Miracle-dz/UniFAR-FTUs huggingface dataset:
Star-galaxy Classification Using Deep Convolutional Neural Networks

Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine to automatically learn the features directly from data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep convolutional neural networks (ConvNets) directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep neural networks require very little, manual feature engineering.
Galaxy halo masses and satellite fractions from galaxy–galaxy lensing in the Sloan Digital Sky Survey: stellar mass, luminosity, morphology and environment dependencies

The relationship between galaxies and dark matter (DM) can be characterized by the halo mass of the central galaxy and the fraction of galaxies that are satellites. Here, we present observational constraints from the Sloan Digital Sky Survey on these quantities as a function of r-band luminosity and stellar mass using galaxy-galaxy weak lensing, with a total of 351 507 lenses. We use stellar masses derived from spectroscopy and virial halo masses derived from weak gravitational lensing to determine the efficiency with which baryons in the halo of the central galaxy have been converted into stars. We find that an L* galaxy with a stellar mass of 6 x 1010 M ⊙ is hosted by a halo with mass of 1.4 x 10 12 h -1 M ⊙ , independent of morphology, yielding baryon conversion efficiencies of 17 +10 -5 per cent (early-types) and 16 +15 -6 per cent (late-types) at the 95 per cent confidence level (statistical, not including systematic uncertainty due to assumption of a universal initial mass function). We find that for a given stellar mass, the halo mass is independent of morphology below M stellar = 10 11 M ⊙ , in contrast to typically a factor of 2 difference in halo mass between ellipticals and spirals at a fixed luminosity. This suggests that stellar mass is a good proxy for halo mass in this range and should be used preferentially whenever a halo mass selected sample is needed. For higher stellar masses, the conversion efficiency is a declining function of stellar mass, and the differences in halo mass between early- and late-types become larger, reflecting the fact that most group and cluster haloes with masses above 10 13 M ⊙ host ellipticals at the centre, while even the brightest central spirals are hosted by haloes of mass below 10 13 M ⊙ We find that the fraction of spirals that are satellites is roughly 10-15 per cent independent of stellar mass or luminosity, while for ellipticals this fraction decreases with stellar mass from 50 per cent at 10 10 M ⊙ to 10 per cent at 3 x 10 11 M ⊙ or 20 per cent at the maximum luminosity considered. We split the elliptical sample by local density, and find that at a given luminosity there is no difference in the signal on scales below 100 h -1 kpc between high- and low-density regions, suggesting that tidal stripping inside large haloes does not remove most of the DM from the early-type satellites. This result is dominated by haloes in the mass range 10 13 -10 14 h -1 M ⊙ , and is an average over all separations from the group or cluster centre.
How and when LMX quality relates to counterproductive performance

Purpose In spite of the recent meta-analysis by Martin et al. (2016), we have very little insight about the theoretical mechanism explaining the leader–member exchange–counterproductive work behavior (LMX–CWB) relationship. Drawing on social cognitive theory, the purpose of this paper is to test if occupational self-efficacy functions as a mediating mechanism to explain the relationship between LMX quality and counterproductive performance directed toward the supervisor. In addition, based on the conservation of resources theory, the paper investigates if supervisor–subordinate relationship tenure acted as a second-stage moderator of this mediated relationship. Design/methodology/approach The authors used two-wave time-lagged data from a sample of 189 high-tech professionals to test the hypotheses, controlling for age, sex, and trust. Findings The results of this paper showed that occupational self-efficacy carried the effect of LMX quality on counterproductive performance, but only for workers who have longer supervisor–subordinate relationship tenure. Originality/value This paper is unique in proposing and testing a social cognitive mechanism to explain the relationship between LMX quality and counterproductive performance. As Johns (2017) advocated, the authors incorporated length of time, a contextual variable into this study by investigating supervisor–subordinate relationship tenure as moderating the proposed mediated relationship.
A Machine Learning Approach for an Indonesian-English Cross Language Question Answering System

