Migration, trapping, and venting of gas in a soft granular material

Gas migration through a soft granular material involves a strong coupling between the motion of the gas and the deformation of the material. This process is relevant to a variety of natural phenomena, such as gas venting from sediments and gas exsolution from magma. Here, we study this process experimentally by injecting air into a quasi-2D packing of soft particles and measuring the morphology of the air as it invades and then rises due to buoyancy. We systematically increase the confining pre-stress in the packing by compressing it with a fluid-permeable piston, leading to a gradual transition in migration regime from fluidization to pathway opening to pore invasion. We find that mixed migration regimes emerge at intermediate confinement due to the spontaneous formation of a compaction layer at the top of the flow cell. By connecting these migration mechanisms with macroscopic invasion, trapping, and venting, we show that mixed regimes enable a sharp increase in the average amount of gas trapped within the packing, as well as much larger venting events. Our results suggest that the relationship between invasion, trapping, and venting could be controlled by modulating the confining stress

Learning to Skim Text

Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. We employ a standard policy gradient method to train the model to make discrete jumping decisions. In our benchmarks on four different tasks, including number prediction, sentiment analysis, news article classification and automatic Q\&A, our proposed model, a modified LSTM with jumping, is up to 6 times faster than the standard sequential LSTM, while maintaining the same or even better accuracy

Maximum Score Estimation of Preference Parameters for a Binary Choice Model under Uncertainty

This paper develops maximum score estimation of preference parameters in the binary choice model under uncertainty in which the decision rule is affected by conditional expectations. The preference parameters are estimated in two stages: we estimate conditional expectations nonparametrically in the first stage and then the preference parameters in the second stage based on Manski (1975, 1985)'s maximum score estimator using the choice data and first stage estimates. The paper establishes consistency and derives rate of convergence of the two-stage maximum score estimator. Moreover, the paper also provides sufficient conditions under which the two-stage estimator is asymptotically equivalent in distribution to the corresponding single-stage estimator that assumes the first stage input is known. These results are of independent interest for maximum score estimation with nonparametrically generated regressors. The paper also presents some Monte Carlo simulation results for finite-sample behavior of the two-stage estimator

Energy transfer from baryons to dark matter as a unified solution to small-scale structure issues of the Λ\LambdaCDM model

Using a semianalytic code, we show how baryon physics in a $\Lambda$CDM cosmology could solve the discrepancy between numerical predictions of dark matter haloes and observations, ranging from dwarf galaxies to clusters, without the need of nonstandard dark matter models as advocated, for example, by [Kaplinghat et al., Phys. Rev. Lett. 116, 041302, (2016)]. Combining well established results, we show, for the first time, how accounting for baryon physics, in particular dynamical friction mechanisms, leads to flat galaxy-cluster profiles and correlations in several of their properties, solves the so-called `diversity problem' and reproduces very well the challenging, extremely low-rising rotation curve of IC2574. We therefore suggest treating baryonic physics properly before introducing new exotic features, albeit legitimate, in the standard cosmological model.Comment: 10 pages, 4 figures, matching the accepted version on Phys. Rev.