Search

Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms: 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions.

Schizophrenia (SZ) is a complex disease. Single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA methylation are all important biomarkers that can be used for the study of SZ. To our knowledge, there has been little effort to combine these three datasets together. In this study, we propose a group sparse joint nonnegative matrix factorization (GSJNMF) model to integrate SNP, fMRI, and DNA methylation for the identification of multi-dimensional modules associated with SZ, which can be used to study regulatory mechanisms underlying SZ at multiple levels. The proposed GSJNMF model projects multiple types of data onto a common feature space, in which heterogeneous variables with large coefficients on the same projected bases are used to identify multi-dimensional modules. We also incorporate group structure information available from each dataset. The genomic factors in such modules have significant correlations or functional associations with several brain activities. At the end, we have applied the method to the analysis of real data collected from the Mind Clinical Imaging Consortium (MCIC) for the study of SZ and identified significant biomarkers. These biomarkers were further used to discover genes and corresponding brain regions, which were confirmed to be significantly associated with SZ.

Multiplex-fluorescence in situ hybridization (M-FISH) is a chromosome imaging technique which can be used to detect chromosomal abnormalities such as translocations, deletions, duplications, and inversions. Chromosome classification from M-FISH imaging data is a key step to implement the technique. In the classified M-FISH image, each pixel in a chromosome is labeled with a class index and drawn with a pseudo-color so that geneticists can easily conduct diagnosis, for example, identifying chromosomal translocations by examining color changes between chromosomes. However, the information of pixels in a neighborhood is often overlooked by existing approaches. In this work, we assume that the pixels in a patch belong to the same class and use the patch to represent the center pixel's class information, by which we can use the correlations of neighboring pixels and the structural information across different spectral channels for the classification. On the basis of assumption, we propose a patch-based classification algorithm by using higher order singular value decomposition (HOSVD). The developed method has been tested on a comprehensive M-FISH database that we established, demonstrating improved performance. When compared with other pixel-wise M-FISH image classifiers such as fuzzy c-means clustering (FCM), adaptive fuzzy c-means clustering (AFCM), improved adaptive fuzzy c-means clustering (IAFCM), and sparse representation classification (SparseRC) methods, the proposed method gave the highest correct classification ratio (CCR), which can translate into improved diagnosis of genetic diseases and cancers. © 2016 International Society for Advancement of Cytometry., (© 2016 International Society for Advancement of Cytometry.)

Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction., Competing Interests: The authors have declared that no competing interests exist.

Exemplar-based algorithms are a popular technique for image inpainting. They mainly have two important phases: deciding the filling-in order and selecting good exemplars. Traditional exemplar-based algorithms are to search suitable patches from source regions to fill in the missing parts, but they have to face a problem: improper selection of exemplars. To improve the problem, we introduce an independent strategy through investigating the process of patches propagation in this paper. We first define a new separated priority definition to propagate geometry and then synthesize image textures, aiming to well recover image geometry and textures. In addition, an automatic algorithm is designed to estimate steps for the new separated priority definition. Comparing with some competitive approaches, the new priority definition can recover image geometry and textures well.

The total variation (TV) regularization method is an effective method for image deblurring in preserving edges. However, the TV based solutions usually have some staircase effects. In order to alleviate the staircase effects, we propose a new model for restoring blurred images under impulse noise. The model consists of an ℓ1-fidelity term and a TV with overlapping group sparsity (OGS) regularization term. Moreover, we impose a box constraint to the proposed model for getting more accurate solutions. The solving algorithm for our model is under the framework of the alternating direction method of multipliers (ADMM). We use an inner loop which is nested inside the majorization minimization (MM) iteration for the subproblem of the proposed method. Compared with other TV-based methods, numerical results illustrate that the proposed method can significantly improve the restoration quality, both in terms of peak signal-to-noise ratio (PSNR) and relative error (ReE).

Recently, extensive researches on estimating the value of e have been studied. In this paper, the structural characteristics of I. Schur type inequalities are exploited to generalize the corresponding inequalities by variable parameter techniques. Some novel upper and lower bounds for the I. Schur inequality have also been obtained and the upper bounds may be obtained with the help of Maple and automated proving package (Bottema). Numerical examples are employed to demonstrate the reliability of the approximation of these new upper and lower bounds, which improve some known results in the recent literature.

The first four smallest values of the spectral radius among all connected graphs with maximum clique size ω ≥ 2 are obtained.

