BCS thermal vacuum of fermionic superfluids and its perturbation theory

The thermal field theory is applied to fermionic superfluids by doubling the degrees of freedom of the BCS theory. We construct the two-mode states and the corresponding Bogoliubov transformation to obtain the BCS thermal vacuum. The expectation values with respect to the BCS thermal vacuum produce the statistical average of the thermodynamic quantities. The BCS thermal vacuum allows a quantum-mechanical perturbation theory with the BCS theory serving as the unperturbed state. We evaluate the leading-order corrections to the order parameter and other physical quantities from the perturbation theory. A direct evaluation of the pairing correlation as a function of temperature shows the pseudogap phenomenon results from the perturbation theory. The BCS thermal vacuum is shown to be a generalized coherent and squeezed state. The correspondence between the thermal vacuum and purification of the density matrix allows a unitary transformation, and we found the geometric phase in the parameter space associated with the transformation.Comment: 14 pages, 2 figure

Robust Human Body Shape and Pose Tracking

Best paper runner up awardInternational audienceIn this paper we address the problem of markerless human performance capture from multiple camera videos. We consider in particular the recovery of both shape and parametric motion information as often required in applications that produce and manipulate animated 3D contents using multiple videos. To this aim, we propose an approach that jointly estimates skeleton joint positions and surface deformations by fitting a reference surface model to 3D point reconstructions. The approach is based on a probabilistic deformable surface registration framework coupled with a bone binding energy. The former makes soft assignments between the model and the observations while the latter guides the skeleton fitting. The main benefit of this strategy lies in its ability to handle outliers and erroneous observations frequently present in multi view data. For the same purpose, we also introduce a learning based method that partition the point cloud observations into different rigid body parts that further discriminate input data into classes in addition to reducing the complexity of the association between the model and the observations. We argue that such combination of a learning based matching and of a probabilistic fitting framework efficiently handle unreliable observations with fake geometries or missing data and hence, it reduces the need for tedious manual interventions. A thorough evaluation of the method is presented that includes comparisons with related works on most publicly available multi-view datasets

A Global Method for a Two-Dimensional Cutting Stock Problem in the Manufacturing Industry

A two-dimensional cutting stock problem (2DCSP) needs to cut a set of given rectangular items from standard-sized rectangular materials with the objective of minimizing the number of materials used. This problem frequently arises in different manufacturing industries such as glass, wood, paper, plastic, etc. However, the current literatures lack a deterministic method for solving the 2DCSP. However, this study proposes a global method to solve the 2DCSP. It aims to reduce the number of binary variables for the proposed model to speed up the solving time and obtain the optimal solution. Our experiments demonstrate that the proposed method is superior to current reference methods for solving the 2DCSP