Reading, Translating, Rewriting: Angela Carter's Translational Poetics

In "Reading, Translating, Rewriting: Angela Carter's Translational Poetics", author Martine Hennard Dutheil de la Rochère delves into Carter's The Fairy Tales of Charles Perrault (1977) to illustrate that this translation project had a significant impact on Carter's own writing practice. Hennard combines close analyses of both texts with an attention to Carter's active role in the translation and composition process to explore this previously unstudied aspect of Carter's work. She further uncovers the role of female fairy-tale writers and folktales associated with the Grimms' Kinder- und Hausmärchen in the rewriting process, unlocking new doors to The Bloody Chamber. Hennard begins by considering the editorial evolution of The Fairy Tales of Charles Perrault from 1977 to the present day, as Perrault's tales have been rediscovered and repurposed. In the chapters that follow, she examines specific linkages between Carter's Perrault translation and The Bloody Chamber, including targeted analysis of the stories of Red Riding Hood, Bluebeard, Puss-in-Boots, Beauty and the Beast, Sleeping Beauty, and Cinderella. Hennard demonstrates how, even before The Bloody Chamber, Carter intervened in the fairy-tale debate of the late 1970s by reclaiming Perrault for feminist readers when she discovered that the morals of his worldly tales lent themselves to her own materialist and feminist goals. Hennard argues that The Bloody Chamber can therefore be seen as the continuation of and counterpoint to The Fairy Tales of Charles Perrault, as it explores the potential of the familiar stories for alternative retellings. While the critical consensus reads into Carter an imperative to subvert classic fairy tales, the book shows that Carter valued in Perrault a practical educator as well as a proto-folklorist and went on to respond to more hidden aspects of his texts in her rewritings. Reading, Translating, Rewriting is informative reading for students and teachers of fairy-tale studies and translation studies.

Translating in the "Tongue of Perrault". The Reception of the "Kinder- und Hausmärchen" in France

To translate the Kinder- und Hausmärchen into French is to confront the spectre of Charles Perrault and his Histoires ou contes du temps passé. Avec des moralités, which have haunted the fairy-tale genre in France since the end of the 17th century. Celebrated for their alleged simplicity and naivety by literary critics and folklorists, Perrault's "contes" have become the paragon of a genre against which fairytales translated into French are implicitly? measured. On the one hand, Perrault has come to play an integrating role, linking foreign texts to the French literary heritage and thereby facilitating their reception. On the other hand, he is simultaneously used as a contrast, to emphasise the originality of foreign authors and emphasise cultural differences. Drawing on contemporary and 19th century examples emphasising the influence of the Histoires ou contes du temps passé on French translations of the KHM, I will show that the Grimms' fairy-tales are translated less in the "tongue of Molière" than in the "tongue of Perrault".

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Phototropism: Translating light into directional growth.

Phototropism allows plants to align their photosynthetic tissues with incoming light. The direction of incident light is sensed by the phototropin family of blue light photoreceptors (phot1 and phot2 in Arabidopsis), which are light-activated protein kinases. The kinase activity of phototropins and phosphorylation of residues in the activation loop of their kinase domains are essential for the phototropic response. These initial steps trigger the formation of the auxin gradient across the hypocotyl that leads to asymmetric growth. The molecular events between photoreceptor activation and the growth response are only starting to be elucidated. In this review, we discuss the major steps leading from light perception to directional growth concentrating on Arabidopsis. In addition, we highlight links that connect these different steps enabling the phototropic response.

Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.