Documenting and analyzing pre-reflective self-consciousness underlying ongoing performance optimization in elite athletes: the theoretical and methodological approach of the course-of-experience framework.

Traditional theories of motor learning emphasize the automaticity of skillful actions. However, recent research has emphasized the role of pre-reflective self-consciousness accompanying skillful action execution. In the present paper, we present the course-of-experience framework as a means of studying elite athletes' pre-reflective self-consciousness in the unfolding activity of performance optimization. We carried out a synthetic presentation of the ontological and epistemological foundation of this framework. Then we illustrated the methodology by an in-depth analysis of two elite windsurfers' courses of experience. The analysis of global and local characteristics of the riders' courses of experience reveal (a) the meaningful activities accompanying the experience of ongoing performance optimization; (b) the multidimensionality of attentional foci and the normativity of performance self-assessment; and (c) a micro-scale phenomenological description of continuous improvement. These results highlight the fruitfulness of the course-of-experience framework to describe the experience of being absorbed in an activity of performance optimization.

Biomechanical Characterization of Preparation for Airs above the Ground: A Mixed Approach.

Equitation in the French tradition is a school of riding that emphasizes harmonious relations between humans and horses. The best-known community is the Cadre Noir of Saumur, whose specialty is the air above the ground (AAG). No study has yet looked at the horse-rider interaction in this specific context. The purpose of this study was to identify and quantify indicators of AAGs based on the empirical perception of the écuyers expressed by a method of self-confrontation interviews. Fourteen training sessions were the subject of phenomenological and biomechanical approaches. Contact, balance, and hoof-beat, decisive for performance quality, were characterized for 49 AAGs, performed by five horses trained by two expert écuyers, with rein tension meters integrated in their double bridle (curb and snaffle reins) and six inertial measurement units fixed on the limbs, sternum, and croup. Their action was characterized by a peak of 65 ± 39 N on the inside curb rein. They considered that their horse was in balance (forehand inclined 13 ± 7° and -12 ± 9° for the hind hand). After the peak, during the 3.3 ± 2 s the horse's trunk was stable and the écuyers released the contact until the AAG was perceived as satisfactory by the écuyer. The mixed approach allowed a pattern of action to be envisaged for the écuyer based on contact, balance, and hoof-beat in the execution of AAGs. The quantification of rein tension, trunk movements, and acceleration of the four limbs objectified the expert écuyers' feeling of developing aptitudes for their actions in the human-horse interactions for improved transmission to young écuyers. The mixed approach used in this study has given rise to new training methods that are transferable to other equestrian activities.

Multi-task deep learning for glaucoma detection from color fundus images.

Glaucoma is an eye condition that leads to loss of vision and blindness if not diagnosed in time. Diagnosis requires human experts to estimate in a limited time subtle changes in the shape of the optic disc from retinal fundus images. Deep learning methods have been satisfactory in classifying and segmenting diseases in retinal fundus images, assisting in analyzing the increasing amount of images. Model training requires extensive annotations to achieve successful generalization, which can be highly problematic given the costly expert annotations. This work aims at designing and training a novel multi-task deep learning model that leverages the similarities of related eye-fundus tasks and measurements used in glaucoma diagnosis. The model simultaneously learns different segmentation and classification tasks, thus benefiting from their similarity. The evaluation of the method in a retinal fundus glaucoma challenge dataset, including 1200 retinal fundus images from different cameras and medical centers, obtained a [Formula: see text] AUC performance compared to an [Formula: see text] obtained by the same backbone network trained to detect glaucoma. Our approach outperforms other multi-task learning models, and its performance pairs with trained experts using [Formula: see text] times fewer parameters than training each task separately. The data and the code for reproducing our results are publicly available.

Coordination between Crew Members on Flying Multihulls: A Case Study on a Nacra 17.

A current trend in sailing sports is the use of boats equipped with hydrofoils, allowing the boats to "fly" over the water surface. In this situation, the handling of the boat requires fine coordination between the crew members to maintain the precarious flight. The purpose of this case study was to analyze the crew activity on a flying multihull and explore the role of the shared sport equipment in the emergence of coordination between crew members. Data were collected during a training session with a crew of expert sailors. A joint analysis of phenomenological and mechanical data was conducted. The aim of the analysis was to categorize the forms of interactions between crew members, boat and environment. Results showed that collective coordination in the studied situation involves six forms of interaction that are associated with stable, unstable or critical states of the flight. Consequently, we discussed the role played by the crew members, the behavior of the boat and the environment in the collective coordination.

Event-Based Media Enrichment Using an Adaptive Probabilistic Hypergraph Model.

Nowadays, with the continual development of digital capture technologies and social media services, a vast number of media documents are captured and shared online to help attendees record their experience during events. In this paper, we present a method combining semantic inference and multimodal analysis for automatically finding media content to illustrate events using an adaptive probabilistic hypergraph model. In this model, media items are taken as vertices in the weighted hypergraph and the task of enriching media to illustrate events is formulated as a ranking problem. In our method, each hyperedge is constructed using the K-nearest neighbors of a given media document. We also employ a probabilistic representation, which assigns each vertex to a hyperedge in a probabilistic way, to further exploit the correlation among media data. Furthermore, we optimize the hypergraph weights in a regularization framework, which is solved as a second-order cone problem. The approach is initiated by seed media and then used to rank the media documents using a transductive inference process. The results obtained from validating the approach on an event dataset collected from EventMedia demonstrate the effectiveness of the proposed approach.