Protein inference is an important issue in proteomics research. Its main objective is to select a proper subset of candidate proteins that best explain the observed peptides. Although many methods have been proposed for solving this problem, several issues such as peptide degeneracy and one-hit wonders still remain unsolved. Therefore, the accurate identification of proteins that are truly present in the sample continues to be a challenging task. Based on the concept of peptide detectability, we formulate the protein inference problem as a constrained Lasso regression problem, which can be solved very efficiently through a coordinate descent procedure. The new inference algorithm is named as ProteinLasso, which explores an ensemble learning strategy to address the sparsity parameter selection problem in Lasso model. We test the performance of ProteinLasso on three datasets. As shown in the experimental results, ProteinLasso outperforms those state-of-the-art protein inference algorithms in terms of both identification accuracy and running efficiency. In addition, we show that ProteinLasso is stable under different parameter specifications. The source code of our algorithm is available at: http://sourceforge.net/projects/proteinlasso., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Motivation: Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved., Results: In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms., Availability: The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/., Contact: zyhe@dlut.edu.cn., Supplementary Information: Supplementary data are available at Bioinformatics Online.

Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Due to the existence of degenerate peptides and 'one-hit wonders', it is very difficult to determine which proteins are present in the sample. In this paper, we review existing protein inference methods and classify them according to the source of peptide identifications and the principle of algorithms. It is hoped that the readers will gain a good understanding of the current development in this field after reading this review and come up with new protein inference algorithms.

In recent years, displays and lighting require color temperature (CT) conversion function because observers have different preferences. This paper proposes effective methods to determine the optimal converting point of CT conversion for display and lighting application. For display application, the concepts of center of gravity and isotemperature line are applied to determine the optimal converting point. The maximal enhancement of luminance between the optimal and average is 18%. For lighting application, this paper proposes two methods to determine the optimal converting point in the CT quadrangle which complies with ANSI C78. 377. The enhancement of luminance in two CT modes (5700K and 6500K) are 14.2% and 23.6%, respectively.

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

This paper proposes an algorithm based on defined scaled tri-factorization (STF) for fast and accurate tensor ring (TR) decomposition. First, based on the fast tri-factorization approach, we define STF and design a corresponding algorithm that can more accurately represent various matrices while maintaining a similar level of computational time. Second, we apply sequential STFs to TR decomposition with theoretical proof and propose a stable (i.e., non-iterative) algorithm named TR-STF. It is a computationally more efficient algorithm than existing TR decomposition algorithms, which is beneficial when dealing with big data. Experiments on multiple randomly simulated data, highly oscillatory functions, and real-world data sets verify the effectiveness and high efficiency of the proposed TR-STF. For example, on the Pavia University data set, TR-STF is nearly 9240 and 39 times faster, respectively, and more accurate than algorithms based on alternating least squares and singular value decomposition. As an extension, we apply sequential STFs to tensor train (TT) decomposition and propose a non-iterative algorithm named TT-STF. Experimental results demonstrate the superiority of the proposed TT-STF compared with the state-of-the-art TT decomposition algorithm. [ABSTRACT FROM AUTHOR]

An efficient method for fast wide-band eigenmode analysis is presented in this paper. The finite-element solution space is projected to a subspace containing frequency-dependent information obtained at a single frequency by a Lanczos algorithmand modified perturbation theory, resulting in a single-point reduced-order model accurate over an extremely wide frequency range. A numerical procedure is developed to extract a desired mode from the reduced-order model for fast frequency sweep and is then applied to several practical problems, which demonstrate the solution accuracy and computational efficiency of the proposed method. Index Terms--Coplanar waveguide (CPW), finite-element method, microstrip line, microwave waveguide, model-orderreduction, non-Hermitian Lanczos algorithm.

• A HSI restoration method using superpixel segmentation of smooth band is proposed, which exploits the spectral smooth and spatial-spectral similarity of the HSI. • Develop an efficient SSSB algorithm to solve the proposed method. • The method can better remove the mixed noise in the HSI, even for heavy noise. Hyperspectral images (HSIs) are inevitably degraded in the acquisition process by mixed noise including Gaussian noise, impulse noise, stripes, and so on. Recently, many low-rank regularization based HSI restoration methods have been proposed to powerfully remove the mixed noise. However, most of them use the square patch based denoising strategy, which destroyed the boundary information of the objects in the HSI. In this paper, we adopt superpixel segmentation to group the pixels of HSI with adjacent position, similar color, texture and luminance into a homogeneous region, whose shape is adaptive. Several homogeneous regions cover the full HSI. This is better than simply dividing the HSI into square patches. By taking advantage of both the low-rank property and the spectral smooth of the HSI, this approach can efficiently remove the mixed noise with few time. Several experiments verify the performance of the proposed approach for HSI restoration. [ABSTRACT FROM AUTHOR]

The local magnitude scale ML is defined by the difference between the observed amplitude (log A ) and the anchored attenuation amplitude (log A 0 ). A previous study in Taiwan established a model of the anchored amplitude as a function of the hypocentral distance R by matching the ML to the corresponding moment magnitude MW. Although the overall performance of the model is adequate, there remain some drawbacks, namely, the correlated empirical station correction problem and relatively low sample size. In this paper, we adopt the concept of the expectation-maximization (EM) algorithm to develop a new method that can simultaneously estimate the anchored amplitude model coefficients and station corrections. The revised catalog using the latest dataset in Taiwan provides an up-to-date accurate estimate of ML. Additionally, the proposed method can systematically obtain statistically meaningful results and be applied to datasets of other regions in the future. [ABSTRACT FROM AUTHOR]

