The twisted gradient flow coupling at one loop

We compute the one-loop running of the $SU(N)$ 't Hooft coupling in a finite volume gradient flow scheme using twisted boundary conditions. The coupling is defined in terms of the energy density of the gradient flow fields at a scale $\tilde{l}$ given by an adequate combination of the torus size and the rank of the gauge group, and is computed in the continuum using dimensional regularization. We present the strategy to regulate the divergences for a generic twist tensor, and determine the matching to the $\overline{\rm MS}$ scheme at one-loop order. For the particular case in which the twist tensor is non-trivial in a single plane, we evaluate the matching coefficient numerically and determine the ratio of $\Lambda$ parameters between the two schemes. We analyze the $N$ dependence of the results and the possible implications for non-commutative gauge theories and volume independence.Comment: 52 pages, 12 figure

Choosing choices: Agenda selection with uncertain issues

This paper studies selection rules i.e. the procedures committees use to choose whether to place an issue on their agenda. The main ingredient of the model is that committee members are uncertain about their final preferences at the selection stage: they only know the probability that they will eventually prefer the proposal to the status quo at the decision stage. This probability is private information. We find that a more stringent selection rule makes the voters more conservative. Hence individual behavior reinforces the effect of the rule instead of balancing it. For a voter, conditional on being pivotal, the probability that the proposal is adopted depends on which option she eventually favors. The probability that the proposal is adopted if she eventually prefers the proposal increases at a higher rate with the selection rule than if she eventually prefers the status quo. In order to compensate for that, the voters become more selective. The decision rule has the opposite effect. We describe optimal rules when there is a fixed cost of organizing the final election.selection rules ; strategic voting ; asymmetric information ; agenda setting ; large deviations ; petitions ; citizens' initiative

Data Augmentation for Skin Lesion Analysis

Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited. Data augmentation can expand the training dataset by transforming input images. In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet). Scenarios include traditional color and geometric transforms, and more unusual augmentations such as elastic transforms, random erasing and a novel augmentation that mixes different lesions. We also explore the use of data augmentation at test-time and the impact of data augmentation on various dataset sizes. Our results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images. The best scenario results in an AUC of 0.882 for melanoma classification without using external data, outperforming the top-ranked submission (0.874) for the ISIC Challenge 2017, which was trained with additional data.Comment: 8 pages, 3 figures, to be presented on ISIC Skin Image Analysis Worksho