Miracle-dz/UniFAR-FTUs huggingface dataset:
ON FUTURE PENSIONS FROM THE SECOND PILLAR PENSION FUNDS

e main focus of this paper lies in the possibility for particular second pillar pension funds participants to get a higher pension, compared with non-participants. ese particular participants are the employees with average wages and average employment history. is analysis is of main importance when it comes to the decision to participate in the second pillar or not. Unit roots tests and cointegration analysis are used as the possible tools to investigate the dynamics of retirement income for participants and non-participants. is research has the intention to determine the conditions when replacement "om the second pillar will o#set the loss "om pay-as-you-go system.
Effects of µ-Opioid Receptor Gene Polymorphism on Postoperative Nausea and Vomiting in Patients Undergoing General Anesthesia with Remifentanil: Double Blinded Randomized Trial

Association between postoperative nausea and vomiting (PONV) and µ-opioid receptor A118G single nucleotide polymorphism (SNP) is undefined and might underlie inconsistent results of studies on PONV occurrence in patients undergoing general anesthesia with the opioid, remifentanil. Four hundred and sixteen Korean women undergoing breast surgery with general anesthesia were randomized to receive remifentanil 10 ng/mL (plasma-site, Minto model) using a target-controlled infusion device and either propofol for total intravenous anesthesia (T group) or sevoflurane for inhalation anesthesia (I group) with bispectral index values maintained between 40 and 60. Blood specimens were collected after anesthesia induction for A118G SNP analysis. PONV and postoperative pain were evaluated. A118G SNP type distribution among Korean female adults studied was AG (n=195)>AA (n=158)>GG (n=63). Regardless of anesthetic technique, patients with GG types had lower PONV scale on arrival at postoperative care unit (PACU) (P=0.002), while T group showed lower PONV scale than I group up to 6 hr after PACU discharge in AA and AG types. No differences were apparent for postoperative pain among opioid receptor polymorphism. PONV occurrence differs according to opioid receptor polymorphism and anesthetic technique in patients undergoing general anesthesia with remifentanil. Graphical Abstract
Matryoshka: Strengthening Software Protection via Nested Virtual Machines

The use of virtual machine technology has become a popular approach for defending software applications from attacks by adversaries that wish to compromise the integrity and confidentiality of an application. In addition to providing some inherent obfuscation of the execution of the software application, the use of virtual machine technology can make both static and dynamic analysis more difficult for the adversary. However, a major point of concern is the protection of the virtual machine itself. The major weakness is that the virtual machine presents a inviting target for the adversary. If an adversary can render the virtual machine ineffective, they can focus their energy and attention on the software application. One possible approach is to protect the virtual machine by composing or nesting virtualization layers to impart virtual machine protection techniques to the inner virtual machines "closest" to the software application. This paper explores the concept and feasibility of nested virtualization for software protection using a high-performance software dynamic translation system. Using two metrics for measuring the strength of protection, the preliminary results show that nesting virtual machines can strengthen protection of the software application. While the nesting of virtual machines does increase run-time overhead, initial results indicate that with careful application of the technique, run-time overhead could be reduced to reasonable levels.
Fast and effective tuning of Echo State Network reservoir parameters using evolutionary algorithms and template matrices

Echo State Network (ESN) is a special type of neural network with a randomly generated structure called the reservoir. The performance of ESN is sensitive to the reservoir parameters, which have to be tuned for best performance. Tuning of the reservoir parameters using evolutionary algorithms can be slow and produce inconsistent results. In this paper, we present a simple method for generating reservoirs based on templates that makes the reservoir matrices deterministic with respect to the parameters. Compared with the traditional method where the reservoir matrices are random, tuning of the reservoir parameters with an evolutionary algorithm needs less time, less number of cost function evaluations, and produces more reliable results using the proposed method.
An Improved Method of Color Image Edge Detection Based on One Order Gradient Operator

In order to research the problem of the gray edge and leaving out some edges during detecting the color edges, the color contrast enhancement algorithm is designed that it dependences on decomposing the interrelated color component of the color images based on RGB color model. And the edge detection improved method is proposed based on the above algorithm. Namely, the components edges are computed by the gradient operators after the components are enhanced and the color image edges are composed. The experimental results show that the color edges are detected through the proposed method and the algorithm has the advantage of keeping the more edge details than the other ways. Because the algorithm is an improved method of pixel processing based on three components of RGB color model, it needn’t transform one color model to the other color model, so it is a more simple method than the other methods.
An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses

