Semi-Supervised Deep Learning for Fully Convolutional Networks

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semi-supervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.Comment: 9 pages, 6 figure

The Schwinger action principle for classical systems

We use the Schwinger action principle to obtain the correct equations of motion in the Koopman-von Neumann operational version of classical mechanics. We restrict our analysis to non-dissipative systems and velocity-independent forces. We show that the Schwinger action principle can be interpreted as a variational principle in these special cases

Projective representation of the Galilei group for classical and quantum-classical systems

A physically relevant unitary irreducible non-projective representation of the Galilei group is possible in the Koopman-von Neumann formulation of classical mechanics. This classical representation is characterized by the vanishing of the central charge of the Galilei algebra. This is in contrast to the quantum case where the mass plays the role of the central charge. Here we show, by direct construction, that classical mechanics also allows for a projective representation of the Galilei group where the mass is the central charge of the algebra. We extend the result to certain kind of quantum-classical hybrid systems

The geometric tensor for classical states

We use the Liouville eigenfunctions to define a classical version of the geometric tensor and study its relationship with the classical adiabatic gauge potential (AGP). We focus on integrable systems and show that the imaginary part of the geometric tensor is related to the Hannay curvature. The singularities of the geometric tensor and the AGP allows us to link the transition from Arnold-Liouville integrability to chaos with some of the mathematical formalism of quantum phase transitions

Mesoscopic mean-field theory for spin-boson chains in quantum optical systems

We present a theoretical description of a system of many spins strongly coupled to a bosonic chain. We rely on the use of a spin-wave theory describing the Gaussian fluctuations around the mean-field solution, and focus on spin-boson chains arising as a generalization of the Dicke Hamiltonian. Our model is motivated by experimental setups such as trapped ions, or atoms/qubits coupled to cavity arrays. This situation corresponds to the cooperative (E⊗β) Jahn-Teller distortion studied in solid-state physics. However, the ability to tune the parameters of the model in quantum optical setups opens up a variety of novel intriguing situations. The main focus of this paper is to review the spin-wave theoretical description of this problem as well as to test the validity of mean-field theory. Our main result is that deviations from mean-field effects are determined by the interplay between magnetic order and mesoscopic cooperativity effects, being the latter strongly size-dependent

Numerical simulation of resistance furnaces by using distributed and lumped models

This work proposes a methodology combining distributed and lumped models to simulate the current distribution in an indirect heat resistance furnace and, in particular, to compute the current to be supplied in order to obtain a desired power. The distributed model is a time-harmonic eddy current problem which has been numerically solved by using a finite element method. The lumped model is based on the computation of a reduced impedance associated to an equivalent circuit model. The effectiveness of the method has been assessed by numerical simulations and plant measurements. The good correlation between the results reveals this approximation is well-suited in order to aid the design and improve the efficiency of the furnace in a short-time

Comparación de curvas de supervivencia gamma estocásticamente ordenadas

En este trabajo se propone un análisis de supervivencia basado en un modelo Gamma. Se obtienen las condiciones teóricas bajo las cuales dos funciones de supervivencia Gamma están estocásticamente ordenadas. Estos resultados se utilizan para proponer un método sencillo que permite comparar dos poblaciones cuando, a priori, se conoce que sus curvas de supervivencia están estocásticamente ordenadas. Los resultados se ejemplifican con el análisis de un banco de datos reales sobre tiempos de desempleo