La structure de propriété et la politique de rachat d’actions en France

Cet article étudie la relation entre la structure d’actionnariat des entreprises françaises et leur politique de rachat d’actions. L’étude porte sur un échantillon de 77 entreprises durant 2003-2008. Les résultats montrent que la concentration est associée négativement au rachat; les institutionnels influencent négativement le rachat ce qui peut être expliqué leur préférence aux dividendes par rapport aux rachats et aux rétentions du bénéfice pour le réinvestir. D’autre part, on a trouvé une relation positive entre l’actionnariat managériale et le rachat expliquée par le pouvoir d’enracinement que peut jouer le rachat en augmentant le pourcentage d’actionnariat managériale rachetant des actions

New Algorithm of Straight or Curved Baseline Detection for Short Arabic Handwritten writing

In this paper we present a new method of baseline detection of online or offline short handwriting. This work is part of a large project for the edification of a dual online / offline Arabic handwriting recognition system. Compared to the existing approaches in the literature, this new method brings three specific novelties: First, the consideration of the agreement between the alignment of the points and their trajectory tangent directions for the detection of aligned points regroupings. Then, the consideration of a topologic characteristics specific to the used writing language, to value the pertinence of the pretender points regroupings to be recognized as baseline. Finally, we showed the aptitude of the algorithm to detect curved baseline

New Algorithm of Straight or Curved Baseline Detection for Short Arabic Handwritten writing

In this paper we present a new method of baseline detection of online or offline short handwriting. This work is part of a large project for the edification of a dual online / offline Arabic handwriting recognition system. Compared to the existing approaches in the literature, this new method brings three specific novelties: First, the consideration of the agreement between the alignment of the points and their trajectory tangent directions for the detection of aligned points regroupings. Then, the consideration of a topologic characteristics specific to the used writing language, to value the pertinence of the pretender points regroupings to be recognized as baseline. Finally, we showed the aptitude of the algorithm to detect curved baseline

Do individual characteristics influence the beta-elliptic modeling errors during ellipse drawing movements?

This paper investigates whether age, gender, and degree of familiarity with writing have an influence on the Beta-elliptic model errors during hand-drawing on a graphical tablet. A database of elliptical hand drawing movements was built within a sample of 99 participants aged between 19 and 85 years. Using the Beta-elliptic model, the velocity profile was modeled by overlapped Beta functions and the drawing trajectory was segmented between velocity extrema and each segment geometry was modeled by elliptic arcs. Average absolute and relative geometric, curvature and curvilinear velocity errors were 0.27 mm, 0.68%, 4.54 mm, 0.48%, 4.68 mm/s, and 8.79% respectively. Statistical analyses revealed not significant or low correlation between modeling errors and age and movement velocity, and no significant or low error differences according to gender or degree of familiarity with writing

Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate