Miracle-dz/UniFAR-FTUs huggingface dataset:
Content Teachers’ Perspectives from a CLIL Methodology Training Course

The purpose of this study is to explore upper secondary teachers’ perceptions, attitudes, and needs regarding a CLIL methodology teacher training course offered by the Language Centre of the University of Molise in the year 2017-2018. To this end, multiple sources of evidence were gathered from e-questionnaires and observations. The findings point to the need to strengthen training in the areas of micro-teaching and teaching practice and to the importance of practice observation, especially from more experienced CLIL teachers. In this respect, the study suggests to consider a more extensive usage of on-line teaching environments where additional support and valuable resources can be provided throughout the course.
Improved Wavelet Denoising by Non-Convex Sparse Regularization Under Double Wavelet Domains

This paper presents a double wavelet denoising (DWAD) method, which can preserve more details of an original signal. Although the noise removal method based on wavelet transform has been widely used, it still performs poorly for the signals with a low signal-to-noise ratio (SNR) or frequency overlap. Different from the wavelet denoising methods based on a single basis function, the DWAD considers filtering the wavelet coefficients of the noisy signal by threshold functions under two different wavelet domains, simultaneously. It considers using the difference of wavelet coefficient distribution and forcing the denoised signals under two wavelet domains to be the same to achieve more retention of details. In addition, the arctangent function is employed as a penalty function for wavelet coefficients to induce strong sparse wavelet coefficients. The DWAD is applied to one-dimensional signals and it is found that some wavelet coefficients which are smaller than the threshold could be retained during noise removal. The experiment results show that the average SNR of different noise levels is improved by at least 4.2 and 2.1 dB compared with the classical soft threshold method for the one-dimensional and image signals, respectively. Besides, the DWAD tends to obtain better performance on the details of original signals.
Assessing the Uncertainties of Four Precipitation Products for Swat Modeling in Mekong River Basin

Using hydrological simulation to evaluate the accuracy of satellite-based and reanalysis precipitation products always suffer from a large uncertainty. This study evaluates four widely used global precipitation products with high spatial and temporal resolutions [i.e., AgMERRA (AgMIP modern-Era Retrospective Analysis for Research and Applications), MSWEP (Multi-Source Weighted-Ensemble Precipitation), PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record), and TMPA (Tropical Rainfall Measuring Mission 3B42 Version7)] against gauge observations with six statistical metrics over Mekong River Basin (MRB). Furthermore, the Soil and Water Assessment Tool (SWAT), a widely used semi-distributed hydrological model, is calibrated using different precipitation inputs. Both model performance and uncertainties of parameters and prediction have been quantified. The following findings were obtained: (1) The MSWEP and TMPA precipitation products have good accuracy with higher CC, POD, and lower ME and RMSE, and the AgMERRA precipitation estimates perform better than PERSIANN-CDR in this rank; and (2) out of the six different climate regions of MRB, all six metrics are worse than that in the whole MRB. The AgMERRA can better reproduce the occurrence and contributions at different precipitation densities, and the MSWEP has the best performance in Cwb, Cwa, Aw, and Am regions that belong to the low latitudes. (3) Daily streamflow predictions obtained using MSWEP precipitation estimates are better than those simulated by other three products in term of both the model performance and parameter uncertainties; and (4) although MSWEP better captures the precipitation at different intensities in different climatic regions, the performance can still be improved, especially in the regions with higher altitude.
University of Birmingham Late Wenlock sequence stratigraphy in central England