In this study, we proposed a motion recognition algorithm based on optimized temporal-spatial features and integrating BOW model. Firstly, detect the key points of the input video and then set up multiple 3D patches in the domain space, enhancing the spatiotemporal characteristic of the key points. With that, randomly sample the key point sets and carry out the description by histogram of oriented gradient (HOG) and optical flow histogram (OFH), thereby obtaining the composite descriptor for features. The clustering algorithm will be then applied to set up a visual dictionary and represent the input video samples. In the final step, a classifier is trained and generated by utilizing multi-core SVM; thus, the motion recognition has been completed. The experimental results suggested that the recognition rate and robustness of the proposed algorithm can outperform most existing motion recognition algorithms. [ABSTRACT FROM AUTHOR]

This paper considers a two-stage three-machine differentiation flow shop that comprises a common machine at stage 1 and two independent dedicated machines at stage 2. Two types of jobs are to be processed. All jobs must visit the stage-1 machine, and then the jobs of each type proceed to their dedicated machine for stage-2 processing. The stage-1 machine processes the jobs in batches, each of which, whenever formed, requires a constant setup time. The objective is to find a schedule that attains the minimum makespan. While the problem is strongly NP-hard, we investigate the case where the processing sequences of the two types of jobs are given and fixed. A polynomial-time dynamic programming algorithm is designed to solve this problem. We then deploy this algorithm to compute the lower bounds of the general problem. [ABSTRACT FROM AUTHOR]

The similarity of two one-dimensional sequences is usually measured by the longest common subsequence (LCS) algorithms. However, these algorithms cannot be directly extended to solve the two or higher dimensional data. Thus, for the two-dimensional data, computing the similarity with an LCS-like approach remains worthy of investigation. In this paper, we utilize a systematic way to give the generalized definition of the two-dimensional largest common substructure (TLCS) problem by referring to the traditional LCS concept. With various matching rules, eight possible versions of TLCS problems may be defined. However, only four of them are shown to be valid. We prove that all of these four TLCS problems are NP -hard and APX -hard. To accomplish the proofs, two of the TLCS problems are reduced from the 3-satisfiability problem, and the other two are reduced from the 3-dimensional matching problem. [ABSTRACT FROM AUTHOR]

Multi-solution problems extensively exist in practice. Particularly, the traveling salesman problem (TSP) may possess multiple shortest tours, from which travelers can choose one according to their specific requirements. However, very few efforts have been devoted to the multi-solution problems in the discrete domain. In order to fill this research gap and to effectively tackle the multi-solution TSP, we propose a niching memetic algorithm in this article. The proposed algorithm is characterized by a niche preservation technique to enable the parallel search of multiple optimal solutions; an adaptive neighborhood strategy to balance the exploration and exploitation; a critical edge-aware method to provide effective guidance to the reproduction; and a selective local search strategy to improve the search efficiency. To evaluate the performance of the proposed algorithm, we conduct comprehensive experiments on a recently published multi-solution optimization test suite. The experimental results show that our algorithm outperforms other compared algorithms. Furthermore, the proposed algorithm is adopted to tackle problems from the well-known TSPLIB library to obtain a set of distinct but good solutions. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and global spectral correlation. Existing nonlocal low-rank tensor approximation methods were mainly based on two common decomposition [Tucker or CANDECOMP/PARAFAC (CP)] methods and achieved the state-of-the-art results, but they are subject to certain issues and do not produce the best approximation for a tensor. For example, the number of parameters for Tucker decomposition increases exponentially according to its dimensions, and CP decomposition cannot better preserve the intrinsic correlation of the HSI. In this article, a novel nonlocal tensor-ring (TR) approximation is proposed for HSI denoising by using TR decomposition to explore the nonlocal self-similarity and global spectral correlation simultaneously. TR decomposition approximates a high-order tensor as a sequence of cyclically contracted third-order tensors, which has strong ability to explore these two intrinsic priors and to improve the HSI denoising results. Moreover, an efficient proximal alternating minimization algorithm is developed to optimize the proposed TR decomposition model efficiently. Extensive experiments on three simulated data sets under several noise levels and two real data sets verify that the proposed TR model provides better HSI denoising results than several state-of-the-art methods in terms of quantitative and visual performance evaluations. [ABSTRACT FROM AUTHOR]

