Miracle-dz/UniFAR-FTUs huggingface dataset:
HUMAN CAPITAL AND LABOR FLOWS OUT OF THE AGRICULTURAL SECTOR: EVIDENCE FROM SLOVENIA / доклад на 25 конференции IAAE, Reshaping Agriculture’s Contribution to Society, International Convention Centre, Durban, South Africa, 16-23 August 2003

This paper analyses the determinants of agricultural labor flows and the role of human capital in this process on the basis of the Slovenian Labor Force Surveys for the years 1993 to 1999. The household heads living in larger households, having a larger farm size, and working full-time (more hours per week) in permanent jobs are more likely to stay in agricultural employment. The empirical evidence clearly suggests that human capital plays a crucial role for labor mobility and labor adjustment. Young, female and educated individuals are more likely to enter into employment in non-agricultural, particularly service activities. There are remarkable circular flows of elderly and less educated persons between being employed in agriculture, unemployment and retirement pools. Small-scale and part-time farming provide temporary employment opportunities. Investments in human capital to improve quality of labor in agriculture and to increase mobility and flexibility of labor are the key issues in synergy reducing labor mismatch and improving efficiency in labor flow adjustment.
An Investigation of Microaggression Frequency and Stress among Chinese International University Students and the Impacts.

This study examined the microaggression experience of Chinese international college students during their stay in the U.S. The goals of the present study were: First, to explore the types of microaggressions that Chinese international students frequently experience both on and off campus; second, to analyze the stress scales of microaggressions pertaining to Chinese international students; third, to explore how Chinese international students navigate the process of dealing with microaggressions; fourth, to examine how microaggression stress and perceived institutional social support may impact the satisfaction of Chinese international students with their host institutions and their overall academic journey in the U.S.; fifth, to explore the support services Chinese international students expect to receive when dealing with microaggressions; finally, to provide both higher education institutions including faculty and staff members as well as Chinese international students practical implications on how to reduce microaggressions. To address the research questions, an international student minority microaggression process model was adopted as the conceptual framework. This model was developed based on Meyer’s (2003) minority stress model, Sue’s (2010b) microagression process model and the model minority myth (S. Lee, 2007). An explanatory sequential mixed-methods design was utilized to investigate the perceived microaggressions at a large public Mid-Western research university (MU) with over 4,100 international students, among which nearly 1600 are Chinese international students.
Modelo de predicción de la satisfacción y diversión en Educación Física a partir de la autonomía y el clima motivacional

espanolEl objetivo de este trabajo fue hallar un modelo de prediccion de la satisfaccion y diversion hacia la Educacion Fisica partiendo del clima de aprendizaje y teniendo en cuenta el apoyo a la autonomia del profesorado y la propia percepcion de autonomia del alumnado. Se utilizo una muestra de 758 alumnos de secundaria con edades entre 13 y 18 anos. Se aplico a los participantes de la investigacion, la version espanola adaptada a la educacion fisica, de los siguientes instrumentos: Learning and Performance Orientations in Physical Education Classes Questionnaire, Learning Climate Questionnaire, Sport Satisfaction Instrument y el factor autonomia del Basic Psychological Needs in Exercise Scale. Los resultados senalaron que el clima motivacional implicado en el aprendizaje predecia positivamente el apoyo a la autonomia por parte del profesor y esta predecia la propia percepcion de autonomia del alumno. Asimismo, la percepcion de autonomia predijo positivamente la satisfaccion/diversion y negativamente el aburrimiento en las clases. Como conclusion, los docentes deben favorecer un clima aprendizaje que apoye la autonomia y favorezca la percepcion de esta en el alumno para conseguir que se diviertan en las clases de Educacion Fisica. EnglishThe aim of this work was to find a model to predict satisfaction and fun to the physical education based on learning climate and considering supporting to the autonomy of teachers and the perception of autonomy of students. A sample of 758 secondary school students aged between 13 and 18 was used. The following instruments were used: the Spanish version adapted to Physical Education of Learning and Performance Orientations in Physical Education Classes Questionnaire, Learning Climate Questionnaire, Sport Satisfaction Instrument and autonomy factor in Basic Psychological Needs in Exercise Scale. The results showed that learning motivational climate positively predicted perceived autonomy support on the teacher part and this predicted the self perception of autonomy on the part of students. In the same way, the perceived autonomy positively predicted satisfaction/fun and negatively boredom in class. In conclusion, teachers should foster a learning climate that supports and promotes the autonomy perceived autonomy in students for them to have fun in physical education classes.
Analysing properties of the C. Elegans neural network: mathematically modeling a biological system