The late Wenlock Series (Homerian Stage) of the northern Midland Platform (central England) comprises silty mudstones and limestones of the upper part of the Coalbrookdale and overlying Much Wenlock Limestone formations. Based on outcrop studies and borehole data, the sequence stratigraphical interpretation developed for the inliers of the West Midlands is slightly revised, and extended to the stratotype sections along Wenlock Edge. A single third-order cycle of sea-level change is identified, punctuated by a regressive–transgressive episode associated with a higher-order glacioeustatic cycle, allowing the upper Wenlock Series of the area to be divided into two subsequences (A and B). Subsequence A and the early transgressive systems tract began with regression associated with the basal sequence boundary in late Cyrtograptus lundgreni Biozone times. This was followed by a period of slow transgression or stillstand, allowing shallower water carbonate environments to prograde. A minor phase of regression followed, resulting in the generation of the shallowest water deposits of both the Lower Quarried Limestone and Farley members (of the Much Wenlock Limestone and Coalbrookdale formations, respectively). The overlying Subsequence B and the late transgressive systems tract are marked by transgression and a period of rapid sea-level fluctuation and are likely contained within the Gothograptus nassa Biozone. A minor highstand is widely recognizable at this time. The rest of Subsequence B consists of an initial phase of weak progradation (highstand systems tract), followed by a marked regression (falling stage systems tract) culminating in an erosive upper sequence boundary at or close to the top of the Monograptus ludensis Biozone, but within the uppermost Much Wenlock Limestone Formation. Above Subsequence B is a marked transgression into the Lower Elton Formation and the Ludlow Series. Both late Wenlock lowstands and the succeeding flooding events have been recognized on other palaeocontinents, reflecting the eustatic nature of sea-level changes reported here.
MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis

While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to use, in part due to instability during training. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator can quickly become uninformative, due to a learning imbalance during training. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this problem which allows the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for generating synchronized multi-scale images. We present a very intuitive implementation of the mathematical MSG-GAN framework which uses the concatenation operation in the discriminator computations. We empirically validate the effect of our MSG-GAN approach through experiments on the CIFAR10 and Oxford102 flowers datasets and compare it with other relevant techniques which perform multi-scale image synthesis. In addition, we also provide details of our experiment on CelebA-HQ dataset for synthesizing 1024 x 1024 high resolution images.
Graduate Writing Groups: Helping L2 Writers Navigate the Murky Waters of Academic Writing

The present study examines how second language (L2) graduate writers are socialized into the academic discourse practices of a U.S. graduate school via Graduate Writing Groups (GWGs) sponsored by the university writing center. More specifically, it sheds light on how the discourse and interactions within a peer-based GWG affect L2 graduate writers’ identities, as well as their socialization into academic disciplines and cross-disciplinary academic conventions. Results of the study revealed that, unlike the simple portrayal of L2 graduate writers as novices and their enculturation into academia as linear and unidirectional, L2 graduate writers have multi-faceted identities as writers, depending on academic task, context, and previous academic literacy experiences. The study suggests that despite the potential challenges inherent in multidisciplinary GWGs, they offer valuable opportunities for L2 graduate writers to enact the identity of disciplinary expert. Furthermore, the peer-based, extracurricular nature of these groups supports graduate writers’ socialization into academic discourse communities.
Instability of $H^1$-stable peakons in the Camassa-Holm equation.

It is well-known that peakons in the Camassa-Holm equation are $H^1$-orbitally stable thanks to the presence of conserved quantities and properties of peakons as constrained energy minimizers. By using the method of characteristics, we prove that piecewise $C^1$ perturbations to peakons grow in time in spite of their stability in the $H^1$-norm. We also show that the linearized stability analysis near peakons contradicts the $H^1$-orbital stability result, hence passage from linear to nonlinear theory is false in $H^1$.
Challenges in Using ML for Networking Research: How to Label If You Must

Leveraging innovations in Machine Learning (ML) research is of great current interest to researchers across the sciences, including networking research. However, using ML for networking poses challenging new problems that have been responsible for slowing the pace of innovation and the adoption of ML in the networking domain. Among the main problems are a well-known lack of data in general and representative data in particular, an overall inability to label data at scale, unknown data quality due to differences in data collection strategies, and data privacy issues that are unique to network data. Motivated by these challenges, we describe the design of Emerge1, a novel framework to support efforts to dEmocratize the use of ML for nEtwoRkinG rEsearch. In particular, Emerge focuses on the problem of providing a low-cost, scalable, and high-quality methodology for labeling networking data. To illustrate the benefits of Emerge, we use publicly available network measurement datasets from Caida's Ark project and create and evaluate data labels for them in a programmable fashion.
Non–uniqueness of admissible weak solutions to the compressible Euler equations with smooth initial data

