Artistic features extraction from chinese calligraphy works via regional guided filter with reference image

Chinese calligraphy is a unique visual art, and and is one of the material basis of China’s traditional cultural heritage. However, time had caused the old calligraphy works to weathering and damages, so it is necessary to utilize advanced technologies to protect those works. One of those technologies is digital imaging, and the obtained images by digital imaging can preserve the visual information of calligraphy works better, furthermore, they can be used in further researches. While the basic works for those researches are to extract the artistic features which include two elements, i.e., form and spirit. However, most of the existing methods only extract the form and ignore the characters’ spirit, especially they are insensitive to the slight variation in complex ink strokes. To solve these problems, this paper proposes an extraction method based on regional guided flter (RGF) with reference images, which is generated by KNN matting and used as the input image for RGF. Since RGF is sensitive to the slight variation of ink, so the detailed information of the inside of strokes can be detected better. Besides, unlike the past works, which filter the whole strokes, RGF filters the inside of strokes and edges in different windows respectively, which results in that the edges are preserved accurately. Results from a deployment of several famous Chinese calligraphy works demonstrate that our method can extract more accurate and complete form and spirit with lower error rate.     

Approximation Algorithms for <Emphasis Type="Italic">k</Emphasis>-hurdle Problems

The polynomial-time solvable k-hurdle problem is a natural generalization of the classical s-t minimum cut problem where we must select a minimum-cost subset S of the edges of a graph such that |p∩S|≥k for every s-t path p. In this paper, we describe a set of approximation algorithms for “k-hurdle” variants of the NP-hard multiway cut and multicut problems. For the k-hurdle multiway cut problem with r terminals, we give two results, the first being a pseudo-approximation algorithm that outputs a (k−1)-hurdle solution whose cost is at most that of an optimal solution for k hurdles. Secondly, we provide a 2(1-\frac1r)2(1-\frac{1}{r})-approximation algorithm based on rounding the solution of a linear program, for which we give a simple randomized half-integrality proof that works for both edge and vertex k-hurdle multiway cuts that generalizes the half-integrality results of Garg et al. for the vertex multiway cut problem. We also describe an approximation-preserving reduction from vertex cover as evidence that it may be difficult to achieve a better approximation ratio than 2(1-\frac1r)2(1-\frac{1}{r}). For the k-hurdle multicut problem in an n-vertex graph, we provide an algorithm that, for any constant ε>0, outputs a ⌈(1−ε)k⌉-hurdle solution of cost at most O(log n) times that of an optimal k-hurdle solution, and we obtain a 2-approximation algorithm for trees.     

Is my model any good: differentially private regression diagnostics

Linear and logistic regression are popular statistical techniques for analyzing multi-variate data. Typically, analysts do not simply posit a particular form of the regression model, estimate its parameters, and use the results for inference or prediction. Instead, they first use a variety of diagnostic techniques to assess how well the model fits the relationships in the data and how well it can be expected to predict outcomes for out-of-sample records, revising the model as necessary to improve fit and predictive power. In this article, we develop \(\epsilon \)-differentially private diagnostics tools for regression, beginning to fill a gap in privacy-preserving data analysis. Specifically, we create differentially private versions of residual plots for linear regression and of receiver operating characteristic (ROC) curves as well as binned residual plot for logistic regression. The residual plot and binned residual plot help determine whether or not the data satisfy the assumptions underlying the regression model, and the ROC curve is used to assess the predictive power of the logistic regression model. These diagnostics improve the usefulness of algorithms for computing differentially private regression output, which alone does not allow analysts to assess the quality of the posited model. Our empirical studies show that these algorithms can be effective tools for allowing users to evaluate the quality of their models.     

Early bankruptcy prediction using ENPC

Bankruptcy prediction has long time been an active research field in finance. One of the main approaches to this issue is dealing with it as a classification problem. Among the range of instruments available, we focus our attention on the Evolutionary Nearest Neighbor Classifier (ENPC). In this work we assess the performance of the ENPC comparing it to six alternatives. The results suggest that this algorithm might be considered a good choice.     