The brain is one of the most studied and highly complex systems in the biological world. It is the information center behind all vertebrate and most invertebrate life, and thus has become a major focus in current research. While many of these studies have concentrated on studying the brain directly, our focus is the structure of the brain itself: at its core an interconnected network of nodes (neurons). A better understanding of the structural aspects of the brain should elucidate some of its functional properties. In this paper we analyze the brain of the nematode Caenorhabditis elegans. Consisting of only 302 neurons, it is one of the better-understood neural networks. Using a Laplacian matrix of the 279-neuron "giant component" of the network, we use an eigenvalue counting function to look for fractal-like self similarity. This matrix representation is also used to plot (in eigenfunction coordinates) both 2 and 3 dimensional visualizations of the neural network. Further analysis examines the small-world properties of the system, including average path length and clustering coefficient. We then test for localization of eigenfunctions, using graph energy and spacial variance. To better understand these results, all of these calculations are also performed on random networks, branching trees, and known fractals, as well as fractals which have been "rewired" to have small-world properties. This analysis is one of many stepping-stones in the research of neural networks. While many of the structures and functions within the brain are known, understanding how the two interact is also important. A firmer grasp on the structural properties of the neural network is a key step in this process
GPU accelerated item-based collaborative filtering for big-data applications

Recommendation systems are a popular marketing strategy for online service providers. These systems predict a customer's future preferences from the past behaviors of that customer and the other customers. Most of the popular online stores process millions of transactions per day; therefore, providing quick and quality recommendations using the large amount of data collected from past transactions can be challenging. Parallel processing power of GPUs can be used to accelerate the recommendation process. However, the amount of memory available on a GPU card is limited; thus, a number of passes may be required to completely process a large-scale dataset. This paper proposes two parallel, item-based recommendation algorithms implemented using the CUDA platform. Considering the high sparsity of the user-item data, we utilize two compression techniques to reduce the required number of passes and increase the speedup. The experimental results on synthetic and real-world datasets show that our algorithms outperform the respective CPU implementations and also the naïve GPU implementation which does not use compression.
Predicting Personality with Social Behavior

In this paper, we examine to which degree behavioral measures can be used to predict personality. Personality is one factor that dictates people's propensity to trust and their relationships with others. In previous work, we have shown that personality can be predicted relatively accurately by analyzing social media profiles. We demonstrated this using public data from facebook profiles and text from Twitter streams. As social situations are crucial in the formation of one's personality, one's social behavior could be a strong indicator of her personality. Given most users of social media sites typically have a large number of friends and followers, considering only these aspects may not provide an accurate picture of personality. To overcome this problem, we develop a set of measures based on one's behavior towards her friends and followers. We introduce a number of measures that are based on the intensity and number of social interactions one has with friends along a number of dimensions such as reciprocity and priority. We analyze these features along with a set of features based on the textual analysis of the messages sent by the users. We show that behavioral features are very useful in determining personality and perform as well as textual features.
Kronecker graphical lasso

We consider high-dimensional estimation of a (possibly sparse) Kronecker-decomposable covariance matrix given i.i. d. Gaussian samples. We propose a sparse covariance estimation algorithm, Kronecker Graphical Lasso (KGlasso), for the high dimensional setting that takes advantage of structure and sparsity. Convergence and limit point characterization of this iterative algorithm is established. Compared to standard Glasso, KGlasso has low computational complexity as the dimension of the covariance matrix increases. We derive a tight MSE convergence rate for KGlasso and show it strictly outperforms standard Glasso and FF. Simulations validate these results and shows that KGlasso outperforms the maximum-likelihood solution (FF), in the high-dimensional small-sample regime.
Are LAS-codes a miracle ?

Large area synchronized (LAS)-CDMA has been proposed to enhance third generation and fourth generation wireless systems. LAS-CDMA is based on multiple access codes that result from a combination of LA codes and pulse compressing LS codes. To reduce multiple access interference and intersymbol interference in time dispersive channels, LS codes have perfect auto-correlation and cross-correlation functions in a certain vicinity of the zero shift. In this paper, we provide systematic methods and the underlying theory for the construction of such codes that go far beyond the examples revealed by LinkAir (2000).
Lessons from Ten Years of TEAC's Accrediting Activity.