We have built a CLQA (Cross Language Question Answering) system for a source language with limited data resources (e.g. Indonesian) using a machine learning approach. The CLQA system consists of four modules: question analyzer, keyword translator, passage retriever and answer finder. We used machine learning in two modules, the question classifier (part of the question analyzer) and the answer finder. In the question classifier, we classify the EAT (Expected Answer Type) of a question by using SVM (Support Vector Machine) method. Features for the classification module are basically the output of our shallow question parsing module. To improve the classification score, we use statistical information extracted from our Indonesian corpus. In the answer finder module, using an approach different from the common approach in which answer is located by matching the named entity of the word corpus with the EAT of question, we locate the answer by text chunking the word corpus. The features for the SVM based text chunking process consist of question features, word corpus features and similarity scores between the word corpus and the question keyword. In this way, we eliminate the named entity tagging process for the target document. As for the keyword translator module, we use an Indonesian-English dictionary to translate Indonesian keywords into English. We also use some simple patterns to transform some borrowed English words. The keywords are then combined in boolean queries in order to retrieve relevant passages using IDF scores. We first conducted an experiment using 2,837 questions (about 10% are used as the test data) obtained from 18 Indonesian college students. We next conducted a similar experiment using the NTCIR (NII Test Collection for IR Systems) 2005 CLQA task by translating the English questions into Indonesian. Compared to the Japanese-English and Chinese-English CLQA results in the NTCIR 2005, we found that our system is superior to others except for one system that uses a high data resource employing 3 dictionaries. Further, a rough comparison with two other Indonesian-English CLQA systems revealed that our system achieved higher accuracy score.
Towards Virtual Machine Migration in Fog Computing

Handoff mechanisms allow mobile users to move across multiple wireless access points while maintaining their voice and/or data sessions. A traditional handoff process is concerned with smoothly transferring a mobile device session from its current access point (or cell) to a target access point (or cell). These handoff characteristics are sufficient for voice calls and background data transfers, however nowadays many mobile applications are heavily based on data and processing capabilities from the cloud. Such applications, especially those that require greater interactivity, often demand not only a smooth session transfer, but also the maintenance of quality of service requirements that impact a user's experience. In this context, the Fog Computing paradigm arises to overcome delays encountered when applications need low latency to access data or offload processing to the cloud. Fog computing introduces a distributed cloud layer, composed of cloudlets (i.e., "small clouds" with lower computational capacity), between the user and the cloud. Cloudlets allow low latency access to data or processing capabilities, which can be accomplished by offering a VM to the user. An overview of Fog computing is first providing, relating it to general concepts in Cloud-based systems, followed by a general architecture to support virtual machine migration in this emerging paradigm - discussing both the benefits and challenges associated with such migration.
Potential Application of Electrical Conductivity (EC) Map for Variable Rate Seeding

A two-year field experiment was conducted to investigate the potential application of soil electrical conductivity for variable rate seeding. Map calculations and geo-statistical techniques were used to establish a relationship among EC, elevation, seeding rate, and yield. Yield data taken from the first year, which was a dry year, showed inconsistent relationships and the overall yield variation within the farm was very low at 700 kg ha -1 . However, yield data from the second year, being a wet year, exhibited relationships where an increase in EC indicated an increased yield potential, while increased seeding rates exhibited fluctuating trends in yield potentials. There is clear evidence that the existing relationship between the site properties as quantified by the EC, the seeding rate, and the crop yield, can favorably be used for Variable Rate Planting (VRP) in this particular production system. Regarding the development of prediction models for use in these situations, linear and non-linear parametric models were tried on the 2003 data, but with little success. A generalized additive model, a non-parametric approach, was used next and the yield model developed was found to regress the relationship adequately. To further improve the prediction accuracy, a Neural Network (NN) technique was used on the data. The diagnostics indicated that the yield estimation was precise (R 2 = 0.89). The NN approach was identified as a very promising technique for using EC data in the successful application of variable rate technology towards maximizing yield potential of sites. However, the results indicate that the modeled relationship is specific to only that particular crop production system.
Frequency, Causes and Reporting Barriers of Nursing Errors in the Operating Room Students

