The ATIS sign language corpus

Systems that automatically process sign language rely on appropriate data. We therefore present the ATIS sign language corpus that is based on the domain of air travel information. It is available for five languages, English, German, Irish sign language, German sign language and South African sign language. The corpus can be used for different tasks like automatic statistical translation and automatic sign language recognition and it allows the specific modelling of spatial references in signing space

Reduction of the sign problem using the meron-cluster approach

The sign problem in quantum Monte Carlo calculations is analyzed using the meron-cluster solution. The concept of merons can be used to solve the sign problem for a limited class of models. Here we show that the method can be used to \textit{reduce} the sign problem in a wider class of models. We investigate how the meron solution evolves between a point in parameter space where it eliminates the sign problem and a point where it does not affect the sign problem at all. In this intermediate regime the merons can be used to reduce the sign problem. The average sign still decreases exponentially with system size and inverse temperature but with a different prefactor. The sign exhibits the slowest decrease in the vicinity of points where the meron-cluster solution eliminates the sign problem. We have used stochastic series expansion quantum Monte Carlo combined with the concept of directed loops.Comment: 8 pages, 9 figure

Spatial Sign Correlation

A new robust correlation estimator based on the spatial sign covariance matrix (SSCM) is proposed. We derive its asymptotic distribution and influence function at elliptical distributions. Finite sample and robustness properties are studied and compared to other robust correlation estimators by means of numerical simulations.Comment: 20 pages, 7 figures, 2 table

Sign language recognition with transformer networks

Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition. Previous research has approached sign language recognition in various ways, using feature extraction techniques or end-to-end deep learning. In this work, we apply a combination of feature extraction using OpenPose for human keypoint estimation and end-to-end feature learning with Convolutional Neural Networks. The proven multi-head attention mechanism used in transformers is applied to recognize isolated signs in the Flemish Sign Language corpus. Our proposed method significantly outperforms the previous state of the art of sign language recognition on the Flemish Sign Language corpus: we obtain an accuracy of 74.7% on a vocabulary of 100 classes. Our results will be implemented as a suggestion system for sign language corpus annotation

Financial asset returns, direction-of-change forecasting, and volatility dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis

A novel compartment, the 'subqpical stem' of the aerial hyphae, is the location of a sigN-dependent, developmentally distinct transcription in Streptomyces coelicolor.

Streptomyces coelicolor has nine SigB-like RNA polymerase sigma factors, several of them implicated in morphological differentiation and/or responses to different stresses. One of the nine, SigN, is the focus of this article. A constructed sigN null mutant was delayed in development and exhibited a bald phenotype when grown on minimal medium containing glucose as carbon source. One of two distinct sigN promoters, sigNP1, was active only during growth on solid medium, when its activation coincided with aerial hyphae formation. Transcription from sigNP1 was readily detected in several whi mutants (interrupted in morphogenesis of aerial mycelium into spores), but was absent from all bld mutants tested, suggesting that sigNP1 activity was restricted to the aerial hyphae. It also depended on sigN, thus sigN was autoregulated. Mutational and transcription studies revealed no functional significance to the location of sigN next to sigF, encoding another SigB-like sigma factor. We identified another potential SigN target, nepA, encoding a putative small secreted protein. Transcription of nepA originated from a single, aerial hyphae-specific and sigN-dependent promoter. While in vitro run-off transcription using purified SigN on the Bacillus subtilis ctc promoter confirmed that SigN is an RNA polymerase sigma factor, SigN failed to initiate transcription from sigNP1 and from the nepA promoter in vitro. Additional in vivo data indicated that further nepA upstream sequences, which are likely to bind a potential activator, are required for successful transcription. Using a nepA–egfp transcriptional fusion we located nepA transcription to a novel compartment, the ‘subapical stem’ of the aerial hyphae. We suggest that this newly recognized compartment defines an interface between the aerial and vegetative parts of the Streptomyces colony and might also be involved in communication between these two compartments