270,104 research outputs found
Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic Text Summarization
In Automatic Text Summarization, preprocessing is an important phase to
reduce the space of textual representation. Classically, stemming and
lemmatization have been widely used for normalizing words. However, even using
normalization on large texts, the curse of dimensionality can disturb the
performance of summarizers. This paper describes a new method for normalization
of words to further reduce the space of representation. We propose to reduce
each word to its initial letters, as a form of Ultra-stemming. The results show
that Ultra-stemming not only preserve the content of summaries produced by this
representation, but often the performances of the systems can be dramatically
improved. Summaries on trilingual corpora were evaluated automatically with
Fresa. Results confirm an increase in the performance, regardless of summarizer
system used.Comment: 22 pages, 12 figures, 9 table
Asymmetric spatial processing under cognitive load
Spatial attention allows us to selectively process information within a certain location in space. Despite the vast literature on spatial attention, the effect of cognitive load on spatial processing is still not fully understood. In this study we added cognitive load to a spatial processing task, so as to see whether it would differentially impact upon the processing of visual information in the left versus the right hemispace. The main paradigm consisted of a detection task that was performed during the maintenance interval of a verbal working memory task. We found that increasing cognitive working memory load had a more negative impact on detecting targets presented on the left side compared to those on the right side. The strength of the load effect correlated with the strength of the interaction on an individual level. The implications of an asymmetric attentional bias with a relative disadvantage for the left (vs the right) hemispace under high verbal working memory (WM) load are discussed
Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey
Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Interactions, structure and properties in poly(lactic acid)/thermoplastic polymer blends
Blends were prepared from poly(lactic acid) (PLA) and three thermoplastics, polystyrene (PS), polycarbonate (PC) and poly(methyl methacrylate) (PMMA). Rheological and mechanical properties, structure and component interactions were determined by various methods. The results showed that the structure and properties of the blends cover a relatively wide range. All three blends have heterogeneous structure, but the size of the dispersed particles differs by an order of magnitude indicating dissimilar interactions for the corresponding pairs. Properties change accordingly, the blend containing the smallest dispersed particles has the largest tensile strength, while PLA/PS blends with the coarsest structure have the smallest. The latter blends are also very brittle. Component interactions were estimated by four different methods, the determination of the size of the dispersed particles, the calculation of the Flory-Huggins interaction parameter from solvent absorption, from solubility parameters, and by the quantitative evaluation of the composition dependence of tensile strength. All approaches led to the same result indicating strong interaction for the PLA/PMMA pair and weak for PLA and PS. A general correlation was established between interactions and the mechanical properties of the blends
Classical light vs. nonclassical light: Characterizations and interesting applications
We briefly review the ideas that have shaped modern optics and have led to
various applications of light ranging from spectroscopy to astrophysics, and
street lights to quantum communication. The review is primarily focused on the
modern applications of classical light and nonclassical light. Specific
attention has been given to the applications of squeezed, antibunched, and
entangled states of radiation field. Applications of Fock states (especially
single photon states) in the field of quantum communication are also discussed.Comment: 32 pages, 3 figures, a review on applications of ligh
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets
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