Introduction: Operating room with respect to multiple invasive procedures can be a context for the incidence of nursing errors. Also, students are in the process of learning that can lead to errors by them. This study aimed to determine the rate, causes and reporting barriers of nursing errors in the operating room students of Kurdistan University of Medical Sciences in 2016. Methods: In this descriptive and cross-sectional study 120 students of operating room that have been choosen by census method participated. To collect data a three-part questionnaire including (demographic characteristics, frequency of error, the causes of errors and error reporting barriers) was used. Data analysis using descriptive statistics by SPSS was performed. Results: Most nursing errors were related to non-compliance with sterile technique (78.3 percent), reactions to blood or blood products (75.5 percent) and retention of foreign bodies in the surgical incision (73.3 percent), respectively. The most important causes of nursing errors from the perspective of students were the lack of sufficient information (82.5 percent), distraction (79.2 percent) and forced to perform multi-tasks simultaneously (78.3 percent) . The most important causes for refusing to report the error were the fear of disclosure of mistakes and following by justice issues (57.5 percent) and fear of the impact of errors in the evaluation and educational consequences (57.5 percent), respectively. Conclusions: Prevention is the best way of reducing errors, so by recognizing the type and causes of nursing errors in the view of operating room students, clinical instructors should perform necessary preventive actions especially in the clinical training period. Promoting culture of reporting errors, especially in college by supporting them and reduce the impact on student evaluation is recommended.
The comparison of cross–education effect in young and elderly females from unilateral training of the elbow flexors

Background: Many studies have reported the increase in strength of the untrained contralateral limb after unilateral training. The aim of this study was to compare the cross education effect in the young and elderly persons. Methods: In this quasi-experimental and pre-post study, 12 young people aged 28.25 ±3.11 years and 12 elderly persons (aged 73.08 ± 5.3 years) participated. The subjects had no history of strength training and upper limb movement impairments. Maximal isometric flexion strength in the dominant limb and the contralateral side before and after training were measured by tensiometer. Subjects performed elbow flexion exercises in the dominant side, using 3 sets of 10 repetition of the 60-70% maximal force for two weeks. Independent and paired t test were used to analyze between and within groups differences. Results: The results showed that short-term isometric resistive exercise led to a significant increase of strength in trained and untrained limbs in both groups (p<0.05). There was not a significant difference between the two groups in the rate of strength increase, both in the upper limb that was exercised and also in the opposite side (p> 0.05). Conclusion: The increased muscle strength observed during training indicates positive effect of training in old adult. The increased muscle strength in untrained limb suggests the capacity of neuromuscular adaptation among old adults, suitable to be used in cases of limb immobility or unilateral impairment.
V ( D ) J Recombinase-mediated HPRT Mutations in Peripheral Blood Lymphocytes of Normal Children 1

Somatic mutations in the hypoxanthine-guanine phosphoribosyltransferase (hprt) gene are rare occurrences in T-lymphocytes of normal individuals. Lacking pathogenic significance, these events can serve as reporters for assessing environmental genotoxicity. The present molecular analyses of ///;/â€¢/ mutations arising spontaneously in normal children show that 30-35% of the genomic hprt changes in children under 5 years of age have ~20 Kb deletions encompassing exons 2 and 3. The frequency of these specific changes are dramatically decreased in older children. Se quence analysis of these deletion breakpoint and joining regions reveal the molecular hallmarks of ViDi.l recombinase-mediated recombination events. This early childhood hprt mutational spectrum is quite distinct from the adult background spectrum but similar to that reported previ ously for newborns, as determined in lymphocytes from placenta! cord blood. The present study also demonstrates that definition of sequences in the hprt deletion joining regions that are analogous to the N-nucleotide insertion hypervariable regions of rearranged T-cell receptor genes allows the same identification of in vivo clonality of mutants as does analysis of the T-cell receptor gene rearrangements themselves. These methods reveal an in vivo clonal amplification of a V(D)J recombinase-mediated hprt mutant clone in one child in the present study. This newly found agefrequency distribution of \ il)). ) recombinase-mediated hprt mutations correlates with the age-frequency distribution of childhood acute lymphocytic leukemia. A significant number of these malignancies, including acute T-cell leukemia, are also characterized by V(D)J recombinasemediated recombinations but in critical regions of the genome, hprt, therefore, captures a pathogenic mutagenic mechanism as a harmless mistake which, when it occurs in other genetic regions, may result in malignancy.
Neurofeedback As a Treatment for Major Depressive Disorder – A Pilot Study

