Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between Social-Emotional Development, Academic Achievement and Parenting Practices in Young Children who Attend Head Start

The Relationship between Social-Emotional Development, Academic Achievement and Parenting Practices in Young Children who Attend Head Start By Emily A. A. Dow Adviser: Dr. Roseanne L. Flores During the preschool years, children develop social-emotional skills – such as cooperation and self-regulation – which predict later academic achievement. Research shows that parents play an important role in the development of these skills. However, it remains unclear how specific parenting practices may facilitate the relationship between social-emotional development and academic success. Often, children who grow up in low-income families are at risk for a variety of cognitive and emotional problems. Head Start is a federal program offered to low-income families that provides services, including early childhood education programs, to help offset these risks. Using Bronfenbrenner’s bioecological theory, the purpose of this dissertation was to explore the relationship among these three factors -social-emotional skills, academic achievement, and parenting practices -in an effort to better understand child development. There were three primary aims of this dissertation: (1) to demonstrate the inter-relatedness of several social-emotional skills for children who attended Head Start at age three; (2) understand the relationship between social-emotional skills during preschool and academic achievement at the end of kindergarten; and (3) understand how parent characteristics can influence the relationship between social-emotional skills in preschool and academic achievement by the end of kindergarten. Using a large, nationally representative data set from the Head Start program, several specific research questions were addressed through secondary data analysis. Findings from backwards regressions and moderation analysis indicate that there was a relationship
Evaluation of Feature Detector-Descriptor for Real Object Matching under Various Conditions of Ilumination and Affine Transformation

This study attempts to provide explanations, descriptions and evaluations of some most popular and current combinations of description and descriptor frameworks, namely SIFT, SURF, MSER, and BRISK for keypoint extractors and SIFT, SURF, BRISK, and FREAK for descriptors. Evaluations are made based on the number of matches of keypoints and repeatability in various image variations. It is used as the main parameter to assess how well combinations of algorithms are in matching objects with different variations. There are many papers that describe the comparison of detection and description features to detect objects in images under various conditions, but the combination of algorithms attached to them has not been much discussed. The problem domain is limited to different illumination levels and affine transformations from different perspectives. To evaluate the robustness of all combinations of algorithms, we use a stereo image matching case.
Sevoflurane did not show better protective effect on endothelial glycocalyx layer compared to propofol during lung resection surgery with one lung ventilation.

Background The endothelial glycocalyx layer (EGL) coats the alveolar capillary endothelium and plays important roles in pulmonary vascular protection, modulation, and hemostasis. Ischemia-reperfusion, which occurs during lung resection surgery with one lung ventilation (OLV), can damage the EGL. Sevoflurane is known for its protective effect against ischemia-reperfusion injury. Therefore, we hypothesized that lung resection surgery produces EGL damage and sevoflurane protects the EGL better than the intravenous anesthetic propofol. Methods Seventy-eight patients undergoing pulmonary resection were randomly allocated into the sevoflurane (n=38) and propofol (n=40) groups. All patients received OLV and protective ventilation under sevoflurane- or propofol-based anesthesia. The concentrations of EGL injury markers (heparan sulfate and human syndecan-1) and an inflammatory marker (vascular cell adhesion molecule-1) were measured from blood samples drawn at five time points (after induction, 60 min after OLV, 120 min after OLV, end of OLV, and end of surgery). Results OLV increased the concentrations of EGL injury markers; heparan sulfate concentrations increased from 120 minutes after OLV (120 minutes after OLV: sevoflurane, 13.3±6.8 ng/mL, P<0.05; propofol, 14.8±6.9 ng/mL, P<0.05). Human syndecan-1 concentrations also increased from 120 minutes after OLV (120 minutes after OLV: sevoflurane, 20.4±8.9 ng/mL, P<0.05; propofol, 20.5±11.8 ng/mL, P>0.05). However, no difference in EGL injury markers was observed between the sevoflurane and propofol groups at any time point. Vascular cell adhesion molecule-1 concentrations did not show any temporal changes in either group. Conclusions Lung resection surgery with OLV produced EGL damage without any increase in inflammation. Although shedding of heparan sulfate induced by EGL injury during lung resection surgery with OLV, was less than propofol, it was not statistically significant.
Multi-representation ensembles and delayed SGD updates improve syntax-based NMT

We explore strategies for incorporating target syntax into Neural Machine Translation. We specifically focus on syntax in ensembles containing multiple sentence representations. We formulate beam search over such ensembles using WFSTs, and describe a delayed SGD update training procedure that is especially effective for long representations like linearized syntax. Our approach gives state-of-the-art performance on a difficult Japanese-English task.
Role of adipose tissue in body-weight regulation: mechanisms regulating leptin production and energy balance

