Integration of fiber coupled high-Q silicon nitride microdisks with atom chips

Micron scale silicon nitride (SiN_x) microdisk optical resonators are demonstrated with Q = 3.6 x 10^6 and an effective mode volume of 15 (\lambda / n)^3 at near visible wavelengths. A hydrofluoric acid wet etch provides sensitive tuning of the microdisk resonances, and robust mounting of a fiber taper provides efficient fiber optic coupling to the microdisks while allowing unfettered optical access for laser cooling and trapping of atoms. Measurements indicate that cesium adsorption on the SiN_x surfaces significantly red-detunes the microdisk resonances. A technique for parallel integration of multiple (10) microdisks with a single fiber taper is also demonstrated.Comment: Published vesion. Minor change

A long-lived spin-orbit-coupled degenerate dipolar Fermi gas

We describe the creation of a long-lived spin-orbit-coupled gas of quantum degenerate atoms using the most magnetic fermionic element, dysprosium. Spin-orbit-coupling arises from a synthetic gauge field created by the adiabatic following of degenerate dressed states comprised of optically coupled components of an atomic spin. Because of dysprosium's large electronic orbital angular momentum and large magnetic moment, the lifetime of the gas is limited not by spontaneous emission from the light-matter coupling, as for gases of alkali-metal atoms, but by dipolar relaxation of the spin. This relaxation is suppressed at large magnetic fields due to Fermi statistics. We observe lifetimes up to 400 ms, which exceeds that of spin-orbit-coupled fermionic alkali atoms by a factor of 10-100, and is close to the value obtained from a theoretical model. Elastic dipolar interactions are also observed to influence the Rabi evolution of the spin, revealing an interacting fermionic system. The long lifetime of this weakly interacting spin-orbit-coupled degenerate Fermi gas will facilitate the study of quantum many-body phenomena manifest at longer timescales, with exciting implications for the exploration of exotic topological quantum liquids.Comment: 11 pages, 8 figures, one appendi

Quantum degenerate dipolar Fermi gas

The interplay between crystallinity and superfluidity is of great fundamental and technological interest in condensed matter settings. In particular, electronic quantum liquid crystallinity arises in the non-Fermi liquid, pseudogap regime neighboring a cuprate's unconventional superconducting phase. While the techniques of ultracold atomic physics and quantum optics have enabled explorations of the strongly correlated, many-body physics inherent in, e.g., the Hubbard model, lacking has been the ability to create a quantum degenerate Fermi gas with interparticle interactions---such as the strong dipole-dipole interaction---capable of inducing analogs to electronic quantum liquid crystals. We report the first quantum degenerate dipolar Fermi gas, the realization of which opens a new frontier for exploring strongly correlated physics and, in particular, the quantum melting of smectics in the pristine environment provided by the ultracold atomic physics setting. A quantum degenerate Fermi gas of the most magnetic atom 161Dy is produced by laser cooling to 10 uK before sympathetically cooling with ultracold, bosonic 162Dy. The temperature of the spin-polarized 161Dy is a factor T/TF=0.2 below the Fermi temperature TF=300 nK. The co-trapped 162Dy concomitantly cools to approximately Tc for Bose-Einstein condensation, thus realizing a novel, nearly quantum degenerate dipolar Bose-Fermi gas mixture.Comment: 6 pages, 3 figure

Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation

In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors have proven empirically to be the leading representation for a large variety of applications. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). Motivated by the assumption that different distributions should be applied for different datasets, we present two other Mixture Models and derive their Expectation-Maximization and Fisher Vector expressions. The first is a Laplacian Mixture Model (LMM), which is based on the Laplacian distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution. An interesting property of the Expectation-Maximization algorithm for the latter is that in the maximization step, each dimension in each component is chosen to be either a Gaussian or a Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we achieve state-of-the-art results for both the image annotation and the image search by a sentence tasks.Comment: new version includes text synthesis by an RNN and experiments with the COCO benchmar