abcOD: Mining Band Order Dependencies

We present the design of and a demonstration plan for abcOD, a tool for efficiently discovering approximate band conditional order dependencies (abcODs) from data. abcOD utilizes a dynamic programming algorithm based on a longest monotonic band. Using real datasets, we demonstrate how the discovered abcODs can help users understand ordered data semantics, identify potential data quality problems, and interactively clean the data

Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants

Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

Extending FuzAtAnalyzer to approach the management of classical negation

FuzAtAnalyzer was conceived as a Java framework which goes beyond of classical tools in formal concept analysis. Specifically, it successfully incorporated the management of uncertainty by means of methods and tools from the area of fuzzy formal concept analysis. One limitation of formal concept analysis is that they only consider the presence of properties in the objects (positive attributes) as much in fuzzy as in crisp case. In this paper, a first step in the incorporation of negations is presented. Our aim is the treatment of the absence of properties (negative attributes). Specifically, we extend the framework by including specific tools for mining knowledge combining crisp positive and negative attributes.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Tomonaga-Luttinger features in the resonant Raman spectra of quantum wires

The differential cross section for resonant Raman scattering from the collective modes in a one dimensional system of interacting electrons is calculated non-perturbatively using the bosonization method. The results indicate that resonant Raman spectroscopy is a powerful tool for studying Tomonaga-Luttinger liquid behaviour in quasi-one dimensional electron systems.Comment: 4 pages, no figur

Large-Scale information extraction from textual definitions through deep syntactic and semantic analysis

We present DEFIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DEFIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations

QCD effects in mono-jet searches for dark matter

LHC searches for missing transverse energy in association with a jet allow to place strong bounds on the interactions between dark matter and quarks. In this article, we present an extension of the POWHEG BOX capable of calculating the underlying cross sections at the next-to-leading order level. This approach enables us to consistently include the effects of parton showering and to apply realistic experimental cuts. We find significant differences from a fixed-order analysis that neglects parton showering effects. In particular, next-to-leading order corrections do not lead to a significant enhancement of the mono-jet cross section once a veto on additional jets is imposed. Nevertheless, these corrections reduce the theoretical uncertainties of the signal prediction and therefore improve the reliability of the derived bounds. We present our results in terms of simple rescaling factors, which can be directly applied to existing experimental analyses and discuss the impact of changing experimental cuts.Comment: Added KITP affiliations, fixed 2 very minor typos, matches version published in JHE