Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of direct numerical simulations to the budget and modelling of the transport equations for passive scalar turbulent fields with wall scalar fluctuations

Abstract We perform direct numerical simulations (DNS) of the fully developed turbulent channel flow with a passive scalar subjected to constant time-averaged scalar fluxes at the wall. The first case (case I) considers a constant time-averaged scalar flux $\left \langle q_w \right \rangle$ along with the specific condition of a non-fluctuating scalar $\theta _w$ imposed at the wall implying zero wall scalar fluctuation $\theta '_w=0$ and zero variance (isoscalar boundary condition) whereas the second case (case II) accounts for a constant instantaneous scalar flux $q_w$ in time and space leading to zero gradient of scalar fluctuation along the normal to the wall $(\partial \theta '/ \partial x_n)_w = 0$ (isoflux boundary condition) implying a non-zero variance. The friction Reynolds number takes on the value $R_\tau =395$ and the molecular Prandtl numbers considered are $P_r=0.01$, 0.1, 1 and 10. The purpose is to investigate the effect of the wall scalar fluctuations on the scalar field. Emphasis is put on the mean passive scalar $\left \langle \theta \right \rangle$, the half-scalar variance $k_\theta$, the turbulent scalar fluxes $\tau _{i \theta }$, the correlation coefficients $R_{i \theta }$, the passive scalar to dynamic time-scale ratio $\mathcal {R}$, the turbulent Prandtl number $Pr_t$ and higher-order scalar statistics. Systematic comparisons between these two scalar fields are undertaken. As a result of interest, it is found that the mean scalar remains almost the same whatever the type of the boundary layer condition but not the scalar variance. The budgets of transport equations for the half-scalar variance and turbulent fluxes reveal that some scalar quantities such as the dissipation-rate and the molecular diffusion terms are highly modified in the near-wall region but not really the production, diffusion and the scalar-pressure gradient correlation terms in a first approximation. Visualization of the instantaneous scalar fields indicates that the topology of the structures is strongly modified as the Prandtl number increases from $P_r=0.01$ up to $10$. Finally, it is shown how to use this DNS database to devise and calibrate scalar flux equation models in the framework of both first and second moment closures. This study suggests that accounting for wall scalar fluctuations should be considered in the simulation of turbulent flows involving fluid and solid combinations at the interface and provides a useful high resolution DNS database.
Homogenizing fluid transport in stratified porous media using an elastic flow instability

Abstract Many key environmental, industrial and energy processes rely on controlling fluid transport within subsurface porous media. These media are typically structurally heterogeneous, often with vertically layered strata of distinct permeabilities – leading to uneven partitioning of flow across strata, which can be undesirable. Here, using direct in situ visualization, we demonstrate that polymer additives can homogenize this flow by inducing a purely elastic flow instability that generates random spatio-temporal fluctuations and excess flow resistance in individual strata. In particular, we find that this instability arises at smaller imposed flow rates in higher-permeability strata, diverting flow towards lower-permeability strata and helping to homogenize the flow. Guided by the experiments, we develop a parallel-resistor model that quantitatively predicts the flow rate at which this homogenization is optimized for a given stratified medium. Thus, our work provides a new approach to homogenizing fluid and passive scalar transport in heterogeneous porous media.
Lattice Boltzmann modelling of colloidal suspensions drying in porous media accounting for local nanoparticle effects

Abstract A two-dimensional (2-D) double-distribution lattice Boltzmann method (LBM) is implemented to study isothermal drying of a colloidal suspension considering local nanoparticle effects. The two LBMs solve isothermal two-phase flow and nanoparticle transport, respectively. The three local nanoparticle effects on the fluid dynamics considered in this paper are viscosity increase, surface tension drop and local drying rate reduction. The proposed model is first validated by the study of the drying of a 2-D suspended colloidal droplet for two different Péclet numbers, where the evolution of the diameter squared agrees well with experimental results. The model is further validated looking at drying of a colloid in a 2-D capillary tube with two open ends. Compared with experimental results, the best agreement in terms of deposition profile and drying time is obtained when considering all three nanoparticle effects. Afterwards, we apply the model to investigate the complicated drying of a colloidal suspension in a 2-D porous asphalt, considering all three local nanoparticle effects. The drying dynamics, resultant nanoparticle transport, accumulation and deposition are first analysed for a base case. Then a parametric study is conducted varying the initial nanoparticle concentration, porous medium contact angle, nanoparticle contact angle and nanoparticle diffusion coefficient. The influence of these parameters on drying dynamics, drying rate, deposition process and final deposition configurations is analysed in detail, together with the mutual influence of local nanoparticle behaviour. Finally, a unified relation between the average drying rate and the studied parameters is proposed and verified, covering the full parameter ranges of simulations.
Filtering Matters: Experiments in Filtering Training Sets for Machine Translation

