Viscous stabilization of 2D drainage displacements with trapping

We investigate the stabilization mechanisms due to viscous forces in the invasion front during drainage displacement in two-dimensional porous media using a network simulator. We find that in horizontal displacement the capillary pressure difference between two different points along the front varies almost linearly as function of height separation in the direction of the displacement. The numerical result supports arguments taking into account the loopless displacement pattern where nonwetting fluid flow in separate strands (paths). As a consequence, we show that existing theories developed for viscous stabilization, are not compatible with drainage when loopless strands dominate the displacement process.Comment: The manuscript has been substantially revised. Accepted in Phys. Rev. Let

Borders, Ethnicity And Trade

This paper uses unique high-frequency data on prices of two agricultural goods to examine the additional costs incurred in cross-border trade between Niger and Nigeria, as well as trade between ethnically distinct markets within Niger. We find a sharp and significant conditional price change of about 20 to 25% between markets immediately across the national border. This price change is significantly lower when markets on either side of the border share a common ethnicity. Within Niger, trade between ethnically distinct regions exhibits an ethnic border effect that is comparable, in its magnitude, to the national border effect between Niger and Nigeria. Our results suggest that having a common ethnicity may reduce the transaction costs associated with agricultural trade, especially the costs associated with communicating and providing credit. (C) 2013 Elsevier B.V. All rights reserved

Analysis methods for the first KATRIN neutrino-mass measurement

We report on the dataset, data handling, and detailed analysis techniques of the first neutrino-mass measurement by the Karlsruhe Tritium Neutrino (KATRIN) experiment, which probes the absolute neutrino-mass scale via the β-decay kinematics of molecular tritium. The source is highly pure, cryogenic T2 gas. The β electrons are guided along magnetic field lines toward a high-resolution, integrating spectrometer for energy analysis. A silicon detector counts β electrons above the energy threshold of the spectrometer, so that a scan of the thresholds produces a precise measurement of the high-energy spectral tail. After detailed theoretical studies, simulations, and commissioning measurements, extending from the molecular final-state distribution to inelastic scattering in the source to subtleties of the electromagnetic fields, our independent, blind analyses allow us to set an upper limit of 1.1 eV on the neutrino-mass scale at a 90% confidence level. This first result, based on a few weeks of running at a reduced source intensity and dominated by statistical uncertainty, improves on prior limits by nearly a factor of two. This result establishes an analysis framework for future KATRIN measurements, and provides important input to both particle theory and cosmolog

Automatic Label Generation for News Comment Clusters

We present a supervised approach to automat- ically labelling topic clusters of reader com- ments to online news. We use a feature set that includes both features capturing proper- ties local to the cluster and features that cap- ture aspects from the news article and from comments outside the cluster. We evaluate the approach in an automatic and a manual, task-based setting. Both evaluations show the approach to outperform a baseline method, which uses tf*idf to select comment-internal terms for use as topic labels. We illustrate how cluster labels can be used to generate cluster summaries and present two alternative sum- mary formats: a pie chart summary and an ab- stractive summary

Automatic Label Generation for News Comment Clusters

We present a supervised approach to automat- ically labelling topic clusters of reader com- ments to online news. We use a feature set that includes both features capturing proper- ties local to the cluster and features that cap- ture aspects from the news article and from comments outside the cluster. We evaluate the approach in an automatic and a manual, task-based setting. Both evaluations show the approach to outperform a baseline method, which uses tf*idf to select comment-internal terms for use as topic labels. We illustrate how cluster labels can be used to generate cluster summaries and present two alternative sum- mary formats: a pie chart summary and an ab- stractive summary

A Graph-Based Approach to Topic Clustering for Online Comments to News

This paper investigates graph-based approaches to labeled topic clustering of reader comments in online news. For graph-based clustering we propose a linear regression model of similarity between the graph nodes (comments) based on similarity features and weights trained using automatically derived training data. To label the clusters our graph-based approach makes use of DBPedia to abstract topics extracted from the clusters. We evaluate the clustering approach against gold standard data created by human annotators and compare its results against LDA – currently reported as the best method for the news comment clustering task. Evaluation of cluster labelling is set up as a retrieval task, where human annotators are asked to identify the best cluster given a cluster label. Our clustering approach significantly outperforms the LDA baseline and our evaluation of abstract cluster labels shows that graph-based approaches are a promising method of creating labeled clusters of news comments, although we still find cases where the automatically generated abstractive labels are insufficient to allow humans to correctly associate a label with its cluster

The SENSEI Overview of Newspaper Readers’ Comments

Automatic summarization of reader comments in on-line news is a challenging but clearly useful task. Work to date has produced extractive summaries using well-known techniques from other areas of NLP. But do users really want these, and do they support users in realistic tasks? We specify an alternative summary type for reader comments, based on the notions of issues and viewpoints, and demonstrate our user interface to present it. An evaluation to assess how well summarization systems support users in time-limited tasks (identifying issues and characterizing opinions) gives good results for this prototype

The SENSEI Annotated Corpus: Human Summaries of Reader Comment Conversations in On-line News

Researchers are beginning to explore how to generate summaries of extended argumentative conversations in social media, such as those found in reader comments in on-line news. To date, however, there has been little discussion of what these summaries should be like and a lack of humanauthored exemplars, quite likely because writing summaries of this kind of interchange is so difficult. In this paper we propose one type of reader comment summary – the conversation overview summary – that aims to capture the key argumentative content of a reader comment conversation. We describe a method we have developed to support humans in authoring conversation overview summaries and present a publicly available corpus – the first of its kind – of news articles plus comment sets, each multiply annotated, according to our method, with conversation overview summaries