Miracle-dz/UniFAR-FTUs huggingface dataset:
The association between specific sports activities and sport performance following hip arthroscopy for femoroacetabular impingement syndrome: A secondary analysis of a cross-sectional cohort study including 184 athletes

Abstract The main purpose of this secondary analysis was to compare the proportion of athletes with moderate-to-extreme difficulties in eight specific sport activities in athletes with optimal versus impaired sport performance after a hip arthroscopy for femoroacetabular impingement syndrome. Subjects were identified in a nationwide registry and invited to answer a return to sport and performance questionnaire, and the Copenhagen Hip and Groin Outcome Score Sport subscale investigating difficulties in eight specific sports activities (HAGOS sport items) as; none, mild, moderate, severe or extreme. Subjects were divided into two groups based on sport performance (optimal or impaired). The proportion of athletes with none-to-mild versus moderate-to-extreme difficulties in the eight specific sport activities was compared between groups. The association between difficulties in sport activities and sport performance were investigated using logistic regression analysis. One hundred and eighty-four athletes (31 athletes with optimal and 153 athletes with impaired sport performance) were included at a mean follow-up of 33.1 ± 16.3 months. Up to six athletes (<20%) with optimal sport performance had moderate-to-extreme difficulties in sport activities. Contrary, 43–108 athletes (28.1–70.6%) with impaired performance had moderate-to-extreme difficulties in sport activities. Furthermore, moderate-to-extreme difficulties in HAGOS sport items: ‘running as fast as you can’ and ‘kicking, skating etc.’ increased the odds (14.7 and 6.1 times, respectively) of having impaired sport performance. Many athletes with impaired sport performance reported moderate-to-extreme difficulties in sport activities, specifically moderate-to-extreme difficulties in ‘running as fast as you can’ and ‘kicking, skating etc.’ were associated with patients having impaired sport performance.
Ranking Passages for Argument Convincingness

In data ranking applications, pairwise annotation is often more consistent than cardinal annotation for learning ranking models. We examine this in a case study on ranking text passages for argument convincingness. Our task is to choose text passages that provide the highest-quality, most-convincing arguments for opposing sides of a topic. Using data from a deployed system within the Bing search engine, we construct a pairwise-labeled dataset for argument convincingness that is substantially more comprehensive in topical coverage compared to existing public resources. We detail the process of extracting topical passages for queries submitted to a search engine, creating annotated sets of passages aligned to different stances on a topic, and assessing argument convincingness of passages using pairwise annotation. Using a state-of-the-art convincingness model, we evaluate several methods for using pairwise-annotated data examples to train models for ranking passages. Our results show pairwise training outperforms training that regresses to a target score for each passage. Our results also show a simple ‘win-rate’ score is a better regression target than the previously proposed page-rank target. Lastly, addressing the need to filter noisy crowd-sourced annotations when constructing a dataset, we show that filtering for transitivity within pairwise annotations is more effective than filtering based on annotation confidence measures for individual examples.
Artificial Error Generation with Fluency Filtering

The quantity and quality of training data plays a crucial role in grammatical error correction (GEC). However, due to the fact that obtaining human-annotated GEC data is both time-consuming and expensive, several studies have focused on generating artificial error sentences to boost training data for grammatical error correction, and shown significantly better performance. The present study explores how fluency filtering can affect the quality of artificial errors. By comparing artificial data filtered by different levels of fluency, we find that artificial error sentences with low fluency can greatly facilitate error correction, while high fluency errors introduce more noise.
Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts

In this work we propose to leverage resources available with discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. In particular, we implement and evaluate a transfer learning approach in which contextualized representations learned from discourse parsing tasks are used as input of argument mining models. As a pilot application, we explore the feasibility of using automatically identified argumentative components and relations to predict the acceptance of papers in computer science venues. In order to conduct our experiments, we propose an annotation scheme for argumentative units and relations and use it to enrich an existing corpus with an argumentation layer.
Analyzing Stock Brokers' Trading Patterns: A Network Decomposition and Spatial Econometrics Approach

Using a unique data set with all the daily transactions from the Santiago Stock Exchange, we develop a novel methodology that combines a network decomposition with a spatial econometrics technique to study how brokers’ characteristics and trading decisions may affect the stock market return. We present suggestive evidence of a mechanism by which structural changes of the transaction network between brokers affect the aggregate returns of the stock market. We find that brokers tend to trade with counterparties with dissimilar intraday selling volume when market return significantly increases. Moreover, brokers with a research department tend to sell to brokers without a research department when the market experiences a considerable increase of its return. From the financial perspective, these results highlight new ways in which intermediaries may affect market equilibrium and the efficiency of the market.
Composing Noun Phrase Vector Representations