Several supermodular losses have been shown to improve the perceptual quality of image segmentation in a discriminative framework such as a structured output support vector machine (SVM). These loss functions do not necessarily have the same structure as the one used by the segmentation inference algorithm, and in general, we may have to resort to generic submodular minimization algorithms for loss augmented inference. Although these come with polynomial time guarantees, they are not practical to apply to image scale data. Many supermodular losses come with strong optimization guarantees, but are not readily incorporated in a loss augmented graph cuts procedure. This motivates our strategy of employing the alternating direction method of multipliers (ADMM) decomposition for loss augmented inference. In doing so, we create a new API for the structured SVM that separates the maximum a posteriori (MAP) inference of the model from the loss augmentation during training. In this way, we gain computational efficiency, making new choices of loss functions practical for the first time, while simultaneously making the inference algorithm employed during training closer to the test time procedure. We show improvement both in accuracy and computational performance on the Microsoft Research Grabcut database and a brain structure segmentation task, empirically validating the use of several supermodular loss functions during training, and the improved computational properties of the proposed ADMM approach over the Fujishige-Wolfe minimum norm point algorithm.
Impact of pseudonym subsequent pre-computation and forwarding in hybrid vehicular networks

Vehicular networks continue to provide a path to increased roadway safety. Pseudonyms are the provided means to balance security and privacy in such networks and their distribution remains an open question. In this work, we examine the impact of having pseudonyms readily available for distribution when the vehicle requests them. Extensive simulation is conducted to estimate a reasonable delay to generate the pseudonyms. A generic roadway grid is then analyzed to estimate the performance of various pseudonym forwarding methods. Finally we consider three cases of vehicular traffic densities given probabilities of zero to one hundred percent that the pseudonyms are immediately available for transmission. From this work, we conclude that not having pseudonyms available for transmission adversely affects system performance and could be a good design enhancement for vehicular networks.
Enlisting Madison Avenue: The Marketing Approach to Earning Popular Support in Theaters of Operation

Abstract : "Shaping," in traditional U.S. military parlance, refers to battlefield activities designed to constrain adversary force options or increase friendly force options. Recent analysis of field requirements and joint urban doctrine has expanded the concept of shaping to include influencing resident populations in military operational theaters. These populations constitute a significant component of stability operations, particularly through their decision to support friendly force objectives or those of the adversary. Virtually every action, message, and decision of a force shapes the opinions of an indigenous population: how coalition personnel treat civilians during cordon-and-search operations, the accuracy or inaccuracy of aerial bombardment, and the treatment of detainees. Given the inherent difficulty in unifying coalition messages across disparate organizations and over time, shaping efforts must be designed, war-gamed, and conducted as a campaign. The goal of such a shaping campaign is to foster positive attitudes among the populace toward U.S. and allied forces. These attitudes help decrease anti-coalition behaviors and motivate the population to act in ways that facilitate friendly force operational objectives and the attainment of desired end states. This study considered how the United States and its coalition partners can shape indigenous attitudes and behavior during stability operations via the character of those operations and the behavior of coalition forces and those responsible for communication. While successes have been achieved in this regard, U.S. forces stand to benefit from the application of select, proven commercial marketing techniques. The authors consider successes and missteps from the marketing and advertising industries and how lessons from those events might assist the U.S. military. They also present recommendations based on observations and insights from previous operations, including ongoing operations in Iraq and Afghanistan.
The Best Brown Dwarf Yet? A Companion to the Hyades Eclipsing Binary V471 Tauri

We have carried out an analysis of about 160 eclipse timings spanning over 30 years of the Hyades eclipsing binary V471 Tauri that shows a long-term quasi-sinusoidal modulation of its observed eclipse arrival times. The O - C values have been analyzed for the "light-time" effect that arises from the gravitational influence of a tertiary companion. The presence of a third body causes the relative distance of the eclipsing pair to the Earth to change as it orbits the barycenter of the triple system. The result of the analysis of the eclipse times yields a light-time semiamplitude of 137.2 ± 12.0 s, an orbital period of P3 = 30.5 ± 1.6 yr, and an eccentricity of e3 = 0.31 ± 0.04. The mass of the tertiary component is M3 sin i3 ≃ 0.0393 ± 0.0038 M☉ when a total mass of 1.61 ± 0.06 M☉ for V471 Tau is adopted. For orbital inclinations i3 ≳ 35°, the mass of the third body would be below the stable hydrogen-burning limit of M ≈ 0.07 M☉, and thus it would be a brown dwarf. In the next several years (near maximum elongation), it should be feasible to obtain IR images and spectra of V471 Tau C that, when combined with the known mass, age, distance, and [Fe/H], will serve as a benchmark for understanding the physical properties and evolution of brown dwarfs.
MENINGKATKAN KEMAMPUAN SAINS MENGENAL BENDA CAIR MELALUI METODE EKSPERIMEN

