Miracle-dz/UniFAR-FTUs huggingface dataset:
Iranian and Swedish adolescents: differences in personality traits and well-being

Introduction. This study addresses the need to further contextualize research on well-being (e.g., Kjell, 2011) in terms of cross-cultural aspects of personality traits among adolescents and by examining two different conceptualizations of well-being: subjective well-being (i.e., life satisfaction, positive and negative affect) and psychological well-being (i.e., positive relations with others, environmental mastery, self-acceptance, autonomy, personal growth, and life purpose). Methods. Iranian (N = 122, mean age 15.23 years) and Swedish (N = 109, mean age 16.69 years) adolescents were asked to fill out a Big Five personality test, as well as questionnaires assessing subjective well-being and psychological well-being. Results. Swedes reported higher subjective and psychological well-being, while Iranians reported higher degree of Agreeableness, Openness and Conscientiousness. Neuroticism and Extraversion did not differ between cultures. Neuroticism was related to well-being within both cultures. Openness was related to well-being only among Iranians, and Extraversion only among Swedes. A mediation analysis within the Swedish sample, the only sample meeting statistical criteria for mediation analysis to be conducted, demonstrated that psychological well-being mediated the relationship between Neuroticism and subjective well-being as well as between Extraversion and subjective well-being. Conclusions. Certain personality traits, such as Extraversion, Openness, and Conscientiousness, relate differently to well-being measures across cultures. Meanwhile, Neuroticism seems to relate similarly across cultures at least with regard to subjective well-being. Furthermore, the results give an indication on how psychological well-being might mediate the relationship between certain personality traits and subjective well-being. Overall, the complexity of the results illustrates the need for more research whilst supporting the importance of contextualizing well-being research.
FPGA implementation and analysis of a multilevel coded modulation scheme

The authors describe a multilevel coded modulation scheme that has been designed for being implemented on FPGA. The goal of the design is to obtain good performance while keeping decoding delay and computational complexity as low as possible. This goal is obtained by a two-level scheme, where the partition chain of the 8-dimensional Gosset lattice is associated to a combination of 16-ary convolutional and block codes, at the first and second level respectively. The motivations behind the design choices are illustrated and the implementation on FPGA is presented. The authors conclude by analyzing the performance of the system and by showing experimental results.
0 Statistical Study of the Galaxy Distribution

Large-scale structures in the Universe provide crucial information about formation of structures and can be used to test cosmological models. The good agreement between large-scale observations and the now-standard Lambda-Cold Dark Matter (ΛCDM) model gives hope for this model to be a lasting foundation. Going a step further, these observations have been used for precision cosmology to constrain cosmological parameters inside the model. Large-scale structures are mainly studied through galaxy surveys, while on the other hand, cosmological models give predictions in terms of the global matter density field. Thus we need to understand the relationship between the matter density field and galaxy field, which is still a subject of research. Yet galaxy surveys have the advantage to map very large volumes. Recent galaxy surveys such as the Sloan Digital Sky Survey (SDSS, York et al. (2000)) and the 2 degree Field (2dF, Colless et al. (2001)) map unprecedented 3D volumes in the Universe. They have notably confirmed the view of large-scale structures as a collection of giant bubble-like voids separated by sheets and filaments of galaxies. This pattern has become known as the "Cosmic Web" (Bond et al. (1996), Springel et al. (2005)). The SDSS survey also enabled the first convincing detection of Baryon Acoustic Oscillations (BAOs) in large-scale structures (Eisenstein et al. (2005)), which opened a new field in precision cosmology. Future galaxy surveys will map always bigger volumes, with more galaxies and continue to improve our knowledge of large-scale structures and cosmological models. Though data sets are of crucial importance, statistical methods extracting the information also have an important role. They should be optimized in order to exploit the full potential of these surveys. The purpose of this chapter is to review the different methods that can be used. A first class of methods that we present is based on Fourier analysis, using the correlation function and the power spectrum. It originates from the simplicity of the Gaussian field, fully described by its second order moments. The Gaussian model gives a simple approximation of the matter density field, that works well on large scales. Another reason for the study of correlation function and power spectrum is that they are well understood and can be predicted in ΛCDM models. Recently, Fourier analysis has been used to study BAOs. These structures are remnants of acoustic waves, traveling in the hot plasma before recombination, and that have stayed frozen in large-scale structures. The detection of BAOs in the SDSS has been a strong support for the ΛCDM model. Besides, they provide a statistical standard ruler since their absolute 8
Large Scale Homing in Honeybees

