Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Moisture on the Orientation Birefringence of Cellulose Esters

Orientation birefringence and its wavelength dispersion are studied for hot-drawn films of cellulose esters such as cellulose triacetate (CTA), cellulose diacetate (CDA), and cellulose acetate propionate (CAP) exposed to three different humidities of environments. Hot-drawn CTA films show negative birefringence that decreases with increasing wavelength. On the other hand, CDA and CAP films show positive birefringence that increases with increasing wavelength, i.e., the so-called extraordinary wavelength dispersion of birefringence. Upon exposure to high humidity environment, the orientation birefringence of CDA and CAP decreases. The decrease is prominent for the samples containing a large amount of water. CTA, however, shows an increase in magnitude of its negative orientation birefringence with increasing moisture content. The results can be explained by the increase of the polarizability anisotropy perpendicular to the stretching direction in the cellulose esters. It is found from ATR-FTIR measurements that hydrogen bonds are formed between carbonyl groups of cellulose esters and water molecules. Considering that orientation birefringence of cellulose esters is determined mainly by ester groups, the formation of hydrogen bonds contributes to the polarizability anisotropy, thus affecting the orientation birefringence.
Phylogenetic Diversity of the Enteric Pathogen Salmonella enterica subsp. enterica Inferred from Genome-Wide Reference-Free SNP Characters

The enteric pathogen Salmonella enterica is one of the leading causes of foodborne illness in the world. The species is extremely diverse, containing more than 2,500 named serovars that are designated for their unique antigen characters and pathogenicity profiles—some are known to be virulent pathogens, while others are not. Questions regarding the evolution of pathogenicity, significance of antigen characters, diversity of clustered regularly interspaced short palindromic repeat (CRISPR) loci, among others, will remain elusive until a strong evolutionary framework is established. We present the first large-scale S. enterica subsp. enterica phylogeny inferred from a new reference-free k-mer approach of gathering single nucleotide polymorphisms (SNPs) from whole genomes. The phylogeny of 156 isolates representing 78 serovars (102 were newly sequenced) reveals two major lineages, each with many strongly supported sublineages. One of these lineages is the S. Typhi group; well nested within the phylogeny. Lineage-through-time analyses suggest there have been two instances of accelerated rates of diversification within the subspecies. We also found that antigen characters and CRISPR loci reveal different evolutionary patterns than that of the phylogeny, suggesting that a horizontal gene transfer or possibly a shared environmental acquisition might have influenced the present character distribution. Our study also shows the ability to extract reference-free SNPs from a large set of genomes and then to use these SNPs for phylogenetic reconstruction. This automated, annotation-free approach is an important step forward for bacterial disease tracking and in efficiently elucidating the evolutionary history of highly clonal organisms.
APRENDIZAJE BASADO EN PROYECTOS: UNA EXPERIENCIA UNIVERSITARIA

Resumen: La finalidad de nuestra experiencia de innovacion ha sido introducir en nuestras aulas universitarias la metodologia de Aprendizaje Basado en Proyectos (ABP), la cual consideramos una estrategia de ensenanza en la que los estudiantes planifican, implementan y evaluan proyectos que tienen aplicaciones en el mundo real y que va mas alla de lo que sucede en las aulas universitarias. Esta innovacion puesta en practica con 107 estudiantes de segundo curso del Grado de Educacion Infantil de la Universidad de Sevilla (Espana) ha tenido como meta fomentar y desarrollar una variedad de competencias relacionadas no solo con el trabajo en equipo, sino tambien con habilidades y estrategias de gestion, localizacion, busqueda y evaluacion de informacion a partir de fuentes documentales electronicas, acercandolos a la reflexion de sus propias investigaciones y la construccion de conocimientos. Aunque el ABP es una metodologia dificil de aplicar en Educacion Superior, los resultados obtenidos ponen de manifiesto las ventajas que ofrece este tipo de metodologia y el alto nivel de satisfaccion de los estudiantes participantes. Ha ofrecido interacciones optimas entre profesores y estudiantes, se ha creado un ambiente positivo de aprendizaje en el aula facilitando la comunicacion y permitiendo un mejor proceso de ensenanza y aprendizaje. Abstract: The purpose of our experience of innovation has been introduced in our university classrooms teaching methodology based on project (PBL), which we consider a teaching strategy in which students plan, implement and evaluate projects that have real-world applications and that goes beyond of what happens in university classrooms. This innovation implementation 140 students's Degree in Early Childhood Education at the University of Seville (Spain) has been aimed to encourage and develop a variety of skills related not only teamwork, but also with the skills and management strategies, locating, searching and evaluating information from electronic documentary sources, bringing them closer to the reflection of their own research and knowledge building. Although the PBL is a difficult methodology applied in higher education, the results show the advantages of this type of methodology and the high level of satisfaction of participating students. It has offered optimal interactions between teachers and students, it has created a positive learning environment in the classroom facilitating communication and enabling better teaching and learning process.
Labour Market Institutions and Income Inequality

