Miracle-dz/UniFAR-FTUs huggingface dataset:
Heritability and Demographic Analyses in the Large Isolated Population of Val Borbera Suggest Advantages in Mapping Complex Traits Genes

Background Isolated populations are a useful resource for mapping complex traits due to shared stable environment, reduced genetic complexity and extended Linkage Disequilibrium (LD) compared to the general population. Here we describe a large genetic isolate from the North West Apennines, the mountain range that runs through Italy from the North West Alps to the South. Methodology/Principal Findings The study involved 1,803 people living in 7 villages of the upper Borbera Valley. For this large population cohort, data from genealogy reconstruction, medical questionnaires, blood, anthropometric and bone status QUS parameters were evaluated. Demographic and epidemiological analyses indicated a substantial genetic component contributing to each trait variation as well as overlapping genetic determinants and family clustering for some traits. Conclusions/Significance The data provide evidence for significant heritability of medical relevant traits that will be important in mapping quantitative traits. We suggest that this population isolate is suitable to identify rare variants associated with complex phenotypes that may be difficult to study in larger but more heterogeneous populations.
Data fusion of left ventricle Electro-Anatomic Mapping and Multislice Computerized Tomography for Cardiac Resynchronisation Therapy optimization

Cardiac Resynchronization Therapy is a treatment for bi-ventricular asynchronism. It can be optimized by the identification of the most effective pacing sites. The aim of this study is to provide a helpful tool to perform this identification by the fusion of electrical and anatomical information resulting from Electro-Anatomic Mapping (EAM) data and Multislice Computerized Tomography (MSCT) imaging. EAM data provide an approximation of the left ventricle (LV) 3D-surface (SEAM). Left cardiac chambers are segmented from MSCT imaging and surfaces are reconstructed (SCT ). In order to represent this information in a unified framework, a three steps method is proposed: (1) the LV is separated from the left auricle on SCT providing S'CT ; (2) a semi-automatic rigid registration method is applied to SEAM and S'CT ; (3) activation time delays are estimated on S'CT from the EAM data. This method results in a graphical interface offering to clinicians means to identify abnormal electrical activity sites.
A hybrid EF/DF protocol with rateless coded network code for two-way relay channel

In this paper, we investigate a rateless code design for a two-way relay channel (TWRC) where a network coding operation is employed at the relay. Rateless codes are used at both two users and the relay nodes as it can cope with various channel conditions. We propose a hybrid estimate and forward (EF)/decode and forward (DF) protocol with rateless codes for the TWRC. In the proposed scheme, the relay uses a joint belief propagation (BP) decoding process to decode the messages of two users. Depending on whether the relay recovers both users' messages, only one user's message or no users' messages, the relay will generate three different types of signals, namely, network code (NC), soft network code (SNC) or soft physical-layer network code (SPNC), respectively. We design the degree distribution of the rateless codes for the three nodes of the TWRC, to make it suitable not only for decoding at two users at high SNR, but also for decoding at the relay at low SNR. We show that the proposed hybrid EF/DF protocol with the designed rateless codes outperforms other schemes significantly in terms of bit error rate (BER) performance.
High-quality image deblurring with panchromatic pixels

Image deblurring has been a very challenging problem in recent decades. In this article, we propose a high-quality image deblurring method with a novel image prior based on a new imaging system. The imaging system has a newly designed sensor pattern achieved by adding panchromatic (pan) pixels to the conventional Bayer pattern. Since these pan pixels are sensitive to all wavelengths of visible light, they collect a significantly higher proportion of the light striking the sensor. A new demosaicing algorithm is also proposed to restore full-resolution images from pixels on the sensor. The shutter speed of pan pixels is controllable to users. Therefore, we can produce multiple images with different exposures. When long exposure is needed under dim light, we read pan pixels twice in one shot: one with short exposure and the other with long exposure. The long-exposure image is often blurred, while the short-exposure image can be sharp and noisy. The short-exposure image plays an important role in deblurring, since it is sharp and there is no alignment problem for the one-shot image pair. For the algorithmic aspect, our method runs in a two-step maximum-a-posteriori (MAP) fashion under a joint minimization of the blur kernel and the deblurred image. The algorithm exploits a combined image prior with a statistical part and a spatial part, which is powerful in ringing controls. Extensive experiments under various conditions and settings are conducted to demonstrate the performance of our method.
The Forecasting Performance of an Estimated Medium Run Model

In recent times DSGE models came more and more into the focus of forecasters and showed promising forecast performances for the short term. We contribute to the existing literature by analyzing the forecast power of a DSGE model including endogenous growth for the medium run. Instead of only calibrating the model we apply a mixture of calibrating and estimating using Bayesian estimation methods. As forecasting benchmarks we take the Smets-Wouters model (2007) and a VAR model. The evaluation of the forecast errors shows that the Medium-Term model outperforms the Smets-Wouters model with respect to some key macroeconomic variables in the medium run. Compared to the VAR model the Medium-Term model forecast performance is competitive. These results show that the forecast ability of DSGE models is also valid for the medium term.
Windows API based Malware Detection and Framework Analysis