Vector representations of words have seen an increasing success over the past years in a variety of NLP tasks. While there seems to be a consensus about the usefulness of word embeddings and how to learn them, it is still unclear which representations can capture the meaning of phrases or even whole sentences. Recent work has shown that simple operations outperform more complex deep architectures. In this work, we propose two novel constraints for computing noun phrase vector representations. First, we propose that the semantic and not the syntactic contribution of each component of a noun phrase should be considered, so that the resulting composed vectors express more of the phrase meaning. Second, the composition process of the two phrase vectors should apply suitable dimensions’ selection in a way that specific semantic features captured by the phrase’s meaning become more salient. Our proposed methods are compared to 11 other approaches, including popular baselines and a neural net architecture, and are evaluated across 6 tasks and 2 datasets. Our results show that these constraints lead to more expressive phrase representations and can be applied to other state-of-the-art methods to improve their performance.
ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge

We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA. Textual inference is the task of finding the semantic relationships between pairs of text. Question entailment involves identifying pairs of questions which have similar semantic content. To improve upon medical natural language inference and question entailment approaches to further medical question answering, we propose a system that incorporates open-domain and biomedical domain approaches to improve semantic understanding and ambiguity resolution. Our models achieve 80% accuracy on medical natural language inference (6.5% absolute improvement over the original baseline), 48.9% accuracy on recognising medical question entailment, 0.248 Spearman’s rho for question answering ranking and 68.6% accuracy for question answering classification.
Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes

The objective of this work is to develop an automated diagnosis system that is able to predict the probability of appendicitis given a free-text emergency department (ED) note and additional structured information (e.g., lab test results). Our clinical corpus consists of about 180,000 ED notes based on ten years of patient visits to the Accident and Emergency (A&E) Department of the National University Hospital (NUH), Singapore. We propose a novel neural network approach that learns to diagnose acute appendicitis based on doctors’ free-text ED notes without any feature engineering. On a test set of 2,000 ED notes with equal number of appendicitis (positive) and non-appendicitis (negative) diagnosis and in which all the negative ED notes only consist of abdominal-related diagnosis, our model is able to achieve a promising F_0. 5-score of 0.895 while ED doctors achieve F_0. 5-score of 0.900. Visualization shows that our model is able to learn important features, signs, and symptoms of patients from unstructured free-text ED notes, which will help doctors to make better diagnosis.
Annotation of Rhetorical Moves in Biochemistry Articles

This paper focuses on the real world application of scientific writing and on determining rhetorical moves, an important step in establishing the argument structure of biomedical articles. Using the observation that the structure of scholarly writing in laboratory-based experimental sciences closely follows laboratory procedures, we examine most closely the Methods section of the texts and adopt an approach of identifying rhetorical moves that are procedure-oriented. We also propose a verb-centric frame semantics with an effective set of semantic roles in order to support the analysis. These components are designed to support a computational model that extends a promising proposal of appropriate rhetorical moves for this domain, but one which is merely descriptive. Our work also contributes to the understanding of argument-related annotation schemes. In particular, we conduct a detailed study with human annotators to confirm that our selection of semantic roles is effective in determining the underlying rhetorical structure of existing biomedical articles in an extensive dataset. The annotated dataset that we produce provides the important knowledge needed for our ultimate goal of analyzing biochemistry articles.
Translating Between Morphologically Rich Languages: An Arabic-to-Turkish Machine Translation System

This paper introduces the work on building a machine translation system for Arabic-to-Turkish in the news domain. Our work includes collecting parallel datasets in several ways for a new and low-resourced language pair, building baseline systems with state-of-the-art architectures and developing language specific algorithms for better translation. Parallel datasets are mainly collected three different ways; i) translating Arabic texts into Turkish by professional translators, ii) exploiting the web for open-source Arabic-Turkish parallel texts, iii) using back-translation. We per-formed preliminary experiments for Arabic-to-Turkish machine translation with neural(Marian) machine translation tools with a novel morphologically motivated vocabulary reduction method.
The Impact of Word Representations on Sequential Neural MWE Identification

Recent initiatives such as the PARSEME shared task allowed the rapid development of MWE identification systems. Many of those are based on recent NLP advances, using neural sequence models that take continuous word representations as input. We study two related questions in neural MWE identification: (a) the use of lemmas and/or surface forms as input features, and (b) the use of word-based or character-based embeddings to represent them. Our experiments on Basque, French, and Polish show that character-based representations yield systematically better results than word-based ones. In some cases, character-based representations of surface forms can be used as a proxy for lemmas, depending on the morphological complexity of the language.
Performant Container Support for HPC Applications