Problems in this research is whether through experiment method can improve the ability of science to know liquid object at group B5 TK Witri I of Bengkulu City? The purpose of this class action research is to increase the ability of science to know the liquid through the experimental method at group B5 TK Witri I of Bengkulu City. This research is a classroom action research (CAR). The result of this research is through experiment method can improve the ability of science to know liquid objects. Evident from the average score of children's science skills in the first cycle reached an average grade of 3.53 with 70.6% classical completeness is on the enough criteria then in cycle II the average value of children reached 4.16 with classical completeness 83.2% is in good criteria. It is suggested for the next researcher to use experimental method in improving children's science skill especially predicting aspect. Key Words : The Science Abilities, Experiment Method, Soluble And Insoluble Objects.
Improving feature location by transforming the query from natural language into requirements

Software maintenance and evolution activities are responsible for the emergence of a great demand of feature location approaches that search relevant code in a large codebase. However, this search is usually performed manually and relies heavily on developers. In this paper, we propose a feature location approach that, instead of searching directly into code from a natural language query as other approaches do, transforms a natural language query to a query that is made up of the requirements that are located as relevant. Furthermore, our approach limits the scope of the code search space by selecting only the code of those products that hold relevant requirements. We evaluate the overall effectiveness of our approach in the industrial domain of train control software. Our results show that our approach improves in 18.1% the results of precision with regard to searching directly into code, which encourages further research in this direction.
Modeling protein-DNA binding time in Stochastic Discrete Event Simulation of Biological Processes

This paper presents a parametric model to estimate the DNA-protein binding time using the DNA and protein structures and details of the binding site. To understand the stochastic behavior of biological systems, we propose an "in silico" stochastic event based simulation that determines the temporal dynamics of different molecules. This paper presents a parametric model to determine the execution time of one biological function (i.e. simulation event): protein-DNA binding by abstracting the function as a stochastic process of microlevel biological events using probability measure. This probability is coarse grained to estimate the stochastic behavior of the biological function. Our model considers the structural configurations of the DNA, proteins and the actual binding mechanism. We use a collision theory based approach to transform the thermal and concentration gradients of this biological process into the probability measure of DNA-protein binding event. This information theoretic approach significantly removes the complexity of the classical protein sliding along the DNA model, improves the speed of computation and can bypass the speed-stability paradox. This model can produce acceptable estimates of DNA-protein binding time to be used by our event-based stochastic system simulator where the higher order (more than second order statistics) uncertainties can be ignored. The results show good correspondence with available experimental estimates. The model depends very little on experimentally generated rate constants
Effects of a Wind-Driven Gyre on Thermohaline Circulation Variability

A simplified model is used to study the possible effects of the horizontal upper-ocean wind-driven circulation (WDC) on the variability of the overturning meridional circulation driven by buoyancy fluxes. It is found that the added interaction with the WDC adds interesting new classes of variability. First, self-sustained variability of the thermohaline circulation (THC) becomes possible, on time scales of interdecades to a few centuries. Furthermore, these oscillations may be either small amplitude or large amplitude and either periodic or chaotic, depending on the amplitude of the freshwater forcing and on the strength of the WDC. Even a relatively weak WDC changes the well-known stability properties of the THC that are seen in numerous models of the THC alone. The variability modes found here may account for similar modes of variability observed in GCM studies.
Partition Decomposition for Roll Call Data

In this paper we bring to bear some new tools from statistical learning on the analysis of roll call data. We present a new data-driven model for roll call voting that is geometric in nature. We construct the model by adapting the "Partition Decoupling Method," an unsupervised learning technique originally developed for the analysis of families of time series, to produce a multiscale geometric description of a weighted network associated to a set of roll call votes. Central to this approach is the quantitative notion of a "motivation," a cluster-based and learned basis element that serves as a building block in the representation of roll call data. Motivations enable the formulation of a quantitative description of ideology and their data-dependent nature makes possible a quantitative analysis of the evolution of ideological factors. This approach is generally applicable to roll call data and we apply it in particular to the historical roll call voting of the U.S. House and Senate. This methodology provides a mechanism for estimating the dimension of the underlying action space. We determine that the dominant factors form a low- (one- or two-) dimensional representation with secondary factors adding higher-dimensional features. In this way our work supports and extends the findings of both Poole-Rosenthal and Heckman-Snyder concerning the dimensionality of the action space. We give a detailed analysis of several individual Senates and use the AdaBoost technique from statistical learning to determine those votes with the most powerful discriminatory value. When used as a predictive model, this geometric view significantly outperforms spatial models such as the Poole-Rosenthal DW-NOMINATE model and the Heckman-Snyder 6-factor model, both in raw accuracy as well as Aggregate Proportional Reduced Error (APRE).
Welcoming Communities? An Assessment of Community Services in Attracting and Retaining Immigrants in the South Okanagan Valley (British Columbia, Canada), with Policy Recommendations

