Miracle-dz/UniFAR-FTUs huggingface dataset:
Just Until Payday

The growth of payday lending markets during the last 15 years, both in the United States and abroad, has been the focus of substantial regulatory attention, producing a dizzying array of initiatives by federal and state policymakers. Those initiatives have conflicting purposes - some seek to remove barriers to entry and others seek to impose limits on the business model and those who participate in it. As is often the case in banking markets, the resulting patchwork of federal and state laws poses a problem when one state is able to dictate the practices of a national industry. For most of this industry's life, just that has happened - states with the least restrictive laws effectively displaced states with more restrictive laws. Recently, however, the Office of the Comptroller of the Currency and the Federal Deposit Insurance Corporation significantly changed their policies on payday lending. Now, for the first time, States can effectively police payday lenders in their borders. Yet as we enter this new stage in which States will be able to regulate payday lending effectively, there has been little clear analysis of how they should do so. This paper responds to that opportunity by providing a detailed explanation of the business models and regulatory regimes that exist today, together with a framework of options designed to implement various perspectives regulators might adopt. We emphasize three main points. The first is the unusual nature of payday lending, with very high interest rates accruing against necessarily limited debt amounts. Unlike credit card lending, the payday loan amount does not increase over time, but the biweekly interest obligation can lead to a semi-permanent cash annuity for the lender. In our view, those features present challenging issues for regulators. Second, we underscore the limitations of existing legal regimes, which often leave loopholes that permit lenders to avoid the statutory framework; this is a particularly serious problem for the majority of States that have tried to limit rollover lending. Third, addressing the majority of jurisdictions that have not banned the product, we advocate a reversal of the current hostility to market activity by large institutions. If the market is to exist, we believe it is better for it to be populated by highly visible national providers than by smaller fly-by-night providers.
Overlapping and distinct roles for PI 3 K b and g isoforms in S 1 P-induced migration of human and mouse endothelial cells

Center for Molecular Biomedicine, Institute of Molecular Cell Biology, Friedrich Schiller University, Jena, Am Leutragraben 3, 07743 Jena, Germany; Australian Centre for Blood Diseases, Monash University, Alfred Medical Research Centre and Education Precinct (AMREP), Melbourne, Victoria, Australia; Medizinische Klinik I/Herz-Kreislaufzentrum, Universitätsklinikum, Julius Maximilian University, Würzburg, Germany; Institute for Biochemistry, Medical Faculty, Justus Liebig University, Giessen, Germany; and Department of Genetics, Biology and Biochemistry, Center for Molecular Biotechnology, University of Torino, Torino, Italy
Prebiotics, probiotics and postbiotics for sustainable poultry production

ABSTRACT Globally, poultry production has been an integral part of human activities, providing a major source of livelihood and food to humans. The continuous increase in the world population with an attendant rise in the demand for safe poultry products requires a global strategy for sustainable poultry production. With growing concerns over antimicrobial resistance, abolition or reduction in the use of antibiotic growth promoters (AGPs), and rising consumer demand for chemical or antibiotic-free products, identifying and applying safe, natural and economical alternatives including prebiotics, probiotics and postbiotics for sustainable poultry production has become imperative. The beneficial effects of the dietary inclusion of prebiotics, probiotics and postbiotics as AGP alternatives in poultry production include improved poultry health, growth performance and feed efficiency. Generally, the mechanisms through which prebiotics, probiotics and postbiotics exert beneficial effects on poultry include competitive exclusion and antagonism of pathogens, modulation of intestinal microbiota, production of antimicrobial substances, stimulation of immune system, and enhancement of nutrient digestibility and intestinal morphology development. The properties and beneficial effects of these emerging alternatives suggest their contribution to the improvement of poultry health, through the stimulation of diverse physiological functions (although not entirely elucidated) with better growth performance and feed efficiency. This review discusses the concept, impacts and mechanisms of the application of prebiotics, probiotics and postbiotics in sustainable poultry production. Their general acceptance and application in the poultry industry will undoubtedly result in more sustainable, safe and economic poultry production for feeding the world.
An Artifical Management Platform Based on Deep Learning Using Cloud Computing for Smart Cities

In today's world; smart city management uses sensors, cameras and mobile devices which is internet-connected. These devices, called Internet of things (IoTs) generate large amounts of data. A big data-based approach is needed to store these data appropriately and to provide real-time access to the data. In smart city management applications that use many different sources of information, traditional machine learning methods for classifying large data and generating meaning can be inadequate. Deep learning approach is widely used today to solve similar problems. In this study, a cloud computing based architectural approach is proposed that enables data mining using deep learning on big data produced by IOTs.
Identification of N-Oxide-Containing Aromatic Heterocycles as Pharmacophores for Rumen Fermentation Modifiers

