Successful Transcatheter Closure of Patent Ductus Arteriosus in Isolated Levocardia with Situs Inversus

Abstract Isolated levocardia with situs inversus is an extremely rare type of situs anomaly with an estimated incidence of 1 per 22,000 in the general population. This autosomal recessive situs anomaly has been described as normal levo position of the heart with dextro position of the abdominal viscera. In this case report, we describe accidental diagnosis of isolated levocardia with situs inversus in pediatric patient while evaluating for heart murmur. Systematic examination of the patient identified the presence of patent ductus arteriosus, anomalous course of inferior vena cava and bovine aortic arch. We shared our experience of successful transcatheter closure of patent ductus arteriosus. This case report is worth reporting both, for demonstrating the possibility of the percutaneous device closure of the patent ductus arteriosus in patient with this unusual situs anomaly to interventional cardiologists and because of scanty of literature

Using the X-IOTA System in Mono- and Bilingual Experiments at CLEF 2005

International audienceThis document describes the CLIPS experiments in the CLEF 2005 campaign. We used a surface-syntactic parser in order to extract new indexing terms. These terms are considered syntactic dependencies. Our goal was to evaluate their relevance for an information retrieval task. We used them in different forms in different information retrieval models, in particular in a language model. For the bilingual task, we tried two simple tests of Spanish and German to French retrieval; for the translation we used a lemmatizer and a dictionary

A soft alignment model for bug deduplication

ABSTRACT: Bug tracking systems (BTS) are widely used in software projects. An important task in such systems consists of identifying duplicate bug reports, i.e., distinct reports related to the same software issue. For several reasons, reporting bugs that have already been reported is quite frequent, making their manual triage impractical in large BTSs. In this paper, we present a novel deep learning network based on soft-attention alignment to improve duplicate bug report detection. For a given pair of possibly duplicate reports, the attention mechanism computes interdependent representations for each report, which is more powerful than previous approaches. We evaluate our model on four well-known datasets derived from BTSs of four popular open-source projects. Our evaluation is based on a ranking-based metric, which is more realistic than decision-making metrics used in many previous works. Achieved results demonstrate that our model outperforms state-of-the-art systems and strong baselines in different scenarios. Finally, an ablation study is performed to confirm that the proposed architecture improves the duplicate bug reports detection

Evaluating Multimedia Features and Fusion for Example-Based Event Detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported

Discovery of latent subcommunities in a blog's readership

The blogosphere has grown to be a mainstream forum of social interaction as well as a commercially attractive source of information and influence. Tools are needed to better understand how communities that adhere to individual blogs are constituted in order to facilitate new personal, socially-focused browsing paradigms, and understand how blog content is consumed, which is of interest to blog authors, big media, and search.We present a novel approach to blog subcommunity characterization by modeling individual blog readers using mixtures of an extension to the LDA family that jointly models phrases and time, Ngram Topic over Time (NTOT), and cluster with a number of similarity measures using Affinity Propagation. We experiment with two datasets: a small set of blogs whose authors provide feedback, and a set of popular, highly commented blogs, which provide indicators of algorithm scalability and interpretability without prior knowledge of agiven blog. The results offer useful insight to the blog authors about their commenting community, and are observed to offer an integrated perspective on the topics of discussion and members engaged in those discussions for unfamiliar blogs. Our approach also holds promise as a component of solutions to related problems, such as online entity resolution and role discovery