Founded in 199 , the Teacher Education Accreditation Council (TEAC) designed a system that balances three sources of evidence in a single accreditation system: (a) that the program’s graduates are qualified, competent, and caring beginning teachers; (b) that the program faculty investigates the factors that improve program quality; and (c) that theprogramhas the capacity for continuousprogramimprovement. One aspect of the third factor is whether that program is offered by a regionally accredited institution andwhether the institution is committed to the program (Murray, 2005). 1 In other words, TEAC asks that programs make systematic inquiries into what their graduates know and can do and uses the evidence that they obtain to assess and improve the effectiveness of the program. TEAC’s system was recognized by the Council of Higher Education Accreditation in 2001 and subsequently by the U.S. Department of Education in 2003 and2005. TheCouncil has 200 institutionalmembers and has accredited 100 teacher education programs in over 15 states. TEAC’s assumption is that the evidence that the program actually
Singularity Profile in the Mean Curvature Flow

In this paper we study the geometry of first time singularities of the mean curvature flow. By the curvature pinching estimate of Huisken and Sinestrari, we prove that a mean curvature flow of hypersurfaces in the Euclidean space R n+1 with positive mean curvature is κ-noncollapsing, and a blow-up sequence converges locally smoothly along a subsequence to a smooth, convex blow-up solution. As a consequence we obtain a local Harnack inequality for the mean convex flow.
A Genome-Wide mQTL Analysis in Human Adipose Tissue Identifies Genetic Variants Associated with DNA Methylation, Gene Expression and Metabolic Traits

Little is known about the extent to which interactions between genetics and epigenetics may affect the risk of complex metabolic diseases and/or their intermediary phenotypes. We performed a genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human adipose tissue of 119 men, where 592,794 single nucleotide polymorphisms (SNPs) were related to DNA methylation of 477,891 CpG sites, covering 99% of RefSeq genes. SNPs in significant mQTLs were further related to gene expression in adipose tissue and obesity related traits. We found 101,911 SNP-CpG pairs (mQTLs) in cis and 5,342 SNP-CpG pairs in trans showing significant associations between genotype and DNA methylation in adipose tissue after correction for multiple testing, where cis is defined as distance less than 500 kb between a SNP and CpG site. These mQTLs include reported obesity, lipid and type 2 diabetes loci, e.g. ADCY3/POMC, APOA5, CETP, FADS2, GCKR, SORT1 and LEPR. Significant mQTLs were overrepresented in intergenic regions meanwhile underrepresented in promoter regions and CpG islands. We further identified 635 SNPs in significant cis-mQTLs associated with expression of 86 genes in adipose tissue including CHRNA5, G6PC2, GPX7, RPL27A, THNSL2 and ZFP57. SNPs in significant mQTLs were also associated with body mass index (BMI), lipid traits and glucose and insulin levels in our study cohort and public available consortia data. Importantly, the Causal Inference Test (CIT) demonstrates how genetic variants mediate their effects on metabolic traits (e.g. BMI, cholesterol, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c) and homeostatic model assessment of insulin resistance (HOMA-IR)) via altered DNA methylation in human adipose tissue. This study identifies genome-wide interactions between genetic and epigenetic variation in both cis and trans positions influencing gene expression in adipose tissue and in vivo (dys)metabolic traits associated with the development of obesity and diabetes.
Kinematic inversion of physically plausible earthquake source models obtained from dynamic rupture simulations

One approach to investigate earthquake source processes is to produce kinematic source models from inversion of seismic records and geodetic data. The setup of the inversion requires a variety of assumptions and constraints to restrict the range of possible models. Here, we evaluate to what extent physically plausible earth- quake scenarios are reliably restituted in spite of these restrictions. We study which characteristics of ruptures, such as rupture velocity, slip distribution, stress drop, rise time, and slip function, can be reliably determined from the inversion of near-field seismic and geodetic data. Using spontaneous dynamic rupture simulations, we gen- erate five earthquake scenarios, each of which has different characteristics of the source process. Then we conduct a blind test by modeling the synthetic near-source data using a standard inversion scheme that optimizes the fit to the observations while searching for solutions with minimum roughness. The inversion procedure assumes a rupture front propagating away from the hypocenter with variable rupture velocity and a simple cosine slip-time function. Our results show that, overall, slip distribution and stress drop are reasonably well determined even for input models with relatively complex histories (such as a subshear rupture transitioning to supershear speeds). Depth-averaged rupture velocities are also reasonably well resolved although their estimate progressively deteriorates away from the hypocenter. The local rise time and slip function are not well resolved, but there is some sensitivity to the rupture pulse width, which can be used to differentiate between pulse-like and crack-like ruptures. Our test for understanding the inaccuracies in Green's functions shows that random 3D perturbations of 5% standard deviation do not lead to significant degradation of the estimation of earthquake source parameters. As remedies to the current limitations, we propose smoothing slip function parameters and using more complicated inversion schemes only if data necessitates them. Online Material: Figures showing snapshots of forward and inverse modeling of rupture, L curves, slip models, and waveform fits.
Pose graph for improved monocular visual odometry

In this paper the monocular visual odometry algorithm augmented with pose graph optimization is presented. The algorithm was tested using five different combinations of feature extractors and descriptors and evaluated using two challenging datasets from KITTI database. The main result of this study is that the implementation of pose graph optimization may lead to reduction of position error ranging between 1.53% to 76.05%. The error reduction depends on a feature type and dataset used.
Uncertainty, innovation, and dynamic sustainable development

