Conservatoire national des arts et métiers

About: Conservatoire national des arts et métiers is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Orthogonal frequency-division multiplexing. The organization has 3573 authors who have published 7127 publications receiving 141430 citations. The organization is also known as: CNAM & Conservatoire des arts et métiers.

Proposal for an Extension of Traditional Named Entitites: from Guidelines to Evaluation, an Overview

Abstract: Within the framework of the construction of a fact database, we defined guidelines to extract named entities, using a taxonomy based on an extension of the usual named entities defini- tion. We thus defined new types of entities with broader coverage including substantive- based expressions. These extended named en- tities are hierarchical (with types and compo- nents) and compositional (with recursive type inclusion and metonymy annotation). Human annotators used these guidelines to annotate a 1.3M word broadcast news corpus in French. This article presents the definition and novelty of extended named entity annotation guide- lines, the human annotation of a global corpus and of a mini reference corpus, and the evalu- ation of annotations through the computation of inter-annotator agreement. Finally, we dis- cuss our approach and the computed results, and outline further work.

Sum-frequency generation of 589 nm light with near-unit efficiency.

Abstract: We report on a laser source at 589 nm based on sum-frequency generation of two infrared laser at 1064 nm and 1319 nm. Output power as high as 800 mW is achieved starting from 370 mW at 1319 nm and 770 mW at 1064 nm, corresponding to converting roughly 90% of the 1319 nm photons entering the cavity. The power and frequency stability of this source are ideally suited for cooling and trapping of sodium atoms.

Scalable mining of large video databases using copy detection

Abstract: Mining the video content itself can bring to light important information regarding the internal structure of large video databases, compensating for a lasting absence of extensive and reliable annotations. Many valuable links between video segments can be identified by content-based copy detection methods, where "copies" are transformed versions of original video sequences. To make this approach viable for large video databases, we put forward a new mining method relying on the definition of a compact keyframe-level descriptor and of a specific index structure. The performance obtained in detecting links between video segments is evaluated with the help of a ground truth and several illustrations are given. The scalability of the approach is then demonstrated for databases of up to 10,000 hours of video.