We consider the isentropic Euler equations of gas dynamics in the whole two-dimensional space and we prove the existence of a $C^\infty$ initial datum which admits infinitely many bounded admissible weak solutions. Taking advantage of the relation between smooth solutions to the Euler system and to the Burgers equation we construct a smooth compression wave which collapses into a perturbed Riemann state at some time instant $T > 0$. In order to continue the solution after the formation of the discontinuity, we adjust and apply the theory developed by De Lellis and Sz\'ekelyhidi and we construct infinitely many solutions. We introduce the notion of an admissible generalized fan subsolution to be able to handle data which are not piecewise constant and we reduce the argument to finding a single generalized subsolution.
human-computer improvisation

Human computer co-improvisation aims to rely on a computer in order to produce a musical accompaniment to a musician’s improvisation. Recently, the notion of guidance has been introduced to enhance the process of human computer co-improvisation. Although this concept has already been studied with a step-by-step guidance or by guiding with a formal temporal structure, it is usually only based on a past memory of events. This memory is derived from an annotated corpus which limits the possibility to infer the potential future improvisation structure. Nevertheless, most improvisations are based on long-term structures or grids. Our study intends to target these aspects and provide short term predictions of the musical structures to improve the quality of the computer co-improvisation. Our aim is to develop a software that interacts in real-time with a musician by inferring expected structures. In order to achieve this goal, we divide the project into two main tasks: a listening module and a symbolic generation module. The listening module extracts the musical structure played by the musician whereas the generative module predicts musical sequences based on these extractions. In this report, we present a first approach towards this goal by introducing an automatic chord extraction module and a chord label sequence generator. Regarding the structure extraction, as the current state-of-the-art results in automatic chord extraction are obtained with Convolutional Neural Networks (CNN), we first study new architectures derived from the CNNs applied to this task. However, as we underline in our study, the low quantity of audio labeled dataset could limit the use of machine learning algorithms. Hence, we also propose the use of Ladder Networks (LN) which can be trained in a semi-supervised way. This allows us to evaluate the use of unlabeled music data to improve labeled chord extraction. Regarding the chord label generator, many recent works showed the success of Recurrent Neural Networks (RNN) for generative temporal applications. Thus, we use a family of recurrent networks, the Long Short-Term Memory (LSTM) unit, for our generative task. Here, we present our implementations and the results of our models by comparing to the current state-of-the-art and show that we obtain comparable results on the seminal evaluation datasets. Finally, we introduce the overall architecture of the software linking both modules and propose some directions of future work.
Pro tip: Screen-based payment methods increase negative feelings in consumers but do not increase tip sizes

Leaving monetary tips for servers is a commonplace occurrence in the United States. Tipping research has proposed that consumers tip to serve a variety of motives such as to ensure good service on future patronage, supplement servers' wages, and comply with social norms. Meanwhile, research on environmental factors that affect tipping behavior, such as method of bill payment, have yielded mixed findings. The advancement of technology has increased the use of screen-based payment methods that feature options with differing suggested tip amounts. This technology may pressure consumers into leaving larger tips or start tipping in situations where they previously would not. Using a computer-based study, we simulated limited-service experiences where customers have short interactions with their servers (e.g., ordering a cup of coffee to-go at a coffee shop). We studied how the availability of screen-based payment methods affected consumer feelings about establishments and tip amounts. Results indicated that, in our simulated coffee shop scenario, people feel negatively about screen-based payment methods and may avoid limited-service establishments that use them, but they did not leave larger tips when compared to other payment methods. Moreover, people did not tip more when a server was visible (versus absent) and empathy did not moderate this effect. Together, these findings suggest that people tip to comply with social norms in limited-service environments even if they may feel negatively about the consumer experience.
Weakly-Supervised Unconstrained Action Unit Detection via Feature Disentanglement