Background There is growing interest in neurofeedback as a treatment for major depressive disorder. Reduction of asymmetry of alpha-activity between left and right prefrontal areas with neurofeedback has been postulated as effective in earlier studies. Unfortunately, methodological shortcomings limit conclusions that can be drawn from these studies. In a pilot-study, we investigated the effectiveness of reduction of asymmetry of alpha-activity with neurofeedback in depressed participants with the use of a stringent methodological approach. Methods Nine participants meeting DSM-IV criteria for major depressive disorder were treated with a maximum of 30 neurofeedback-sessions, aimed at reducing asymmetry of alpha-activity, over a 10-week period. No changes in the use of antidepressants were allowed 6 weeks before and during the intervention. Changes in depressive symptomatology were assessed with the Quick Inventory of Depressive Symptoms, self-report version. Results We observed response in 1 and remission in 4 out of a total of 9 participants. The effectiveness appeared largest in female participants. The mean asymmetry of alpha-activity decreased significantly over sessions in a quadratic fashion. This decrease was associated with clinical response. Conclusions This pilot study suggests that neurofeedback aimed at a reduction of frontal asymmetry of alpha-activity may be effective as a treatment for depression. However, this was an open label pilot study. Non-specific effects of the procedure and/or a beneficial natural course may have confounded the results. Randomized controlled trials will have to establish the efficacy of neurofeedback for depression. Trial Registration Nederlands Trial Register NTR1629
The transcriptional transactivator Tat selectively regulates viral splicing

HIV-1 gene expression requires both viral and cellular factors to control and coordinate transcription. While the viral factor Tat is known for its transcriptional transactivator properties, we present evidence for an unexpected function of Tat in viral splicing regulation. We used a series of HIV-1 reporter minigenes to demonstrate that Tat’s role in splicing is dependent on the cellular co-transcriptional splicing activators Tat-SF1 and CA150. Surprisingly, we show that this Tat-mediated splicing function is independent from transcriptional activation. In the context of the full-length viral genome, this mechanism promotes an autoregulatory feedback that decreases expression of tat and favors expression of the env-specific mRNA. Our data demonstrate that Tat-mediated regulation of transcription and splicing can be uncoupled and suggest a mechanism for the involvement of specific transcriptional activators in splicing.
Timber Products Output and Timber Harvests in Alaska : An Addendum

Updated projections of demand for Alaska timber were published July 2006. Their application in land management planning for the Tongass National Forest has resulted in numerous questions and requests for clarification. This note discusses a broad range of these questions from the context of why we do projections, the model we used, the assumptions that determine the levels of timber harvest, our use of scenario planning, comments about how producers in Alaska compete with other North American producers, and the potential that some significant changes in southeast Alaska markets have changed the demand projections.
Racial and ethnic health disparities among fifth-graders in three cities.

BACKGROUND For many health-related behaviors and outcomes, racial and ethnic disparities among adolescents are well documented, but less is known about health-related disparities during preadolescence. METHODS We studied 5119 randomly selected public-school fifth-graders and their parents in three metropolitan areas in the United States. We examined differences among black, Latino, and white children on 16 measures, including witnessing of violence, peer victimization, perpetration of aggression, seat-belt use, bike-helmet use, substance use, discrimination, terrorism worries, vigorous exercise, obesity, and self-rated health status and psychological and physical quality of life. We tested potential mediators of racial and ethnic disparities (i.e., sociodemographic characteristics and the child's school) using partially adjusted models. RESULTS There were significant differences between black children and white children for all 16 measures and between Latino children and white children for 12 of 16 measures, although adjusted analyses reduced many of these disparities. For example, in unadjusted analysis, the rate of witnessing a threat or injury with a gun was higher among blacks (20%) and Latinos (11%) than among whites (5%), and the number of days per week on which the student performed vigorous exercise was lower among blacks (3.56 days) and Latinos (3.77 days) than among whites (4.33 days) (P<0.001 for all comparisons). After statistical adjustment, these differences were reduced by about half between blacks and whites and were eliminated between Latinos and whites. Household income, household highest education level, and the child's school were the most substantial mediators of racial and ethnic disparities. CONCLUSIONS We found that harmful health behaviors, experiences, and outcomes were more common among black children and Latino children than among white children. Adjustment for socioeconomic status and the child's school substantially reduced most of these differences. Interventions that address potentially detrimental consequences of low socioeconomic status and adverse school environments may help reduce racial and ethnic differences in child health. (Funded by the Centers for Disease Control and Prevention.).