The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD), has obtained promising results in hyperspectral image (HSI) denoising. However, the framework of the t-SVD lacks flexibility for handling different correlations along different modes of HSIs, leading to suboptimal denoising performance. This article mainly makes three contributions. First, we introduce a new tensor rank named tensor fibered rank by generalizing the t-SVD to the mode- ${k}$ t-SVD, to achieve a more flexible and accurate HSI characterization. Since directly minimizing the fibered rank is NP-hard, we suggest a three-directional tensor nuclear norm (3DTNN) and a three-directional log-based tensor nuclear norm (3DLogTNN) as its convex and nonconvex relaxation to provide an efficient numerical solution, respectively. Second, we propose a fibered rank minimization model for HSI mixed noise removal, in which the underlying HSI is modeled as a low-fibered-rank component. Third, we develop an efficient alternating direction method of multipliers (ADMMs)-based algorithm to solve the proposed model, especially, each subproblem within ADMM is proven to have a closed-form solution, although 3DLogTNN is nonconvex. Extensive experimental results demonstrate that the proposed method has superior denoising performance, as compared with the state-of-the-art competing methods on low-rank matrix/tensor approximation and noise modeling. [ABSTRACT FROM AUTHOR]

On May 31, 2003, the scan line corrector (SLC) of the Enhanced Thematic Mapper Plus (ETM+) on-board the Landsat-7 satellite failed, resulting in strips of data lost in all ETM+ images acquired since then. In this paper, we proposed a novel inpainting algorithm for recovering the ETM+ SLC-off images. The two slopes of the boundaries of each missing stripe were extracted through the Hough transform, ignoring the slope of the edge of the strip that overlaps the edge of the image. An adaptive dictionary was then developed and trained using ETM+ SLC-on images acquired before May 31, 2003 so that the physical characteristics and geometric features of the ground coverage of the data-missing strips can be considered during recovery. To make the algorithm computationally efficient, data-missing strips were repaired along their slope directions by using the logdet $\left ({\cdot }\right)$ low-rank nonconvex model along with the dictionary. The algorithm was tested using the simulated ETM+ SLC-off images created from a multiband ETM+ SLC-on image file and compared to the high accuracy low-rank tensor completion (HaLRTC), logDet, and tensor nuclear norm (TNN) algorithms. The results show that the ETM+ images restored using the new algorithm have lower RMSE, higher PSNR and structure similarity (SSIM) values, and better visualization. These results indicate that the new algorithm performs better than the other three algorithms and can efficiently and accurately restore the data-missing stripes. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose the restarted global full orthogonalization method (Gl-FOM) and global generalized minimum residual (Gl-GMRES) method to solve the Stein-like matrix equation X + M (X) = C with M (X) = A X B , M (X) = A X ⊤ B , M (X) = A X ¯ B or M (X) = A X H B , respectively, where X is an unknown matrix to be solved. First, by using a real inner product in complex matrix spaces, a generalized global Arnoldi process is given. Then we demonstrate how to employ the restarted Gl-FOM and Gl-GMRES algorithms for solving the Stein-like matrix equation. The proposed algorithms take advantage of the shifted structure of the matrix equation and are implemented by the original coefficient matrices. Finally, some numerical examples are given to illustrate the effectiveness with comparison to some existing methods. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing has attracted much attention in recent years. Single sparse unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number of spectral signatures from large, ever-growing, and available spectral libraries. Joint-sparsity (or row-sparsity) model typically enforces all pixels in a neighborhood to share the same set of spectral signatures. The two sparse models are widely used in the literature. In this paper, we propose a joint-sparsity-blocks model for abundance estimation problem. Namely, the abundance matrix of size $m\times n$ is partitioned to have one row block and $s$ column blocks and each column block itself is joint-sparse. It generalizes both the single (i.e., $s=n$) and the joint (i.e., $s=1$) sparsities. Moreover, concatenating the proposed joint-sparsity-blocks structure and low rankness assumption on the abundance coefficients, we develop a new algorithm called joint-sparse-blocks and low-rank unmixing. In particular, for the joint-sparse-blocks regression problem, we develop a two-level reweighting strategy to enhance the sparsity along the rows within each block. Simulated and real-data experiments demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to enhance the visual quality of images, while preserving image details of stripe-free regions. Instead of solving the underlying image by variety of algorithms, we first estimate the stripe noise from the degraded images, then compute the final destriping image by the difference of the known stripe image and the estimated stripe noise. In this paper, we propose a non-convex l0 sparse model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise, and the locally continuous property of the underlying image as well. Moreover, the proposed non-convex model is solved by a proximal alternating direction method of multipliers (PADMM) based algorithm. In addition, we also give the corresponding theoretical analysis of the proposed algorithm. Extensive experimental results on simulated and real data demonstrate that the proposed method outperforms recent competitive destriping methods, both visually and quantitatively. [ABSTRACT FROM AUTHOR]