— Detection of zero day malware has been the great challenge for researchers from long time. Traditional signature based anti-malware scanners detect malware based on their unique signatures. The major drawback of such traditional signatures based scanners is that it has no protection against zero-day or unseen malware. Further usage of packers and obfuscation techniques empowered the malware writers to recreate malware variants quickly with slight or no change in malcode. These new variants are undetectable by traditional signature based scanner until their signatures are not present in database. Therefore researchers are working towards finding patterns or features which have unchangeable characteristics of malware even though the malware mutates or obfuscates itself. To address the limitation of traditional signature based scanner, we propose the malware detection method based on extracting relevant application programming interface (API) calls from sub categories of malware. These malware are categorized based on their infection mechanism and actions performed. And because of their fundamental difference in infection mechanism, they do not share similar type of API calls in all malware categories. In this paper, we elucidate an automated framework for analyzing and classifying executables based on their relevant API calls. We explain all the software components used to make the framework fully automatic for extracting API calls. We further explain the Document Class wise Frequency feature selection measure (DCFS) to get the relevant API calls from the extracted API calls to increase the detection rate. We conclude the paper with our experimental result and discussion.
Volatility Model For Financial Market Risk Management : An Analysis on JSX Index Return Covariance Matrix

In measuring risk, practitioners have practiced one of the two extreme approaches for so long, i.e. historical simulation or risk metrics. Meanwhile, academicians tend to apply methods based on the latest development in financial econometrics. In this study, we try to assess one of important issues in financial econometric development that focuses on market risk measurement and management employing asset-based models, i.e. models that apply dimensional covariance matrix, which is relevant to practice world. We compare covariance matrix model with Exponential Smoothing Model and GARCH Derivation and the Associated Derivation Models, using JSX Stock price Index data in 2000-2005. The result of this study shows how applicable the observed financial econometric instrument in Financial Market Risk Management practice.
Active exploration by searching for experiments that falsify the computed control policy

We propose a strategy for experiment selection - in the context of reinforcement learning - based on the idea that the most interesting experiments to carry out at some stage are those that are the most liable to falsify the current hypothesis about the optimal control policy. We cast this idea in a context where a policy learning algorithm and a model identification method are given a priori. Experiments are selected if, using the learnt environment model, they are predicted to yield a revision of the learnt control policy. Algorithms and simulation results are provided for a deterministic system with discrete action space. They show that the proposed approach is promising.
A Test of the Rational Electoral-Cycle Hypothesis

This paper develops a three-step empirical methodology to test the rational electoralcycle hypothesis. The first step consists of testing for the existence of electoral cycles in fiscal policy. The second step conducts three tests for how such cycles should depend on election outcomes as suggested by recent political agency models. The third step is to regress electoral success on fiscal policy. This three-step approach is applied to a panel of Swedish local governments with more than 2000 observations from elections. The findings are as follows: (i) spending is raised and taxes are cut in the election year, (ii) in the election year, spending is higher for a government that will be re-elected as compared to those that will not be re-appointed, (iii) in the post-election year, spending is higher and taxes are lower for re-elected governments than for newly elected ones, (iv) reelected governments spend less and tax more in the post-election year as compared to the election year, (v) conditional on taxes, spending is positively related to electoral success. These set of findings are consistent with Rogoff’s equilibrium budget cycle model where a government signals its competence through cycles in fiscal policy.
Reduced Discrimination in the Tritanopic Confusion Line for Congenital Color Deficiency Adults

In congenital color blindness the red–green discrimination is impaired resulting in an increased confusion between those colors with yellow. Our post-receptoral physiological mechanisms are organized in two pathways for color perception, a red–green (protanopic and deuteranopic) and a blue–yellow (tritanopic). We argue that the discrimination losses in the yellow area in congenital color vision deficiency subjects could generate a subtle loss of discriminability in the tritanopic channel considering discrepancies with yellow perception. We measured color discrimination thresholds for blue and yellow of tritanopic channel in congenital color deficiency subjects. Chromaticity thresholds were measured around a white background (0.1977 u′, 0.4689 v′ in the CIE 1976) consisting of a blue–white and white–yellow thresholds in a tritanopic color confusion line of 21 congenital colorblindness subjects (mean age = 27.7; SD = 5.6 years; 14 deuteranomalous and 7 protanomalous) and of 82 (mean age = 25.1; SD = 3.7 years) normal color vision subjects. Significant increase in the whole tritanopic axis was found for both deuteranomalous and protanomalous subjects compared to controls for the blue–white (F2,100 = 18.80; p < 0.0001) and white–yellow (F2,100 = 22.10; p < 0.0001) thresholds. A Principal Component Analysis (PCA) found a weighting toward to the yellow thresholds induced by deuteranomalous subjects. In conclusion, the discrimination in the tritanopic color confusion axis is significantly reduced in congenital color vision deficiency compared to normal subjects. Since yellow discrimination was impaired the balance of the blue–yellow channels is impaired justifying the increased thresholds found for blue–white discrimination. The weighting toward the yellow region of the color space with the deuteranomalous contributing to that perceptual distortion is discussed in terms of physiological mechanisms.
A new management approach for finding an optimal path in an ad-hoc network