"Labour market institutions are a crucial determinant of wage inequality, the wage share in aggregate income, and the unemployment rate. Since these variables affect, in turn, the distribution of income across households, the question arises of whether stronger labour market institutions have an impact on income inequality. Institutions can in principle have conflicting effects. For example, a higher unemployment benefit tends to increase the wage share, which in turn reduces inequality, but it also increases the unemployment rate thus making the distribution of income more unequal. This paper examines what is the overall impact of labour market institutions on household income inequality. The evidence indicates that stronger institutions are associated with lower income inequality, but in some cases also with higher rates of unemployment. We explore the magnitude of this trade-off, and quantify the changes in inequality and unemployment that we would observe if a common labour standard were imposed on members of the European Union. " Copyright (c) CEPR, CES, MSH, 2008.
Distributed input and state estimation for linear discrete-time systems

This paper provides a solution for distributed input and state estimation, simultaneously. A set of sensors with the capability of exchanging information is used to collect data from a discrete-time system. Various distributed implementations of Kalman filter have already been developed to track system states in such a setup when the system is subject to noise with known stochastic properties. However, practical systems might be subject to unknown input signals as well as stochastic noise, which leads to a biased state estimation. This study proposes new distributed filters that calculate state estimation in the presence of unknown inputs. In addition, the filter provides an estimation of the unknown inputs. A consensus-based distributed estimation algorithm is proposed in this paper. The algorithm gives an optimal unbiased minimum variance estimation if perfect consensus is achieved. Simulation results show the efficiency of the method.
Case study on semantic clone detection based on code behavior

The activities of copy and paste fragments of code from a source code into the other source code, with or without modification, are known as the code cloning. The process is often done by software developers because it's easier than generate code manually. In the other hand, this behavior leads to the increasing of effort to maintenance the code. Research on detecting the presence of cloning has been done. Detection of cloned semantically is a detection method that still needs a deepening. One of the detection methods of semantic cloning is based on the behavior of the code. The code behavior detected by looking at input, output and the effects of the method. Methods with the same input, output and effect will show that the function of the method is the same. However, the detection method based on input, output and effect could not be used in a void method and method without parameters. There is no explanations about input, output and effect in that kind of methods. In this case, the solution is needed to find the input, output and the effects of the method. This research is a concern on how to find an input, output and effect in void and non-parameterized method. Detection of input, output and effects done using PDG (Program Dependence Graph). The result is used to reconstruct void and non-parameterizes method. Trial is performed on each method (without exception) using random input data to get the behavior methods. Trial is done using small size source code from jDraw. From the result of the trial process, can be concluded that semantic clone detection using the methods behavior approach is sound very promising. This method can also detect type 1, type 2 and type 3 clone beside the semantic clone itself with an accuracy about 89%.
HARAM: A Hierarchical ARAM Neural Network for Large-Scale Text Classification

With the rapid development of the Web, the need for text classification of large data volumes is permanently growing. Texts represented as bags-of-words possess usually very high dimensionality in the input space and often also in the output space if labeled with many categories. As a result, neural classifiers should be adapted to such large-scale data. We present here a well scalable extension to the fuzzy Adaptive Resonance Associative Map (ARAM) neural network which was specially developed for high-dimensional and large data. This extension aims at increasing the classification speed by adding an extra ART layer for clustering learned prototypes into large clusters. In this case the activation of all prototypes can be replaced by the activation of a small fraction of them, leading to a significant reduction of the classification time. This extension can be especially useful for multi-label classification tasks.
Chinese Medicine Formula Lingguizhugan Decoction Improves Beta-Oxidation and Metabolism of Fatty Acid in High-Fat-Diet-Induced Rat Model of Fatty Liver Disease

Lingguizhugan decoction (LGZG), a classic traditional Chinese medicine (TCM) formula, has been used to treat obesity and hyperlipidemia in recent years, but the related mechanisms underlying the regulation of lipid metabolism by LGZG are not clear yet. Here, we reported the effectiveness and possible mechanisms of LGZG on rats with fatty liver disease induced by high-fat diet (HFD). Our results demonstrated that LGZG significantly attenuated HFD-induced fatty liver disease, as measured by body weight, liver index, epididymal fat pad-body weight ratio (EFP/BW), liver injury, and hepatic triglycerides (TG) probably through increasing serum thyroid hormone levels, improving beta-oxidation (via modulation of TRβ1 and CPT1A expression), metabolism and transport (through modulation of SREBP-1c, ACSL and ApoB100 expression) of fatty acid. In addition, we discovered the herbal combination with the properties of warming yang to relieve water retention in the formula and proposed the biological basis of LGZG conventional effect via further study on disassembled formula. This study, for the first time, revealed the mechanisms through which LGZG regulates lipid metabolism. Furthermore, our study suggested that it might be feasible to understand the scientific implications of TCM from the perspective of classic formulas' conventional efficacy.
Small Target Detection combining Foreground and Background Manifolds