Abstract Sustainable development is a rich concept that has helped shape the discussion of human society’s interaction with the biosphere. However, the term “sustainable development” is contentious, and some dismiss it outright as an oxymoron. The seemingly contradictory “sustainable” and “development” can be reconciled by accepting that due to two factors, the inherent complexity and uncertainty of human and natural systems, and the ability of human society to innovate, sustainable development must be dynamic. It must be an ongoing process, not a goal. A sustainable society must constantly evaluate its relationship with nature as it adopts new innovations and encounters unexpected events. The role of feedback and suitable application of the precautionary principle are key elements of a dynamic sustainable development process. The example of nuclear waste management in Canada demonstrates the beginning of such a process.
Organizational behavior modification goes to Russia: Replicating an experimental analysis across cultures and tasks

This study examined whether organizational behavior modification interventions that produced improvements in performance related behaviors among U.S. workers in a retail sales setting could be replicated among ethnic Russian workers in a textile mill. Procedures used by Luthans and his colleagues (Luthans, Paul & Baker, 1981; Luthans, Paul & Taylor, 1985) to identify, categorize, operationalize, measure, and change functional and dysfunctional performance-related behaviors among retail workers in the U.S. served as a model for replicating these dimensions of the U.S. intervention among textile workers in Russia. Supervisors of a sample of workers (N = 33) in the largest textile mill in Russia were trained to contingently administer social rewards (attention and praise) and positive feedback. They administered these rewards when they observed their workers engaged in identified functional performance-related behaviors dealing with specific procedures, repairs, and interpersonal relations that contributed to the production of quality fabric. In addition, corrective feedback was given for dysfunctional behaviors that detracted from producing quality fabric. Among the Russian workers, frequencies of observed functional behaviors rose from baseline to intervention and frequencies of dysfunctional behavior fell from baseline to the intervention within an A-B-A reversal design. No reversal in frequencies of the behaviors occurred following withdrawal of the intervention. Implications of the study are discussed in terms of extending the generality of the intervention across functional and dysfunctional behaviors associated with different task types and across different cultures.
Hot C-rich white dwarfs: testing the DB-DQ transition through pulsations

Hot DQ white dwarfs constitute a new class of white dwarf stars, uncovered recently within the framework of the SDSS project. Recently, three hot DQ white dwarfs have been reported to exhibit photometric variability with periods compatible with pulsation g-modes. Here, we present a nonadiabatic pulsation analysis of the recently discovered carbon-rich hot DQ white dwarf stars. One of our main aims is to test the convective-mixing picture for the origin of hot DQs through the pulsational properties. Our study relies on the full evolutionary models of hot DQ white dwarfs recently developed by Althaus et al. (2009), that consistently cover the whole evolution from the born-again stage to the white dwarf cooling track. Specifically, we present a stability analysis of white dwarf models from stages before the blue edge of the DBV instability strip until the domain of the hot DQ white dwarfs, including the transition DB --> hot DQ white dwarf. We found that at evolutionary phases in which the models are characterized by He-dominated atmospheres, they exhibit unstable $g$-mode pulsations typical of DBV stars, and when the models become DQ white dwarfs with carbon-dominated atmospheres, they continue being pulsationally unstable with similar characteristics than DB models, and in agreement with the periods detected in variable hot DQ white dwarfs. Our calculations provide strong support to the convective-mixing picture for the formation of hot DQs. In particular, our results point to the existence of pulsating DB white dwarfs with very thin He-rich envelopes, which after passing the DBV instability strip become variable hot DQ stars. The existence of these DB stars with very thin envelopes could be investigated through asteroseismology.
Hierarchical interconnects for on-chip clustering

In the sub-micron technology era, wire delays are becoming much more important than gate delays, making it particularly attractive to go for clustered designs. A common form of clustering adopted in processors is to replace the centralized instruction scheduler with multiple smaller schedulers that work in parallel within a single chip. Studies have found that existing interconnects connecting onchip clusters, as well as proposed instruction distribution algorithms, are not scalable. The objective of this paper is to investigate alternate interconnects (hierarchical interconnects) that provide scalable performance with increase in on-chip clusters. We also investigate distribution algorithms that are best suited for these interconnects. Experimental results of these new interconnects with appropriate distribution techniques show that they more scalable than the existing techniques. They achieve an IPC that is around 15-20% more than the most scalable existing configuration, and is also within 2% of that achieved by a hypothetical ideal processor having a 1-cycle latency crossbar interconnect, irrespective of the number of clusters; confirming their utility and applicability. We also discuss the many other design advantages that are obtained by the use of hierarchical interconnects.