The demand for the ability to easily customize, reproduce and migrate applications and workflows has been steadily increasing among the HPC community as software environments and applications grow in complexity. Lightweight containers that are suitable for HPC applications at scale are considered to be a viable approach to meet this demand. Previous studies have addressed the performance aspects of most existing containers using microbenchmarks and revealed the performance overheads of the best implementations to be small. However, the feasibility of providing containerized, real-world HPC applications on HPC systems, and the impact on overall application performance at scale, has not yet been explored. Here we present a basic feasibility and performance study using the Singularity container. We evaluate what is required to enable container images to utilize the high-speed fabric present on most HPC systems and explore their performance by comparing real-world applications run both within a container and in the absence of a container (natively). The results indicate lightweight Singularity images are a promising approach to the HPC communities demands for not only customizability, reproducibility and portability, but also performance.
Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA

We explore the extent to which neural networks can learn to identify semantically equivalent sentences from a small variable dataset using an end-to-end training. We collect a new noisy non-standardised user-generated Algerian (ALG) dataset and also translate it to Modern Standard Arabic (MSA) which serves as its regularised counterpart. We compare the performance of various models on both datasets and report the best performing configurations. The results show that relatively simple models composed of 2 LSTM layers outperform by far other more sophisticated attention-based architectures, for both ALG and MSA datasets.
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings

In this paper, we tackle the problem of “root extraction” from words in the Semitic language family. A challenge in applying natural language processing techniques to these languages is the data sparsity problem that arises from their rich internal morphology, where the substructure is inherently non-concatenative and morphemes are interdigitated in word formation. While previous automated methods have relied on human-curated rules or multiclass classification, they have not fully leveraged the various combinations of regular, sequential concatenative morphology within the words and the internal interleaving within templatic stems of roots and patterns. To address this, we propose a constrained sequence-to-sequence root extraction method. Experimental results show our constrained model outperforms a variety of methods at root extraction. Furthermore, by enriching word embeddings with resulting decompositions, we show improved results on word analogy, word similarity, and language modeling tasks.
Efficient Language Modeling with Automatic Relevance Determination in Recurrent Neural Networks

Reduction of the number of parameters is one of the most important goals in Deep Learning. In this article we propose an adaptation of Doubly Stochastic Variational Inference for Automatic Relevance Determination (DSVI-ARD) for neural networks compression. We find this method to be especially useful in language modeling tasks, where large number of parameters in the input and output layers is often excessive. We also show that DSVI-ARD can be applied together with encoder-decoder weight tying allowing to achieve even better sparsity and performance. Our experiments demonstrate that more than 90% of the weights in both encoder and decoder layers can be removed with a minimal quality loss.
The Shear Strength and Dilatancy Behavior of Wheat Stored in Silos

This paper focuses on the shear and dilatancy behavior of wheat stored in silos with various densities and normal stresses. The goal is to find a quantitative relationship modeling the peak friction angle and maximum dilatancy angle of wheat stored in silos. A total of 48 direct shear tests were carried out to research the evolution of shear and dilatancy of stored wheat in silos. It is revealed that strength of wheat in bulk attributes to the combination of frictional and dilatant during shearing, in particular attributing to its elliptic shape. An increase in relative density enhances the peak friction angle as well as the dilation. The relationships between relative density, peak friction angle, and dilatancy angle were presented based on the tests data and Bolton’s theory. Then an advanced model is developed to evaluate the peak shear behavior of wheat stored in silos considering the dilatancy of the stored wheat. It is a practical method to predict the strength and dilatancy behavior of wheat stored in silos.
The Swedish PoliGraph: A Semantic Graph for Argument Mining of Swedish Parliamentary Data

As part of a larger project on argument mining of Swedish parliamentary data, we have created a semantic graph that, together with named entity recognition and resolution (NER), should make it easier to establish connections between arguments in a given debate. The graph is essentially a semantic database that keeps track of Members of Parliament (MPs), in particular their presence in the parliament and activity in debates, but also party affiliation and participation in commissions. The hope is that the Swedish PoliGraph will enable us to perform named entity resolution on debates in the Swedish parliament with a high accuracy, with the aim of determining to whom an argument is directed.
The geologic history of seawater oxygen isotopes from marine iron oxides