This paper focuses on the detection of small objects (e.g. vehicules in aerial images) on complex back-grounds (e.g. natural backgrounds). A key contribution of the paper is to show that, in such situations, learning a target model and a background model separately is better than training a unique discriminative model. This contrasts with standard object detection approaches for which objects vs. background classi fiers use the same types of visual features/models for both. The second contribution lies in the use of manifold learning approaches to build these models. The proposed detection algorithm is validated on the publicly available OIRDS dataset, on which we obtain state-of-the-art results.
Implicit Theories of Intelligence and Personality: Relations to Intelligence, Motivation and Personality

Implicit theories (IT) reflect core beliefs about malleability of cognitive and personality human attributes. IT participate in the interpretation of the social world, regulate behaviors (through goal setting, adjustments after failures, learning strategies, etc.), and are valid predictors of achievement (Dweck, 2006). Nevertheless, little is known about the IT’s relationship to the components of the intellectual and personality human potential. The purpose of this research is to examine the extent to which IT are related to cognitive (intelligence) and personality (Big-Five personality traits, motivation) structures. A sample of 307 students completed the intelligence test (ICAR), the Ten-Item Personality Inventory and the Edwards Personal Preference Schedule; additionally, GPA was obtained for 49 students. Results demonstrate similar as well as distinctive correlations between the measurements in men and women. In particular, in both men and women, malleable intelligence beliefs do not depend on intelligence level, fluid or crystallized, but are largely related to personality characteristics: conscientiousness (in both men and women), openness to experience (in women), and intraception motivation (in men). Malleability of personality beliefs correlates negatively with crystallized intelligence (only in women). Mastery goal orientation in both men and women is related to openness; academic achievement is predicted by conscientiousness. The results are discussed from the perspective of the integrated intellectual and personality potential.
Virtual Borders : Online Nominal Rigidities and International Market Segmentation

Do prices respond to macro shocks? Does the mere presence of international frontiers hinder trade? We revisit these questions by studying a dataset of online book prices for a number of US and Canadian retailers. We believe our dataset is well suited to this task for a number of reasons: (1) data for multiple retailers are available; (2) the products sold are identical across retailers; (3) the sample spans a period of large uctuations in the bilateral exchange rate; (4) the nature of the industry means that physical distance is irrelevant beyond shipping costs which are observable; (5) nominal frictions in the form of menu costs are arguably minimal; and (6) proxies for sales are available for some retailers. Given the unique nature of our dataset, the rst objective of the paper is to document the degree of price rigidity and price dispersion. Our main ndings are: online book prices display signi cant stickiness; there is a large degree of heterogeneity across retailers in terms of price rigidity and pricing strategy; price dispersion is high both within and across borders. Also, price levels do not appear to respond to exchange rate uctuations. A simple two-country, multi rm model is used to illustrate that rms should react to exchange rate shocks under some degrees of market segmentation. We then make an attempt at determining whether nominal rigidities or market segmentation are behind this disconnect by exploiting information contained both in prices
Ratings are Overrated!

Are ratings of any use in human-computer interaction and user studies at large? If ratings are of limited use, is there a better alternative for quantitative subjective assessment? Beyond the intrinsic shortcomings of human reporting, there are a number of supplementary limitations and fundamental methodological flaws associated with rating-based questionnaires --- i.e. questionnaires that ask participants to rate their level of agreement with a given statement such as a Likert item. While the effect of these pitfalls has been largely downplayed, recent findings from diverse areas of study question the reliability of using ratings. Rank-based questionnaires --- i.e. questionnaires that ask participants to rank two or more options --- appear as the evident alternative that not only eliminates the core limitations of ratings but also simplifies the use of sound methodologies that yield more reliable models of the underlying reported construct: user emotion, preference, or opinion. This paper solicits recent findings from various disciplines interlinked with psychometrics and offers a quick guide for the use, processing and analysis of rank-based questionnaires for the unique advantages they offer. The paper challenges the traditional state-of-practice in human-computer interaction and psychometrics directly contributing towards a paradigm shift in subjective reporting.
Biogeographic Ancestry, Cognitive Ability and Socioeconomic Outcomes

