, the gradient is directly obtained which satisfies the monotonicity condition. It can be shown that the resulting numerical scheme with our new method fulfils a discrete maximum principle with fair relaxed geometric constraints on grids. Numerical results demonstrate that our method achieves satisfactory numerical accuracy with theoretical guarantee of local discrete maximum principle. [ABSTRACT FROM AUTHOR]

Images measured through the atmosphere are degraded by scattering and absorption from aerosols along the path and by atmospheric turbulence. In the presence of heavy scattering at visible and infrared wavelengths, the distances over which reasonable observations are possible are quite short compared to astronomical imaging paradigms, and aerosol scattering effects dominate the degradation of the point spread function (PSF) due to atmospheric effects. In addition to aerosol-induced blurring, measurement noise effects are present in observed images. We examine the problem of reconstructing images degraded by aerosol blur and measurement noise using estimates of the overall PSF, which account for unscattered and scattered radiation detected by the imaging system. Representative images of a spoke target acquired under various conditions of scattering and photon flux levels were simulated, and reconstruction of the degraded images is performed using two linear reconstruction algorithms: a Wiener filter and a constrained least squares filter. Results of the reconstructions show that spatial resolution can be recovered in badly blurred images up to the limit imposed by the noise effective cutoff spatial frequency of the measurement. [ABSTRACT FROM AUTHOR]

Evolution in the surgical treatment of left ventricular aneurysm post myocardial infarction resulted in considerable decrease of intra and postoperative mortality in these patients, but the optimal technique for correction remains questionable, each technique presenting advantages and disadvantages. The purpose of this study was to carry out a review of publications on left ventricular reconstruction techniques for left ventricular aneurysm after acute myocardial infarction, from the first attempts to currently used techniques. [ABSTRACT FROM AUTHOR]

SUMMARY On unstructured meshes, the cell-centered finite volume (CCFV) formulation, where the finite control volumes are the mesh elements themselves, is probably the most used formulation for numerically solving the two-dimensional nonlinear shallow water equations and hyperbolic conservation laws in general. Within this CCFV framework, second-order spatial accuracy is achieved with a Monotone Upstream-centered Schemes for Conservation Laws-type (MUSCL) linear reconstruction technique, where a novel edge-based multidimensional limiting procedure is derived for the control of the total variation of the reconstructed field. To this end, a relatively simple, but very effective modification to a reconstruction procedure for CCFV schemes, is introduced, which takes into account geometrical characteristics of computational triangular meshes. The proposed strategy is shown not to suffer from loss of accuracy on grids with poor connectivity. We apply this reconstruction in the development of a second-order well-balanced Godunov-type scheme for the simulation of unsteady two-dimensional flows over arbitrary topography with wetting and drying on triangular meshes. Although the proposed limited reconstruction is independent from the Riemann solver used, the well-known approximate Riemann solver of Roe is utilized to compute the numerical fluxes, whereas the Green-Gauss divergence formulation for gradient computations is implemented. Two different stencils for the Green-Gauss gradient computations are implemented and critically tested, in conjunction with the proposed limiting strategy, on various grid types, for smooth and nonsmooth flow conditions. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel semi-supervised learning approach is proposed. It assumes that, for the ith sample , the samples from ''s sparse neighborhood have the same label with and the label of can be linearly reconstructed by the labels of those samples from ''s sparse neighborhood. Our algorithm firstly selects the sparse neighborhood for each sample, and then in that sparse neighborhood finds the sparse coefficients to represent the local geometry structure, finally seeks a label propagation way. Different from many existing methods, we construct the adapting graph, simultaneously, give the weight of each edge. What''s more, we highlight the role of those samples in that sparse neighborhood, meanwhile, eliminate the role of those samples out of that sparse neighborhood. The experimental results on face recognition and document classification demonstrate the effectiveness and efficiency of our proposed approach in this paper. [Copyright &y& Elsevier]

The article focuses on the spectral clustering algorithm method used in obtaining similarity matrix based on local neighborhood in kernel space (SC-LNK). It mentions that SC-LNK assumes the data to a feature space by Mercer kernel and uses a linear reconstruction for sparse matrix. It states that linear reconstruction-based spectral clustering (SC) in kernel space outperforms other algorithms such as traditional SC, self-tuning spectral clustering (SSC), and locality spectral clustering (LSC).

Abstract: A new, second-order accurate, volume conservative, material-order-independent interface reconstruction method for multi-material flow simulations is presented. First, materials are located in multi-material computational cells using a piecewise linear reconstruction of the volume fraction function. These material locator points are then used as generators to reconstruct the interface with a weighted Voronoi diagram that matches the volume fractions. The interfaces are then improved by minimizing an objective function that smoothes interface normals while enforcing convexity and volume constraints for the pure material subcells. Convergence tests are shown demonstrating second-order accuracy. Static and dynamic examples are shown illustrating the superior performance of the method over existing material-order-dependent methods. [Copyright &y& Elsevier]

Abstract: In modern communication systems such as the Internet, random losses of information can be mitigated by oversampling the source. This is equivalent to expanding the source using overcomplete systems of vectors (frames), as opposed to the traditional basis expansions. Dependencies among the coefficients in frame expansions often allow for better performance compared to bases under random losses of coefficients. We show that for any n-dimensional frame, any source can be linearly reconstructed from only randomly chosen frame coefficients, with a small error and with high probability. Thus every frame expansion withstands random losses better (for worst case sources) than the orthogonal basis expansion, for which the bound is attained. The proof reduces to M. Rudelson''s selection theorem on random vectors in the isotropic position, which is based on the non-commutative Khinchine''s inequality. [Copyright &y& Elsevier]

A previous paper has shown that, a particular class of complex processes with power spectrum covering (−2π,2π) can be reconstructed using separately real and imaginary parts of samples. This class has been defined by a necessary and sufficient condition giving a relationship between real and imaginary parts of samples. In this paper, this property is generalized to processes with any kind of power spectrum, even unlimited. [Copyright &y& Elsevier]