Miracle-dz/UniFAR-FTUs huggingface dataset:
Usability in e-Learning Platforms : heuristics comparison between Moodle , Sakai and dotLRN

Recently, educational institutions largely adopted open source elearning platforms. Several studies and comparisons between those platforms were conducted. However, these analyses were more focused into purely technical issues (standards support, development platform used, scalability, etc.) than into functional issues. It was noted that most of the e-learning solutions are not optimal from the end-user point of view: they can be hard and tedious to use, potentially resulting in higher abandon rates. There is therefore a need to focus into usability to work toward more usable and user-centered elearning platforms. This paper details the used methodology based on heuristics to allow an objective usability evaluation of three of the main e-learning open source platforms: Moodle, Sakai and dotLRN. The results of the comparative evaluations are exposed.
Separating signal and noise in atmospheric temperature changes: The importance of timescale

We compare global-scale changes in satellite estimates of the temperature of the lower troposphere (TLT) with model simulations of forced and unforced TLT changes. While previous work has focused on a single period of record, we select analysis timescales ranging from 10 to 32 years, and then compare all possible observed TLT trends on each timescale with corresponding multi-model distributions of forced and unforced trends. We use observed estimates of the signal component of TLT changes and model estimates of climate noise to calculate timescale-dependent signal-to-noise ratios (S/N). These ratios are small (less than 1) on the 10-year timescale, increasing to more than 3.9 for 32-year trends. This large change in S/N is primarily due to a decrease in the amplitude of internally generated variability with increasing trend length. Because of the pronounced effect of interannual noise on decadal trends, a multi-model ensemble of anthropogenically-forced simulations displays many 10-year periods with little warming. A single decade of observational TLT data is therefore inadequate for identifying a slowly evolving anthropogenic warming signal. Our results show that temperature records of at least 17 years in length are required for identifying human effects on global-mean tropospheric temperature. Copyright 2011 by the American Geophysical Union.
Working from strengths: Venturing towards strength-based adult education

This practitioner resource has been developed to introduce adult literacy educators to the principles of strength-based practice. Strength-based practice is a technique that concentrates on the strengths, capacities and aspirations of individuals and uses these as a catalyst for positive change and growth. The practice is more commonly used in the health and welfare sectors. The resource is based on a project undertaken by Peter Waterhouse and Crina Virgona and was funded by the former Department of Education, Science and Training (whose responsibilities have been assumed by the Department of Education, Employment and Workplace Relations) on behalf of the Australian Government and state and territory governments, as part of the Adult Literacy and Numeracy Research Program, a national program managed by the National Centre for Vocational Education Research (NCVER). This practical guide for adult educators highlights the relevance of strength-based practice for the adult literacy and basic education sectors. It provides examples of key strength-based practice processes and techniques, as well as information about where to access additional resources relating to strength-based practice.
Study on the Volatile Oxidation Compounds and Quantitative Prediction of Oxidation Parameters in Walnut (
 Carya cathayensis
 Sarg.) Oil

Walnut (Carya cathayensis Sarg.) oil contains over 85% unsaturated fatty acids, which are easily oxidized during storage. As a result, a large number of volatile oxidation compounds (VOCs) are formed during oxidation. The qualitative composition of VOCs in walnut oil and quantitative prediction of the oxidation parameters (peroxide value [POV], acid value [AV], and p‐anisidine value [p‐AnV]) by VOCs are investigated through SPME/GC‐MS combined with partial least‐squares (PLS) regression analysis. Eighteen VOCs including aldehydes, alcohols, and acids are detected by SPME/GC‐MS. According to the comprehensive scores of principal component analysis (PCA), 2‐octenal, hexanal, 2‐heptenal, 1‐octen‐3‐ol, hexanoic acid, and nonanal are the main products formed during oxidation. Then PLS regression is applied to developing quantitative prediction models of oxidation parameters (POV, AV, and p‐AnV) by VOCs. The PLS prediction models have a good performance, with determination coefficients (R²ₚ) of 0.993–0.997 for the prediction sets of the three oxidation parameters. Practical Applications: The quantitative relationship between VOCs and oxidation parameters is developed in this study, which provided a new method for monitoring the quality of walnut oil. The SPME/GC‐MS combined with PLSR is a feasible and potential method for simultaneous qualitative and quantitative analysis of oxidation process. This method is proven to have a precise predictive ability and provided a potential application in the quality assessment of other nut products. Volatile oxidation compounds (VOCs) are detected by SPME/GC‐MS in oxidized walnut (Carya cathayensis Sarg.) oil. Following this determination, partial least‐squares regression (PLSR) is applied to developing quantitative prediction models of oxidation parameters by VOCs.
Converting the Schoolyard Into a Learning Space: A Strategy for Public Education