Abstract: Background: Septal ventricular outflow tract ventricular arrhythmias (OT‐VAs) are defined as septal origin VAs from the right ventricular or left ventricular OT. Patients with septal OT‐VAs may require a sequential bilateral OT ablation. This study aimed to evaluate the electrophysiological characteristics and ablation outcome in patients with septal OT‐VAs. Methods: We retrospectively analyzed the electrocardiography and electrophysiological parameters in 96 patients (mean age 49 ± 15 years, 49 male) undergoing bilateral activation mapping before catheter ablation of idiopathic septal OT‐VAs. The patients were categorized into three groups based on the successful ablation sites, including the right ventricular outflow tract (RVOT), RVOT/left ventricular outflow tract (LVOT), and LVOT. Results: Mapping in the three groups demonstrated a gradually decreasing and increasing trend in the earliest activation time obtained from the RVOT and LVOT, respectively. The absolute earliest activation time discrepancy (AEAD) of ≤18 milliseconds could predict the requirement for a sequential bilateral ablation with a sensitivity and specificity of 100.0% and 93.7%, respectively. The small AEAD (≤21 milliseconds) was associated with a higher recurrence rate in patients receiving a successful unilateral ablation, while patients with a longer distance between the bilateral OT earliest activation sites (DEA > 26 mm) increased future recurrences after an initially successful sequential bilateral ablation. Conclusions: The application of bilateral OT‐VA activation mapping and the measurement of the AEAD and DEA provided not only pivotal information for the ablation strategy, but also prognostic implications for recurrences in patients with septal OT‐VAs. [ABSTRACT FROM AUTHOR]

One method of solving the single-image super-resolution problem is to use Heaviside functions. This has been done previously by making a binary classification of image components as “smooth” and “non-smooth”, describing these with approximated Heaviside functions (AHFs), and iteration including l1 regularization. We now introduce a new method in which the binary classification of image components is extended to different degrees of smoothness and non-smoothness, these components being represented by various classes of AHFs. Taking into account the sparsity of the non-smooth components, their coefficients are l1 regularized. In addition, to pick up more image details, the new method uses an iterative refinement for the residuals between the original low-resolution input and the downsampled resulting image. Experimental results showed that the new method is superior to the original AHF method and to four other published methods. [ABSTRACT FROM AUTHOR]

In this paper, the exponential weighted entropy (EWE) and exponential weighted mutual information (EWMI) are proposed as the more generalized forms of Shannon entropy and mutual information (MI), respectively. They are position-related and causal systems that redefine the foundations of information-theoretic metrics. As the special forms of the weighted entropy and the weighted mutual information, EWE and EWMI have been proved that they preserve nonnegativity and concavity properties similar to Shannon frameworks. They can be adopted as the information measures in spatial interaction modeling. Paralleling with the normalized mutual information (NMI), the normalized exponential weighted mutual information (NEWMI) is also investigated. Image registration experiments demonstrate that EWMI and NEWMI algorithms can achieve higher aligned accuracy than MI and NMI algorithms. [ABSTRACT FROM AUTHOR]

Computing PageRank plays an important part in determining the importance of Web pages. Based on the classical power method and the inner–outer iteration proposed by Gleich et al. (2010), Gu et al. (2015) presented a two-step splitting iteration, i.e., the power-inner–outer (PIO) iteration, for the computation of PageRank. In this paper, we develop a variant of the PIO iteration by applying multi-step power method to combine with the inner–outer iteration. The new method is denoted as the MPIO iteration, its convergence is analyzed in detail. Numerical experiments on several PageRank problems are used to illustrate the effectiveness of the MPIO iteration. [ABSTRACT FROM AUTHOR]

In Web link structures, similar link distributions often occur, especially for pages from same hosts. For PageRank problems, similar in-link distributions of pages result in similar row patterns of the transition matrix. We demonstrate that common row patterns in the transition matrix determine identical sub-rows. Thus the identical sub-rows with a large proportion of nonzeros can be eliminated to decrease the density of PageRank problems. We propose an elimination strategy that exploits such identical sub-rows and generates an elimination operator for transferring the problem to an equivalent but more sparse problem. Numerical experiments are reported to illustrate the effectiveness of this strategy for decreasing the computational cost of solving PageRank problems. [ABSTRACT FROM AUTHOR]

This paper formulates the PageRank problem A x = x into a consistent singular linear system ( I − A ) x = 0 , and applies the full orthogonalization method (FOM) to solve it. This singular system is characterized by index one, namely i n d e x ( I − A ) = 1 . We analyze the breakdown performance of FOM on a general singular linear system, and conclude that FOM can determine a solution if it converges, without any unfortunate breakdowns for our target problem. Then we propose to use a vector extrapolation method to speed up the convergence performance of FOM. This extrapolation procedure is based on Ritz values, which directly stems from the Arnoldi-Extrapolation algorithm (Wu and Wei, 2010). Eventually numerical experiments are presented to illustrate the effectiveness of our approaches. [ABSTRACT FROM AUTHOR]

