CoCAS: a ChIP-on-chip analysis suite

Motivation: High-density tiling microarrays are increasingly used in combination with ChIP assays to study transcriptional regulation. To ease the analysis of the large amounts of data generated by this approach, we have developed ChIP-on-chip Analysis Suite (CoCAS), a standalone software suite which implements optimized ChIP-on-chip data normalization, improved peak detection, as well as quality control reports. Our software allows dye swap, replicate correlation and connects easily with genome browsers and other peak detection algorithms. CoCAS can readily be used on the latest generation of Agilent high-density arrays. Also, the implemented peak detection methods are suitable for other datasets, including ChIP-Seq output

Towards the development of ecosystem-based indicators of mangroves functioning state in the context of the EU water framework directive

European Water Framework Directive is enforced in five tropical French Oversea Territories where mangroves are present. Developing bioindication tools to support the ecosystem-based management approach of the Directive is needed. A series of expert workshops was organized and led to the proposal of a strategy and of an applied research program to develop bioindication tools. The proceedings of the workshops are presented as a case study, as this is the first time such an integrative ecosystem-based approach is proposed in mangroves, combining structural and functional aspects, from forest structure to benthic community functioning

Domain adaptation for pen trajectory reconstruction from kinematic sensors

International audienceHandwriting with connected pen becomes one of the major human-computer easy interaction methods. In comparison with traditional touch screen handwriting systems, the pen-based interaction method has the advantage of producing the online handwriting signal without surface constraints. Indeed, people who write on a paper obtain the corresponding pen trajectory coordinates that represent the online handwriting signal. Furthermore, the feeling of writing on paper is important particularly for the children during the learning of writing. In this work which is part of ANR Franco-German KIHT project with Stabilo, we introduce a domain adaptation-based approach that reconstructs the paper handwriting traces of the digital stylus Digipen of STABILO which is equipped with a wireless trajectory tracking system based on kinematic sensors. We use unsupervised domain adaptation method, to pass from the tablet domain where the ground truth (the online trace of the writing on the tablet) is known, to the paper domain where only the input sensors data are known

Domain adaptation for pen trajectory reconstruction from kinematic sensors

International audienceHandwriting with connected pen becomes one of the major human-computer easy interaction methods. In comparison with traditional touch screen handwriting systems, the pen-based interaction method has the advantage of producing the online handwriting signal without surface constraints. Indeed, people who write on a paper obtain the corresponding pen trajectory coordinates that represent the online handwriting signal. Furthermore, the feeling of writing on paper is important particularly for the children during the learning of writing. In this work which is part of ANR Franco-German KIHT project with Stabilo, we introduce a domain adaptation-based approach that reconstructs the paper handwriting traces of the digital stylus Digipen of STABILO which is equipped with a wireless trajectory tracking system based on kinematic sensors. We use unsupervised domain adaptation method, to pass from the tablet domain where the ground truth (the online trace of the writing on the tablet) is known, to the paper domain where only the input sensors data are known

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info:eu-repo/semantics/nonPublishe

Domain adaptation for pen trajectory reconstruction from kinematic sensors

International audienceHandwriting with connected pen becomes one of the major human-computer easy interaction methods. In comparison with traditional touch screen handwriting systems, the pen-based interaction method has the advantage of producing the online handwriting signal without surface constraints. Indeed, people who write on a paper obtain the corresponding pen trajectory coordinates that represent the online handwriting signal. Furthermore, the feeling of writing on paper is important particularly for the children during the learning of writing. In this work which is part of ANR Franco-German KIHT project with Stabilo, we introduce a domain adaptation-based approach that reconstructs the paper handwriting traces of the digital stylus Digipen of STABILO which is equipped with a wireless trajectory tracking system based on kinematic sensors. We use unsupervised domain adaptation method, to pass from the tablet domain where the ground truth (the online trace of the writing on the tablet) is known, to the paper domain where only the input sensors data are known

Toward Deep neural network for pen trajectory reconstruction from kinematic sensors

International audienc

Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network

International audienceHandwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction. In this work, we focus on a digital pen equipped with sensors from which one wants to reconstruct the OH trajectory. Such a pen allows to write on any surface and to get the digital trace, which can help learning to write, by writing on paper, and can be useful for many other applications such as collaborative meetings, etc. In this paper, we introduce a novel processing pipeline that maps the sensor signals of the pen to the corresponding OH trajectory. Notably, in order to tackle the difference of sampling rates between the pen and the tablet (which provides ground truth information), our preprocessing pipeline relies on Dynamic Time Warping to align the signals. We introduce a dedicated neural network architecture, inspired by a Temporal Convolutional Network, to reconstruct the online trajectory from the pen sensor signals. Finally, we also present a new benchmark dataset on which our method is evaluated both qualitatively and quantitatively, showing a notable improvement over its most notable competitor

Toward Deep neural network for pen trajectory reconstruction from kinematic sensors

International audienc

Toward Deep neural network for pen trajectory reconstruction from kinematic sensors

International audienc