The cause(s) of ubiquitous cognitive differences between American self-identified racial/ethnic groups (SIREs) is uncertain. Evolutionary-genetic models posit that ancestral genetic selection pressures are the ultimate source of these differences. Conversely, sociological models posit that these differences result from racial discrimination. To examine predictions based on these models, we conducted a global admixture analysis using data from the Pediatric Imaging, Neurocognition, and Genetics Study (PING; N = 1,369 American children). Specifically, we employed a standard methodology of genetic epidemiology to determine whether genetic ancestry significantly predicts cognitive ability, independent of SIRE. In regression models using four different codings for SIRE as a covariate, we found incremental relationships between genetic ancestry and both general cognitive ability and parental socioeconomic status (SES). The relationships between global ancestry and cognitive ability were partially attenuated when parental SES was added as a predictor and when cognitive ability was the outcome. Moreover, these associations generally held when subgroups were analyzed separately. Our results are congruent with evolutionary-genetic models of group differences and with certain environmental models that mimic the predictions of evolutionary-genetic ones. Implications for research on race/ethnic differences in the Americas are discussed, as are methods for further exploring the matter.
Bidirectional Activity-Dependent Regulation of Neuronal Ion Channel Phosphorylation

Activity-dependent dephosphorylation of neuronal Kv2.1 channels yields hyperpolarizing shifts in their voltage-dependent activation and homoeostatic suppression of neuronal excitability. We recently identified 16 phosphorylation sites that modulate Kv2.1 function. Here, we show that in mammalian neurons, compared with other regulated sites, such as serine (S)563, phosphorylation at S603 is supersensitive to calcineurin-mediated dephosphorylation in response to kainate-induced seizures in vivo, and brief glutamate stimulation of cultured hippocampal neurons. In vitro calcineurin digestion shows that supersensitivity of S603 dephosphorylation is an inherent property of Kv2.1. Conversely, suppression of neuronal activity by anesthetic in vivo causes hyperphosphorylation at S603 but not S563. Distinct regulation of individual phosphorylation sites allows for graded and bidirectional homeostatic regulation of Kv2.1 function. S603 phosphorylation represents a sensitive bidirectional biosensor of neuronal activity.
Distributed Representations of Words and Documents for Discriminating Similar Languages

Discriminating between similar languages or language varieties aims to detect lexical and semantic variations in order to classify these varieties of languages. In this work we describe the system built by the Pattern Recognition and Human Language Technology (PRHLT) research center - Univer-sitat Polit`ecnica de Val`encia and Autoritas Consulting for the Discriminating between similar languages (DSL) 2015 shared task. In order to determine the language group of similar languages, we ﬁrst employ a simple approach based on distances with language prototypes with 99.8% accuracy in the test sets. For classifying intra-group languages we focus on the use of distributed representations of words and documents using the continuous Skip-gram model. Experimental results of classiﬁcation of languages in 14 categories yielded accuracies of 92.7% and 90.8% when classifying unmodiﬁed texts and text with hidden named entities, respectively.
The impact of the initial condition on robust tests for a linear trend

This article examines the behaviour of some recently proposed ‘robust’ (to the order of integration of the data) tests for the presence of a deterministic linear trend in a univariate times series in situations where the magnitude of the initial condition of the series is non‐negligible. We demonstrate that the asymptotic size and/or local power properties of these tests are extremely sensitive to the initial condition. Straightforward modifications to the trend tests are suggested, based on the use of trimmed data, which are shown to help reduce this sensitivity.
Thin-Film Silicon MEMS for Dynamic Mass Sensing in Vacuum and Air: Phase Noise, Allan Deviation, Mass Sensitivity and Limits of Detection

Microelectromechanical sensors have extreme mass sensitivities, making them promising for mass sensing, gas sensing, and biosensing applications. Despite the numerous examples of MEMS/NEMS mass sensors in the literature, the characterization of the resonator or oscillator system is often incomplete. Resonant mass sensor studies should include phase noise (or Allan deviation), as the use of these metrics allows for the identification of the noise mechanisms, the optimization of the measurement and actuation parameters, the prediction of the limit of detection, and the comparison of different types of resonators or vibration modes. In this paper, we summarize the metrics we consider to be the most important in the characterization of dynamic mass sensors, and then apply them to low-cost, hydrogenated amorphous silicon (a-Si:H) thin-film MEMS cantilever resonators. The effects of the actuation voltage on the phase noise were studied. The mass sensitivities of these resonators were calibrated using patterned thin-films localized at the tip of the cantilevers as well as uniform ultra-thin films of SiO2. The experimental mass sensitivities in vacuum ranged −98 Hz/pg to −4.6 kHz/pg. The limits of detection were calculated to be 100-833 fg in vacuum and 37-846 pg at atmospheric pressure. [2019-0006]