Different strategies have been used to mitigate greenhouse gas emissions from domesticated ruminants, including the removal of protozoa (defaunation). The objective of the present work was to analyze the potential of different N-oxide-containing aromatic heterocycles with known antiprotozoal activity as rumen-gas-abating agents. Nineteen pure compounds, belonging to seven different N-oxide chemotypes from our chemo-library were studied together with monensin in an in vitro rumen simulation assay. Fermentation profiles, i.e., gas production, pH, and short carboxylic acid concentrations, were compared to an untreated control at 96 h post inoculation. In our study, we investigated whole-ruminal fluid, with and without compound treatments, by NMR spectroscopy focusing on concentrations of the metabolites acetate, propionate, butyrate, and lactate. From data analysis, three of the compounds from different N-oxide chemotypes, including quinoxaline dioxide, benzofuroxan, and methylfuroxan, were able to diminish the production of gases such as monensin with similar gas production lag times for each of them. Additionally, unlike monensin, one methylfuroxan did not decrease the rumen pH during the analyzed incubation time, shifting rumen fermentation to increase the molar concentrations of propionate and butyrate. These facts suggest interesting alternatives as feed supplements to control gas emissions from dairy ruminants.
Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis

As an indicator of human attention gaze is a subtle behavioral cue which can be exploited in many applications. However, inferring 3D gaze direction is challenging even for deep neural networks given the lack of large amount of data (groundtruthing gaze is expensive and existing datasets use different setups) and the inherent presence of gaze biases due to person-specific difference. In this work, we address the problem of person-specific gaze model adaptation from only a few reference training samples. The main and novel idea is to improve gaze adaptation by generating additional training samples through the synthesis of gaze-redirected eye images from existing reference samples. In doing so, our contributions are threefold:(i) we design our gaze redirection framework from synthetic data, allowing us to benefit from aligned training sample pairs to predict accurate inverse mapping fields; (ii) we proposed a self-supervised approach for domain adaptation; (iii) we exploit the gaze redirection to improve the performance of person-specific gaze estimation. Extensive experiments on two public datasets demonstrate the validity of our gaze retargeting and gaze estimation framework.
Acute insertion effects of penetrating cortical microelectrodes imaged with quantitative optical coherence angiography

Abstract. The vascular response during cortical microelectrode insertion was measured with amplitude decorrelation-based quantitative optical coherence angiography (OCA). Four different shank-style microelectrode configurations were inserted in murine motor cortex beneath a surgically implanted window in discrete steps while OCA images were collected and processed for angiography and flowmetry. Quantitative measurements included tissue displacement (measured by optical flow), perfused capillary density, and capillary flow velocity. The primary effect of insertion was mechanical perturbation, the effects of which included tissue displacement, arteriolar rupture, and compression of a branch of the anterior cerebral artery causing a global decrease in flow. Other effects observed included local flow drop-out in the region immediately surrounding the microelectrode. The mean basal capillary network velocity for all animals was 0.23 (±0.05 SD) and 0.18 (±0.07 SD) mm/s for capillaries from 100 to 300 μm and 300 to 500 μm, respectively. Upon insertion, the 2-shank electrode arrays caused a decrease in capillary flow density and velocity, while the results from other configurations were not different from controls. The proximity to large vessels appears to play a larger role than the array configuration. These results can guide neurosurgeons and electrode designers to minimize trauma and ischemia during microelectrode insertion.
As innovations drive economic growth, do they also raise well-being?

While there is little doubt that innovations drive economic growth, their effects on well-being are less clear. One reason for this are ambivalent effects of innovations on well-being that result from pecuniary and technological externalities of innovations, argued to be inevitably. Another major reason lies in the fact that, as a result of innovations, preferences can change over time. Under such conditions, a time-consistent measuring rod for changes in well-being is hard to construct. Existing conceptions of well-being are shown not yet to solve the problem in a way that provides an unambiguous answer to the question in the title.
Variable Selection for Health Care Demand in Germany

This document reproduces the data analysis presented in Wang et al. (2015). In an effort to optimize the computing algorithms, the penalized regression can be slightly different. For a description of the theory behind application illustrated here we refer to the original manuscript. Riphahn et al. (2003) utilized a part of the German Socioeconomic Panel (GSOEP) data set to analyze the number of doctor visits. The original data have twelve annual waves from 1984 to 1995 for a representative sample of German households, which provide broad information on the health care utilization, current employment status, and the insurance arrangements under which subjects are protected. The data set contains number of doctor office visits for 1,812 West German men aged 25 to 65 years in the last three months of 1994. As shown in the figure, many doctor office visits are zeros, which can be difficult to fit with a Poisson or negative binomial model. Therefore, zero-inflated negative binomial (ZINB) model is considered.
Hematological and Biochemical Effects of Meloxicam in Male Albino Rats