A hybrid file system with high flexibility and performance, called Trident file system (TridentFS), is proposed to manage three types of storage with different performance characteristics, that is, Non-Volatile RAM (NVRAM), flash memory and magnetic disk. Unlike previous NVRAM-based hybrid file systems, novel techniques are used in TridentFS to improve the flexibility and performance. TridentFS is flexible by the support of various forms of flash memory and a wide range of NVRAM size. The former is achieved on the basis of the concept of stackable file systems, and the latter is achieved by allowing data eviction from the NVRAM. TridentFS achieves high performance by keeping hot data in the NVRAM and allowing data evicted from the NVRAM to be parallel distributed to the flash memory and disk. A data eviction policy is proposed to determine the data to be evicted from the NVRAM. Moreover, a data distribution algorithm is proposed to effectively leverage the parallelism between flash memory and disk during data distribution. TridentFS is implemented as a loadable module on Linux 2.6.29. The performance results show that it works well for both small-sized and large-sized NVRAM, and the proposed eviction policy outperforms LRU by 27%. Moreover, by effectively leveraging the parallelism between flash memory and disk, the proposed data distribution algorithm outperforms the RAID-0 and a size-based distribution method by up to 471.6% and 82.6%, respectively. By considering the data size and performance characteristics of the storage, the proposed data distribution algorithm outperforms the greedy algorithm by up to 15.5%. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Some approaches based on learning basis and self-similarities have shown powerful results apply to image denoising, reconstruction and some other classification tasks. Among these restoration techniques, BM3D and LASSC demonstrate amazing stability in dealing with natural images of different noise level. These methods make full use of nonlocal self-similarity and group sparsity. Taking advantage of the superiority, we propose a two-step group-based adaptive soft-thresholding algorithm for image denoising. We apply adaptive soft-thresholding to higher order singular value decomposition (HOSVD) and consider this operation as the first step, obtaining a basic estimate of the clean image. In the second step, denoising is also realized in the group-based framework and the final result is obtained by applying the basic estimate to the model which makes up of a F -norm fidelity term and an adaptive weighted nuclear norm regularization term. Compared with several state-of-the-art denoising methods, the proposed method requires less iterations and execution time. Experiment results demonstrate obvious improvement over BM3D and LASSC in PSNR value and visual effect. [ABSTRACT FROM AUTHOR]

In this study, we derive a new iterative algorithm (including its preconditioned version) which is a hybridized variant of the biconjugate A -orthogonal residual stabilized (BiCORSTAB) method and the generalized product-type solvers based on BiCOR (GPBiCOR) method. The proposed method, which is named GPBiCOR( m , ℓ ) similarly to the GPBiCG( m , ℓ ) method proposed by Fujino (2002), can be regarded as an extension of the BiCORSTAB2 method introduced by Zhao and Huang (2013). Inspired by Fujino’s idea for improving the BiCGSTAB2 method, in the established GPBiCOR( m , ℓ ) method the parameters computed by the BiCORSTAB method are chosen at successive m iteration steps, and afterwards the parameters of the GPBiCOR method are utilized in the subsequent ℓ iteration steps. Therefore, the proposed method can inherit the low computational cost of BiCORSTAB and the attractive convergence of GPBiCOR. Extensive numerical convergence results on selected real and complex matrices are shown to assess the performances of the proposed GPBiCOR( m , ℓ ) method, also against other popular non-Hermitian Krylov subspace methods. [ABSTRACT FROM AUTHOR]

Restoration of images degraded by blurring and impulse noise has received considerable attention recently. Guo et al. (2009) proposed a fast l 1 -total variation algorithm for grayscale image restoration with impulse noise. In this paper, we extend their idea for deblurring color images with impulse noise. An alternating iteration scheme is adopted for solving the corresponding problem. More importantly, we employ the five-point property to analyze the convergence of the proposed alternating algorithm. Numerical experiments demonstrate that the proposed method could deblur color images with good quality. [ABSTRACT FROM AUTHOR]

The rank- (L , L , 1) block term decomposition (BTD) of the tensor has recently received increasing attention in diverse high-order data processing, e.g., hyperspectral image restoration and blind source separation. However, the standard alternating least squares algorithm for the rank- (L , L , 1) BTD (BTD-ALS) is computationally expensive, which hinders its application on real large-scale data. In this paper, we propose a fast sketching-based algorithm for the rank- (L , L , 1) BTD (FastBTD) to address the computational burden of BTD-ALS. Since the dominant cost of BTD-ALS at each iteration is the sequence of large-scale least squares subproblems, we first project these original subproblems into the low-dimensional subspace by the tensor sketching operator. Then, we can fastly obtain the approximate solutions of the original subproblems by solving these sketched small-scale subproblems at each iteration of FastBTD. The computational complexity of FastBTD (O (2 J I L R + J I R)) is significantly lower than that of BTD-ALS (O (2 I 3 L R + I 3 R)) for solving the least squares subproblems at each iteration, when J ≪ I 2 . Moreover, we provide the theoretical error bound for FastBTD. Extensive experiments on both synthetic and real data demonstrate that FastBTD achieves substantial speedup while maintaining accuracy compared with BTD-ALS. [ABSTRACT FROM AUTHOR]