Ad hoc networks, and particularly sensors networks, are composed of nodes in which the batteries and processing capacities are limited. Each node can adjust its power to some extent, that determines all its neighbors that can potentially participate in a hop. The traffic expedition through multiple hops is used when the foreseen destination is not in the immediate extension. The nodes have initially limited energy and consume it in different proportions, according to power level and foreseen receivers. In this paper, a new approach to choose the data paths is proposed, in order to maximize the lifetime of node batteries. This algorithm, basically inspired from project management theory, is simple and allows to find the longest life time of an ad hoc network by calculating the optimal path between a source and a destination.
Acoustic Emission Source Location Based on Signal Features

Good knowledge of acoustic emission (AE) source location is the basic requirement for further damage mechanism characterization. Calculation of the AE source location is mostly based on arrival time differences of the signals recorded by different transducers. Error free arrival time determination is the crucial factor for the localization results accuracy together with the exact elastic wave velocity measurement. In the paper difficulties and limitations of the elastic wave velocity computation are shown. To solve the velocity and the time differences problems, new approach to AE source localization is described. The new method estimates the AE source coordinates using artificial neural network (ANN) processing extracted signal parameters. The ANN do not uses neither arrival time differences nor elastic wave velocities as input data. The new approach advantages are discussed in cases of both numerical and practical experiments. The experiments results are promising for the use of designed localization method in praxis.
Chapter 2 Introduction to the Physics of Ultrasound

From an acoustical point of view, bone is a complex medium as it is heterogeneous, anisotropic and viscoelastic. This chapter reviews the basic notions of physical acoustics which are necessary to tackle the problem of the ultrasonic propagation in bone, in the perspective of the application of quantitative ultrasound (QUS) techniques to bone characterization. The first section introduces the basic phenomena related to the field of medical ultrasound. Basic description of wave propagation is introduced. Mechanical bases are necessary to understand the elastodynamic nature of the interaction between bone and ultrasound. The physical determinants of the speed of sound of the different types of waves corresponding to the propagation in a liquid and in a solid are considered. The effects of boundary conditions (guided waves) are also detailed. The second section describes the physical interaction between an ultrasonic wave and bone tissue, by introducing reflection/refraction, attenuation and scattering phenomena.
Semidefinite programming and combinatorial optimization

We describe a few applications of semideenite programming in combinatorial optimization. Semideenite programming is a special case of convex programming where the feasible region is an aane subspace of the cone of positive semideenite matrices. There has been much interest in this area lately, partly because of applications in com-binatorial optimization and in control theory and also because of the development of eecient interior-point algorithms. The use of semideenite programming in combinatorial optimization is not new though. Eigenvalue bounds have been proposed for combinatorial optimization problems since the late 60's, see for example the comprehensive survey by Mohar and Poljak 20]. These eigenvalue bounds can often be recast as semideenite programs 1]. This reformulation is useful since it allows to exploit properties of convex programming such as duality and polynomial-time solvability, and it avoids the pitfalls of eigenvalue optimization such as non-diierentiability. An explicit use of semideenite programming in combinatorial optimization appeared in the seminal work of Lovv asz 16] on the so-called theta function, and this lead Grr otschel, Lovv asz and Schrijver 9, 11] to develop the only known (and non-combinatorial) polynomial-time algorithm to solve the maximum stable set problem for perfect graphs. In this paper, we describe a few applications of semideenite programming in combinatorial optimization. Because of space limitations, we restrict our attention to the Lovv asz theta function, the maximum cut problem 8], and the automatic generation of valid inequalities a la Lovv asz-Schrijver 17, 18]. This survey is much inspired by another (longer) survey written by the author 7]. However, new results on the power and limitations of the Lovv asz-Schrijver procedure are presented as well as a study of the maximum cut relaxation for graphs arising from association schemes.
Naturalizing Anthropomorphism: Behavioral Prompts to Our Humanizing of Animals

