Wild Ramification and the Cotangent Bundle

We define the characteristic cycle of a locally constant \'etale sheaf on a smooth variety in positive characteristic ramified along boundary as a cycle in the cotangent bundle of the variety, at least on a neighborhood of the generic point of the divisor on the boundary. The crucial ingredient in the definition is an additive structure on the boundary induced by the groupoid structure of multiple self products. We prove a compatibility with pull-back and local acyclicity in non-characteristic situations. We also give a relation with the characteristic cohomology class under a certain condition and a concrete example where the intersection with the 0-section computes the Euler-Poincar\'e characteristic.Comment: 56 pages. In v2, the local acyclicity is proved in Proposition 3.14. In v3, errors in Examples 2.18.2 and 3.18 are corrected. In v4, the assumption in Proposition 3.14 on local acyclicity is weakened. In v5, Conjectures on the integrality of the characteristic cycle and on the total dimension of nearby cycles are formulated. In v6, some corrections are made and explanations are adde

On the modelling of highly elastic flows of amorphous thermoplastics

Two approaches to the kinematic structuring of constitutive models for highly elastic flows of polymer melts have been examined systematically, assuming either: (1) additivity of elastic and viscous velocity gradients or (2) multiplicability of elastic and viscous deformation gradients. A series of constitutive models were compared, with differing kinematic structure but the same linear responses in elastic and viscous limits. They were solved numerically and their predictions compared, and they were also compared to those of the Giesekus model. Several variants, previously proposed as separate models, are shown to be equivalent and qualitatively in agreement with experiment, and therefore a sound basis for construction of models. But the assignment of viscous spin is critical: if it is assumed equal to the total spin with approach (1), or equal to zero with approach (2), then unphysical viscoelastic behaviour is predicted. © 2008 Elsevier Ltd. All rights reserved

Analysis and Synthesis Prior Greedy Algorithms for Non-linear Sparse Recovery

In this work we address the problem of recovering sparse solutions to non linear inverse problems. We look at two variants of the basic problem, the synthesis prior problem when the solution is sparse and the analysis prior problem where the solution is cosparse in some linear basis. For the first problem, we propose non linear variants of the Orthogonal Matching Pursuit (OMP) and CoSamp algorithms; for the second problem we propose a non linear variant of the Greedy Analysis Pursuit (GAP) algorithm. We empirically test the success rates of our algorithms on exponential and logarithmic functions. We model speckle denoising as a non linear sparse recovery problem and apply our technique to solve it. Results show that our method outperforms state of the art methods in ultrasound speckle denoising

Logarithmic extensions of minimal models: characters and modular transformations

We study logarithmic conformal field models that extend the (p,q) Virasoro minimal models. For coprime positive integers $p$ and $q$, the model is defined as the kernel of the two minimal-model screening operators. We identify the field content, construct the W-algebra W(p,q) that is the model symmetry (the maximal local algebra in the kernel), describe its irreducible modules, and find their characters. We then derive the SL(2,Z) representation on the space of torus amplitudes and study its properties. From the action of the screenings, we also identify the quantum group that is Kazhdan--Lusztig-dual to the logarithmic model.Comment: 43pp., AMSLaTeX++. V3: Some explanatory comments added, notational inaccuracies corrected, references adde

LASSO ISOtone for High Dimensional Additive Isotonic Regression

Additive isotonic regression attempts to determine the relationship between a multi-dimensional observation variable and a response, under the constraint that the estimate is the additive sum of univariate component effects that are monotonically increasing. In this article, we present a new method for such regression called LASSO Isotone (LISO). LISO adapts ideas from sparse linear modelling to additive isotonic regression. Thus, it is viable in many situations with high dimensional predictor variables, where selection of significant versus insignificant variables are required. We suggest an algorithm involving a modification of the backfitting algorithm CPAV. We give a numerical convergence result, and finally examine some of its properties through simulations. We also suggest some possible extensions that improve performance, and allow calculation to be carried out when the direction of the monotonicity is unknown