We explore different approaches for filtering parallel data for MT training, whether the same filtering approaches suit different datasets, and if separate filters should be applied to a dataset depending on the translation direction. We evaluate the results of different approaches, both manually and on a downstream NMT task. We find that, first, it is beneficial to inspect how well different filtering approaches suit different datasets and, second, that while MT systems trained on data prepared using different filters do not differ substantially in quality, there is indeed a statistically significant difference. Finally, we find that the same training sets do not seem to suit different translation directions.
Generating Errors: OCR Post-Processing for Icelandic

We describe work on enhancing the performance of transformer-based encoder-decoder models for OCR post-correction on modern and historical Icelandic texts, where OCRed data are scarce. We trained six models, four from scratch and two fine-tuned versions of Google’s ByT5, on a combination of real data and texts populated with artificially generated errors. Our results show that the models trained from scratch, as opposed to the fine-tuned versions, benefited the most from the addition of artificially generated errors.
Boosting Norwegian Automatic Speech Recognition

In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokmål and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech datasets. Additionally, we measure the performance of these models against previous state-of-the-art ASR models, as well as on out-of-domain datasets. We improve the state of the art on the Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of 17.10% to 7.60%, with models achieving 5.81% for Bokmål and 11.54% for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.
Named Entity layer in Estonian UD treebanks

In this paper we will introduce two new language resources, two NE-annotated corpora for Estonian: Estonian Universal Dependencies Treebank (EDT, 440,000 tokens) and Estonian Universal Dependencies Web Treebank (EWT, 90,000 tokens). Together they make up the largest publicly available Estonian named entity gold annotation dataset. Eight NE categories are manually annotated in this dataset, and the fact that it is also annotated for lemma, POS, morphological features and dependency syntactic relations, makes it more valuable. We will also show that dividing the set of named entities into clear-cut categories is not always easy.
Dyslexia Prediction from Natural Reading of Danish Texts

Dyslexia screening in adults is an open challenge since difficulties may not align with standardised tests designed for children. We collect eye-tracking data from natural reading of Danish texts from readers with dyslexia while closely following the experimental design of a corpus of readers without dyslexia. Research suggests that the opaque orthography of the Danish language affects the diagnostic characteristics of dyslexia. To the best of our knowledge, this is the first attempt to classify dyslexia from eye movements during reading in Danish. We experiment with various machine-learning methods, and our best model yields 0.85 F1 score.
Multi-way Variational NMT for UGC: Improving Robustness in Zero-shot Scenarios via Mixture Density Networks

This work presents a novel Variational Neural Machine Translation (VNMT) architecture with enhanced robustness properties, which we investigate through a detailed case-study addressing noisy French user-generated content (UGC) translation to English. We show that the proposed model, with results comparable or superior to state-of-the-art VNMT, improves performance over UGC translation in a zero-shot evaluation scenario while keeping optimal translation scores on in-domain test sets. We elaborate on such results by visualizing and explaining how neural learning representations behave when processing UGC noise. In addition, we show that VNMT enforces robustness to the learned embeddings, which can be later used for robust transfer learning approaches.
Comparing Methods for Segmenting Elementary Discourse Units in a French Conversational Corpus

While discourse parsing has made considerable progress in recent years, discourse segmentation of conversational speech remains a difficult issue. In this paper, we exploit a French data set that has been manually segmented into discourse units to compare two approaches to discourse segmentation: fine-tuning existing systems on manual segmentation vs. using hand-crafted labelling rules to develop a weakly supervised segmenter. Our results show that both approaches yield similar performance in terms of f-score while data programming requires less manual annotation work. In a second experiment we play with the amount of training data used for fine-tuning systems and show that a small amount of hand labelled data is enough to obtain good results (although significantly lower than in the first experiment using all the annotated data available).
Machine vs. Human: Exploring Syntax and Lexicon in German Translations, with a Spotlight on Anglicisms

Machine Translation (MT) has become an integral part of daily life for millions of people, with its output being so fluent that users often cannot distinguish it from human translation. However, these fluid texts often harbor algorithmic traces, from limited lexical choices to societal misrepresentations. This raises concerns about the possible effects of MT on natural language and human communication and calls for regular evaluations of machine-generated translations for different languages. Our paper explores the output of three widely used engines (Google, DeepL, Microsoft Azure) and one smaller commercial system. We translate the English and French source texts of seven diverse parallel corpora into German and compare MT-produced texts to human references in terms of lexical, syntactic, and morphological features. Additionally, we investigate how MT leverages lexical borrowings and analyse the distribution of anglicisms across the German translations.
Description of Two New Labeo (Labeoninae; Cyprinidae) Endemic to the Lulua River in the Democratic Republic of Congo (Kasai Ecoregion); a Hotspot of Fish Diversity in the Congo Basin