The present study was planned to investigate the hemato-biochemical alterations induced by Meloxicam, a new non-steroidal anti-inflammatory drug, in albino rats and to determine the modulatory action of vitamin C against the adverse effects of Meloxicam. Seventy-eight male albino rats (140-160 g) were divided into five groups. The first group (G1) was considered as control group, in which rats were given 1 ml/kg of saline solution. The second group (G2) was given orally the therapeutic (low) dose of Meloxicam (0.2mg/kg) alone. The third group (G3) was given three-fold the therapeutic (high) dose of Meloxicam (0.6 mg/kg). The forth group (G4) was treated with the low dose of Meloxicam along with vitamin C orally (200 mg/Kg). The fifth group (G5) was treated with the high dose of Meloxicam along with vitamin C (200 mg/Kg). At the end of the experimental period (four weeks), the obtained results showed that Meloxicam at both doses did not alter the levels of RBCs, PCV% and total protein compared with the control group, while, the low and high doses of Meloxicam induced significant increases in total WBCs counts, blood Hb, platelets, serum creatinine, total cholesterol, LDL-C, AST, ALT and MDA and decreases in serum HDL-C, and SOD. Administration of vitamin C along with Meloxicam intoxication led to decrease the severe biochemical and hematological alterations induced by Meloxicam by preventing the rise in the levels of MDA, ALT, AST, total cholesterol, LDL-C, creatinine and the values of WBCs, platelets and Hb, and ameliorated the decline in the levels of SOD and HDL-C. We could conclude that Meloxicam at both dose levels causes hepatotoxicity, nephrotoxicity and produces lesion in hematological parameters. Supplementation with vitamin C could counteract the toxic effects of Meloxicam.
Adversarial Energy Disaggregation

Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. NILM aims to help households understand how the energy is used and consequently tell them how to effectively manage the energy, thus allowing energy efficiency, which is considered as one of the twin pillars of sustainable energy policy (i.e., energy efficiency and renewable energy). Although NILM is unidentifiable, it is widely believed that the NILM problem can be addressed by data science. Most of the existing approaches address the energy disaggregation problem by conventional techniques such as sparse coding, non-negative matrix factorization, and the hidden Markov model. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM since DNNs can inherently learn the discriminative signatures of the different appliances. In this article, we propose a novel method named adversarial energy disaggregation based on DNNs. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task. Our method trains a generator and multiple discriminators via an adversarial fashion. The proposed method not only learns shared representations for different appliances but captures the specific multimode structures of each appliance. Extensive experiments on real-world datasets verify that our method can achieve new state-of-the-art performance.
Technical Report : Exception-enriched Rule Learning from Knowledge Graphs

Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. These KGs are inevitably bound to be incomplete. To fill in the gaps, data correlations in the KG can be analyzed to infer Horn rules and to predict new facts. However, Horn rules do not take into account possible exceptions, so that predicting facts via such rules introduces errors. To overcome this problem, we present a method for effective revision of learned Horn rules by adding exceptions (i.e., negated atoms) into their bodies. This way errors are largely reduced. We apply our method to discover rules with exceptions from real-world KGs. Our experimental results demonstrate the effectiveness of the developed method and the improvements in accuracy for KG completion by rule-based fact prediction.
Inkjet Printing of Sc-Doped TiO2 with Enhanced Photoactivity

Here we report the methodology for nanocomposite fabrication based on the inkjet printing technique. Doped TiO2 nanoparticles with Sc contents up to 10 wt. % were synthesized and adapted towards a facile fabrication of microscale structures and thin film printing. Implementation of the state-of-the-art low-temperature synthesis allowed to us successfully incorporate high concentrations of Sc3+ ions into the TiO2 lattice and improve the light absorption characteristics of the resulting materials. Without affecting the anatase structure substantially, Sc doping gave rise to an intensified absorbance capacity and provided the means for the efficient fabrication of Sc-TiO2 microarchitectures via the inkjet printing technique. The changes in the spectral and structural characteristics of the Sc-TiO2 composites were observed by Energy Dispersive X-Ray spectroscopy (EDX), X-ray diffraction (XRD), and UV-vis methods. The rheological parameters of the colloidal suspension based on the synthesized Sc-TiO2 nanoparticles were adapted for inkjet printing in terms of the optimal viscosity, morphology, and surface tension. The developed individual ink characteristics allowed us to produce a close coherence between the enhanced optical properties of the Sc-TiO2 prepared the sol–gel method and the inkjet-printed films. The introduced methodology features the possibility to inkjet-print doped and pure TiO2 robust films for potential large-scale fabrication.
Robot Intelligent Grasp of Unknown Objects Based on Multi-Sensor Information