The urban bias of Canadian immigration has led to policies intended to redirect immigration away from major metropolitan areas. Policy makers have identified the Okanagan Valley in British Columbia as a region that could benefit from additional immigration. Whether the policy succeeds depends on the presence of (a) quality services in a welcoming community; (b) affordable, suitable, and adequate housing; (c) educational opportunities; (d) employment opportunities that offer an adequate income; and (e) opportunities to integrate into the community. This study evaluates community services and their role in attracting and retaining immigrants to the South Okanagan, a sub-region of the Okanagan Valley. The study uses data from four focus groups with 31 immigrants, 10 semi-structured interviews with immigrants, and 15 interviews with key informants. The researchers found that immigrants face two major obstacles in their service use: physical access, given the near-absence of an effective public transportation system; and financial instability, as many of the surveyed immigrants rely on low-paying 'survival jobs' in the cyclical tourism and service industry. These findings have led to recommendations to improve regional socio-economic conditions. Keywords: Regionalization of immigration, community services, immigration, economic development, Okanagan Valley, Canada
FUSION OF ACOUSTIC, PERCEPTUAL AND PRODUCTION FEATURES FOR ROBUST SPEECH RECOGNITION IN HIGHLY NON-STATIONARY NOISE

Improving the robustness of speech recognition systems to cope with adverse background noise is a challenging research topic. Extraction of noise robust acoustic features is one of the prominent methods used for incorporating robustness in speech recognition systems. Prior studies have proposed several perceptually motivated noise robust acoustic features, and the normalized modulation cepstral coefficient (NMCC) is one such feature which uses amplitude modulation estimates to create cepstrum-like parameters. Studies have shown that articulatory features in combination with traditional mel-cepstral features help to improve robustness of speech recognition systems in noisy conditions. This paper shows that fusion of multiple noise robust feature streams motivated by speech production and perception theories help to significantly improve the robustness of traditional speech recognition systems. Keyword recognition accuracies on the CHiME-2 noisy-training task reveal that utilizing an optimal combination of noise robust features help to improve the accuracies by more than 6% absolute across all the different signal-to-noise ratios. Index Terms— Robust speech recognition, Modulation features, Articulatory features, Noise robust speech processing, Robust acoustic features, key word recognition.
Absence of an Intron Splicing Silencer in Porcine Smn1 Intron 7 Confers Immunity to the Exon Skipping Mutation in Human SMN2

Spinal Muscular Atrophy is caused by homozygous loss of SMN1. All patients retain at least one copy of SMN2 which produces an identical protein but at lower levels due to a silent mutation in exon 7 which results in predominant exclusion of the exon. Therapies targeting the splicing of SMN2 exon 7 have been in development for several years, and their efficacy has been measured using either in vitro cellular assays or in vivo small animal models such as mice. In this study we evaluated the potential for constructing a mini-pig animal model by introducing minimal changes in the endogenous porcine Smn1 gene to maintain the native genomic structure and regulation. We found that while a Smn2-like mutation can be introduced in the porcine Smn1 gene and can diminish the function of the ESE, it would not recapitulate the splicing pattern seen in human SMN2 due to absence of a functional ISS immediately downstream of exon 7. We investigated the ISS region and show here that the porcine ISS is inactive due to disruption of a proximal hnRNP A1 binding site, while a distal hnRNP A1 binding site remains functional but is unable to maintain the functionality of the ISS as a whole.
How Australian welfare reforms shape low-income single mothers’ food provisioning practices and their children’s nutritional health

ABSTRACT This paper examines the food provisioning practices of low-income single mothers affected by market-driven welfare reforms in Australia. We explore the tensions between women’s care responsibilities and the government’s welfare-to-work reform, and the effects of these tensions on the nutritional health and wellbeing of women and their children. Conducting a thematic analysis of 20 interviews with single mothers receiving either Parenting Payment Single, Newstart Allowance (NSA), or the Disability Support Pension, we found that when women’s income and time were significantly compromised, especially when women were transferred onto the less generous unemployment benefit NSA, that food provisioning became more psychologically taxing and nutritional health decreased. The findings suggest that public health researchers must focus on challenging the structural antecedents of women’s food work and hunger to alleviate the responsibilities of single mothers in countries facing similar circumstances.