Honeybee foragers frequently fly several kilometres to and from vital resources, and communicate those locations to their nest mates by a symbolic dance language. Research has shown that they achieve this feat by memorizing landmarks and the skyline panorama, using the sun and polarized skylight as compasses and by integrating their outbound flight paths. In order to investigate the capacity of the honeybees' homing abilities, we artificially displaced foragers to novel release spots at various distances up to 13 km in the four cardinal directions. Returning bees were individually registered by a radio frequency identification (RFID) system at the hive entrance. We found that homing rate, homing speed and the maximum homing distance depend on the release direction. Bees released in the east were more likely to find their way back home, and returned faster than bees released in any other direction, due to the familiarity of global landmarks seen from the hive. Our findings suggest that such large scale homing is facilitated by global landmarks acting as beacons, and possibly the entire skyline panorama.
Do parental involvement interventions increase attainment? A review of the evidence.

This briefing paper summarises a review of the evidence linking interventions to improve parental engagement in their children’s education with improved attainment. The review illustrates that there is not yet enough evidence that any intervention will work, and also that a far higher standard of basic evaluation is required, and should be expected by those who commission and fund research. Full findings are presented in the report: What do rigorous evaluations tell us about the most promising parental involvement interventions? A critical review of what works for disadvantaged children in different age groups. The report is available to download from www.nuffieldfoundation. org/parental-involvement
Barriers to Entry, Deregulation and Workplace Training

We develop a theoretical and empirical analysis of the impact of barriers to entry on workplace training. Our theoretical model yields ambiguous predictions on the sign of this relationship. On the one hand, given the number of firms, a deregulation reduces profits per unit of output, and thereby reduces training. On the other hand, the number of firms increases, and so does the output gain from training, which facilitates the investment in training. Our numerical simulation shows that for reasonable values of the parameters a negative relationship prevails. We use repeated cross section data from the European Labour Force Survey to investigate empirically the relationship between product market regulation and training incidence in a sample of 15 European countries and 13 industrial sectors, which we follow for about 7 years. Our empirical results are unambiguous and show that an increase in product market deregulation generates a sizeable increase in training incidence.
National Poverty Center Working Paper Series # 06-07 May 2006 Welfare Reform and Family Expenditures : How are Single Mothers Adapting to the New Welfare and Work Regime ?

Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the National Poverty Center or any sponsoring agency. Abstract We study the effect of welfare reform, broadly defined to include social policy changes in the 1990s, on the material well-being and consumption patterns of poor single-mother families using the Consumer Expenditure Surveys. We find that welfare reform did not have any statistically significant effect on total expenditures in households headed by low-educated (education high-school) single mothers. However, patterns of expenditure did change. There is strong evidence that the policy change was associated with an increase in spending on transportation and food away from home, and some evidence of an increase in spending on adult clothing and footwear. Welfare reform was also associated with an increase in ownership of microwave ovens, phones and cars. These increases were higher, in absolute as well as relative terms, among families headed by very low educated (education < high-school) single mothers. In contrast, we find no statistically significant changes in expenditures on childcare or learning and enrichment activities, and, if anything, a relative decline in ownership of computers in low-educated, single mother households. This pattern of results suggests that welfare reform has shifted affected families' expenditures towards items that facilitate work outside the home, but, at least so far, has not allowed families to catch up in terms of their expenditures on learning and enrichment items.
Multi-DOF Micro-Macro Bilateral Controller Using Oblique Coordinate Control

In this study, we show that tasks can be realized by appropriate coordinate transformation. This approach, oblique coordinate control, decouples tasks. Even though a system is large, they can be designed by a combination of small tasks. Therefore, tasks can be regarded as reusable components. In this study, micro-macro bilateral control is achieved as a complicated task. Small objects can be manipulated as large objects in micro-macro bilateral control. However, ideal micro-macro bilateral controllers are usually derived only for single-degree-of-freedom (DOF) systems. Then, we propose a micro-macro bilateral controller for multi-DOF on the basis of oblique coordinate control. To obtain multi-DOF bilateral control, we first derive control goals as kinematic relations. This study also considers the case where scaling gains of position and force are selected in a different manner. The goals of micro-macro bilateral control are then controlled by oblique coordinate control. The validity of the proposed method is experimentally verified.
**full Title** Asp Conference Series, Vol. **volume**, **year of Publication** **names of Editors** the Internal Structure of the Asteroids

Knowledge of the internal structure of the asteroids is rather poorly developed,mainly due to lack of reliable data (masses or densities and radii). This contribution has two aims: to review the basics of existing knowledge and explore the possibility of advancing the field by introducing and using in it some well known ideas from solid state physics.
Research on an approach of the distributed jobshop scheduling basing on multi-agent

