Singularity in the boundary resistance between superfluid 4^4He and a solid surface

We report new measurements in four cells of the thermal boundary resistance $R$ between copper and $^4$He below but near the superfluid-transition temperature $T_\lambda$. For $10^{-7} \leq t \equiv 1 - T/T_\lambda \leq 10^{-4}$ fits of $R = R_0 t^{x_b} + B_0$ to the data yielded $x_b \simeq 0.18$, whereas a fit to theoretical values based on the renormalization-group theory yielded $x_b = 0.23$. Alternatively, a good fit of the theory to the data could be obtained if the {\it amplitude} of the prediction was reduced by a factor close to two. The results raise the question whether the boundary conditions used in the theory should be modified.Comment: 4 pages, 4 figures, revte

Rotons and Quantum Evaporation from Superfluid 4He

The probability of evaporation induced by $R^+$ and $R^-$ rotons at the surface of superfluid helium is calculated using time dependent density functional theory. We consider excitation energies and incident angles such that phonons do not take part in the scattering process. We predict sizable evaporation rates, which originate entirely from quantum effects. Results for the atomic reflectivity and for the probability of the roton change-mode reflection are also presented.Comment: 11 pages, REVTEX, 3 figures available upon request or at http://anubis.science.unitn.it/~dalfovo/papers/papers.htm

Robust Prostate Cancer Classification with Siamese Neural Networks

Nuclear magnetic resonance (NMR) is a powerful and non–invasive diagnostic tool. However, NMR scanned images are often noisy due to patient motions or breathing. Although modern Computer Aided Diagnosis (CAD) systems, mainly based on Deep Learning (DL), together with expert radiologists, can obtain very accurate predictions, working with noisy data can induce a wrong diagnose or require a new acquisition, spending time and exposing the patient to an extra dose of radiation. In this paper, we propose a new DL model, based on a Siamese neural network, able to withstand random noise perturbations. We use data coming from the ProstateX challenge and demonstrate the superior robustness of our model to random noise compared to a similar architecture, albeit deprived of the Siamese branch. In addition, our approach is also resistant to adversarial attacks and shows overall better AUC performance