Miracle-dz/UniFAR-FTUs huggingface dataset:
SOCIAL SOLIDARITY AND WELLBEING AFTER CRITICAL INCIDENTS: THREE CASES OF MASS SHOOTINGS

Critical incidents often result in amplified social solidarity among the members of a traumatized community. Some argue this solidarity accelerates recovery and supportive social environments decrease the likelihood of PTSD and other health problems after traumatic events. However, little research investigates if social solidarity influences wellbeing independent from the social support that accompanies heightened solidarity. That is, does feeling attached to a group protect one from negative events even if social support is lacking? We investigate the relationship between solidarity and wellbeing after tragedies by analyzing three mass shootings: a mall shooting in Omaha, Nebraska and two school shootings in Finland. Our results indicate that social solidarity decreases depressive symptomology in all three cases and promotes wellbeing in both the short and long-term. The influence of solidarity on wellbeing remains even when controlling for other known predictors of depression, including social support. Therefore, solidarity’s influence on wellbeing appears to be substantial and enduring. Importantly, our research also demonstrates that the relationship between solidarity and wellbeing holds across cultures.
Systematic Review of Breast Cancer Biology in Developing Countries (Part 1): Africa, the Middle East, Eastern Europe, Mexico, the Caribbean and South America

There has been no systematic appraisal of ethnicity-based variations in breast cancer (BC) biology amongst women from developing countries. A qualitative systematic review was conducted of breast cancer size, stage, grade, histological type, extra-mammary involvement, hormone receptor status as well as patient demographics. This review includes patients from Africa, the Middle East, Eastern Europe, Mexico, the Caribbean and South America. BC in these regions present at an earlier age with large aggressive tumours. Distant metastases are frequently present at the time of diagnosis. African women have a higher frequency of triple negative tumours. Over half of Middle Eastern women have lymph node involvement at the time of diagnosis. Despite experiencing a lower incidence compared to the Ashkenazi Jewish population, Palestinian women have poorer five-year survival outcomes. The majority of women from Mexico and South America have stage two or three disease whilst over sixty percent of women from Eastern Europe have either stage one or stage two disease. The biological characteristics of BC in the Caribbean cannot be fully assessed due to a paucity of data from the region. BC amongst the developing world is characterised by an early peak age of onset with aggressive biological characteristics. Strategies that improve breast cancer awareness, address amenable risk factors and improve early detection are essential.
Randomized Derivative-Free Optimization of Noisy Convex Functions

We propose STARS, a randomized derivative-free algorithm for unconstrained optimization when the function evaluations are contaminated with random noise. STARS takes dynamic, noise-adjusted smoothing step-sizes that minimize the least-squares error between the true directional derivative of a noisy function and its finite difference approximation. We provide a convergence rate analysis of STARS for solving convex problems with additive or multiplicative noise. Experimental results show that (1) STARS exhibits noise-invariant behavior with respect to different levels of stochastic noise; (2) the practical performance of STARS in terms of solution accuracy and convergence rate is significantly better than that indicated by the theoretical result; and (3) STARS outperforms a selection of randomized zero-order methods on both additive and multiplicative-noisy functions.
The Maze of APP Processing in Alzheimer’s Disease: Where Did We Go Wrong in Reasoning?

Why has Alzheimer’s disease (AD) remained a conundrum today? The main reason is the stagnation in understanding the origins of plaques and tangles. While they are widely thought to be the products of the “aberrant” pathways, we believe that plaques and tangles result from natural aging. From this new perspective, we have proposed that age-related inefficiency of α-secretase is the underpinning for Aβ overproduction. This view contrasts sharply with the current doctrine that Aβ overproduction is the product of the “overactivated” β- and γ-secretases. Following this doctrine, it has been claimed that the two secretases are “positively identified” and that their inhibitors have “successfully reduced Aβ levels. ” But, why have these studies not led to the understanding of AD or successful clinical trials? And if so, where did they go off course in reasoning? These questions may touch the basics of biological science and must be answered. In this paper, I dissected several prevailing assumptions and some influential reports with an attempt to trace the origins of the conundrum. This work led me to an original model for Aβ overproduction and also to a serious question: given the universal knowledge that boosting α-secretase reduces Aβ, a straightforward highway for intervention, then why is there such an obsession on “inhibiting β- and γ-secretases,” a much more costly and twisting road even if possible? This issue requires the attention of policymakers and all researchers. I therefore call for a game change in AD study.
Image denoising and quality assessment through the Renyi entropy