Objectives To examine the influence of quality measures of psychopharmacological medication ( PPM) prescribing on all-cause mortality in a Medicare long-stay nursing home ( NH) population. Design Longitudinal. Setting 2007-09 Medicare data linked to Minimum Data Set 2.0 files. Participants Four new-user cohorts of residents initiating antipsychotic (n = 13,105), antidepressant (n = 14,251), anxiolytic and sedative-hypnotic (n = 10,789), and any PPM (n = 14,568) medication. Measurements Three measures of PPM prescribing quality were assessed monthly with a 6-month look-back: evidence of appropriate indication, dose (modified standardized daily dose ( mSDD); below (<1), at (1), and above (>1) recommended geriatric dose), and duration of therapy (DOT; ≤30, 31-60, 61-90, 91-180 days from medication initiation). Complementary log-log models with quality measures as time-dependent variables were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality. Results Appropriate use of antidepressants, anxiolytics and sedative-hypnotics, and any PPMs, as evidenced by appropriate indications, was significantly associated with lower mortality risk (HRantidepressants = 0.81, 95% CI = 0.76-0.86; HRanxiolytics and sedative-hypnotics = 0.81, 0.75-0.88; HRPPM = 0.89, 0.83-0.95). Antipsychotic and anxiolytic and sedative-hypnotic users with a mSDD of less than 1 had lower mortality risk than those with a mSDD greater than 1, whereas a protective effect was observed in antidepressant users with a mSDD greater than 1. In all four cohorts, those with a DOT of 91 to 180 days had lower mortality than those with a DOT of 1 month or less; the lower risk of mortality was detected after antipsychotic use for 31 days or longer. Conclusion Optimal PPM prescribing quality, as measured by indication and duration, is associated with low mortality. The benefit related to drug dosage varied by therapeutic class. When prescribing PPMs to NH residents, providers should consider not only drug choice, but also dose and duration of prescribed regimens. [ABSTRACT FROM AUTHOR]

Background: Procalcitonin (PCT)-based algorithms have been used to guide antibiotic therapy in several clinical settings. However, evidence supporting PCT-based algorithms for secondary peritonitis after emergency surgery is scanty. In this study, we aimed to investigate whether a PCT-based algorithm could safely reduce antibiotic exposure in this population. Methods/Principal Findings: From April 2012 to March 2013, patients that had secondary peritonitis diagnosed at the emergency department and underwent emergency surgery were screened for eligibility. PCT levels were obtained pre-operatively, on post-operative days 1, 3, 5, and 7, and on subsequent days if needed. Antibiotics were discontinued if PCT was <1.0 ng/mL or decreased by 80% versus day 1, with resolution of clinical signs. Primary endpoints were time to discontinuation of intravenous antibiotics for the first episode and adverse events. Historical controls were retrieved for propensity score matching. After matching, 30 patients in the PCT group and 60 in the control were included for analysis. The median duration of antibiotic exposure in PCT group was 3.4 days (interquartile range [IQR] 2.2 days), while 6.1 days (IQR 3.2 days) in control (p < 0.001). The PCT algorithm significantly improves time to antibiotic discontinuation (p < 0.001, log-rank test). The rates of adverse events were comparable between 2 groups. Multivariate-adjusted extended Cox model demonstrated that the PCT-based algorithm was significantly associated with a 87% reduction in hazard of antibiotic exposure within 7 days (hazard ratio [HR] 0.13, 95% CI 0.07–0.21, p < 0.001), and a 68% reduction in hazard after 7 days (adjusted HR 0.32, 95% CI 0.11–0.99, p  =  0.047). Advanced age, coexisting pulmonary diseases, and higher severity of illness were significantly associated with longer durations of antibiotic use. Conclusions/Significance: The PCT-based algorithm safely reduces antibiotic exposure in this study. Further randomized trials are needed to confirm our findings and incorporate cost-effectiveness analysis. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12612000601831 [ABSTRACT FROM AUTHOR]