ABSTRACT Labeo mbimbii, n. sp. , and Labeo manasseeae, n. sp. , two small-bodied Labeo species, are described from the lower and middle reaches of the Lulua River (Kasai ecoregion, Congo basin) in the Democratic Republic of Congo. The two new species are members of the L. forskalii species group and are genetically distinct from all other species of that clade. Morphologically they can be distinguished from central African L. forskalii group congeners except L. dhonti, L. lukulae, L. luluae, L. parvus, L. quadribarbis, and L. simpsoni in the possession of 29 or fewer (vs. 30 or more) vertebrae and from those congeners by a wider interpectoral, among other features. The two new species are endemic to the Lulua River and, although overlapping in geographical range and most meristic and morphometric measures, are readily differentiated by differing numbers of fully developed supraneural bones, predorsal vertebrae, snout morphology, and additional osteological features. The description of these two species brings the total of Labeo species endemic to the Lulua basin to three. The third endemic species, L. luluae, was previously known only from the juvenile holotype, but numerous additional specimens have now been identified. The cooccurrence of 14 Labeo species in the Lulua River, three of which are endemic, highlights this system as a hotspot of Labeo diversity in the Congo basin and across the continent.
Insulin resistance, cognition, and Alzheimer disease

Chronic diseases of aging are increasingly common. Dementia, often due to multiple etiologies including Alzheimer disease (AD), is at the forefront. Previous studies have reported higher rates of dementia among persons with diabetes, yet less is known about how insulin resistance relates to cognition. This article reviews recently published data on the relationship of insulin resistance to cognition and AD, and remaining knowledge gaps in the field are discussed. A structured review of studies was conducted over a 5‐year period, investigating insulin and cognitive function in adults with a baseline mean age of ≥65 years. This search yielded 146 articles, of which 26 met the predetermined inclusion and exclusion criteria. Among the nine studies that specifically examined insulin resistance and cognitive dysfunction and/or decline, eight studies suggest an association, but some only in subanalyses. Results are mixed in studies relating insulin to structural and functional changes on brain imaging, and data on intranasal insulin for cognition remain unclear. Future avenues are proposed to elucidate the impact of insulin resistance on brain structure and function, including cognition, in persons with and without AD.
Admittance signature of piezoceramic transducers bonded on different materials

In the electro-mechanical impedance technique (EMI) for structural health monitoring, the admittance change between the free state and bonded state can play a crucial role in the selection of the optimal frequency range prior to attachment. However, the frequency shift after bonding has so far been ignored. This paper investigates the change in the admittance of the PZT patch after bonding to different materials via experimentation and numerical simulation. The results show that the frequency shift of the first resonance frequency is dependent on the material of the substrate. For passive materials, the frequency shift is between −15 and 15 kHz. With active materials, the frequency shift is between 40 and 80 kHz. Furthermore, the frequency shift is related to the elastic modulus of the materials, with a high elastic modulus resulting in a large frequency shift. The findings lay the foundation for a more comprehensive understanding of the admittance at the bonded state and for the proper application of the EMI technique.
Antimutagenic 1,4-Dihydropyridine AV-153 Normalizes Expression of GLUT1, GLUT4, INOS, PARP1, and Gamma H2AX Histone in Myocardium of Rats with Streptozotocin Model of Diabetes Mellitus

Abstract Diabetic cardiomyopathy increases the risk of heart failure and worsens prognosis for diabetes mellitus (DM) patients. Its development depends on many factors, including modification of nitric oxide production and impaired DNA repair. The goal of the present work was to study in vivo effects of a 1,4-dihydropyridine AV-153, known as antimutagen and DNA-binder, on DNA integrity, and on the expression of several proteins involved in glucose transport, nitric oxide metabolism, and DNA repair in myocardium in diabetic rats. DM was induced in rats by streptozotocin (STZ) injection. Expression of proteins was studied by means of immunohistochemistry. Development of the STZ-induced DM significantly induced PARP1 and gamma H2AX histone, markers of DNA breakage, protein expression in heart tissue, while AV-153 administration decreased PARP1 and H2AX protein expression. In this model of diabetes, myocardial expression of iNOS was also significantly increased, but administration of AV-153 reduced it to normal levels. AV-153 also upregulated the expression of insulin-dependent GLUT4 and insulin-independent GLUT1 glucose transporters up to normal level in diabetic rats. Thus, AV-153 appears to be prospective for creation of a remedy for prevention of diabetic cardiomyopathy.
Collaborative Behavior or Collusion? Investigating Unpermitted Collaboration on Homework Among College Students

