A generic approach for desining on-line handwritten shapes recognizers

This paper presents a generic approach for designing on-line handwritten shapes recognizers. Our approach allows designing very different recognition engines that correspond to various needs in pen-based interfaces. In particular, it allows dealing with a wide class of symbols and characters. We present in detail our system and make the link between our models and more standard statistical models such as Hierarchical Hidden Markov Models and Dynamic Bayesian Networks. We then evaluate fundamental properties of our approach: learning from scratch any symbol, learning from very few training sample. We show experimentally that, using our approach, one can learn both a state-of-the-art writerindependent recognizer for alphanumeric characters, and a writer-dependent recognizer working with any twodimensional shapes that learns a new symbol with a few training samples and requires very few machines resources.Dans ce papier, nous présentons une approche générique pour le développement de moteurs de reconnaissance de symboles manuscrits en ligne. Cette approche permet de concevoir des systèmes de reconnaissance de types très variés correspondant à différents contextes des interfaces stylo, pouvant notamment fonctionner sur diverses classes de caractères ou symboles. Nous présentons en détail notre approche et faisons le lien avec d’une part les modèles de Markov hiérarchiques et d’autre part les réseaux bayésiens dynamiques. Nous évaluons ensuite les propriétés fondamentales de notre approche qui lui confèrent une grande flexibilité. Puis nous montrons que l’on peut, avec cette approche générique, concevoir aussi bien des systèmes omni-scripteur rivalisant avec les meilleurs systèmes actuels sur des caractères alphanumériques usuels, que des systèmes mono-scripteur pour des symboles graphiques quelconques, nécessitant très peu d’exemples d’apprentissage et peu gourmands en ressources machine

A hybrid nonlinear-discriminant analysis feature projection technique

Feature set dimensionality reduction via Discriminant Analysis (DA) is one of the most sought after approaches in many applications. In this paper, a novel nonlinear DA technique is presented based on a hybrid of Artificial Neural Networks (ANN) and the Uncorrelated Linear Discriminant Analysis (ULDA). Although dimensionality reduction via ULDA can present a set of statistically uncorrelated features, but similar to the existing DA's it assumes that the original data set is linearly separable, which is not the case with most real world problems. In order to overcome this problem, a one layer feed-forward ANN trained with a Differential Evolution (DE) optimization technique is combined with ULDA to implement a nonlinear feature projection technique. This combination acts as nonlinear discriminant analysis. The proposed approach is validated on a Brain Computer Interface (BCI) problem and compared with other techniques. © 2008 Springer Berlin Heidelberg

Role of small Rhizaria and diatoms in the pelagic silica production of the Sourther Ocean

We examined biogenic silica production and elementary composition (biogenic Si, particulate organic carbon and particulate organic nitrogen) of Rhizaria and diatoms in the upper 200 m along a transect in the Southwest Pacific sector of the Southern Ocean during austral summer (January–February 2019). From incubations using the 32Si radioisotope, silicic acid uptake rates were measured at 15 stations distributed in the Polar Front Zone, the Southern Antarctic Circumpolar Current and the Ross Sea Gyre. Rhizaria cells are heavily silicified (up to 7.6 nmol Si cell−1), displaying higher biogenic Si content than similar size specimens found in other areas of the global ocean, suggesting a higher degree of silicification of these organisms in the silicic acid rich Southern Ocean. Despite their high biogenic Si and carbon content, the Si/C molar ratio (average of 0.05 ± 0.03) is quite low compared to that of diatoms and relatively constant regardless of the environmental conditions. The direct measurements of Rhizaria's biogenic Si production (0.8–36.8 μmol Si m−2 d−1) are of the same order of magnitude than previous indirect estimations, confirming the importance of the Southern Ocean for the global Rhizaria silica production. However, diatoms largely dominated the biogenic Si standing stock and production of the euphotic layer, with low rhizarians' abundances and biogenic Si production (no more than 1%). In this manuscript, we discuss the Antarctic paradox of Rhizaria, that is, the potential high accumulation rates of biogenic Si due to Rhizaria in siliceous sediments despite their low production rates in surface waters.Versión del editor3,38