Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic isolation through time: allochronic differentiation of a phenologically atypical population of the pine processionary moth

Allochronic speciation refers to a mode of sympatric speciation in which the differentiation of populations is primarily due to a phenological shift without habitat or host change. However, it has been so far rarely documented. The present paper reports on a plausible case of allochronic differentiation between sympatric populations of the pine processionary moth (PPM), Thaumetopoea pityocampa. The PPM is a Mediterranean insect with winter larval development. A phenologically atypical population with early adult activity and summer larval development was detected 10 years ago in Portugal. Mitochondrial and nuclear sequences strongly suggest that the ‘summer’ individuals are closely related to the sympatric winter population, while microsatellite data show a reduction in allelic richness, a distortion of allelic frequencies and significant genetic differentiation. Moreover, monitoring of adult flights suggests that reproductive activity does not overlap between the summer and winter populations. We postulate that the summer population appeared after a sudden phenological shift of some individuals of the sympatric winter population, leading to a founder effect and complete reproductive isolation. Given that the individuals showing this new phenology are subject to different selection pressures, the observed allochronic differentiation may rapidly lead to deeper divergence.
CUDA Compatible GPU as an Efficient Hardware Accelerator for AES Cryptography

This paper presents a study of the efficiency in applying modern graphics processing units in symmetric key cryptographic solutions. It describes both traditional style approaches based on the OpenGL graphics API and new ones based on the recent technology trends of major hardware vendors. It presents an efficient implementation of the advanced encryption standard (AES) algorithm in the novel CUDA platform by Nvidia. AES is currently the most widely adopted modern symmetric key encryption standard. The performance of the new fastest GPU solution is compared with those of the reference sequential implementations running on an Intel Pentium IV 3.0 GHz CPU. Unlike previous research in this field, the results of this effort show for the first time the GPU can perform as an efficient cryptographic accelerator. The developed solutions run up to 20 times faster than OpenSSL and in the same range of performance of existing hardware based implementations.
Facial expression recognition based on Liquid State Machines built of alternative neuron models

This paper presents an approach to facial expression recognition based on the theory of liquid computing. Up to date, no emotion recognition systems based on spiking neural networks exist, and our work is the first attempt in this direction. We investigated the pattern recognition ability of Liquid State Machines based on various neural models, such as integrate-and-fire, resonate-and-fire, FitzHugh-Nagumo, Morris-Lecal, Hindmarsh-Rose and Izhikevich's models. No single Liquid State Machine provided particularly good results, but a global classifier we defined merging the response of the different models achieved a very satisfactory performance in expression recognition.
A Sample Selection Model for Unit and Item Nonresponse in Cross-Sectional Surveys

We consider a general sample selection model where unit and item nonresponse simultaneously affect a regression relationship of interest, and both types of nonresponse are potentially correlated. We estimate both parametric and semiparametric specifications of the model. The parametric specification assumes that the errors in the latent regression equations follow a trivariate Gaussian distribution. The semiparametric specification avoids distributional assumptions about the underlying regression errors. In our empirical application, we estimate Engel curves for consumption expenditure using data from the first wave of SHARE (Survey on Health, Aging and Retirement in Europe).
Evolution of bootstrap techniques in low-voltage CMOS digital VLSI circuits for SOC applications

This paper reports the evolution of the bootstrap techniques in low-voltage CMOS digital VLSI circuits in the past. Combining bootstrap and DTMOS techniques, low-voltage CMOS digital VLSI circuits using a very low power supply voltage such as 0.5V have been developed for low-power SOC applications.
Predicting and Explaining Automatic Testing Tool Effectiveness

Automatic white-box test generation is a challenging problem. Many existing tools rely on complex code analyses and heuristics. As a result, structural features of an input program may impact tool effectiveness in ways that tool users and designers may not expect or understand. We develop a technique that uses structural program metrics to both predict and explain the test coverage achieved by three automatic test generation tools. We use coverage and structural metrics extracted from 11 software projects to train several decision-tree classifiers. These classifiers can predict high or low coverage with success rates of 82% to 94%. In addition, they show tool users and designers the program structures that impact tool effectiveness.
Recent advancements in the forward scattering with the Cylindrical Wave Approach