Technology and globalization have shaped the generation of students in the 21st century. However, actions to configure school space remain stuck in 20th century standards. This article describes a study that contributes with strategies of innovative pedagogies by exploring the use of available spaces in Brazilian public schools. The study involved teaching and learning school curricular subjects in the schoolyard through physical interventions that were designed using playground equipment. Despite the absence of spatial boundaries, usually made by walls in seminar rooms, the interventions polarized the distribution of students in the courtyard with no loss of pedagogical control. An implicit hierarchy was established in the relationship between teacher and students, since the teacher’s position did not differ spatially from the student’s position, offering freedom for interaction.
Super Resolution Based on Inter-frame Translation Model

Inter-frame motion estimation is a crucial step in the super resolution reconstruction. However, the requirement of accuracy and complex computation for motion estimation is an important problem. In order to achieve accurate estimation as well as low complexity, we present a weighted inter-frame translation model. This paper mainly discusses a video sequences super resolution algorithm based on inter-frame translation model without motion-estimation. At first, the theory of Kalman filtering is introduced. Secondly, a weighted translational motion model instead of the explicit motion estimation. In order to reduce the complexity of computation, the weighted translational observation model is used to simplify the state transition equations. Thirdly, the quality of recovery image is improved due to the employment of Kalman filter framework for the super resolution algorithm. The advantages of the proposed algorithm are demonstrated by the simulation result, more specifically, the PSNR of the proposed algorithm is improved about 1 dB.
Transcriptome Profiling, Molecular Biological, and Physiological Studies Reveal a Major Role for Ethylene in Cotton Fiber Cell Elongation[W][OA]

Upland cotton (Gossypium hirsutum) produces the most widely used natural fibers, yet the regulatory mechanisms governing fiber cell elongation are not well understood. Through sequencing of a cotton fiber cDNA library and subsequent microarray analysis, we found that ethylene biosynthesis is one of the most significantly upregulated biochemical pathways during fiber elongation. The 1-Aminocyclopropane-1-Carboxylic Acid Oxidase1-3 (ACO1-3) genes responsible for ethylene production were expressed at significantly higher levels during this growth stage. The amount of ethylene released from cultured ovules correlated with ACO expression and the rate of fiber growth. Exogenously applied ethylene promoted robust fiber cell expansion, whereas its biosynthetic inhibitor l-(2-aminoethoxyvinyl)-glycine (AVG) specifically suppressed fiber growth. The brassinosteroid (BR) biosynthetic pathway was modestly upregulated during this growth stage, and treatment with BR or its biosynthetic inhibitor brassinazole (BRZ) also promoted or inhibited, respectively, fiber growth. However, the effect of ethylene treatment was much stronger than that of BR, and the inhibitory effect of BRZ on fiber cells could be overcome by ethylene, but the AVG effect was much less reversed by BR. These results indicate that ethylene plays a major role in promoting cotton fiber elongation. Furthermore, ethylene may promote cell elongation by increasing the expression of sucrose synthase, tubulin, and expansin genes.
Predicting vortex merging and ensuing turbulence characteristics in shear layers from initial conditions

Unstable shear layers in environmental and industrial flows roll up into a series of vortices, which often form complex nonlinear merging patterns such as pairs and triplets. These patterns crucially determine the subsequent turbulence, mixing and scalar transport. We show that the late-time, highly nonlinear merging patterns are predictable from the linearized initial state. The initial asymmetry between consecutive wavelengths of the vertical velocity field provides an effective measure of the strength and pattern of vortex merging. The predictions of this measure are substantiated using direct numerical simulations. We also show that this measure has significant implications in determining the route to turbulence and the ensuing turbulence characteristics.
Data‐Driven Optimal Transport

The problem of optimal transport between two distributions ρ(x) and μ(y) is extended to situations where the distributions are only known through a finite number of samples {xi} and {yj}. A weak formulation is proposed, based on the dual of the Kantorovich formulation, with two main modifications: replacing the expected values in the objective function by their empirical means over the {xi} and {yj}, and restricting the dual variables u(x) and v(y) to a suitable set of test functions adapted to the local availability of sample points. A procedure is proposed and tested for the numerical solution of this problem, based on a fluidlike flow in phase space, where the sample points play the role of active Lagrangian markers. © 2016 Wiley Periodicals, Inc.
Matrix Model from String Field Theory

We show that the boundary string field theory (BSFT) on unstable D0-branes in 2d string theory is equivalent to the double scaled c = 1 matrix model (i.e. quadratic action), even though we naively expect many interaction terms in BSFT. It is checked that S-matrices are trivial in the open string theory on the D0-branes. We discuss how to take the closed string decoupling limit in our model in order to make its holographic interpretation clear. We also find some useful lessons from our results in 2d string toward the further understanding of the higher dimensional BSFT action. takayana@bose.harvard. edu seijit@physics.rutgers. edu
NONCOMPLIANCE BIAS CORRECTION BASED ON COVARIATES IN RANDOMIZED EXPERIMENTS

