Global versus Localized Generative Adversarial Nets

In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data. Compared with the classic GAN that {\em globally} parameterizes a manifold, the Localized GAN (LGAN) uses local coordinate charts to parameterize distinct local geometry of how data points can transform at different locations on the manifold. Specifically, around each point there exists a {\em local} generator that can produce data following diverse patterns of transformations on the manifold. The locality nature of LGAN enables local generators to adapt to and directly access the local geometry without need to invert the generator in a global GAN. Furthermore, it can prevent the manifold from being locally collapsed to a dimensionally deficient tangent subspace by imposing an orthonormality prior between tangents. This provides a geometric approach to alleviating mode collapse at least locally on the manifold by imposing independence between data transformations in different tangent directions. We will also demonstrate the LGAN can be applied to train a robust classifier that prefers locally consistent classification decisions on the manifold, and the resultant regularizer is closely related with the Laplace-Beltrami operator. Our experiments show that the proposed LGANs can not only produce diverse image transformations, but also deliver superior classification performances

Magnetothermoelectric DC conductivities from holography models with hyperscaling factor in Lifshitz spacetime

We investigate an Einstein-Maxwell-Dilaton-Axion holographic model and obtain two branches of a charged black hole solution with a dynamic exponent and a hyperscaling violation factor when a magnetic field presents. The magnetothermoelectric DC conductivities are then calculated in terms of horizon data by means of holographic principle. We find that linear temperature dependence resistivity and quadratic temperature dependence inverse Hall angle can be achieved in our model. The well-known anomalous temperature scaling of the Nernst signal and the Seebeck coefficient of cuprate strange metals are also discussed.Comment: 1+23 pages, 4 figures, references adde

Interventional few-shot learning

Ministry of Education, Singapore under its Academic Research Funding Tier 1 and 2; Alibaba Innovative Research (AIR) programm

Rabi Spectroscopy of Super-Bloch Oscillations in Optical Lattice Clock

Super-Bloch oscillations(SBOs) is giant Bloch oscillations (BOs) when applying both static and periodically driving force to free atoms in lattice at the condition that Bloch oscillations are close to integer times of driving frequencies. Rather than observe SBOs in real space, this paper presents a method to observe it using Rabi spectroscopy of Optical lattice clock(OLC). An effective model of OLC with atoms been added both static and time-periodical forces is derived. Based on that, we propose an experimental scheme and give the Rabi spectrum under lab achievable parameters. Utilizing the precision spectroscopy of OLC, force with a large range could be accurately measured by measuring the Period of SBOs. We also gave the best parameter condition of measuring gravity by calculating Fisher information. Our work paves the way to study other exotic dynamics behaviors in Floquet driving OLC