Reconstructing mass profiles of simulated galaxy clusters by combining Sunyaev-Zeldovich and X-ray images

We present a method to recover mass profiles of galaxy clusters by combining data on thermal Sunyaev-Zeldovich (tSZ) and X-ray imaging, thereby avoiding to use any information on X-ray spectroscopy. This method, which represents a development of the geometrical deprojection technique presented in Ameglio et al. (2007), implements the solution of the hydrostatic equilibrium equation. In order to quantify the efficiency of our mass reconstructions, we apply our technique to a set of hydrodynamical simulations of galaxy clusters. We propose two versions of our method of mass reconstruction. Method 1 is completely model-independent, while Method 2 assumes instead the analytic mass profile proposed by Navarro et al. (1997) (NFW). We find that the main source of bias in recovering the mass profiles is due to deviations from hydrostatic equilibrium, which cause an underestimate of the mass of about 10 per cent at r_500 and up to 20 per cent at the virial radius. Method 1 provides a reconstructed mass which is biased low by about 10 per cent, with a 20 per cent scatter, with respect to the true mass profiles. Method 2 proves to be more stable, reducing the scatter to 10 per cent, but with a larger bias of 20 per cent, mainly induced by the deviations from equilibrium in the outskirts. To better understand the results of Method 2, we check how well it allows to recover the relation between mass and concentration parameter. When analyzing the 3D mass profiles we find that including in the fit the inner 5 per cent of the virial radius biases high the halo concentration. Also, at a fixed mass, hotter clusters tend to have larger concentration. Our procedure recovers the concentration parameter essentially unbiased but with a scatter of about 50 per cent.Comment: 13 pages, 11 figures, submitted to MNRA

Toward a Motor Theory of Sign Language Perception

Researches on signed languages still strongly dissociate lin- guistic issues related on phonological and phonetic aspects, and gesture studies for recognition and synthesis purposes. This paper focuses on the imbrication of motion and meaning for the analysis, synthesis and evaluation of sign language gestures. We discuss the relevance and interest of a motor theory of perception in sign language communication. According to this theory, we consider that linguistic knowledge is mapped on sensory-motor processes, and propose a methodology based on the principle of a synthesis-by-analysis approach, guided by an evaluation process that aims to validate some hypothesis and concepts of this theory. Examples from existing studies illustrate the di erent concepts and provide avenues for future work.Comment: 12 pages Partiellement financ\'e par le projet ANR SignCo

Building an Assessment Use Argument for sign language: the BSL Nonsense Sign Repetition Test

In this article, we adapt a concept designed to structure language testing more effectively, the Assessment Use Argument (AUA), as a framework for the development and/or use of sign language assessments for deaf children who are taught in a sign bilingual education setting. By drawing on data from a recent investigation of deaf children's nonsense sign repetition skills in British Sign Language, we demonstrate the steps of implementing the AUA in practical test design, development and use. This approach provides us with a framework which clearly states the competing values and which stakeholders hold these values. As such, it offers a useful foundation for test-designers, as well as for practitioners in sign bilingual education, for the interpretation of test scores and the consequences of their use

Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

Hand Gesture Recognition within a Linguistics-Based Framework

An approach to recognizing human hand gestures from a monocular temporal sequence of images is presented. Of particular concern is the representation and recognition of hand movements that are used in single handed American Sign Language (ASL). The approach exploits previous linguistic analysis of manual languages that decompose dynamic gestures into their static and dynamic components. The first level of decomposition is in terms of three sets of primitives, hand shape, location and movement. Further levels of decomposition involve the lexical and sentence levels and are part of our plan for future work. We propose and subsequently demonstrate that given a monocular gesture sequence, kinematic features can be recovered from the apparent motion that provide distinctive signatures for 14 primitive movements of ASL. The approach has been implemented in software and evaluated on a database of 592 gesture sequences with an overall recognition rate of 86.00% for fully automated processing and 97.13% for manually initialized processing