Based on the intelligent and agile characteristics of a multi-agents system and the optimization capacity of genetic algorithm, a distributed jobshop scheduling method is presented. A multi-agents based model of distributed scheduling system is established, then a distributed scheduling mechanism integrating multi-agents and GA and dynamic scheduling strategies based on multi-agent negotiation are proposed. Finally, a simulation experiment on solving the distributed dynamic scheduling with the methods is given. Simulation results show that the presented scheduling strategy is feasible and effective.
Comparing Classroom Note Taking across Multiplatform Devices

Many educators have suggested that note taking can be beneficial for the students' educational growth. Note taking is the core activity for students in a classroom and there been a large amount of research conducted, both from industry and from academia, into facilitating the note taking process. As such, there are many available systems for taking notes. However, what has not been given as much attention is how different devices, such as Tablet PCs and PDAs, effect this task. In this paper, we study students' current note taking behavior and the changes caused by the use of different platforms for this activity. Our goal is to provide ideas and general design guidelines for future note taking systems.
Blockade of Endothelial-Mesenchymal Transition by a Smad3 Inhibitor Delays the Early Development of Streptozotocin-Induced Diabetic Nephropathy

OBJECTIVE A multicenter, controlled trial showed that early blockade of the renin-angiotensin system in patients with type 1 diabetes and normoalbuminuria did not retard the progression of nephropathy, suggesting that other mechanism(s) are involved in the pathogenesis of early diabetic nephropathy (diabetic nephropathy). We have previously demonstrated that endothelial-mesenchymal-transition (EndoMT) contributes to the early development of renal interstitial fibrosis independently of microalbuminuria in mice with streptozotocin (STZ)-induced diabetes. In the present study, we hypothesized that blocking EndoMT reduces the early development of diabetic nephropathy. RESEARCH DESIGN AND METHODS EndoMT was induced in a mouse pancreatic microvascular endothelial cell line (MMEC) in the presence of advanced glycation end products (AGEs) and in the endothelial lineage–traceble mouse line Tie2-Cre;Loxp-EGFP by administration of AGEs, with nonglycated mouse albumin serving as a control. Phosphorylated Smad3 was detected by immunoprecipitation/Western blotting and confocal microscopy. Blocking studies using receptor for AGE siRNA and a specific inhibitor of Smad3 (SIS3) were performed in MMECs and in STZ-induced diabetic nephropathy in Tie2-Cre;Loxp-EGFP mice. RESULTS Confocal microscopy and real-time PCR demonstrated that AGEs induced EndoMT in MMECs and in Tie2-Cre;Loxp-EGFP mice. Immunoprecipitation/Western blotting showed that Smad3 was activated by AGEs but was inhibited by SIS3 in MMECs and in STZ-induced diabetic nephropathy. Confocal microscopy and real-time PCR further demonstrated that SIS3 abrogated EndoMT, reduced renal fibrosis, and retarded progression of nephropathy. CONCLUSIONS EndoMT is a novel pathway leading to early development of diabetic nephropathy. Blockade of EndoMT by SIS3 may provide a new strategy to retard the progression of diabetic nephropathy and other diabetes complications.
Goal Directed Model Inversion : a Characterization of Learning Behavior

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one". The algorithm makes use of these intermediate "successes" to achieve the final goal One issue for an earlier version of GDMI was referred to as "the priming problem" and relates to the relative difficulty in achieving the very first external target. In this paper we report modifications to the GDMI algorithm which eliminate the priming problem. Key Word 1: Learning Algorithms and Training
Antiphospholipase A2 Receptor Autoantibodies: A Step Forward in the Management of Primary Membranous Nephropathy

Since the identification of PLA2R (M-type phospholipase A2 receptor) as the first human antigenic target in primary membranous nephropathy (MN), perpetual progress has been made in understanding the pathogenesis of this disease. Accumulating clinical data support a pathogenic role for the anti-PLA2R antibodies (PLA2R ABs), but confirmation in an animal model is still lacking. However, PLA2R ABs were related to disease activity and outcome, as well as to response therapy. Accordingly, PLA2R ABs assay seems to be promising tool not only to diagnose MN but also to predict the course of the disease and could open the way to personalize therapy. Nevertheless, validation of a universal assay with high precision and definition of cut-off levels, followed by larger studies with a prolonged follow-up period, are needed to confirm these prospects.
Rural-Urban Transformation in Nigeria

