501 research outputs found
Historical Determinants of Fintech Development : Evidence from Initial Coin Offerings
Peer reviewedPostprin
Sharpness-Aware Minimization with Dynamic Reweighting
Deep neural networks are often overparameterized and may not easily achieve
model generalization. Adversarial training has shown effectiveness in improving
generalization by regularizing the change of loss on top of adversarially
chosen perturbations. The recently proposed sharpness-aware minimization (SAM)
algorithm conducts adversarial weight perturbation, encouraging the model to
converge to a flat minima. SAM finds a common adversarial weight perturbation
per-batch. Although per-instance adversarial weight perturbations are stronger
adversaries and they can potentially lead to better generalization performance,
their computational cost is very high and thus it is impossible to use
per-instance perturbations efficiently in SAM. In this paper, we tackle this
efficiency bottleneck and propose sharpness-aware minimization with dynamic
reweighting ({\delta}-SAM). Our theoretical analysis motivates that it is
possible to approach the stronger, per-instance adversarial weight
perturbations using reweighted per-batch weight perturbations. {\delta}-SAM
dynamically reweights perturbation within each batch according to the
theoretically principled weighting factors, serving as a good approximation to
per-instance perturbation. Experiments on various natural language
understanding tasks demonstrate the effectiveness of {\delta}-SAM
Novel Tactile-SIFT Descriptor for Object Shape Recognition
Using a tactile array sensor to recognize an object often requires multiple touches at different positions. This process is prone to move or rotate the object, which inevitably increases difficulty in object recognition. To cope with the unknown object movement, this paper proposes a new tactile-SIFT descriptor to extract features in view of gradients in the tactile image to represent objects, to allow the features being invariant to object translation and rotation. The tactile-SIFT segments a tactile image into overlapping subpatches, each of which is represented using a dn-dimensional gradient vector, similar to the classic SIFT descriptor. Tactile-SIFT descriptors obtained from multiple touches form a dictionary of k words, and the bag-of-words method is then used to identify objects. The proposed method has been validated by classifying 18 real objects with data from an off-the-shelf tactile sensor. The parameters of the tactile-SIFT descriptor, including the dimension size dn and the number of subpatches sp, are studied. It is found that the optimal performance is obtained using an 8-D descriptor with three subpatches, taking both the classification accuracy and time efficiency into consideration. By employing tactile-SIFT, a recognition rate of 91.33% has been achieved with a dictionary size of 50 clusters using only 15 touches
Athletes in boardrooms:Evidence from the world
International audienceThis study examines the relation between the athletic experience of board directors and corporate outcomes. We predict that athletes’ attributes, such as physical fitness, mental resilience, leadership, and team-working skills, enhance their monitoring role. Using a large sample from 71 countries, we find that athletic experience is associated with better firm performance. The benefits are more pronounced when the experience is of team sports and confrontational sports, and for firms experiencing financial crisis. The results remain consistent when we instrument the athletic experience of directors with the number of Olympic medals won and Olympic sports participated in by the country in question at the previous Olympic Games
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