This paper presents a new image denoising method based on truncating the original noisy coefficients of a Pseudo- Wigner distribution (PWD) calculated through 1D directional windows. This method has been tested both for additive and multiplicative noisy images. The coefficients are selected according to their local directionality to take into account the image anisotropy. Next, the PWD is inverted and the set of different directional images are averaged. When the ground truth image reference is available, the peak signal-to-noise ratio (PSNR) metric is used to evaluate the resulting denoised images in comparison with other alternative methods. The described method is based on the use of the Renyi entropy extracted from a joint spatial frequency representation such as the Wigner distribution. A comparison with other competitive techniques is described and tested for real-world images. In particular, some experimental results are presented in the area of synthetic aperture radar (SAR) and retinal imaging, showing the effectiveness of the method in comparison with other alternative techniques through the use of two different non-reference image quality metrics.
Female Students get More Marks as Compared to Male Students: A Statistical Study

The main purpose of the study is to find various causes which leads towards the success of the female students rather than male students. Nowadays in educational institutions most of the position holders are females. For these investigations sample of 200 students of University of Peshawar selected from different departments and investigated through a questionnaire. There were 55.5% male respondents and 44.5% are females respondents. 75% respondents were agreed that female students get more marks rather than male students, 25% were disagreed. The investigation of marks percentage showed that 8.11% male students and 1.12% female students were in the range of 51-60 percentage marks, low range of marks have less number of female students. As the range increased from 50-60 percentage to interval of 71-80 percentage of marks so there were found 44.14% male students and 46.06% female students. And in the range of 81% and above there were 18.01% males and 40.45% females students. These results showed that more female students have high range of marks as compared to male students. And at low range of marks only 1.12% females compared to 8.11% males. Over all comparison showed that more female students have high marks rather than male students or female students perform better than male students. 60.5% of the respondents believe that female students are more studios as compare to male students. And 21% responses were thinking that female students are not more studios than male students and 18.5% respondents had no opinion that who are more studios. 76% of the respondents said that female students are more serious toward their studies comparatively to male students. Technique of odds ratio analysis was performed to check at the association of female students get more marks as compare to male students with females attend classes (lectures) more regularly, females know the art of attempting paper in a better way, females ask more questions in the class from teachers. The odds ratio analysis suggests that the female students get more marks as compare to male students were significantly associated with females attend classes (lectures) more regularly, females know the art of attempting paper in a better way, and females ask more questions in the class from teachers.
On a continuous mixed strategies model for evolutionary game theory

We consider an integro-differential model for evolutionary game theory which describes the evolution of a population adopting mixed strategies. Using a reformulation based on the first moments of the solution, we prove some analytical properties of the model and global estimates. The asymptotic behavior and the stability of solutions in the case of two strategies is analyzed in details. Numerical schemes for two and three strategies which are able to capture the correct equilibrium states are also proposed together with several numerical examples.
Subspace Methods for Computing the Pseudospectral Abscissa and the Stability Radius

The pseudospectral abscissa and the stability radius are well-established tools for quantifying the stability of a matrix under unstructured perturbations. Based on first-order eigenvalue expansions, Guglielmi and Overton [SIAM J. Matrix Anal. Appl. , 32 (2011), pp. 1166--1192] recently proposed a linearly converging iterative method for computing the pseudospectral abscissa. In this paper, we propose to combine this method and its variants with subspace acceleration. Each extraction step computes the pseudospectral abscissa of a small rectangular matrix pencil, which is comparably cheap and guarantees monotonicity. We observe local quadratic convergence and prove local superlinear convergence of the resulting subspace methods. Moreover, these methods extend naturally to computing the stability radius. A number of numerical experiments demonstrate the robustness and efficiency of the subspace methods.
Development of magnesium calcium phosphate biocement for bone regeneration