A process mining-based analysis of business process work-arounds

Business process work-arounds are specific forms of incompliant behavior, where employees intentionally decide to deviate from the required procedures although they are aware of them. Detecting and understanding the work-arounds performed can guide organizations in redesigning and improving their processes and support systems. Existing process mining techniques for compliance checking and diagnosis of incompliant behavior rely on the available information in event logs and emphasize technological capabilities for analyzing this information. They do not distinguish intentional incompliance and do not address the sources of this behavior. In contrast, the paper builds on a list of generic types of work-arounds found in practice and explores whether and how they can be detected by process mining techniques. Results obtained for four work-around types in five real-life processes are reported. The remaining two types are not reflected in events logs and cannot be currently detected by process mining. The detected work-around data are further analyzed for identifying correlations between the frequency of specific work-around types and properties of the processes and of specific activities. The analysis results promote the understanding of work-around situations and sources.     

Modelling salient visual dynamics in videos

Automatic video annotation is a critical step for content-based video retrieval and browsing. Detecting the focus of interest in video frames automatically can benefit the tedious manual labeling process. However, producing an appropriate extent of visually salient regions in video sequences is a challenging task. Therefore, in this work, we propose a novel approach for modeling dynamic visual attention based on spatiotemporal analysis. Our model first detects salient points in three-dimensional video volumes, and then uses the points as seeds to search the extent of salient regions in a novel motion attention map. To determine the extent of attended regions, we use the maximum entropy in the spatial domain to analyze the dynamics derived by spatiotemporal analysis. Our experiment results show that the proposed dynamic visual attention model achieves high precision value of 70% and reveals its robustness in successive video volumes.     

Beyond the financial crisis: a way forward with development paradigm

As the impact of the financial crisis spreads worldwide, it has become a top priority of various countries, international institutions, entrepreneurs and scholars to find innovative and creative ways to face this challenge. As Hazel Henderson (2002) has pointed out, “the world has not fallen into a financial crisis, but fundamentally fell into a crisis of development paradigm.” We need to reflect seriously on this paradigm and rethink of the social and economic models and cultural values for meeting the challenges of this crisis. The paper introduces key issues of development within a global context, and explores the shift of the value system from hard technology centered to soft technology-based future direction of human development.     

Linear-Dispersion Division Multiple-Access for MIMO systems

Comprehensive study on novel Linear-Dispersion Division Multiple-Access（LDDMA） for multi-user uplink Multiple-Input Multiple-Output（MIMO）systems is proposed.In the new multiplexing scheme,each user’s information symbol is dispersed by a User-Specific Matrix（USM）both in space and time domain and linearly combined at base-station side.And a simple random search algorithm,based on capacity maximization criteria,is developed to generate a bank of USMs.Simulation results are presented to demonstrate the advantages of LDDMA.When the Bit Error Rate（BER） reaches 10^–3,the performance gains are 3dB and 5dB,compared with Time-Division Linear Dispersion Codes（TD-LDC）and BLAST,respectively.     

Sphingolipid Catabolism and Glycerophospholipid Levels Are Altered in Erythrocytes and Plasma from Multiple Sclerosis Patients

Multiple sclerosis (MS) is an autoimmune, inflammatory, degenerative disease of the central nervous system. Changes in lipid metabolism have been suggested to play important roles in MS pathophysiology and progression. In this work we analyzed the lipid composition and sphingolipid-catabolizing enzymes in erythrocytes and plasma from MS patients and healthy controls. We observed reduction of sphingomyelin (SM) and elevation of its products—ceramide (CER) and shingosine (SPH). These changes were supported by the detected up-regulation of the activity of acid sphingomyelinase (ASM) in MS plasma and alkaline ceramidase (ALCER) in erythrocytes from MS patients. In addition, Western blot analysis showed elevated expression of ASM, but not of ALCER. We also compared the ratios between saturated (SAT), unsaturated (UNSAT) and polyunsaturated fatty acids and suggest, based on the significant differences observed for this ratio, that the UNSAT/SAT values could serve as a marker distinguishing erythrocytes and plasma of MS from controls. In conclusion, the application of lipid analysis in the medical practice would contribute to definition of more precise diagnosis, analysis of disease progression, and evaluation of therapeutic strategies. Based on the molecular changes of blood lipids in neurodegenerative pathologies, including MS, clinical lipidomic analytical approaches could become a promising contemporary tool for personalized medicine.