Since the late 1960s, Nigeria's structural and rural-urban transformations have been driven largely by developments in its oil sector. Due to increased oil revenues, incentives for the production of internationally tradable agricultural commodities declined and the agricultural sector stagnated. Incentives to produce domestically consumed agricultural products also declined as oil exports financed increasing food imports. The decline of the agricultural sector fueled rural-urban migration. Migration continued into the 1980s and 1990s, even when large parts of the economy - including manufacturing - collapsed due to lower oil prices, unsustainable policies, and foreign debt. Growth in the urban informal sector, fueled by migrants has posed challenges in terms of urban poverty and the growth of urban and peri-urban slums. The government of Nigeria has attempted to stem the tide of rural-urban migration through rural development programs. However these programs have had limited impact and were unable to stem the flow of migrants into urban areas. Key remaining questions center on how to increase productivity in the agricultural sector and at the same time provide more lucrative opportunities in urban areas.
Self-Consistent Evolution of Ring Galaxies

Ring galaxies are commonly known as objects where a burst of star formation was triggered by a close encounter with an intruder, maybe a satellite galaxy. BVRI CCD observations of five ring galaxies have been performed. Here we present the results of a self-consistent approach to reproduce their observed morphology and spectral energy distribution using updated $N$--body simulations and evolutionary population synthesis models extending from UV to far--IR wavelengths. Some suggestions about the evolutionary properties of these starburst galaxies are then derived.
On probabilistic data association for achieving near-exponential diversity over fading channels

Machine-to-Machine (M2M) wireless communication requires the transmission of short blocks of data with high reliability over fading channels. We discuss the use of the probabilistic data association (PDA) detector in conjunction with precoding to design high-performance systems for these links. First, the performance of the traditional PDA algorithm with precoding over ideal Rayleigh fading links is analyzed, which provides insight into its performance, and evidence of an error floor at high SNRs. Then, a novel ordering mechanism is proposed that takes advantage of the precoder characteristics. It is shown by simulation that the proposed modified algorithm can achieve near-ML performance for block sizes as small as 32 symbols.
Type Assisted Synthesis of Recursive Transformers on Algebraic Data Types

In this paper, we show how synthesis can help implement interesting functions involving pattern matching and algebraic data types. One of the novel aspects of this work is the combination of type inference and counterexample-guided inductive synthesis (CEGIS) in order to support very high-level notations for describing the space of possible implementations that the synthesizer should consider. The paper also describes a set of optimizations that significantly improve the performance and scalability of the system. The approach is evaluated on a set of case studies which most notably include synthesizing desugaring functions for lambda calculus that force the synthesizer to discover Church encodings for pairs and boolean operations, as well as a procedure to generate constraints for type inference.
Identification of Peer-to-Peer VoIP Sessions Using Entropy and Codec Properties

Voice over Internet Protocol (VoIP) applications based on peer-to-peer (P2P) communications have been experiencing considerable growth in terms of number of users. To overcome filtering policies or protect the privacy of their users, most of these applications implement mechanisms such as protocol obfuscation or payload encryption that avoid the inspection of their traffic, making it difficult to identify its nature. The incapacity to determine the application that is responsible for a certain flow raises challenges for the effective management of the network. In this paper, a new method for the identification of VoIP sessions is presented. The proposed mechanism classifies the flows, in real-time, based on the speech codec used in the session. To make the classification lightweight, the behavioral signatures for each analyzed codec were created using only the lengths of the packets. Unlike most previous approaches, the classifier does not use the lengths of the packets individually. Instead, it explores their level of heterogeneity in real time, using entropy to emphasize such feature. The results of the performance evaluation show that the proposed method is able to identify VoIP sessions accurately and simultaneously recognize the used speech codec.
Compositional temporal analysis model for incremental hard real-time system design

The incremental design and analysis of parallel hard real-time stream processing applications is hampered by the lack of an intuitive compositional temporal analysis model that supports arbitrary cyclic dependencies between tasks. This paper introduces a temporal analysis model for hard real-time systems, called the Compositional Temporal Analysis (CTA) model, in which arbitrary cyclic dependencies can be specified. The CTA model also supports hierarchical composition and incremental design of timed components. The internals of a component in the CTA model can be hidden without changing the temporal properties of the component. Furthermore, the composition operation in the CTA model is associative, which enables composing components in an arbitrary order. Besides all these properties, also latency constraints and periodic sources and sinks can be specified and analyzed. We also show in this paper that for the CTA model efficient algorithms exist for buffer sizing, verifying consistency of compositions and to compute the temporal properties of compositions. The CTA model can be used as an abstraction of timed dataflow models. The CTA model uses components with transfer rates per port, in contrast to dataflow models that use actors with firing rules. Unlike dataflow models, the CTA model is not executable. An audio echo cancellation application is used to illustrate the applicability of the CTA model for a stream processing application with throughput and latency constraints, and to illustrate incremental design.