Magnesium calcium phosphate biocement (MCPB) with rapid-setting characteristics was fabricated by using the mixed powders of magnesium oxide (MgO) and calcium dihydrogen phosphate (Ca(H2PO4)2·H2O). The results revealed that the MCPB hardened after mixing the powders with water for about 7 min, and the compressive strength reached 43 MPa after setting for 1 h, indicating that the MCPB had a short setting time and high initial mechanical strength. After the acid–base reaction of MCPB containing MgO and Ca(H2PO4)2·H2O in a molar ratio of 2 : 1, the final hydrated products were Mg3(PO4)2 and Ca3(PO4)2. The MCPB was degradable in Tris–HCl solution and the degradation ratio was obviously higher than calcium phosphate biocement (CPB) because of its fast dissolution. The attachment and proliferation of the MG63 cells on the MCPB were significantly enhanced in comparison with CPB, and the alkaline phosphatase activity of MG63 cells on the MCPB was significantly higher than on the CPB at 7 and 14 days. The MG63 cells with normal phenotype spread well on the MCPB surfaces, and were attached in close proximity to the substrate, as seen by scanning electron microscopy (SEM). The results demonstrated that the MCPB had a good ability to support cell attachment, proliferation and differentiation, and exhibited good cytocompatibility.
Visualizing Memory Behavior of Programs with Complex Data-Structures

The performance of an application on modern computers has become increasingly dependent on its memory behavior. While existing tools are able to pin-point memory bottlenecks to data-structures and lines of code, they are often ineffective at indicating the cause of poor performance, especially for scientific codes that use complicated data structures. This paper presents a new framework for visualizing the memory behavior of programs with complex data structures. We develop a tool that enables the user to visualize data layout and access patterns, and to correlate this information with memory hierarchy behavior. The tool presents a visual representation of user defined data structures along with a view of the cache. The reference pattern and cache statistics can then be viewed in the region of interest. This offers insight into bad memory behavior that can be used in determining algorithmic changes to exploit the cache hierarchy.
Locales framework: exploring foundations for collaboration support

We believe that virtual simulation of physical environments is an insufficient basis for building collaborative support systems. In response, we present a locales framework which frames collaboration as the interaction of individuals within social worlds regardless of whether that interaction is physical or virtual. The overview describes the five aspects of the framework: locale foundations, where locales are the basic structures providing the affordances for the work of social worlds; mutuality in interaction, including presence and awareness issues; individual views of multiple locales of interest, defined by the individual's participation in multiple social worlds; interaction trajectories, capturing the temporal dimensions of interaction; and civic structures, which embed locales and the collaborative work of groups in the larger public sphere.
The membrane-associated proteins FCHo and SGIP are allosteric activators of the AP2 clathrin adaptor complex

The AP2 clathrin adaptor complex links protein cargo to the endocytic machinery but it is unclear how AP2 is activated on the plasma membrane. Here we demonstrate that the membrane-associated proteins FCHo and SGIP1 convert AP2 into an open, active conformation. We screened for Caenorhabditis elegans mutants that phenocopy the loss of AP2 subunits and found that AP2 remains inactive in fcho-1 mutants. A subsequent screen for bypass suppressors of fcho-1 nulls identified 71 compensatory mutations in all four AP2 subunits. Using a protease-sensitivity assay we show that these mutations restore the open conformation in vivo. The domain of FCHo that induces this rearrangement is not the F-BAR domain or the µ-homology domain, but rather is an uncharacterized 90 amino acid motif, found in both FCHo and SGIP proteins, that directly binds AP2. Thus, these proteins stabilize nascent endocytic pits by exposing membrane and cargo binding sites on AP2. DOI: http://dx.doi. org/10.7554/eLife.03648. 001
The Eclipse of the Uncertainty Concept in Mainstream Economics

This paper examines the decline in use of the Knight-Keynes uncertainty concept in mainstream economics. Using electronic archives, it shows that the frequency of its appearance in leading journals of economics has fallen rapidly from the 1950s. As well as to the declining popularity of Keynesian ideas since about 1970, the decrease in this use of the uncertainty concept is additionally related to the increasing mathematical formalization of economics and to the prevalence of a positivist emphasis on prediction. Some possible causes of this formalization are examined. Finally the essay discusses the prospects for a broadening of economics within universities, beyond a relatively narrow preoccupation with predictive formalism and including a reinvigorated Keynesianism.
Information Technology Adoption in Small and Medium-sized Enterprises; An Appraisal of Two Decades Literature

Small to medium-sized enterprises (SMEs) account for major source of employment, technological advancements, and competitive advantages for both developed and developing countries. Owing to the intensified competitive pressure and necessity for entering to global market undergone by SMEs, these businesses are incrementally employing Information Technology (IT) to take advantage of its substantial benefits. Most of prior researches have more focused on IT adoption in large organizations. However, and with regard to the limited resources controlled by SMEs, the process of IT adoption in this business sector is considerably different. The purpose of this paper is to analyze and contrast the internal and external issues affecting the process of IT adoption in SMEs to provide clearer understanding of this process by reviewing IT adoption literature, which includes more than 20 years of empirical research and case studies from a variety of databases with high concentration on certain SME-related issues. Proposed integrated framework demonstrates the process of IT adoption in SMEs through reviewing exiting perspectives in the literature. This study will assist different parties involved with adoption process including managers, vendors, consultants, and governments to achieve a practical synopsis of the IT adoption process in SMEs, which is believed to assist them with successful adoption.
Accelerating the local outlier factor algorithm on a GPU for intrusion detection systems