Motivation: Statistical validation of protein identifications is an important issue in shotgun proteomics. The false discovery rate (FDR) is a powerful statistical tool for evaluating the protein identification result. Several research efforts have been made for FDR estimation at the protein level. However, there are still certain drawbacks in the existing FDR estimation methods based on the target-decoy strategy.Results: In this article, we propose a decoy-free protein-level FDR estimation method. Under the null hypothesis that each candidate protein matches an identified peptide totally at random, we assign statistical significance to protein identifications in terms of the permutation P-value and use these P-values to calculate the FDR. Our method consists of three key steps: (i) generating random bipartite graphs with the same structure; (ii) calculating the protein scores on these random graphs; and (iii) calculating the permutation P value and final FDR. As it is time-consuming or prohibitive to execute the protein inference algorithms for thousands of times in step ii, we first train a linear regression model using the original bipartite graph and identification scores provided by the target inference algorithm. Then we use the learned regression model as a substitute of original protein inference method to predict protein scores on shuffled graphs. We test our method on six public available datasets. The results show that our method is comparable with those state-of-the-art algorithms in terms of estimation accuracy.Availability: The source code of our algorithm is available at: https://sourceforge.net/projects/plfdr/Contact: zyhe@dlut.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

SUMMARY An incomplete Cholesky (IC) factorization with multi-parameters is presented. The marked virtue of the proposed IC factorization algorithm is to dynamically control the number of nonzero elements in each column of the IC factorization preconditioner L with the help of these involved parameters. Parameter setting strategies are also given. Numerical results show that the total computing time for both computation of the preconditioner L and iterative solution is evidently reduced for almost all test matrices. In general, these parameters can obviously enhance the effectiveness and performance of the IC factorization. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: By a block representation of factorization for a general matrix introduced by Amodio and Mazzia [P. Amodio, F. Mazzia, A new approach to the backward error analysis in the factorization algorithm, BIT 39 (1999) 385–402], a block representation of block factorization for block tridiagonal block -matrices is obtained and some properties on the factors of the factorization are presented. Perturbation theory for the block factorization of block tridiagonal block -matrices is also considered. Then a rounding error analysis of the block factorization for block tridiagonal block -matrices is given, and some bounds for the growth factor are proposed. Finally, a numerical example is presented to illustrate our theoretical results. [Copyright &y& Elsevier]

In this paper we study convergence of the overlapping Schwarz waveform relaxation (OSWR) algorithm for reaction-diffusion equations with time delay. We first prove linear convergence of the algorithm in the continuous case on infinite time intervals at a rate depending on the size of the overlaps, the time-delay argument and the coefficients of the equations. For the special case, i.e., the heat equation with a fixed time delay, the convergence rates of the OSWR algorithm presented in this paper are sharper than the existing results. We then prove that the linear convergence remains valid after spatial discretization and the convergence rates are robust with respect to mesh refinement. The convergence behaviour of the algorithm with an arbitrary number of subdomains is also investigated and it is shown that the convergence rate deteriorates with as number of subdomains increases and ameliorates as the overlap size increases. [ABSTRACT FROM PUBLISHER]

Abstract: The existence of block factorization without pivoting for complex symmetric block tridiagonal matrices whose real and imaginary parts are positive definite and every block has the same property is assured. Some properties of the factors of the block factorization for this kind of matrices are presented. By the block representation of the factorization, the growth factor proposed by Amodio and Mazzia [P. Amodio, F. Mazzia, A new approach to the backward error analysis in the factorization algorithm, BIT 39 (1999) 385–402], sometimes, is less than or equal to 1. Based on the growth factor, an error analysis is also considered and it shows that the factorization is stable under some reasonable assumptions. Finally, a numerical experiment on a model problem is used to verify our results. [Copyright &y& Elsevier]

This paper presents a new approach for the calibration and control of spark ignition engines using a combination of neural networks and sliding mode control technique. Two parallel neural networks are utilized to realize a neuro-sliding mode control (NSLMC) for self-learning control of automotive engines. The equivalent control and the corrective control terms are the outputs of the neural networks. Instead of using error backpropagation algorithm, the network weights of equivalent control are updated using the Levenberg-Marquardt algorithm. Moreover, a new approach is utilized to update the gain of corrective control. Both modifications of the NSLMC are aimed at improving the transient performance and speed of convergence. Using the data from a test vehicle with a V8 engine, we built neural network models for the engine torque (TRQ) and the air-to-fuel ratio (AFR) dynamics and developed NSLMC controllers to achieve tracking control. The goal of TRQ control and AFR control is to track the commanded values under various operating conditions. From simulation studies, the feasibility and efficiency of the approach are illustrated. For both control problems, excellent tracking performance has been achieved. [ABSTRACT FROM AUTHOR]

Abstract: An algorithm of diagonal transformation for the Perron root of nonnegative matrices is proposed by Duan and Zhang [F. Duan, K. Zhang, An algorithm of diagonal transformation for Perron root of nonnegative irreducible matrices, Appl. Math. Comput. 175 (2006) 762–772]. This method can be used for all nonnegative irreducible matrices. In this paper, an improved algorithm which is based on this method is proposed. The new algorithm inherits all the above-mentioned advantages of the original algorithm and has higher efficiency. It is testified by numerical testing that the efficiency of the new algorithm is improved greatly. [ABSTRACT FROM AUTHOR]