Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback

Distributed Generation and Resilience in Power Grids

We study the effects of the allocation of distributed generation on the resilience of power grids. We find that an unconstrained allocation and growth of the distributed generation can drive a power grid beyond its design parameters. In order to overcome such a problem, we propose a topological algorithm derived from the field of Complex Networks to allocate distributed generation sources in an existing power grid.Comment: proceedings of Critis 2012 http://critis12.hig.no

Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma

OBJECTIVE: We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique. METHODS AND RESULTS: The multilayer perceptron (MLP) analysis was performed in the GSE10846 series, divided into discovery (n = 100) and validation (n = 414) sets. The top 25 gene-probes from a total of 54,614 were selected based on their normalized importance for outcome prediction (dead/alive). By Gene Set Enrichment Analysis (GSEA) the association to unfavorable prognosis was confirmed. In the validation set, by univariate Cox regression analysis, high expression of ARHGAP19, MESD, WDCP, DIP2A, CACNA1B, TNFAIP8, POLR3H, ENO3, SERPINB8, SZRD1, KIF23 and GGA3 associated to poor, and high SFTPC, ZSCAN12, LPXN and METTL21A to favorable outcome. A multivariate analysis confirmed MESD, TNFAIP8 and ENO3 as risk factors and ZSCAN12 and LPXN as protective factors. Using a risk score formula, the 25 genes identified two groups of patients with different survival that was independent to the cell-of-origin molecular classification (5-year OS, low vs. high risk): 65% vs. 24%, respectively (Hazard Risk = 3.2, P < 0.000001). Finally, correlation with known DLBCL markers showed that high expression of all MYC, BCL2 and ENO3 associated to the worst outcome. CONCLUSION: By artificial intelligence we identified a set of genes with prognostic relevance

Kinetic description of avalanching systems

Avalanching systems are treated analytically using the renormalization group (in the self-organized-criticality regime) or mean-field approximation, respectively. The latter describes the state in terms of the mean number of active and passive sites, without addressing the inhomogeneity in their distribution. This paper goes one step further by proposing a kinetic description of avalanching systems making use of the distribution function for clusters of active sites. We illustrate application of the kinetic formalism to a model proposed for the description of the avalanching processes in the reconnecting current sheet of the Earth magnetosphere.Comment: 9 page

Seven years of marine environmental changes monitoring at coastal OOCS stations (Catalan Sea, NW Mediterranean)

Since March 2009 up to the present (more than 7 years now), the Operational Observatory of the Catalan Sea (OOCS; http://www2.ceab.csic.es/ oceans/) remains a witness of persistent marine environmental changes. The OOCS has two fixed observation stations at the head of the Blanes Canyon (200 m depth, 41.66°N; 2.91°E) and at the Blanes bay (20 m depth, 41.67°N; 2.80°E) in the Catalan Sea, NW Mediterranean. At the canyon station, a multi-parametric buoy presently installed delivers high frequency (by 30 min) and multi-parametric oceanographic (i.e. salinity, temperature, chlorophyll, turbidity, as well as light intensity in the PAR range for the upper 50 m depth) and atmospheric (air temperature, relative humidity, wind speed and direction and PAR) data. Subsurface photos and videos by an IP high resolution fisheye camera attached to the buoy are also delivered at 4-hour basis. Data and multimedia are transmitted in near real time for public access, via combined GSM/GPRS and 3G connections. At both stations, CTD profiles and water samples (collected for nutrients and picoplankton analyses) are carried out on board a research vessel at fortnightly basis. Numerical simulations along with the time series of in-situ observations show inter-annual seasonality anomalies possibly linked to global environmental changes. The lower-atmosphere and upper-sea environmental time series data collected prove the occurrence of shifting patterns of heat and matter fluxes impacting pelagic and benthic organisms.Peer Reviewe