Adipose tissue performs complex metabolic and endocrine functions. Among the endocrine products produced by adipose tissue are tumour necrosis factor α, interleukin 6, acylation-stimulating protein and leptin. The present review will focus primarily on mechanisms regulating leptin production and leptin action, and the implications of this regulation in the control of energy balance. Leptin acts in the central nervous system where it interacts with a number of hypothalamic neuropeptide systems to regulate feeding behaviour and energy expenditure. The presence of extreme obesity in animals and human subjects with mutations of the leptin gene or the leptin receptor demonstrates that normal leptin production and action are critical for maintaining energy balance. Insulin is the major regulator of leptin production by adipose tissue. Insulin infusions increase circulating leptin concentrations in human subjects. Plasma leptin levels are markedly decreased in insulin-deficient diabetic rodents, and the low leptin levels contribute to diabetic hyperphagia. Based on in vitro studies, the effect of insulin to stimulate leptin production appears to involve increased glucose metabolism. Blockade of glucose transport or glycolysis inhibits leptin expression and secretion in isolated adipocytes. Evidence suggests that anaerobic metabolism of glucose to lactate does not stimulate leptin production. Alterations in insulin-mediated glucose metabolism in adipose tissue are likely to mediate the effects of energy restriction to decrease, and refeeding to increase, circulating leptin levels. Changes in glucose metabolism may also explain the observation that high-fat meals lower 24 h circulating leptin levels relative to high-carbohydrate meals in human subjects, suggesting a mechanism that may contribute to the effects that high-fat diets have in promoting increased energy intake, weight gain and obesity. The decreased circulating leptin observed during energy restriction is related to increased sensations of hunger in human subjects. Thus, decreases in leptin during energy-restricted weight-loss regimens may contribute to the strong propensity for weight regain. A better understanding of the precise mechanisms regulating leptin production and leptin action may lead to new approaches for managing obesity.
Estimating the Precision of the Likelihood-Ratio Output of a Forensic-Voice-Comparison System

The issues of validity and reliability are important in forensic science. Within the likelihood-ratio framework for the evaluation of forensic evidence, the log-likelihood-ratio cost (Cllr) has been applied as an appropriate metric for evaluating the accuracy of the output of a forensic-voice-comparison system, but there has been little research on developing a quantitative metric of precision. The present paper describes two procedures for estimating the precision of the output of a forensic-comparison system, a non-parametric estimate and a parametric estimate of its 95% credible interval. The procedures are applied to estimate the precision of a basic automatic forensic-voice-comparison system presented with different amounts of questioned-speaker data. The importance of considering precision is discussed.
Experience and Expectations of Ovarian Cancer Patients in Australia

Some of the most significant advances in ovarian cancer treatment have been those that result in improvements in progression-free survival (PFS); however there is little research to understand the value that patients place on accessing therapies that result in PFS as a clinical outcome related to survivorship. This study therefore aimed to understand the experience and expectations of women with ovarian cancer in Australia in relation to quality of life (QoL) and treatment options. An online survey collected demographic information and 13 investigator-derived structured interview questions were developed to understand the experience of women with ovarian cancer, their understanding of terminology associated with their condition, and expectations of future treatment. This study demonstrated that ovarian cancer patients equate PFS with being in remission and that patients expect QoL during that time to be good to excellent. Women in this study described excellent QoL as feeling positive and happy and not worrying about cancer, feeling fit and healthy without side effects, and being able to live life as they did before their diagnosis, including the absence of fear of progression or recurrence. It is therefore suggested that there is a positive relationship between PFS and QoL. While it is difficult to quantify QoL and further research is needed, the results of this study suggest that the minimum time that women with ovarian cancer expect in relation to treatments that result in PFS is approximately six months. In the absence of this information, decision-makers are left to make assumptions about the value women place on access to therapeutics that increase PFS, which for this type of cancer is an important aspect of survivorship.
Microsatellite genotyping of clinical Candida parapsilosis isolates

Background and Purpose: Candida parapsilosis is a predominant species found in nosocomial infection, particularly in hospitalized patients. The molecular epidemiology of the clinical strains of this species has not been well studied. The present study was performed with the aim of investigating the microsatellite genotyping of Candida parapsilosis among the Iranian clinical isolates. Materials and Methods: This study was conducted on 81 independent clinical C. parapsilosis isolates that were genotyped by using a panel of six microsatellite markers. Results: The short tandem repeat (STR) typing of clinical C. parapsilosis isolates demonstrated 68 separate genotypes, among which 57 genotypes were observed once and the remaining 11 cases were identified for multiple times. The Simpson’s diversity index for the panel of combined six markers yielded a diversity index of 0.9951. The heterogeneity was observed among the Iranian and the Netherlands clinical C. parapsilosis isolates. Conclusion: As the findings indicated, the clinical C. parapsilosis isolates from Iran showed a high genetic diversity. It can be concluded that molecular epidemiology could be useful for screening during outbreak investigation where C. parapsilosis is involved.
Learning Explicit Deep Representations from Deep Kernel Networks