The Cylindrical Wave Approach is a numerical-analytical technique for the simulation of forward scattering by buried cylindrical targets. Such a technique implements the cylindrical-wave expansions to deal with the scattered field by circular cross-section cylinders in different environments. The method is capable to solve reflection and transmission from flat or slightly rough boundaries, through suitable cylindrical waves of expansions. Therefore, targets may by buried under an interface or placed in a stratified medium, with a high flexibility in the simulation of possible scattering scenarios. Applications are in the buried object detection and localization through electromagnetic techniques.
Skeletal muscle ACC2 S212 phosphorylation is not required for the control of fatty acid oxidation during exercise

During submaximal exercise fatty acids are a predominant energy source for muscle contractions. An important regulator of fatty acid oxidation is acetyl‐CoA carboxylase (ACC), which exists as two isoforms (ACC1 and ACC2) with ACC2 predominating in skeletal muscle. Both ACC isoforms regulate malonyl‐CoA production, an allosteric inhibitor of carnitine palmitoyltransferase 1 (CPT‐1); the primary enzyme controlling fatty acyl‐CoA flux into mitochondria for oxidation. AMP‐activated protein kinase (AMPK) is a sensor of cellular energy status that is activated during exercise or by pharmacological agents such as metformin and AICAR. In resting muscle the activation of AMPK with AICAR leads to increased phosphorylation of ACC (S79 on ACC1 and S221 on ACC2), which reduces ACC activity and malonyl‐CoA; effects associated with increased fatty acid oxidation. However, whether this pathway is vital for regulating skeletal muscle fatty acid oxidation during conditions of increased metabolic flux such as exercise/muscle contractions remains unknown. To examine this we characterized mice lacking AMPK phosphorylation sites on ACC2 (S212 in mice/S221 in humans‐ACC2‐knock‐in [ACC2‐KI]) or both ACC1 (S79) and ACC2 (S212) (ACC double knock‐in [ACCD‐KI]) during submaximal treadmill exercise and/or ex vivo muscle contractions. We find that surprisingly, ACC2‐KI mice had normal exercise capacity and whole‐body fatty acid oxidation during treadmill running despite elevated muscle ACC2 activity and malonyl‐CoA. Similar results were observed in ACCD‐KI mice. Fatty acid oxidation was also maintained in muscles from ACC2‐KI mice contracted ex vivo. These findings indicate that pathways independent of ACC phosphorylation are important for regulating skeletal muscle fatty acid oxidation during exercise/muscle contractions.
Brain tumor segmentation based on Self Organising Map and Discrete Wavelet Transform

One of the serious kind of disease in the medical field is considered to be the brain tumor. So it must to have the fast and accurate detection. Different algorithms are provided for the tumour detection and segmentation. The important approach in the brain tumor segmentation is to identify the various stages includes benign, malignant and the normal. For the process of classifying voxels, a classifier called Self Organising Map(SOM) is used. Self-Organizing Map (SOM) which includes the unsupervised learning algorithm and Learning Vector Quantization (LVQ) with high diversity data like tumour appearance and its contour deformation. On comparing with the other approaches, instead of using Multimodal MRI images for clustering of voxels SOM has been used. A feature vector has been constructed in which the features for the constructing vector have been gained from the Discrete Wavelet Transform (DWT) coefficients. This has been constructed mainly for identifying tissue types which includes White Matter (WM), Grey Matter (GM), Cerebrospinal Fluid (CSF) and sometimes pathological tissues. The accuracies and the performance are provided in terms of training performance and the classification accuracies. The results which has been been simulated, shows the better accuracies of classifier and segmentation on comparing with the previous method.
Simulations of three-dimensional flows with the lattice Boltzmann equation on the IBM 3090/VF

