Dynamical properties of dipolar Fermi gases

We investigate dynamical properties of a one-component Fermi gas with dipole-dipole interaction between particles. Using a variational function based on the Thomas-Fermi density distribution in phase space representation, the total energy is described by a function of deformation parameters in both real and momentum space. Various thermodynamic quantities of a uniform dipolar Fermi gas are derived, and then instability of this system is discussed. For a trapped dipolar Fermi gas, the collective oscillation frequencies are derived with the energy-weighted sum rule method. The frequencies for the monopole and quadrupole modes are calculated, and softening against collapse is shown as the dipolar strength approaches the critical value. Finally, we investigate the effects of the dipolar interaction on the expansion dynamics of the Fermi gas and show how the dipolar effects manifest in an expanded cloud.Comment: 14 pages, 8 figures, submitted to New J. Phy

TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.Comment: MICCAI2017 Ora

Giant Colloidal Diffusivity on Corrugated Optical Vortices

A single colloidal sphere circulating around a periodically modulated optical vortex trap can enter a dynamical state in which it intermittently alternates between freely running around the ring-like optical vortex and becoming trapped in local potential energy minima. Velocity fluctuations in this randomly switching state still are characterized by a linear Einstein-like diffusion law, but with an effective diffusion coefficient that is enhanced by more than two orders of magnitude.Comment: 4 pages, 4 figure

Heavy Quark diffusion from lattice QCD spectral functions

We analyze the low frequency part of charmonium spectral functions on large lattices close to the continuum limit in the temperature region $1.5\lesssim T/T_c\lesssim 3$ as well as for $T \simeq 0.75T_c$. We present evidence for the existence of a transport peak above $T_c$ and its absence below $T_c$. The heavy quark diffusion constant is then estimated using the Kubo formula. As part of the calculation we also determine the temperature dependence of the signature for the charmonium bound state in the spectral function and discuss the fate of charmonium states in the hot medium.Comment: 4 pages, Proceedings for Quark Matter 2011 Conference, May 23-28, 2011, Annecy, Franc

Machine learning invariants of arithmetic curves

We show that standard machine learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around 105, we demonstrate the utility of machine learning in classification problems pertaining to the BSD invariants of an elliptic curve (including its rank and torsion subgroup), and the analogous invariants of a genus 2 curve. Our results show that a trained machine can efficiently classify curves according to these invariants with high accuracies (>0.97). For problems such as distinguishing between torsion orders, and the recognition of integral points, the accuracies can reach 0.998

Machine-learning the Sato-Tate conjecture

We apply some of the latest techniques from machine-learning to the arithmetic of hyperelliptic curves. More precisely we show that, with impressive accuracy and confidence (between 99 and 100 percent precision), and in very short time (matter of seconds on an ordinary laptop), a Bayesian classifier can distinguish between Sato–Tate groups given a small number of Euler factors for the L-function. Our observations are in keeping with the Sato-Tate conjecture for curves of low genus. For elliptic curves, this amounts to distinguishing generic curves (with Sato–Tate group SU(2)) from those with complex multiplication. In genus 2, a principal component analysis is observed to separate the generic Sato–Tate group USp(4) from the non-generic groups. Furthermore in this case, for which there are many more non-generic possibilities than in the case of elliptic curves, we demonstrate an accurate characterisation of several Sato–Tate groups with the same identity component. Throughout, our observations are verified using known results from the literature and the data available in the LMFDB. The results in this paper suggest that a machine can be trained to learn the Sato–Tate distributions and may be able to classify curves efficiently