Evaluation of Linguistic Features for Word Sense Disambiguation with Self-Organized Document Maps

Word sense disambiguation automatically determines the appropriate senses of a word in context. We have previously shown that self-organized document maps have properties similar to a large-scale semantic structure that is useful for word sense disambiguation. This work evaluates the impact of different linguistic features on self-organized document maps for word sense disambiguation. The features evaluated are various qualitative features, e.g. part-of-speech and syntactic labels, and quantitative features, e.g. cut-off levels for word frequency. It is shown that linguistic features help make contextual information explicit. If the training corpus is large even contextually weak features, such as base forms, will act in concert to produce sense distinctions in a statistically significant way. However, the most important features are syntactic dependency relations and base forms annotated with part of speech or syntactic labels. We achieve 62.9% ± 0.73% correct results on the fine grained lexical task of the English SENSEVAL-2 data. On the 96.7% of the test cases which need no back-off to the most frequent sense we achieve 65.7% correct results.     

Parallelization of a multi-blocked CFD code via three strategies for fluid flow and heat transfer analysis

This paper reports on a parallel implementation of a general 3D multi-block CFD code. The parallelization is achieved by using three strategies. Firstly, it is done on dual-processor PC-clusters where Windows NT systems are running. A multi-thread programming model is adopted for the multi-block code, where one thread corresponds to a block. Shared-memory is used for the exchange of inner-boundaries between neighboring blocks (threads) on the same node, while WinSockets are employed for those on different nodes. Secondly, the parallelization is extended to UNIX operating system. MPI is applied for all the message passing between different processors, including those on the same node. Thirdly, Pthreads (POSIX threads), a standardized application interface for threads, are adopted to take the advantage of the shared-memory feature of the SMP nodes, while MPI is only applied for the message passing between processors on different nodes. In all the strategies, a static load-balancing method is employed for equitable distribution of computational work to specified nodes. The parameters of the present code is studied in detail to facilitate the explanation of the speedup results. Two examples are provided to show the speedup and load balancing of the parallel calculation. Detailed comparison is made to evaluate the efficiency of different strategies.     

Academic trajectories of newcomer immigrant youth.

Immigration to the United States presents both challenges and opportunities that affect students' academic achievement. Using a 5-year longitudinal, mixed-methods approach, we identified varying academic trajectories of newcomer immigrant students from Central America, China, the Dominican Republic, Haiti, and Mexico. Latent class growth curve analysis revealed that although some newcomer students performed at high or improving levels over time, others showed diminishing performance. Multinomial logistic regressions identified significant group differences in academic trajectories, particularly between the high-achieving youth and the other groups. In keeping with ecological–developmental and stage–environment fit theories, School Characteristics (school segregation rate, school poverty rate, and student perceptions of school violence), Family Characteristics (maternal education, parental employment, and household structure), and Individual Characteristics (academic English proficiency, academic engagement, psychological symptoms, gender, and 2 age-related risk factors, number of school transitions and being overaged for grade placement) were associated with different trajectories of academic performance. A series of case studies triangulate many of the quantitative findings as well as illuminate patterns that were not detected in the quantitative data. Thus, the mixed-methods approach sheds light on the cumulative developmental challenges that immigrant students face as they adjust to their new educational settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Microwave radiation as heating method in the synthesis of titanium dioxide nanoparticles from hexafluorotitanate-organic salts

Nanocrystalline anatase was obtained from ionic liquid-like precursors containing hexafluorotitanate-organic salts and less than 25 wt.% of water, and using boric acid as fluoride scavenger. Two alternative heating methods were explored using either a conventional oven or a domestic microwave apparatus. A significant reduction in the reaction time from 24 h to only few minutes was obtained using the microwave route. The as-prepared materials were characterized by X-ray diffraction, transmission electron microscopy, nitrogen sorption analysis, and attenuated total reflectance Fourier transformed infrared, X-ray photoelectronic and Raman spectroscopes. The convenience of using the microwave heating option was a function of the organic cation present in the precursor. Thus, organic ammonium cations containing only hydrocarbon substituents, such as diethylammonium, phenylammonium and benzyltrimethylammonium led to the precipitation of nanocrystalline anatase powder with high specific surface area (up to 120 m² g⁻¹) in a short processing time (1-3 min). Otherwise, alcohol and carboxylate functionalized cations decomposed under microwave treatment. Moreover, the choice of the organic cation allowed tuning several properties of the end material, such as particle size and pore morphology.     

Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.     

Prediction of Losses in Flow Through the Last Stage of LP Steam Turbine

The aim of presented work was the prediction of the losses in the wet steam flow through the last stage of 200MW steam turbine LP part.To this end,three numerical tools were used.The first method was the streamline curvature method (SCM) used on the meridional plane with losses correlations.The next two methods,TASCflow commercial CFD code and an in-house CFD code,based on the solution of the Reynolds- averaged Navier-Stokes equations (RANS).Ap- plication of three independent numerical tools al- lowed to make the more reliable losses analysis and made possible to compare applied numerical methods with each other. For the flow modeling in the last stage of LP steam turbine the various two-phase flow models were used and compared.The equilibrium model and non-equi- librium models with homogeneous and/or heterogeneous condensation were considered.The boundary conditions at the inlet and outlet from the stage were selected in such way to get the beginning of the homogeneous condensation process in the sta- tot.It corresponded to the part load of the turbine, i.e.140MW power and pressure in condenser 2.7kPa.     

A semantic role labelling-based framework for learning ontologies from Spanish documents

Currently, most of the information available in the Web is adapted primarily for human consumption, but there is so much information that can no longer be processed by a person in a reasonable time, either in digital or physical formats. To solve this problem, the idea of the Semantic Web arose. The Semantic Web deals with adding machine-readable information to Web pages. Ontologies represent a very important element of this web, as they provide a valid and robust structure to represent knowledge based on concepts, relations, axioms, etc. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semiautomatic methods to learn ontologies. In this sense, this paper proposes a new ontology learning methodology based on semantic role labeling from digital Spanish documents. The method makes it possible to represent multiple semantic relations specially taxonomic and partonomic ones in the standardized OWL 2.0. A set of experiments has been performed with the approach implemented in educational domain that show promising results.     

Efficient implementation of the implicit error correction nVoD schema

Implicit Error Correction is a near Video-on-Demand (nVoD) schema that trades bandwidth utilization for initial playback delay while potentially allowing an infinite number of users. Additionally, it provides error protection without further bandwidth increase by exploiting the implicit redundancy of nVoD protocols, using linear combinations of the segments transmitted in a given time slot. This paper introduces a new implementation that outperforms the original schema by adding two new mechanisms: redundancy channels and feedback error correction.     

Emptiness and Finiteness for Tree Automata with Global Reflexive Disequality Constraints

In recent years, several extensions of tree automata have been considered. Most of them are related with the capability of testing equality or disequality of certain subterms of the term evaluated by the automaton. In particular, tree automata with global constraints are able to test equality and disequality of subterms depending on the state to which they are evaluated. The emptiness problem is known decidable for this kind of automata, but with a non-elementary time complexity, and the finiteness problem remains unknown. In this paper, we consider the particular case of tree automata with global constraints when the constraint is a conjunction of disequalities between states, and the disequality predicate is forced to be reflexive. This restriction is significant in the context of XML definitions with monadic key constraints. We prove that emptiness and finiteness are decidable in triple exponential time for this kind of automata.     

Applicability assessment of a real-time PCR assay for the specific detection of bovine,ovine and caprine material in feedstuffs

A TaqMan real-time polymerase chain reaction (PCR) method was developed for specific detection of bovine, ovine and caprine processed animal protein (PAP) in industrial feedstuffs. The method uses species-specific primers and probes targeting short mitochondrial D-loop sequences, and a positive amplification control based on 18S rRNA gene. The applicability of the real-time PCR protocol was assessed through analysis of 126 industrial feed samples that were manufactured to reproduce rendering processes of commercial feeds destined for farmed animals. The assay successfully classified samples as positive or negative according to the ruminant composition, enabling qualitative detection of banned material in feeds at levels as low as 0.1%. Although the method provides quantitative potential, results suggest that the real quantitative capability of the assay is limited by the existing variability in terms of composition and processing treatments of the feeds, which affect the amount and quality of amplifiable DNA.