We illustrate the basic features of the Lattice Boltzmann Equation (LBE), a new finite-difference scheme that arises from the microdynamics of the Frisch-Hasslacher-Pomcau cellular automation once, instead of tracking the individual history of each particle, one only solves for the mean values of the particle populations living in the lattice. Details on the actual coding of the LBE scheme are presented and discussed along with performance data pertaining to the vector and parallel implementation on the IBM 3090/600 vector multiprocessor. Finally, two applications in the field of two and three-dimensional hydrodynamics are briefly illustrated.
Impact of Internal Marketing on Market Orientation and Business Performance

The purpose of this paper is to investigate the impact of internal marketing on commercial banks’ organization commitment, market orientation, and business performance. Data collection from 12 commercial banks of Pakistan was targeted. Quantitative approaches were used for data collection from 500 bank employees and effective data was further analyzed by using inferential techniques on SPSS 18.00. The findings of the study suggested that internal marketing programs had a significant impact on employees’ commitment, their market orientation and overall profitability of the firm. In addition, the mediating relationship of organization commitment with internal marketing and market orientation was not supported. The paper investigates the associations by adding a cultural value aspect of Hofstede suggested by Awwad and Agti (2011), and study is extended by incorporating element of business performance and market orientation in a single study. The findings support the originality of the work. Additionally, parsimonious aspect is followed by taking the only mediating variable i.e. organization commitment. Lastly, this paper is one of few papers that focus on internal marketing in Pakistani banking industry adds to its originality.
Radio observations and spectral index study of the SNR G126.2+1.6

We present new images of the low radio surface brightness Supernova Remnant (SNR) G126.2+1.6, based on the 408 MHz and 1420 MHz continuum emission and the HI-line emission data of the Canadian Galactic Plane Survey (CGPS). We find the SNR's flux densities at 408 MHz ($9.7\pm3.9$ Jy) and 1420 MHz ($6.7\pm2.1$ Jy) which have been corrected for flux densities from compact sources within the SNR. We list properties of the 19 brightest compact sources within G126.2+1.6 at both 408 MHz and 1420 MHz. The integrated flux density based spectral index ($S_{\nu} \propto \nu ^{-\alpha}$) is $0.30\pm0.41$. The respective T-T plot spectral index is $0.30 \pm0.08$. We also find spatial variations of spectral index within the SNR: 0.2–0.6. HI observations show structures probably associated with the SNR, i.e., features associated with the SNR's southeastern filaments in the radial velocity range of –33 to –42 km s -1 , and with its northwestern filaments in –47 to –52 km s -1 . This association suggests a distance of 5.6 kpc for SNR G126.2+1.6. The estimated Sedov age for G126.2+1.6 is less than $2.1\times10$ 5 yr.
Bearing Capacity Modeling of Composite Pile Foundation Using Parameter-Optimized RBF Neural Networks

Radial basis function-artificial neural networks (RBF-ANNs) are used for bearing capacity modeling of composite foundation reinforced with deep mixing piles. Although RBF-ANNs possess significant advantages in terms of strong generalization, flexible adaptability to multi-independent variables and sufficient avoidance of local minima, their performance may be directly affected by two uncertain parameters, the width of radial basis kernel function (spread) and the goal error of training (err_goal). Up to now still no mature methods to determine the optimal parameter values. As an exploration, a novel method is proposed to determine the optimal parameter values by thoroughly searching over the possible interval of uncertain parameters. Moreover, a technique of reconstructing more samples from few original samples is put forward to improve the prediction precision of the RBF-ANNs. The proposed techniques are applied to the bearing capacity modeling of composite foundation reinforced with deep mixing piles. The results demonstrate that the uncertain parameter optimization and sample reconstruction techniques are capable of significantly improving the performance of RBF-ANNs
Long-Term Evolution of Massive Black Hole Binaries. II. Binary Evolution in Low-Density Galaxies