The Local Outlier Factor (LOF) is a very powerful anomaly detection method available in machine learning and classification. The algorithm defines the notion of local outlier in which the degree to which an object is outlying is dependent on the density of its local neighborhood, and each object can be assigned an LOF which represents the likelihood of that object being an outlier. Although this concept of a local outlier is a useful one, the computation of LOF values for every data object requires a large number of k-nearest neighbor queries -- this overhead can limit the use of LOF due to the computational overhead involved. Due to the growing popularity of Graphics Processing Units (GPU) in general-purpose computing domains, and equipped with a high-level programming language designed specifically for general-purpose applications (e.g., CUDA), we look to apply this parallel computing approach to accelerate LOF. In this paper we explore how to utilize a CUDA-based GPU implementation of the k-nearest neighbor algorithm to accelerate LOF classification. We achieve more than a 100X speedup over a multi-threaded dual-core CPU implementation. We also consider the impact of input data set size, the neighborhood size (i.e., the value of k) and the feature space dimension, and report on their impact on execution time.
Multilevel Autoregressive Modeling in Psychology: Snags and Solutions

Psychological processes are of interest in all areas of psychology, and all such processes occur within individuals over time. Some examples of psychological processes are the regulation of daily mood, the effect of job motivation on job performance and vice versa, or social interactions between a parent and child. In order to study these processes it is necessary to take many repeated measures for each individual. Multilevel autoregressive models are statistical models than can be used to analyze this kind of data - data that consist of many repeated measures, for many individuals. The approach of autoregressive models is summarized well with the saying ``The best predictor of future behavior is past behavior'': In autoregressive models, current observations are used to predict future observations. By extending the autoregressive model to a multilevel autoregressive model, it becomes possible to model the repeated measures for many individuals at the same time, while also modeling the differences between the processes of each individual. Multilevel autoregressive models are increasing in popularity within psychology, however, the methods for analyzing psychological data with these models are still being developed. The aim for this dissertation was to further investigate, explicate, and if possible remedy certain difficulties in fitting and interpreting multilevel autoregressive models in the context of psychological science. Specifically, in Chapter 1 it is discussed why it is important to collect many repeated measures for studying psychological processes, and why it is important to model these processes on an individual level, as well as the similarities and differences between the individuals' processes. In Chapter 2 a difficulty with specifying an Inverse-Wishart prior distribution for the covariance matrix of the random parameters is explored, in the context of fitting the multilevel autoregressive model in a Bayesian framework. In Chapter 3 it is discussed how to standardize the model parameters, such that we can make meaningful comparisons of the strength of the cross-lagged effects in a multivariate model. In Chapters 4 and 5 the consequences of ignoring measurement errors for the estimation of the model parameters are investigated for respectively a single-subject autoregressive model and the multilevel autoregressive model, as well as how to account for measurement errors in these models. The final chapter of this dissertation contains a summary of the work presented in the previous chapters, and a discussion of some limitations of the multilevel autoregressive modeling approach.
Effects of Intervention on Self-Regulated Learning for Second Language Learners

Abstract The study investigated the effects of an intervention program on self-regulated learning designed for second language learners. One hundred and twenty participants who were sophomore English majors at a university in China were randomly assigned to either the treatment or the control group. The intervention was composed of six weekly two-hour training sessions that focus on five main variables of self-regulatory processes: goal setting, self-efficacy, time and study environment management, language learning strategies, and attribution. The evaluation of the effectiveness of the intervention included multiple outcome variables, which were grouped into three categories: students’ motivational beliefs, students’ strategy use, and students’ academic performance. The results of the immediate training effects on goal setting, self-efficacy, attribution, time and study environment management, memory strategy, compensation strategy, metacognitive strategy and second language proficiency confirmed that academic self-regulation is a trainable student characteristic and self-regulation training can be used effectively in a second language classroom setting. The feature of the current study design allows for systematically examining and evaluating both motivational variables and learning strategies in the context of second language learning.