Investigating Properties of a Family of Quantum Renyi Divergences

Audenaert and Datta recently introduced a two-parameter family of relative R\'{e}nyi entropies, known as the $\alpha$-$z$-relative R\'{e}nyi entropies. The definition of the $\alpha$-$z$-relative R\'{e}nyi entropy unifies all previously proposed definitions of the quantum R\'{e}nyi divergence of order $\alpha$ under a common framework. Here we will prove that the $\alpha$-$z$-relative R\'{e}nyi entropies are a proper generalization of the quantum relative entropy by computing the limit of the $\alpha$-$z$ divergence as $\alpha$ approaches one and $z$ is an arbitrary function of $\alpha$. We also show that certain operationally relevant families of R\'enyi divergences are differentiable at $\alpha = 1$. Finally, our analysis reveals that the derivative at $\alpha = 1$ evaluates to half the relative entropy variance, a quantity that has attained operational significance in second-order quantum hypothesis testing.Comment: 15 pages, v2: journal versio

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, these methods are computationally wasteful in attempt to predict 3D shapes, where information is rich only on the surfaces. In this paper, we propose a novel 3D generative modeling framework to efficiently generate object shapes in the form of dense point clouds. We use 2D convolutional operations to predict the 3D structure from multiple viewpoints and jointly apply geometric reasoning with 2D projection optimization. We introduce the pseudo-renderer, a differentiable module to approximate the true rendering operation, to synthesize novel depth maps for optimization. Experimental results for single-image 3D object reconstruction tasks show that we outperforms state-of-the-art methods in terms of shape similarity and prediction density

Light Dark Matter: Models and Constraints

We study the direct detection prospects for a representative set of simplified models of sub-GeV dark matter (DM), accounting for existing terrestrial, astrophysical and cosmological constraints. We focus on dark matter lighter than an MeV, where these constraints are most stringent, and find three scenarios with accessible direct detection cross sections: (i) DM interacting via an ultralight kinetically mixed dark photon, (ii) a DM sub-component interacting with nucleons or electrons through a light scalar or vector mediator, and (iii) DM coupled with nucleons via a mediator heavier than ~ 100 keV.Comment: 44 pages, 13 figures, reference added and minor updates to some of the constraints, conclusions unchange

LHC limits on axion-like particles from heavy-ion collisions

In these proceedings we use recent LHC heavy-ion data to set a limit on axion-like particles coupling to electromagnetism with mass in the range 10-100 GeV. We recast ATLAS data as per the strategy proposed in 1607.06083, and find results in-line with the projections given there.Comment: 4 pages, 3 figures, conference proceeding for PHOTON201

Deep-LK for Efficient Adaptive Object Tracking

In this paper we present a new approach for efficient regression based object tracking which we refer to as Deep- LK. Our approach is closely related to the Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et al. We make the following contributions. First, we demonstrate that there is a theoretical relationship between siamese regression networks like GOTURN and the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further, we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance of the currently tracked frame. We argue that this missing property in GOTURN can be attributed to its poor performance on unseen objects and/or viewpoints. Second, we propose a novel framework for object tracking - which we refer to as Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive results demonstrating that Deep-LK substantially outperforms GOTURN. Additionally, we demonstrate comparable tracking performance to current state of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS) computationally efficient