The Energetic Reasoning Checker Revisited

Energetic Reasoning (ER) is a powerful filtering algorithm for the Cumulative constraint. Unfortunately, ER is generally too costly to be used in practice. One reason of its bad behavior is that many intervals are considered as relevant by the checker of ER, although most of them should be ignored. In this paper, we provide a sharp characterization that allows to reduce the number of intervals by a factor seven. Our experiments show that associating this checker with a Time-Table filtering algorithm leads to promising results.Comment: CP Doctoral Program 2013, Uppsala : Sweden (2013

A New Characterization of Relevant Intervals for Energetic Reasoning

International audienceEnergetic Reasoning (ER) is a powerful filtering algorithm for the Cumulative constraint. Unfortunately, ER is generally too costly to be used in practice. One reason of its bad behavior is that many intervals are considered as relevant, although most of them should be ignored. In the literature, heuristic approaches have been developed in order to reduce the number of intervals to consider, leading to a loss of filtering. In this paper, we provide a sharp characterization that allows to reduce the number of intervals by a factor seven without loss of filtering

On graph-based reentrancy-free semantic parsing

We propose a novel graph-based approach for semantic parsing that resolves two problems observed in the literature: (1) seq2seq models fail on compositional generalization tasks; (2) previous work using phrase structure parsers cannot cover all the semantic parses observed in treebanks. We prove that both MAP inference and latent tag anchoring (required for weakly-supervised learning) are NP-hard problems. We propose two optimization algorithms based on constraint smoothing and conditional gradient to approximately solve these inference problems. Experimentally, our approach delivers state-of-the-art results on Geoquery, Scan and Clevr, both for i.i.d. splits and for splits that test for compositional generalization.Comment: This work has been accepted for publication in TACL. This version is a pre-MIT Press publication versio

Structural generalization in COGS: Supertagging is (almost) all you need

In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples. In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work, we extend a neural graph-based semantic parsing framework in several ways to alleviate this issue. Notably, we propose: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) a reduction of the graph prediction problem to the maximum matching problem; (3) the design of an incremental early-stopping training strategy to prevent overfitting. Experimentally, our approach significantly improves results on examples that require structural generalization in the COGS dataset, a known challenging benchmark for compositional generalization. Overall, our results confirm that structural constraints are important for generalization in semantic parsing.Comment: accepted at EMNLP 202

Transcatheter tricuspid valve implantation: A multicentre French study

SummaryBackgroundTranscatheter valve-in-valve (VIV) implantation in failing bioprosthesis is an emerging field in cardiology.AimTo report on a French multicentre experience and a literature review of tricuspid VIV implantation.MethodsWe approached different institutions and collected 10 unpublished cases; a literature review identified 71 patients, including our 10 cases. Clinical aspects and haemodynamic data are discussed.ResultsAmong our 10 unpublished cases, the reason for implantation was significant tricuspid stenosis (n=4), significant tricuspid regurgitation (n=1) or mixed lesion (n=5). Implantation was performed under general anaesthesia at mean age 28±17 years. The 22mm Melody valve was implanted in seven patients; the Edwards SAPIEN valve was implanted in three patients. The procedure succeeded in all cases, despite two embolizations in the right cardiac chambers; in both cases, the valve was stabilized close to the tricuspid annulus using a self-expandable stent, before implantation of a second Edwards SAPIEN valve. Functional class improved in all but one case. Mean diastolic gradient decreased from 9±2.45mmHg to 3.65±0.7mmHg (p=0.007); no more than trivial regurgitation was noticed. Among the published cases, the Melody valve was implanted in 41 patients, the Edwards SAPIEN valve in 29 patients and the Braile valve in one patient. Short-term results were similar for our 10 cases, but mid-term results are not yet available.ConclusionsTricuspid VIV implantation using the Melody or Edwards SAPIEN valves is a feasible and effective procedure for selected patients with failing bioprosthesis

Increased Muscle Stress-Sensitivity Induced by Selenoprotein N Inactivation in Mouse: A Mammalian Model for SEPN1-Related Myopathy

Selenium is an essential trace element and selenoprotein N (SelN) was the first selenium-containing protein shown to be directly involved in human inherited diseases. Mutations in the SEPN1 gene, encoding SelN, cause a group of muscular disorders characterized by predominant affection of axial muscles. SelN has been shown to participate in calcium and redox homeostasis, but its pathophysiological role in skeletal muscle remains largely unknown. To address SelN function in vivo, we generated a Sepn1-null mouse model by gene targeting. The Sepn1−/− mice had normal growth and lifespan, and were macroscopically indistinguishable from wild-type littermates. Only minor defects were observed in muscle morphology and contractile properties in SelN-deficient mice in basal conditions. However, when subjected to challenging physical exercise and stress conditions (forced swimming test), Sepn1−/− mice developed an obvious phenotype, characterized by limited motility and body rigidity during the swimming session, as well as a progressive curvature of the spine and predominant alteration of paravertebral muscles. This induced phenotype recapitulates the distribution of muscle involvement in patients with SEPN1-Related Myopathy, hence positioning this new animal model as a valuable tool to dissect the role of SelN in muscle function and to characterize the pathophysiological process

Auto-encodeurs variationnels : contrecarrer le problème de posterior collapse grâce à la régularisation du décodeur

International audienceLes auto-encodeurs variationnels sont des modèles génératifs utiles pour apprendre des représentations latentes. En pratique, lorsqu’ils sont supervisés pour des tâches de génération de textes, ils ont tendance à ignorer les variables latentes lors du décodage. Nous proposons une nouvelle méthode de régularisation fondée sur le dropout « fraternel » pour encourager l’utilisation de ces variables latentes. Nous évaluons notre approche sur plusieurs jeux de données et observons des améliorations dans toutes les configurations testées