We use direct-summation N-body integrations to follow the evolution of binary black holes at the centers of galaxy models with large, constant-density cores. Particle numbers as large as 0.4 × 106 are considered. The results are compared with the predictions of loss-cone theory under the assumption that the supply of stars to the binary is limited by the rate at which they can be scattered into the binary's influence sphere by gravitational encounters. The agreement between theory and simulation is quite good; in particular, we are able to quantitatively explain the observed dependence of binary hardening rate on N. We do not verify a recent claim that the hardening rate of the binary stabilizes when N exceeds a particular value or that Brownian wandering of the binary has a significant effect on its evolution. When scaled to real galaxies, our results suggest that massive black hole binaries in gas-poor nuclei would be unlikely to reach gravitational wave coalescence in a Hubble time.
Lack of Association of P2RX7 Gene rs2230912 Polymorphism with Mood Disorders: A Meta-Analysis

Background To assess the association of P2RX7 gene rs2230912 polymorphism with mood disorders using a meta-analysis. Methods Data were collected from the following electronic databases: PubMed, Excerpta Medica Database, Elsevier Science Direct, Cochrane Library, and Chinese Biomedical Literature Database, with the last report up to April 1, 2013. Odds ratio (OR) with 95% confidence interval (CI) was used to assess the strength of the association. Dependent on the results of heterogeneity test among individual studies, the fixed effect model (Mantel–Haenszel) or random effect model (DerSimonian–Laird) was selected to summarize the pooled OR. Results We identified 13 separate studies using search (6,962 cases and 9,262 controls). We detected significant between-study heterogeneity. No significant association of this polymorphism with mood disorders was found (P>0.05). We also performed disease-specific meta-analysis in unipolar depression and bipolar disorder. No significant association of this polymorphism with unipolar depression or bipolar disorder was found (P>0.05). Additionally, we performed subgroup analysis by different types of cases. No significant association of this polymorphism with mood disorders in clinical cohorts or population-based cohorts (P>0.05). A significant association of this polymorphism with mood disorders was found for the allele contrast in family-based cohorts (OR = 1.26, 95%CI = 1.05–1.50, P = 0.01). Conclusions Overall, our meta-analysis suggests that P2RX7 gene rs2230912 polymorphism may not contribute to the risk of developing mood disorders using a case-control design. Given the discordance in the subgroup analysis by different types of cases, further studies based on larger sample size are still needed.
Gene Assembly from Chip‐Synthesized Oligonucleotides

De novo synthesis of long double‐stranded DNA constructs has a myriad of applications in biology and biological engineering. However, its widespread adoption has been hindered by high costs. Cost can be significantly reduced by using oligonucleotides synthesized on high‐density DNA chips. However, most methods for using off‐chip DNA for gene synthesis have failed to scale due to the high error rates, low yields, and high chemical complexity of the chip‐synthesized oligonucleotides. We have recently demonstrated that some commercial DNA chip manufacturers have improved error rates, and that the issues of chemical complexity and low yields can be solved by using barcoded primers to accurately and efficiently amplify subpools of oligonucleotides. This unit includes protocols for computationally designing the DNA chip, amplifying the oligonucleotide subpools, and assembling 500‐ to 800‐bp constructs. Curr. Protoc. Chem. Biol. 4:1‐17 © 2012 by John Wiley & Sons, Inc.
Robust egomotion for large-scale trajectories

This paper presents an effective egomotion solution based on high curvature image features described using local intensity histograms for stereo matching and tracking. To robustify the visual processing system, we propose feature extraction over moment image representation to overcome the adverse effects of illumination changes. A bundle adjustment optimisation technique, thoroughly analysed for different reprojection strategies, is developed for motion estimation of an autonomous platform. The quality of results is shown to be on par with high quality GPS-corrected-INS systems, even for long-range trajectories.
Hybrid Algorithm for Network Design Problem with Uncertain Demands