Facial action unit (AU) detection in the wild is a challenging problem, with current methods either depending on impractical labor-intensive labeling by experts, or inaccurate pseudo labels. In this paper, we aim to exploit accurate AU labels from a well-constrained source domain for training an AU detector for the target domain of unconstrained in-the-wild images. Instead of attempting to map directly from source to target domains, we propose to generate a new feature domain to combine source-domain facial inner landmark features (denoted as shape features) with target-domain global pose and texture features (denoted as texture features), so as to train target AU detector with source AU labels. Specifically, we first disentangle the rich features learned from images into shape features and texture features by introducing a novel shape adversarial loss and a shape classification loss. After swapping the shape features of unpaired source and target images, the combined source shape features and target texture features are translated to the new domain by learning a mapping that maximizes the AU detector performance. A further disentanglement and swap is applied to cross-cyclically reconstruct the original rich features. Moreover, our framework can also be naturally extended for use with pseudo AU labels. Extensive experiments show that our method soundly outperforms the baseline, upper-bound methods and state-of-the-art approaches on the challenging benchmark dataset EmotioNet.
Expert-augmented machine learning

Significance Machine learning is increasingly used across fields to derive insights from data, which further our understanding of the world and help us anticipate the future. The performance of predictive modeling is dependent on the amount and quality of available data. In practice, we rely on human experts to perform certain tasks and on machine learning for others. However, the optimal learning strategy may involve combining the complementary strengths of humans and machines. We present expert-augmented machine learning, an automated way to automatically extract problem-specific human expert knowledge and integrate it with machine learning to build robust, dependable, and data-efficient predictive models. Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.
Fine‐grained material associated with a large sulfide returned from Comet 81P/Wild 2

In a consortium analysis of a large particle captured from the coma of comet 81P/Wild 2 by the Stardust spacecraft, we report the discovery of a field of fine‐grained material (FGM) in contact with a large sulfide particle. The FGM was partially located in an embayment in the sulfide. As a consequence, some of the FGM appears to have been protected from damage during hypervelocity capture in aerogel. Some of the FGM particles are indistinguishable in their characteristics from common components of chondritic‐porous interplanetary dust particles, including glass with embedded metals and sulfides and equilibrated aggregates. The sulfide exhibits surprising Ni‐rich lamellae, which may indicate that this particle experienced a long‐duration heating event after its formation but before incorporation into Wild 2.
Cardiovascular adaptations associated with exercise in patients on hemodialysis

Patients on hemodialysis are physically inactive. Less than 50% of hemodialysis patients undertake exercise once a week and such patients have increased mortality compared to patients who undertake regular exercise. The reasons for physical inactivity and reduced functional capacity are complex and inter‐related, with skeletal muscle catabolism, chronic inflammation, anemia, malnutrition, uremia, the burden of co‐morbid diseases, and “enforced” sedentary time during hemodialysis all contributing. Many of these factors drive cardiovascular disease (CVD) processes in this cohort of patients and in the general population, exercise interventions have been shown to modify many of these risk factors. Whilst there is increasing evidence about the beneficial effects of exercise interventions on quality of life, functional capacity, aerobic fitness, and muscular strength, there are few compelling data on the effects of such programs on cardiovascular outcome measures. The reasons for this are manifold and include: limitations in study size; inconsistencies in study design; the heterogeneous nature of exercise interventions; assessment of nonstandardized outcome measures and; a lack of understanding of what changes in certain traditional measures of CVD (such as blood pressure or lipid profile) mean for patients on hemodialysis. This review summarizes the current evidence base for the effects of exercise on traditional and nontraditional cardiovascular risk factors and the effects of exercise interventions on cardiovascular structure and function, including a review of study limitations and future research priorities.
Dynamic Online Conversation Recommendation

Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner. Here we study dynamic online conversation recommendation, to help users engage in conversations that satisfy their evolving interests. While most prior work assumes static user interests, our model is able to capture the temporal aspects of user interests, and further handle future conversations that are unseen during training time. Concretely, we propose a neural architecture to exploit changes of user interactions and interests over time, to predict which discussions they are likely to enter. We conduct experiments on large-scale collections of Reddit conversations, and results on three subreddits show that our model significantly outperforms state-of-the-art models that make a static assumption of user interests. We further evaluate on handling “cold start”, and observe consistently better performance by our model when considering various degrees of sparsity of user’s chatting history and conversation contexts. Lastly, analyses on our model outputs indicate user interest change, explaining the advantage and efficacy of our approach.