Chinese–Spanish neural machine translation enhanced with character and word bitmap fonts

Recently, machine translation systems based on neural networks have reached state-of-the-art results for some pairs of languages (e.g., German–English). In this paper, we are investigating the performance of neural machine translation in Chinese–Spanish, which is a challenging language pair. Given that the meaning of a Chinese word can be related to its graphical representation, this work aims to enhance neural machine translation by using as input a combination of: words or characters and their corresponding bitmap fonts. The fact of performing the interpretation of every word or character as a bitmap font generates more informed vectorial representations. Best results are obtained when using words plus their bitmap fonts obtaining an improvement (over a competitive neural MT baseline system) of almost six BLEU, five METEOR points and ranked coherently better in the human evaluation.Peer ReviewedPostprint (published version

Fusing Data with Correlations

Many applications rely on Web data and extraction systems to accomplish knowledge-driven tasks. Web information is not curated, so many sources provide inaccurate, or conflicting information. Moreover, extraction systems introduce additional noise to the data. We wish to automatically distinguish correct data and erroneous data for creating a cleaner set of integrated data. Previous work has shown that a na\"ive voting strategy that trusts data provided by the majority or at least a certain number of sources may not work well in the presence of copying between the sources. However, correlation between sources can be much broader than copying: sources may provide data from complementary domains (\emph{negative correlation}), extractors may focus on different types of information (\emph{negative correlation}), and extractors may apply common rules in extraction (\emph{positive correlation, without copying}). In this paper we present novel techniques modeling correlations between sources and applying it in truth finding.Comment: Sigmod'201

Knee surgery and its evidence base

Introduction Evidence driven orthopaedics is gaining prominence. It enables better management decisions and therefore better patient care. The aim of our study was to review a selection of the leading publications pertaining to knee surgery to assess changes in levels of evidence over a decade. Methods Articles from the years 2000 and 2010 in The Knee, the Journal of Arthroplasty, Knee Surgery, Sports Traumatology, Arthroscopy, the Journal of Bone and Joint Surgery (American Volume) and the Bone and Joint Journal were analysed and ranked according to guidelines from the Centre for Evidence-Based Medicine. The intervening years (2003, 2005 and 2007) were also analysed to further define the trend. Results The percentage of high level evidence (level I and II) studies increased albeit without reaching statistical significance. Following a significant downward trend, the latter part of the decade saw a major rise in levels of published evidence. The most frequent type of study was therapeutic. Conclusions Although the rise in levels of evidence across the decade was not statistically significant, there was a significant drop and then rise in these levels in the interim. It is therefore important that a further study is performed to assess longer-term trends. Recent developments have made clear that high quality evidence will be having an ever increasing influence on future orthopaedic practice. We suggest that journals implement compulsory declaration of a published study's level of evidence and that authors consider their study designs carefully to enhance the quality of available evidence

EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR , the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets

Expression of GPR17 receptor in a murine model of perinatal brain neuroinflammation and its possible interaction with Wnt pathway

Oligodendrocyte precursor cells (OPCs) are generated in specific germinal regions and progressively maturate to myelinating cells. Oligodendrocytes (OLs) differentiation is regulated by a complex interplay of intrinsic, epigenetic and extrinsic factors, including Wnt and the G protein-coupled receptor referred to as GPR17 (Mitew et al., 2014). This receptor responds to both extracellular nucleotides (UDP, UDP-glucose) and cysteinyl-leukotrienes (Ciana et al., 2006), endogenous signaling molecules involved in inflammatory response and in the repair of brain lesions. GPR17 is highly expressed in OPCs during the transition to immature OLs, but it is down-regulated in mature cells. Accordingly, GPR17-expressing OPCs are already present in mice at birth, increase over time, reach a peak at P10, before the peak of myelination, and then decline in the adult brain (Boda et al., 2011). Of note, in cultured OPCs, early GPR17 silencing has been shown to profoundly affect their ability to generate mature OLs (Fumagalli et al., 2011, 2015). Myelination defects characterize many brain disorders, including perinatal brain injury caused by systemic inflammation (Favrais et al., 2011), which is a leading cause of preterm birth. It has already been suggested that an imbalance in the Wnt/\u3b2-catenin/TCF4 pathway could be involved in the maturation arrest of OLs that is observed in premature infants (Yuen et al., 2014). No data are currently available on GPR17 in perinatal brain injury and on its possible interaction with Wnt pathway. Based on these premises, the aim of this work was to assess if the maturational blockade of OLs due to mild systemic perinatal inflammation, induced by intraperitoneal injections of interleukin-1\u3b2 (IL- 1\u3b2), is accompanied by defects in GPR17 expression and whether the Wnt pathway is involved in the regulation of GPR17. Data showed that in newborn mice exposed to IL-1\u3b2, which induces a blockade of oligodendrocyte maturation, GPR17 expression is not affected at early time point (P5), but it is downregulated at P10, when its expression should be maximal. Moreover, in vitro studies revealed that the maturation blockade of the oligodendroglial cell line Oli-Neu, after treatment with a Wnt Agonist II, is accompanied by a severe inhibition of GPR17 expression. In conclusion, our data have shown that myelination defects observed in perinatal brain injury are associated with defects in GPR17 expression; further studies are needed to characterize the molecular link between Wnt pathway and GPR17 receptor

Altered Auditory Feedback In-The-Ear Devices

Purpose: This study examined objective and subjective measures of the effect of a self-contained ear-level device delivering altered auditory feedback (AAF) for those who stutter 12 months following initial fitting with and without the device. Method: Nine individuals with developmental stuttering participated. In Experiment 1, the proportion of stuttering was examined during reading and monologue. A self-report inventory inquiring about behavior related to struggle, avoidance and expectancy associated with stuttering was examined in Experiment 2. In Experiment 3, naive listeners rated the speech naturalness of speech produced by the participants during reading and monologue. Results: The proportions of stuttering events were significantly (p < 0.05) reduced at initial fitting and remained so 12 months post follow-up. After using the device for 12 months, self- reported perception of struggle, avoidance and expectancy were significantly (p < 0.05) reduced relative to pre-fitting. Naive listeners rated the speech samples produced by those who stutter while wearing the device significantly more natural sounding than those produced without the device for both reading and monologue (p < 0.0001). Conclusions: These findings support the notion that a device delivering AAF is a viable therapeutic alternative in the treatment of stuttering

Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks

Retinopathy of Prematurity (ROP) is an ocular disease observed in premature babies, considered one of the largest preventable causes of childhood blindness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP detector and with small modifications also return an approximate Bayesian posterior over disease presence. To the best of our knowledge, this is the first completely automated ROP detection system. (2) To further aid grading, we train a second CNN to return novel feature map visualizations of pathologies, learned directly from the data. These feature maps highlight discriminative information, which we believe may be used by clinicians with our classifier to aid in screening

Elevation and cholera: an epidemiological spatial analysis of the cholera epidemic in Harare, Zimbabwe, 2008-2009

BACKGROUND: In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009. METHODS: We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs. RESULTS: This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.66-0.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%. CONCLUSION: This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies

ABSTRACT: BACKGROUND: Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. METHODS: We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. RESULTS: We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. CONCLUSIONS: We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application