The purpose of the paper is to present a hybrid algorithm to solve a transportation optimization model with random demand parameters and network design variables. At first, the classical deterministic linear transportation model with network design 0-1 variables is introduced. Then, randomness is considered for demand parameters and modeled by here-and-now approach. The obtained scenario-based model leads to a mixed integer linear program (MILP) that can be solved by common integer programming techniques, see e.g. the branch-and-bound algorithm implemented in the CPLEX solver. Such a program may reach solvability limitations of MIP algorithms for large scale real world data, so a suitable heuristic development is welcome. Therefore, the idea of combination of traditional optimization algorithm and genetic algorithm is discussed and developed. At the end, the results are illustrated and also verified for a small test instance by figures.
CONSUMER PERCEIVED BRAND CONCEPT & CLOSE BRAND EXTENSION: A MULTI-MEDIATION MODEL ANALYSIS.

ABSTRACT Findings: Three hypotheses were tested with multi-mediation test by Preacher and Hayes. All three were accepted with most significant being Parent Brand Conviction and Brand loyalty jointly mediate the relationship between Consumer Perceived Brand Concept and Successful Brand Extension Evaluation for close extension. Methodology: One independent variable consumer perceived brand concept was used with the dependent variable “Successful brand extension evaluation” with two mediating variables. Real life close brand extension as stimuli was used in this study. Self developed questionnaire was filled by 462 respondents by simple random sampling technique. Purpose: This paper investigates how brand extension strategy can be used successfully by the companies while launching close brand extensions in the market. What are the antecedents (Mediating variables) of successful brand extension? Key Words: Parent Brand Conviction, Brand Loyalty, Consumer Perceived Brand Concept, Close Brand Extension & Multi-Mediation.
Insights into capacity-constrained optimal transport

A variant of the classical optimal transportation problem is the following: among all joint measures with fixed marginals and that are dominated by a given density, find the optimal one. Existence and uniqueness of solutions to this variant were established by Korman and McCann. In the present article, we expose an unexpected symmetry leading to explicit examples in two and more dimensions. These are inspired in part by simulations in one dimension that display singularities and topology and in part by two further developments: the identification of all extreme points in the feasible set and an approach to uniqueness based on constructing feasible perturbations.
Stuffing Mind into Computer: Knowledge and Learning for Intelligent Systems

The task of somehow putting mind into a computer is one that has been pursued by artiicial intelligence researchers for decades, and though we are getting closer, we have not caught it yet. Mind is an incredibly complex and poorly understood thing, but we should not let this stop us from continuing to strive toward the goal of intelligent computers. Two issues that are essential to this endeavor are knowledge and learning. These form the basis of human intelligence, and most people believe they are fundamental to achieving similar intelligence in computers. This paper explores issues surrounding knowledge acquisition and learning in intelligent artiicial systems in light of both current philosophies of mind and the present state of artiicial intelligence research. Its scope ranges from the mundane to the (almost) outlandish, with the goal of stimulating serious thought about where we are, where we would like to go, and how to get there in our attempts to render an intelligence in silicon.
Does the Yield Spread Predict Recessions in the Euro Area?

This paper studies the informational content of the slope of the yield curve as a predictor of recessions in the euro area. In particu-lar, the historical predictive power of ten yield spreads, for di®erent segments of the yield curve, is tested using a probit model. The yield spread between the ten-year government bond rate and the three-month interbank rate outperforms all the other spreads in predicting recessions in the euro area. The result is con¯rmed when the au-toregressive series of the state of the economy is added in the same model. The forecast accuracy of the spread between 10-year and 3-month interest rates is explored in an exercise of out-of-sample forecasting. This yield spread appears to contain information which goes beyond the information already available in the history of output, providing further evidence of the potential usefulness of this indicator for mon-etary policy purposes. JEL Classification: E44, E52, C53