We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity embedding and linking module that identifies entity mentions while generalizing across tables. We also i...

Increasing access to large-scale, high-dimensional and non-stationary streams in many real applications has made it necessary to design new dynamic classification algorithms. Most existing approaches for the textual stream classification are able to train the model relying on labeled data. However, only a limited number of instances can be labeled in a real streaming environment since large-sca...

A semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces is presented. Based on existing scattering models and data sets measured by polarimetric scatterometers and the JPL AirSAR, the parameters of the co-polarized phase-difference probability density function, namely the degree of correlation and the co-polarized phase-di...

Built-in sensors in most modern smartphones open multiple opportunities for novel context-aware applications. Although the Human Activity Recognition field seized such opportunity, many challenges are yet to be addressed, such as the differences in movement by people doing the same activities. This paper exposes empirical research on Online Semi-supervised Learning (OSSL), an under-explored inc...

A semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces is presented. Based on existing scattering models and data sets measured by polarimetric scatterometers and the JPL AirSAR, the parameters of the co-polarized phase-difference probability density function, namely the degree of correlation and the co-polarized-phase-di...

A “semi-probabilistic” alignment algorithm which combines ideas from Smith-Waterman and probabilistic alignment is proposed and studied in detail. It is predicted that the score statistics of this “hybrid” algorithm is of the universal Gumbel form, with the key Gumbel parameter λ taking on a fixed asymptotic value for a wide variety of scoring parameters. We have also characterized the “extrema...

Résumé. Nous nous intéressons à l’extraction d’entités nommées avec comme but d’exploiter un ensemble de rapports pour en extraire une liste de partenaires. À partir d’une liste initiale, nous utilisons un premier ensemble de documents pour identifier des schémas de phrase qui sont ensuite validés par apprentissage supervisé sur des documents annotés pour en mesurer l’efficacité avant d’être ut...

A coordinate-free Lie-group formulation for generating ensembles of DNA conformations in solution is presented. In this formulation, stochastic differential equations define sample paths on the Euclidean motion group. The ensemble of these paths exhibits the same behavior as solutions of the Fokker-Planck equation for the stochastically forced elastica. Longer chains for which the effects of ex...

The Monte Carlo (MC) method for simulation of semi-classical transport in semiconductors evolved for more than three decades to meet the challenges in the research and development of new semiconductor devices. The main MC algorithms used to date were initially devised from merely physical considerations, viewing the method as a direct emulation of the elementary events underlying the transport ...

The prediction of the small-scale spatial-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic downscaling models to generate ensemble rainfall predictions to be used as inputs to rainfall-runoff model...