Abstract Although postsecondary students’ rate of academic dishonesty has been consistent over the last 50 years, the most significant increase in cheating has been in unpermitted collaboration. Given the changing learning environments necessitated by COVID-19, this study investigates how 12 college students at a highly selective Research 1 institution view collaboration, specifically unpermitted collaboration on homework, and how their attitudes are shaped by faculty guidance or lack thereof. We conclude by discussing how both student and faculty perceptions of collaboration may be affected by the impact of COVID-19 and how academic integrity may be influenced by this impact.
Robot programming for a child with autism spectrum disorder: a pilot study

Children with autism spectrum disorder (ASD) show great interest in technological devices, and especially in robots. Several studies in the field have suggested that socially assistive robotics (SARs) can help children with ASD in the enhancement of their social skills and communication, and in the reduction of their stereotypical behaviors. Few published research results are available regarding robot programming or coding in the context of STEM education (Science, Technology, Engineering and Mathematics) for these children. In this pilot study, the authors designed and implemented educational activities with the robot ‘Codey Rocky’, a ready-to-use robot designed for code learning and programming by primary school children. In this pilot study, the participation of two eight-year-old schoolchildren, a girl with ASD and intellectual deficit and a boy of typical development in triadic interactions with the robot, led to the enhancement of the social and communication skills of the girl with ASD. A decrease in her challenging behaviors was also observed although she manifested repetitive and stereotyped behaviors throughout the educational sessions. The benefits, risks, and implications of the use of SARs for children with ASD are discussed.
Annotating Affect in the Field: A Case Study on the Usability of a Minimalist Smartwatch User Interface for Affect Annotation

Successful empathetic interaction requires an accurate understanding of the interaction partner’s affect dynamics. Self-reported annotations provide a way to better understand affect and empathy in real-life; however, the necessary user interactions for collecting such data must be designed to be as unobtrusive as possible. To address this challenge, we explore the potential of a smartwatch annotation application for affect that aims to minimize user interaction effort while maximizing usability. In a field study conducted as part of a student career fair (N=9), we evaluated the feasibility and usability of our app. Participants reported high usability scores and our data collection successfully captured self-reported affect labels at a high temporal resolution. Our work contributes to the challenge of providing minimal obtrusive applications for the collection of self-reported labels of affective states.
G-DAIC: A Gaze Initialized Framework for Description and Aesthetic-Based Image Cropping

We propose a new gaze-initialised optimisation framework to generate aesthetically pleasing image crops based on user description. We extended the existing description-based image cropping dataset by collecting user eye movements corresponding to the image captions. To best leverage the contextual information to initialise the optimisation framework using the collected gaze data, this work proposes two gaze-based initialisation strategies, Fixed Grid and Region Proposal. In addition, we propose the adaptive Mixed scaling method to find the optimal output despite the size of the generated initialisation region and the described part of the image. We address the runtime limitation of the state-of-the-art method by implementing the Early termination strategy to reduce the number of iterations required to produce the output. Our experiments show that G-DAIC reduced the runtime by 92.11%, and the quantitative and qualitative experiments demonstrated that the proposed framework produces higher quality and more accurate image crops w.r. t. user intention.
Exploring Dwell-time from Human Cognitive Processes for Dwell Selection

In order to develop future implicit interactions, it is important to understand the duration a user needs to recognize a visual object. By providing interactions that are triggered after a user recognizes an object, confusion resulting from the discrepancy between completing a cognitive process, which we define as the process from perceiving a visual stimulus to determining a selection, and triggering an interaction can be reduced. To understand this duration, we developed a model to derive dwell-times, allowing dwell selection to be performed after completing a cognitive process based on the Model Human Processor and the number of fixations. Our model revealed a minimum dwell-time of 144.2 ms for a colored target selection task. For an image selection task, the minimum dwell-time was 272.5 ms, which increased to 835.8 ms when a participant had not previously fixated on the object.
Diffusion tensor imaging: Influence of segmentation on fiber tracking in the supraspinatus muscle–An inter-operator reliability analysis

The ability of muscle to generate force depends on its architecture and quality. MR-based diffusion tensor imaging of muscle (mDTI) is an innovative approach for showing the fiber arrangement for the whole muscle volume. For accurate calculations of fiber metrics, muscle segmentation prior to tractography seems mandatory. Since segmentation is known to be operator dependent, it is important to understand how segmentation affects tractography. The aim of this study was to compare the results of deterministic fiber tracking based on muscle models generated by two independent operators. In addition, this study compares the results with a segmentation-free approach. Fifteen subjects underwent mDTI of the right shoulder. The results showed that mDTI can be successfully applied to complex joints such as the human shoulder. Furthermore, operators segmentation did not influence the results of fiber tracking and showed excellent intraclass correlation estimates (≥ 0.975). As an exploratory approach, the segmentation-free fiber tracking showed significant differences in terms of mean fascicle length. Based on these findings, we assume that tractography does not require a detailed muscle segmentation. Furthermore, it implies that mDTI and automatic segmentation approaches or even a segmentation-free analysis can be considered for evaluation of muscle changes.