Deep kernel learning aims at designing nonlinear combinations of multiple standard elementary kernels by training deep networks. This scheme has proven to be effective, but intractable when handling large-scale datasets especially when the depth of the trained networks increases; indeed, the complexity of evaluating these networks scales quadratically w.r. t. the size of training data and linearly w.r. t. the depth of the trained networks. In this paper, we address the issue of efficient computation in Deep Kernel Networks (DKNs) by designing effective maps in the underlying Reproducing Kernel Hilbert Spaces. Given a pretrained DKN, our method builds its associated Deep Map Network (DMN) whose inner product approximates the original network while being far more efficient. The design principle of our method is greedy and achieved layer-wise, by finding maps that approximate DKNs at different (input, intermediate and output) layers. This design also considers an extra fine-tuning step based on unsupervised learning, that further enhances the generalization ability of the trained DMNs. When plugged into SVMs, these DMNs turn out to be as accurate as the underlying DKNs while being at least an order of magnitude faster on large-scale datasets, as shown through extensive experiments on the challenging ImageCLEF and COREL5k benchmarks.
An Anti-fraud System for Car Insurance Claim Based on Visual Evidence

Automatically scene understanding using machine learning algorithms has been widely applied to different industries to reduce the cost of manual labor. Nowadays, insurance companies launch express vehicle insurance claim and settlement by allowing customers uploading pictures taken by mobile devices. This kind of insurance claim is treated as small claim and can be processed either manually or automatically in a quick fashion. However, due to the increasing amount of claims every day, system or people are likely to be fooled by repeated claims for identical case leading to big lost to insurance companies.Thus, an anti-fraud checking before processing the claim is necessary. We create the first data set of car damage images collected from internet and local parking lots. In addition, we proposed an approach to generate robust deep features by locating the damages accurately and efficiently in the images. The state-of-the-art real-time object detector YOLO \cite{redmon2016you}is modified to train and discover damage region as an important part of the pipeline. Both local and global deep features are extracted using VGG model\cite{Simonyan14c}, which are fused later for more robust system performance. Experiments show our approach is effective in preventing fraud claims as well as meet the requirement to speed up the insurance claim prepossessing.
Associated with Dose-Dependent Inhibition of Rat Mammary Carcinogenesis by Dry Bean ( Phaseolus vulgaris , L . ) 1 – 3

The purpose of this study was to determine whether a dry bean (Phaseolus vulgaris, L.) containing diet exerts an inhibitory effect on mammary carcinogenesis in a well-characterized rodent model for breast cancer. Twenty-one-d-old female Sprague Dawley rats were given an intraperitoneal injection of 1-methyl-1-nitrosourea and 7 d after carcinogen injection were randomized to 1 of 5 groups fed a modification of the AIN-93G diet formulation containing 0, 7.5, 15, 30, or 60% (wt:wt) small red dry bean incorporated as cooked, freeze-dried, and milled powder. All experimental diets had the same macronutrient content based on proximate analysis. Compared with the control group, dry bean consumption resulted in dose-dependent reductions in mammary cancer incidence (P 1⁄4 0.046), cancer multiplicity (P 1⁄4 0.001), and tumor burden (P 1⁄4 0.01). Dry bean consumption was associated with dose-dependent reductions in plasma concentrations of glucose, insulin, insulin-like growth factor-1, C-reactive protein, and interleukin-6 in food-deprived rats. Analysis of mammary adenocarcinomas indicated that a dominant mechanism accounting for reduced tumor burden was the induction of apoptosis. B cell lymphoma 2 and X-linked inhibitor of apoptosis protein levels decreased and BCL-2–associated X protein increased with increasing dry bean consumption, findings consistent with the induction of apoptosis via the mitochondrial pathway. These data demonstrate that a legume without noteworthy content of isoflavones inhibits the development of mammary carcinogenesis and are consistent with a recent report from the Nurses Health Study that bean or lentil intake is associated with a lower risk for breast cancer. J. Nutr. 138: 2091–2097, 2008.
Bridging the Gap Between LTL Synthesis and Automated Planning

Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. Recent work has explored the close connection between automated planning and LTL synthesis but has not provided a full mapping between the two problems nor have its practical implications been explored. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We study LTL interpreted over both finite and infinite traces. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.
Long‐term persistence of horse fecal DNA in the environment makes equids particularly good candidates for noninvasive sampling

Abstract Fecal DNA collected noninvasively can provide valuable information about genetic and ecological characteristics. This approach has rarely been used for equids, despite the need for conservation of endangered species and management of abundant feral populations. We examined factors affecting the efficacy of using equid fecal samples for conservation genetics. First, we evaluated two fecal collection methods (paper bag vs. ethanol). Then, we investigated how time since deposition and month of collection impacted microsatellite amplification success and genotyping errors. Between May and November 2014, we collected feral horse fecal samples of known age each month in a feral horse Herd Management Area in western Colorado and documented deterioration in the field with photographs. Samples collected and dried in paper bags had significantly higher amplification rates than those collected and stored in ethanol. There was little difference in the number of loci that amplified per sample between fresh fecal piles and those that had been exposed to the environment for up to 2 months (in samples collected in paper bags). After 2 months of exposure, amplification success declined. When comparing fresh (0–2 months) and old (3–6 months) fecal piles, samples from fresh piles had more matching genotypes across samples, better amplification success and less allelic dropout. Samples defecated during the summer and collected within 2 months of deposition had highest number of genotypes matching among samples, and lowest rates of amplification failure and allelic dropout. Due to the digestive system and amount of fecal material produced by equids, as well as their occurrence in arid ecosystems, we suggest that they are particularly good candidates for noninvasive sampling using fecal DNA.
Room Recognition Using Discriminative Ensemble Learning with Hidden Markov Models for Smartphones

An accurate room localization system is a powerful tool for providing location-based services. Considering that people spend most of their time indoors, indoor localization systems are becoming increasingly important in designing smart environments. In this work, we propose an efficient ensemble learning method to provide room level localization in smart buildings. Our proposed localization method achieves high room-level localization accuracy by combining Hidden Markov Models with simple discriminative learning methods. The localization algorithms are designed for a terminal-based system, which consists of commercial smartphones and Wi-Fi access points. We conduct experimental studies to evaluate our system in an office-like indoor environment. Experiment results show that our system can overcome traditional individual machine learning and ensemble learning approaches.
Intertemporal Labor Supply Substitution? Evidence from the Swiss Income Tax Holidays

This paper estimates intertemporal labor supply responses to two-year long income tax holidays staggered across Swiss cantons. Cantons shifted from an income tax system based on the previous two years’ income to a standard annual pay as you earn system, leaving two years of income untaxed. We find significant but quantitatively very small responses of wage earnings with an intertemporal elasticity of 0.025 overall. High wage income earners and especially the self-employed display larger responses with elasticities around 0.1 and 0.25, respectively, most likely driven by tax avoidance. We find no effects along the extensive margin at all. (JEL H24, H26, J22, J23, J31, R23)
Genetic features of multicentric/multifocal intramucosal gastric carcinoma

Chronic gastritis caused by Helicobacter pylori (H. pylori) infection could lead to the development of gastric cancer. The finding that multiple gastric cancers can develop synchronously and/or metachronously suggests the development of field cancerization in chronically inflamed, H. pylori‐infected gastric mucosa. The genetic basis of multiple tumorigenesis in the inflamed stomach, however, is not well understood. In this study, we analyzed the microsatellite instability (MSI) status and copy number aberrations (CNAs) of 41 multiple intramucosal early gastric cancers that synchronously or metachronously developed in 19 patients with H. pylori infection. Among the 41 intramucosal gastric carcinomas, 9 (22%) exhibited MSI, and the remaining 32 (78%) exhibited the microsatellite stable (MSS) phenotype. Metachronous multiple intramucosal gastric carcinoma exhibit inter‐tumor heterogeneity by individually acquiring genetic aberrations. All synchronous multiple intramucosal gastric carcinoma pairs shared a common MSI/MSS profile, and CNA analysis revealed that synchronous multiple intramucosal gastric carcinoma pairs with the MSS phenotype shared common aberrations of representative tumor‐suppressor genes, including focal deletion of APC, TP53, CDKN2A, and CDKN2B. Multiregional CNA analysis revealed that heterogeneous gene amplifications/deletions, including PDL1 amplification, evolved under the presence of shared “trunk” genetic alterations in a subpopulation of individual intramucosal gastric carcinomas. These data suggest that multiple gastric carcinomas develop in a multicentric/multifocal manner exhibiting features of inter‐ and intra‐tumor heterogeneity in H. pylori‐infected gastric mucosa, whereas synchronous multiple intramucosal gastric carcinomas could share partially common genetic alterations, possibly via common oncogenic pathways.