Sr0.9_{0.9}K0.1_{0.1}Zn1.8_{1.8}Mn0.2_{0.2}As2_{2}: a ferromagnetic semiconductor with colossal magnetoresistance

A bulk diluted magnetic semiconductor (Sr,K)(Zn,Mn)$_{2}$As$_{2}$ was synthesized with decoupled charge and spin doping. It has a hexagonal CaAl$_{2}$Si$_{2}$-type structure with the (Zn,Mn)$_{2}$As$_{2}$ layer forming a honeycomb-like network. Magnetization measurements show that the sample undergoes a ferromagnetic transition with a Curie temperature of 12 K and \revision{magnetic moment reaches about 1.5 $\mu_{B}$/Mn under $\mu_0H$ = 5 T and $T$ = 2 K}. Surprisingly, a colossal negative magnetoresistance, defined as $[\rho(H)-\rho(0)]/\rho(0)$, up to $-$38\% under a low field of $\mu_0H$ = 0.1 T and to $-$99.8\% under $\mu_0H$ = 5 T, was observed at $T$ = 2 K. The colossal magnetoresistance can be explained based on the Anderson localization theory.Comment: Accepted for publication in EP

Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols

Deep Vectorization of Technical Drawings

We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based cleaning stage to eliminate the background and imperfections in the image and fill in missing parts, (2) a transformer-based network to estimate vector primitives, and (3) optimization procedure to obtain the final primitive configurations. We train the networks on synthetic data, renderings of vector line drawings, and manually vectorized scans of line drawings. Our method quantitatively and qualitatively outperforms a number of existing techniques on a collection of representative technical drawings

La transposition d’une controverse scientifique sur l’apprentissage moteur au sein des sciences et techniques des activités physiques et sportives

Les recherches sur l’apprentissage moteur dans le champ des sciences et techniques des activités physiques et sportives (STAPS) mobilisent plusieurs théories construites en dehors de cet espace. Nous proposons d’analyser les déterminants de la controverse qui a opposé l’approche dynamique à l’approche cognitive. L’investigation, menée à partir de l’analyse d’un corpus de textes scientifiques complété d’entretiens, montre que l’opposition tient à des interprétations différentes des phénomènes, mais implique également des conceptions différenciées de la validité scientifique. L’article met également en lumière la façon dont cette controverse, née en dehors des STAPS, est réceptionnée dans cet espace : sa transposition hors de son champ d’origine modifie les rapports d’opposition entre les théories portées par des groupes de chercheurs distincts

Indexing & Retrieval of Graphic Document Images: a New System Scheme

This draft deals with the Indexing and Retrieval (I & R) of graphic document images. We present first a general introduction on this topic and review then the main existing systems in the literature. Based on this review, we conclude on the unadaptability of existing systems to solve real-life indexing problems. We propose then a new system scheme based on three characteristics : a formalism allowing the multi-representation formalism, a perceptive based control, and the use of a high-level representation as image' map to direct the indexing process

A Performance Characterization Algorithm for Symbol Localization

In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm aims to be more “generic ” and “fuzzy ” to characterize the performance. It exploits only single points as the results of localization and compare them with the groundtruth, using information about context. Probability scores are computed for each localization point, depending on the spatial distribution of the regions in the groundtruth. Final characterization results are given with a detection rates/probability error plot, describing the sets of possible interpretations of the localization results. We present experiments and results done with the symbol localization system of [1], using a synthetic dataset of floorplans (100 images, 2500 symbols). We conclude about the performance of this system, in terms of localization accuracy and precision level (false alarms and multiple detections)