Georgia Institute of Technology College of Sciences

The systematical study of model selection procedures, especially since the early nineties, has led to the design of penalties that often allow to achieve minimax rates of convergence and adaptivity for the selected model, in the general setting of risk minimization (Koltchinskii [3], Massart [4]). However, the proposed penalties often su.er form their dependencies on unknown or unrealistic constants. As a matter of fact, under-penalization has generally disastrous e.ects in terms of e¢ ciency. Indeed, the model selection procedure then looses any bias-variance trade-o. and so, tends to select one of the biggest models in the collection. Birgé and Massart ([2]) proposed quite recently a method that empirically adjusts the level of penalization in a linear Gaussian setting. This method of calibration is called "slope heuristics" by the authors, and is proved to be optimal in their setting. It is based on the existence of a minimal penalty, which is shown to be half the optimal one. Arlot and Massart ([1]) have then extended the slope heuristics to the more general framework of empirical risk minimization. They succeeded in proving the optimality of the method in heteroscedastic least-squares regression, a case where the ideal penalty is no longer linear in the dimension of the models, not even a function of it. However, they restricted their analysis to histograms for technical reasons. They conjectured a wide range of applicability for the method. We will present some results that prove the validity of the slope heuristics in heteroscedastic least-squares regression for more general linear models than histograms. The models considered here are equipped with a localized orthonormal basis, among other things. We show that some piecewise polynomials and Haar expansions satisfy the prescribed conditions. We will insist on the analysis when the model is .xed. In particular, we will focus on deviations bounds for the true and empirical excess risks of the estimator. Empirical process theory and concentration inequalities are central tools here, and the results at a .xed model may be of independent interest.

A well known result of Giroux tells us that isotopy classes of contact structures on a closed three manifold are in one to one correspondence with stabilization classes of open book decompositions of the manifold. We will introduce a stabilization-invariant property of open books which corresponds to tightness of the corresponding contact structure. We will mention applications to the classification of contact 3-folds, and also to the question of whether tightness is preserved under Legendrian surgery.

A 1986 article with this title, written by M. Zuker and published by the AMS, outlined several major challenges in the area. Stating the folding problem is simple; given an RNA sequence, predict the set of (canonical, nested) base pairs found in the native structure. Yet, despite significant advances over the past 25 years, it remains largely unsolved. A fundamental problem identified by Zuker was, and still is, the "ill-conditioning" of discrete optimization solution approaches. We revisit some of the questions this raises, and present recent advances in considering multiple (sub)optimal structures, in incorporating auxiliary experimental data into the optimization, and in understanding alternative models of RNA folding.