Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A flow-dependent estimation of constriction size distribution in gap-graded soils: an integrated statistical approach

The clogging-unclogging process in gap-graded soils is a result of the migration of seepage-driven fines, which subsequently induces measurable changes in the local hydraulic gradients. This process can be temporally observed in the variations of Darcy's hydraulic conductivity (K). The current study proposes an integrated statistical Monte Carlo approach combining the discrete element method and 2D computational fluid dynamics simulations to estimate the flow-dependent constriction size distribution (CSD) for a gap-graded soil. The computational inferences were supported with experimental results using an internally stable soil, which was subjected to one-dimensional flow stimulating desired hydraulic loadings: a hydraulic gradient lower than the critical gradient applied as a multi-staged loading pattern. The 35th percentile size of the flow-dependent CSD (Dc35) for both internally stable and unstable gap-graded soils becomes approximately equal to Dc35 at steady-state. However, a greater variation of larger constrictions persists for the unstable soils. This pilot study has shown the applicability of the proposed method to estimate flow-dependent CSD for a wide range of experimentally observed K values.

Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

Development of novel clinical examination scales for the measurement of disease severity in Creutzfeldt-Jakob disease

ObjectiveTo use a robust statistical methodology to develop and validate clinical rating scales quantifying longitudinal motor and cognitive dysfunction in sporadic Creutzfeldt-Jakob disease (sCJD) at the bedside.MethodsRasch analysis was used to iteratively construct interval scales measuring composite cognitive and motor dysfunction from pooled bedside neurocognitive examinations collected as part of the prospective National Prion Monitoring Cohort study, October 2008–December 2016.A longitudinal clinical examination dataset constructed from 528 patients with sCJD, comprising 1030 Motor Scale and 757 Cognitive Scale scores over 130 patient-years of study, was used to demonstrate scale utility.ResultsThe Rasch-derived Motor Scale consists of 8 items, including assessments reliant on pyramidal, extrapyramidal and cerebellar systems. The Cognitive Scale comprises 6 items, and includes measures of executive function, language, visual perception and memory. Both scales are unidimensional, perform independently of age or gender and have excellent inter-rater reliability. They can be completed in minutes at the bedside, as part of a normal neurocognitive examination. A composite Examination Scale can be derived by averaging both scores. Several scale uses, in measuring longitudinal change, prognosis and phenotypic heterogeneity are illustrated.ConclusionsThese two novel sCJD Motor and Cognitive Scales and the composite Examination Scale should prove useful to objectively measure phenotypic and clinical change in future clinical trials and for patient stratification. This statistical approach can help to overcome obstacles to assessing clinical change in rapidly progressive, multisystem conditions with limited longitudinal follow-up.

The rise of grasslands is linked to atmospheric CO2 decline in the late Palaeogene

Grasslands are predicted to experience a major biodiversity change by the year 2100. A better understanding of how grasslands have responded to past environmental changes will help predict the outcome of current and future environmental changes. Here, we explore the relationship between past atmospheric CO2 and temperature fluctuations and the shifts in diversification rate of Poaceae (grasses) and Asteraceae (daisies), two exceptionally species-rich grassland families (~11,000 and ~23,000 species, respectively). To this end, we develop a Bayesian approach that simultaneously estimates diversification rates through time from time-calibrated phylogenies and correlations between environmental variables and diversification rates. Additionally, we present a statistical approach that incorporates the information of the distribution of missing species in the phylogeny. We find strong evidence supporting a simultaneous increase in diversification rates for grasses and daisies after the most significant reduction of atmospheric CO2 in the Cenozoic (~34 Mya). The fluctuations of paleo-temperatures, however, appear not to have had a significant relationship with the diversification of these grassland families. Overall, our results shed new light on our understanding of the origin of grasslands in the context of past environmental changes.