We propose some practical solutions for causal efiects estimation when compliance to assignments is only partial and some of the standard assumptions do not hold. We follow the potential outcome approach but in contrast to Imbens and Rubin (1997), we require no prior classiflcation of the compliance behaviour. When noncompliance is not ignorable, it is known that adjusting for arbitrary covariates can actually increase the estimation bias. We propose an approach where a covariate is adjusted for only when the estimate of the selection bias of the experiment as provided by that covariate is consistent with the data and prior information on the study. Next, we investigate cases when the overlap assumption does not hold and, on the basis of their covariates, some units are excluded from the experiment or equivalently, never comply with their assignments. In that context, we show that a consistent estimation of the causal efiect of the treatment is possible based on a regression model estimation of the conditional expectation of the outcome given the covariates. We illustrate the methodology with several examples such as the access to in∞uenza vaccine experiment (McDonald et al (1992) and the PROGRESA experiment (Shultz (2004)).
A real-time x-ray mobile application using augmented reality and google street view

X-ray view can be defined as the ability one has to see through real surfaces. Although this skill is often associated with superheroes and medical examination, there are several researches conducted to employ X-ray view in numerous applications. However, the generation of X-ray visualization includes numerous challenges regarding occlusion, realistic appearance, and depth perception. In this paper, we present a mobile application that uses Augmented Reality and Google Street View to allow users experience real-time X-ray vision. The proposed application was designed to enhance previous Augmented Reality X-ray systems, by introducing a silhouette computation method to provide visual context from the occluder and a perspective estimation system that improves the projection of occluded images into the real scene. Additionally, we implemented two usability studies to assess qualitative aspects of both silhouettes and perspective estimation to generate better X-ray effects. Results indicate good acceptance of the novel X-ray visualization method and a great usability score on the SUS scale for the mobile application.
Delving into 3D Action Anticipation from Streaming Videos

Action anticipation, which aims to recognize the action with a partial observation, becomes increasingly popular due to a wide range of applications. In this paper, we investigate the problem of 3D action anticipation from streaming videos with the target of understanding best practices for solving this problem. We first introduce several complementary evaluation metrics and present a basic model based on frame-wise action classification. To achieve better performance, we then investigate two important factors, i.e., the length of the training clip and clip sampling method. We also explore multi-task learning strategies by incorporating auxiliary information from two aspects: the full action representation and the class-agnostic action label. Our comprehensive experiments uncover the best practices for 3D action anticipation, and accordingly we propose a novel method with a multi-task loss. The proposed method considerably outperforms the recent methods and exhibits the state-of-the-art performance on standard benchmarks.
Intensity fluctuations in random waveguides

An asymptotic analysis of wave propagation in randomly perturbed waveguides is carried out in order to identify the effective Markovian dynamics of the guided mode powers. The main result consists in a quantification of the fluctuations of the mode powers and wave intensities that increase exponentially with the propagation distance. The exponential growth rate is studied in detail so as to determine its dependence with respect to the waveguide geometry, the statistics of the random perturbations, and the operating wavelength.
Baryon Physics and Tight Coupling Approximation in Boltzmann Codes

We provide two derivations of the baryonic equations that can be straightforwardly implemented in existing Einstein–Boltzmann solvers. One of the derivations begins with an action principle, while the other exploits the conservation of the stress-energy tensor. While our result is manifestly covariant and satisfies the Bianchi identities, we point out that this is not the case for the implementation of the seminal work by Ma and Bertschinger and in the existing Boltzmann codes. We also study the tight coupling approximation up to the second order without choosing any gauge using the covariant full baryon equations. We implement the improved baryon equations in a Boltzmann code and investigate the change in the estimate of cosmological parameters by performing an MCMC analysis. With the covariantly correct baryon equations of motion, we find 1 % deviation for the best fit values of the cosmological parameters that should be taken into account. While in this paper, we study the Λ CDM model only, our baryon equations can be easily implemented in other models and various modified gravity theories.
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis

In clinical diagnosis, doctors are able to see biological tissues and early lesions more clearly with the assistance of high-resolution(HR) medical images, which is of vital significance for improving diagnosis accuracy. In order to address the issue that medical images would suffer from severe blurring caused by lack of high-frequency details, this paper develops a novel image super-resolution(SR) algorithm called SR-DBAN via dense neural network and blended attention mechanism. Specifically, a novel blended attention block is proposed and introduced to dense neural network(DenseNet), so that the neural network can concentrate more attention to the regions and channels with sufficient high-frequency details adaptively. In the framework of SR-DBAN, batch normalization layers in the original DenseNet are removed to avoid loss of high-frequency texture details, final HR images are obtained by deconvolution at the very end of the network. Furthermore, inspired by the impressive performance of generative adversarial network, this paper develops a novel image SR algorithm called SR-DBAGAN via dense blended attention generative adversarial network. SR-DBAGAN consists a generator and a discriminator, the generator uses our proposed SR-DBAN to generate HR images and try to fool the discriminator while the discriminator is designed based on Wasserstein GAN(WGAN) to discriminate. We deployed our algorithms on blurry prostate MRI images, and experimental results showed that our proposed algorithms have generated considerable sharpness and texture details and have a significant improvement on the peak signal-to-noise ratio(PSNR) and structural similarity index(SSIM), respectively, compared with mainstream interpolation-based and deep learning-based image SR algorithms, which fully proves the effectiveness and superiority of our proposed algorithms.
K-SVD Meets Transform Learning: Transform K-SVD

Recently there has been increasing attention directed towards the analysis sparsity models. Consequently, there is a quest for learning the operators which would enable analysis sparse representations for signals in hand. Analysis operator learning algorithms such as the Analysis K-SVD have been proposed. Sparsifying transform learning is a paradigm which is similar to the analysis operator learning, but they differ in some subtle points. In this paper, we propose a novel transform operator learning algorithm called as the Transform K-SVD, which brings the transform learning and the K-SVD based analysis dictionary learning approaches together. The proposed Transform K-SVD has the important advantage that the sparse coding step of the Analysis K-SVD gets replaced with the simple thresholding step of the transform learning framework. We show that the Transform K-SVD learns operators which are similar both in appearance and performance to the operators learned from the Analysis K-SVD, while its computational complexity stays much reduced compared to the Analysis K-SVD.
Sequences of Enumerative Geometry: Congruences and Asymptotics

We study the integer sequence υn of numbers of lines in hypersurfaces of degree 2 n − 3 of ℙ n , n > 1. We prove a number of congruence properties of these numbers of several different types. Furthermore, the asymptotics of the υ n are described (in an appendix by Don Zagier). Finally, an attempt is made at carrying out a similar analysis for numbers of rational plane curves.
Optimal-Location-Selection Query Processing in Spatial Databases

This paper introduces and solves a novel type of spatial queries, namely, optimal-location-selection (OLS) search, which has many applications in real life. Given a data object set DA, a target object set DB, a spatial region R, and a critical distance dc in a multidimensional space, an OLS query retrieves those target objects in DB that are outside R but have maximal optimality. Here, the optimality of a target object b isin DB located outside R is defined as the number of the data objects from DA that are inside R and meanwhile have their distances to b not exceeding dc. When there is a tie, the accumulated distance from the data objects to b serves as the tie breaker, and the one with smaller distance has the better optimality. In this paper, we present the optimality metric, formalize the OLS query, and propose several algorithms for processing OLS queries efficiently. A comprehensive experimental evaluation has been conducted using both real and synthetic data sets to demonstrate the efficiency and effectiveness of the proposed algorithms.
Electronic word of mouth communications and consumer's information adoption on the Internet

Increasing consumer motivation to pursue online reviews and information has recently become a potential area of study. Perusing the literature reviews, it is fairly evident that disseminating information largely depends on the innovativeness and the interpretative caliber of writers. Individuals that are involved in electronic word of mouth communication may have high ambitions to realize the needs and requirements of the online community. Different consumers have different concerns such as helping the company, obtaining social benefits, communicating post-purchase opinions, self-enhancement, and the expression of positive and negative viewpoints. Therefore, more studies are required to unveil the comprehensive features of online information and portal members' behavior.
the Australian

All derived sequences have been deposited in the NCBI database (accession nos. KC619625–KC620125). Abstract The Australian numbat, Myrmecobius fasciatus , is the only marsupial that feeds almost exclusively on termites and that has a life following the diurnally restricted and dynamic geographical distribution of termites. The millions of years of this adaptation led to unique morphological and anatomical features, especially basicranial and dental characteristics, that make it difﬁcult to identify a clear phylogenetic afﬁliation to other marsupials. From DNA sequence analyses, the family Myrmecobiidae is placed within the dasyuromorph marsupials, but the exact position varies from study to study, and support values are mostly rather modest. Here, we report the recovery and analysis of approximately 110,000 quasi-fossilized traces of mobile element insertions into the genome of a dasyurid marsupial (Tasmanian devil), 25 of which are phylogenetically informative for early dasyuromorphial evolution. Fourteen of these ancient retroposon insertions are shared by the 16 Dasyuromorphia species analyzed, including the numbat, but are absent in the outgroups. An additional 11 other insertions are present in all Dasyuridae but are absent in the numbat. These ﬁndings place numbats as the sister group to all living Dasyuridae and show that the investigated Dasyuromorphia, including the Myrmecobiidae, constitutes a monophyletic group that is separated from Peramelemorphia, Notoryctemorphia, and other marsupials.