Robots frequently need to work in human environments and handle many different types of objects. There are two problems that make this challenging for robots: human environments are typically cluttered, and the multi-finger robot hand needs to grasp and to lift objects without knowing their mass and damping properties. Therefore, this study combined vision and robot hand real-time grasp control action to achieve reliable and accurate object grasping in a cluttered scene. An efficient online algorithm for collision-free grasping pose generation according to a bounding box is proposed, and the grasp pose will be further checked for grasp quality. Finally, by fusing all available sensor data appropriately, an intelligent real-time grasp system was achieved that is reliable enough to handle various objects with unknown weights, friction, and stiffness. The robots used in this paper are the NTU 21-DOF five-finger robot hand and the NTU 6-DOF robot arm, which are both constructed by our Lab.
Data-Driven Decentralized Algorithm for Wind Farm Control with Population-Games Assistance

In wind farms, the interaction between turbines that operate close by experience some problems in terms of their power generation. Wakes caused by upstream turbines are mainly responsible of these interactions, and the phenomena involved in this case is complex especially when the number of turbines is high. In order to deal with these issues, there is a need to develop control strategies that maximize the energy captured from a wind farm. In this work, an algorithm that uses multiple estimated gradients based on measurements that are classified by using a simple distributed population-games-based algorithm is proposed. The update in the decision variables is computed by making a superposition of the estimated gradients together with the classification of the measurements. In order to maximize the energy captured and maintain the individual power generation, several constraints are considered in the proposed algorithm. Basically, the proposed control scheme reduces the communications needed, which increases the reliability of the wind farm operation. The control scheme is validated in simulation in a benchmark corresponding to the Horns Rev wind farm.
Cooperstock An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques

This paper presents an empirical study investigating the effects of training data quantity, measurement error, pixel coordinate noise, and the choice of camera model, on camera calibration accuracy, based on three publicly available techniques, developed by Tsai, Heikkilä and Zhang. Results are first provided for a simulated camera system and then verified through carefully controlled experiments using real-world measurements. Our aims are to provide suggestions to researchers who require an immediate solution for camera calibration and a warning of the practical difficulties one may encounter in a real environment. In addition, we offer some insight into the factors to keep in mind when selecting a calibration technique. Finally, we hope that this paper can serve as an introduction to the field for those newly embarking upon calibration-related research.
Crystal structure of Cex 1 p reveals the mechanism of tRNA trafficking between nucleus and cytoplasm

In all eukaryotes, transcribed precursor tRNAs are maturated by processing and modification processes in nucleus and are transported to the cytoplasm. The cytoplasmic export protein (Cex1p) captures mature tRNAs from the nuclear export receptor (Los1p) on the cytoplasmic side of the nuclear pore complex, and it delivers them to eukaryotic elongation factor 1a. This conserved Cex1p function is essential for the quality control of mature tRNAs to ensure accurate translation. However, the structural basis of how Cex1p recognizes tRNAs and shuttles them to the translational apparatus remains unclear. Here, we solved the 2.2 Å resolution crystal structure of Saccharomyces cerevisiae Cex1p with C-terminal 197 disordered residues truncated. Cex1p adopts an elongated architecture, consisting of N-terminal kinase-like and a C-terminal a-helical HEAT repeat domains. Structure-based biochemical analyses suggested that Cex1p binds tRNAs on its inner side, using the positively charged HEAT repeat surface and the C-terminal disordered region. The N-terminal kinase-like domain acts as a scaffold to interact with the Ran-exportin (Los1p·Gsp1p) machinery. These results provide the structural basis of Los1p·Gsp1p·Cex1p·tRNA complex formation, thus clarifying the dynamic mechanism of tRNA shuttling from exportin to the translational
Quora InsincereQuestion Classification

Machine learning models have been very successful when dealing with text classification problems. In this project we are tasked with classifying questions from a dataset provided by the popular website Quora, as ‘sincere’ or ‘insincere’ . The large-scale dataset, provided by the website Kaggle, contains over 1,300,000 questions with labels to train our models on. A separate test set that contains over 300,000 unlabeled questions is used by Kaggle to test our model. We implement three different models, logistic regression, Naive Bayes, and recurrent neural networks, and use different pre-trained word embedding’s to achieve the best results. The best model, Recurrent Neural Networks, achieves a 0.67 F1 score and is shown to be very useful in text classification problems because of its ability to ‘memorize’ sequential