Building Disease Detection Algorithms with Very Small Numbers of Positive Samples

Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive samples also create unbalanced datasets which limit the true positive rates of trained models. As unbalanced datasets are mostly unavoidable, it is greatly beneficial if we can extract useful knowledge from negative samples to improve classification accuracy on limited positive samples. To this end, we propose a new strategy for building medical image analysis pipelines that target disease detection. We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier. We show that using the feature maps of a trained segmentation network, deviations from normal anatomy can be learned by a two-class classification network on an extremely unbalanced training dataset with as little as one positive for 17 negative samples. We demonstrate that even though the segmentation network is only trained on normal cardiac computed tomography images, the resulting feature maps can be used to detect pericardial effusion and cardiac septal defects with two-class convolutional classification networks

Symplectic Geometry on Quantum Plane

A study of symplectic forms associated with two dimensional quantum planes and the quantum sphere in a three dimensional orthogonal quantum plane is provided. The associated Hamiltonian vector fields and Poissonian algebraic relations are made explicit.Comment: 12 pages, Late

QCD corrections to Upsilon production via color-octet states at the Tevatron and LHC

The NLO QCD corrections to Upsilon production via S-wave color-octet states Upsilon(^1S_0^8,^3S_1^8) at the Tevatron and LHC is calculated. The K factors of total cross section (ratio of NLO to LO) are 1.313 and 1.379 for Upsilon(^1S_0^8) and Upsilon(^3S_1^8) at the Tevatron, while at the LHC they are 1.044 and 1.182, respectively. By fitting the experimental data from the D0, the matrix elements for S-wave color-octet states are obtained. And new predictions for Upsilon production are presented. The prediction for the polarization of inclusive Upsilon contains large uncertainty rising from the polarization of Upsilon from feed-down of chi_b. To further clarify the situation, new measurements on the production and polarization for direct Upsilon are expected.Comment: 13 pages, 10 Figure

Work statistics across a quantum phase transition

We investigate the statistics of the work performed during a quench across a quantum phase transition using the adiabatic perturbation theory. It is shown that all the cumulants of work exhibit universal scaling behavior analogous to the Kibble-Zurek scaling for the average density of defects. Two kinds of transformations are considered: quenches between two gapped phases in which a critical point is traversed, and quenches that end near the critical point. In contrast to the scaling behavior of the density of defects, the scaling behavior of the work cumulants are shown to be qualitatively different for these two kinds of quenches. However, in both cases the corresponding exponents are fully determined by the dimension of the system and the critical exponents of the transition, as in the traditional Kibble-Zurek mechanism (KZM). Thus, our study deepens our understanding about the nonequilibrium dynamics of a quantum phase transition by revealing the imprint of the KZM on the work statistics

Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems

The proximal gradient algorithm has been popularly used for convex optimization. Recently, it has also been extended for nonconvex problems, and the current state-of-the-art is the nonmonotone accelerated proximal gradient algorithm. However, it typically requires two exact proximal steps in each iteration, and can be inefficient when the proximal step is expensive. In this paper, we propose an efficient proximal gradient algorithm that requires only one inexact (and thus less expensive) proximal step in each iteration. Convergence to a critical point %of the nonconvex problem is still guaranteed and has a $O(1/k)$ convergence rate, which is the best rate for nonconvex problems with first-order methods. Experiments on a number of problems demonstrate that the proposed algorithm has comparable performance as the state-of-the-art, but is much faster

Spin textures in slowly rotating Bose-Einstein Condensates

Slowly rotating spin-1 Bose-Einstein condensates are studied through a variational approach based upon lowest Landau level calculus. The author finds that in a gas with ferromagnetic interactions, such as $^{87}$Rb, angular momentum is predominantly carried by clusters of two different types of skyrmion textures in the spin-vector order parameter. Conversely, in a gas with antiferromagnetic interactions, such as $^{23}$Na, angular momentum is carried by $\pi$-disclinations in the nematic order parameter which arises from spin fluctuations. For experimentally relevant parameters, the cores of these $\pi$-disclinations are ferromagnetic, and can be imaged with polarized light.Comment: 14 pages, 12 low resolution bitmapped figures, RevTeX4. High resolution figures available from author. Suplementary movies available from autho