Not as hot as we thought Earth's early oceans appear not to have been as hot as some have suggested. The oxygen isotope composition of marine carbonates has changed markedly over the past 3.5 billion years. However, it has been difficult to determine whether that is because of a cooling of the seawater (from temperatures as high as 70°C) or an actual change in the isotope composition of the water. Galili et al. calibrated the temperature-dependent oxygen isotope fractionation between iron oxides and aqueous solutions and constructed an oxygen isotope record in marine iron oxides covering the past 2 billion years. Their findings suggest that a change in the isotope composition of the water, rather than its cooling, underlies the observed geological trend. Science, this issue p. 469 The oxygen isotopic composition of seawater has evolved over the past 2 billion years. The oxygen isotope composition (δ18O) of marine sedimentary rocks has increased by 10 to 15 per mil since Archean time. Interpretation of this trend is hindered by the dual control of temperature and fluid δ18O on the rocks’ isotopic composition. A new δ18O record in marine iron oxides covering the past ~2000 million years shows a similar secular rise. Iron oxide precipitation experiments reveal a weakly temperature-dependent iron oxide–water oxygen isotope fractionation, suggesting that increasing seawater δ18O over time was the primary cause of the long-term rise in δ18O values of marine precipitates. The 18O enrichment may have been driven by an increase in terrestrial sediment cover, a change in the proportion of high- and low-temperature crustal alteration, or a combination of these and other factors.
3D bioprinting of collagen to rebuild components of the human heart

If I only had a heart 3D bioprinting is still a fairly new technique that has been limited in terms of resolution and by the materials that can be printed. Lee et al. describe a 3D printing technique to build complex collagen scaffolds for engineering biological tissues (see the Perspective by Dasgupta and Black). Collagen gelation was controlled by modulation of pH and could provide up to 10-micrometer resolution on printing. Cells could be embedded in the collagen or pores could be introduced into the scaffold via embedding of gelatin spheres. The authors demonstrated successful 3D printing of five components of the human heart spanning capillary to full-organ scale, which they validated for tissue and organ function. Science, this issue p. 482; see also p. 446 Biofabrication of contractile ventricles, heart valves, vascular networks, and organ-scale scaffolds using collagen is explored. Collagen is the primary component of the extracellular matrix in the human body. It has proved challenging to fabricate collagen scaffolds capable of replicating the structure and function of tissues and organs. We present a method to 3D-bioprint collagen using freeform reversible embedding of suspended hydrogels (FRESH) to engineer components of the human heart at various scales, from capillaries to the full organ. Control of pH-driven gelation provides 20-micrometer filament resolution, a porous microstructure that enables rapid cellular infiltration and microvascularization, and mechanical strength for fabrication and perfusion of multiscale vasculature and tri-leaflet valves. We found that FRESH 3D-bioprinted hearts accurately reproduce patient-specific anatomical structure as determined by micro–computed tomography. Cardiac ventricles printed with human cardiomyocytes showed synchronized contractions, directional action potential propagation, and wall thickening up to 14% during peak systole.
Enteric and non-enteric adenoviruses associated with acute gastroenteritis in pediatric patients in Thailand, 2011 to 2017

Human adenovirus (HAdV) is known to be a common cause of diarrhea in children worldwide. Infection with adenovirus is responsible for 2–10% of diarrheic cases. To increase a better understanding of the prevalence and epidemiology of HAdV infection, a large scale and long-term study was needed. We implemented a multi-year molecular detection and characterization study of HAdV in association with acute gastroenteritis in Chiang Mai, Thailand from 2011 to 2017. Out of 2,312 patients, HAdV was detected in 165 cases (7.2%). The positive rate for HAdV infection was highest in children of 1 and 2 years of age compared to other age groups. HAdV subgroup C (40.6%) was the most prevalent, followed by subgroups F (28.5%), B (20.6%), A and D (4.8% each), and E (0.6%). Of these, HAdV-F41 (22.4%), HAdV-C2 (18.2%), HAdV-B3 (15.2%), and HAdV-C1 (13.3%) were the most common genotypes detected. HAdV infection occurred throughout the year with a higher detection rate between May and July. In conclusion, our study demonstrated the infection rate, seasonal distribution and genotype diversity of HAdV infection in children with diarrhea in Chiang Mai, Thailand over a period of 7 year. Not only enteric adenovirus (F40 and F41) but also non-enteric adenovirus (B3, C1, C2) may play an important role in gastroenteritis in this area. The information will be beneficial for the prevention and control of HAdV outbreaks in the future.