ABSTRACT Anthropomorphism is the use of human characteristics to describe or explain nonhuman animals. In the present paper, we propose a model for a unified study of such anthropomorphizing. We bring together previously disparate accounts of why and how we anthropomorphize and suggest a means to analyze anthropomorphizing behavior itself. We introduce an analysis of bouts of dyadic play between humans and a heavily anthropomorphized animal, the domestic dog. Four distinct patterns of social interaction recur in successful dog–human play: directed responses by one player to the other, indications of intent, mutual behaviors, and contingent activity. These findings serve as a preliminary answer to the question, “What behaviors prompt anthropomorphisms? ” An analysis of anthropomorphizing is potentially useful in establishing a scientific basis for this behavior, in explaining its endurance, in the design of “lifelike” robots, and in the analysis of human interaction. Finally, the relevance of this developing scientific area to contemporary debates about anthropomorphizing behavior is discussed.
Modeling Time Series Analysis between Feedstuff and Hog Prices in Taiwan

Pork in Taiwan has played an important role in terms of domestic consumption, production and price fluctuation in several decades. This study attempts to investigate the causal relationship between hog and feedstuff prices in Taiwan by using monthly time series over the period of January 2000 to October 2010. Prices from hog, feedstuff, soybean meal and corn are considered and performed through a multivariate vector autoregressive (VAR) model. As the empirical results, the long-run equilibrium is captured identifying price elasticity among the variables using the Johansen cointegration diagnosis. Then, the Granger causality approach shows that bidirectional relationship is detected running from feedstuff to corn and from soybean meal to hog as well as unidirectional relationship running from corn to hog, from feedstuff to hog and from soybean meal to feedstuff. In the conclusions, we point out to raw material prices of feedstuff, namely corn and soybean meal that have an importance to hog price as the cost of meat production. Pig farmers and policy-makers should understand the behavior of price interactions to manage the risks on hog market and to increase the profitability on suitable price.
PTEN gene mutations correlate to poor prognosis in glioma patients: a meta-analysis

Background We conducted this meta-analysis based on eligible trials to investigate the relationship between phosphatase and tensin homolog (PTEN) genetic mutation and glioma patients’ survival. Methods PubMed, Web of Science, and EMBASE were searched for eligible studies regarding the relationship between PTEN genetic mutation and glioma patients’ survival. The primary outcome was the overall survival of glioma patient with or without PTEN genetic mutation, and second outcome was prognostic factors for the survival of glioma patient. A fixed-effects or random-effects model was used to pool the estimates according to the heterogeneity among the included studies. Results Nine cohort studies, involving 1,173 patients, were included in this meta-analysis. Pooled results suggested that glioma patients with PTEN genetic mutation had a significant shorter overall survival than those without PTEN genetic mutation (hazard ratio [HR] =2.23, 95% confidence interval [CI]: 1.35, 3.67; P=0.002). Furthermore, subgroup analysis indicated that this association was only observed in American patients (HR =2.19, 95% CI: 1.23, 3.89; P=0.008), but not in Chinese patients (HR =1.44, 95% CI: 0.29, 7.26; P=0.657). Histopathological grade (HR =1.42, 95% CI: 0.07, 28.41; P=0.818), age (HR =0.94, 95% CI: 0.43, 2.04; P=0.877), and sex (HR =1.28, 95% CI: 0.55, 2.98; P=0.564) were not significant prognostic factors for the survival of patients with glioma. Conclusion Current evidence indicates that PTEN genetic mutation is associated with poor prognosis in glioma patients. However, this finding is derived from data in observational studies, potentially subject to selection bias, and hence well conducted, high-quality randomized controlled trials are warranted.
Notice of RetractionCash Flow Risk Measurement for Chinese Non-life Insurance Industry Based on Estimation of ES and NRR

It is very important to measure cash flow risks for the non-life insurance industry, especially in China. A couple of risk measures named cash flow expected shortfall (ES) and cash flow non-recovery ratio (NRR) are proposed based on several assumptions. Therefore monthly premiums and claims of Chinese insurance industry are fitted by seasonal autoregressive integrated moving average model (SARIMA). Monte Carlo simulation method is applied to generate potential cash outflow and inflow in the next 2 years according to SARIMA model. Finally, cash flow ES and cash flow NRR of the next 2 years are estimated. It is briefly concluded that cash flow ES in the future 2 years is RMB 3-8 billion yuan and cash flow NRR is 3%-13% when expense ratio is between 25% and 35%, which means cash flow of Chinese non-life insurance industry is considerably safe because of adequate buffer asset of 130.1 billion yuan at the end of 2007.
Democracy and power

The strange death of political philosophy is democracy special? - does democracy cause inflation? - the political ideas of some economists political accommodation and consociational democracy the consociational model and its dangers self-government revisited exit, voice, and loyalty power - an economic analysis is it better to be powerful or lucky? - the obscurities of power the uses of "power".