Silicon Waveguides and Ring Resonators at 5.5 {\mu}m

We demonstrate low loss ridge waveguides and the first ring resonators for the mid-infrared, for wavelengths ranging from 5.4 to 5.6 {\mu}m. Structures were fabricated using electron-beam lithography on the silicon-on-sapphire material system. Waveguide losses of 4.0 +/- 0.7 dB/cm are achieved, as well as Q-values of 3.0 k.Comment: 4 pages, 4 figures, includes supplemental material

Lattice Boltzmann Simulation of Non-Ideal Fluids

A lattice Boltzmann scheme able to model the hydrodynamics of phase separation and two-phase flow is described. Thermodynamic consistency is ensured by introducing a non-ideal pressure tensor directly into the collision operator. We also show how an external chemical potential can be used to supplement standard boundary conditions in order to investigate the effect of wetting on phase separation and fluid flow in confined geometries. The approach has the additional advantage of reducing many of the unphysical discretisation problems common to previous lattice Boltzmann methods.Comment: 11 pages, revtex, 4 Postscript figures, uuencode

UAVM: Towards Unifying Audio and Visual Models

Conventional audio-visual models have independent audio and video branches. In this work, we unify the audio and visual branches by designing a Unified Audio-Visual Model (UAVM). The UAVM achieves a new state-of-the-art audio-visual event classification accuracy of 65.8% on VGGSound. More interestingly, we also find a few intriguing properties of UAVM that the modality-independent counterparts do not have.Comment: Published in Signal Processing Letters. Code at https://github.com/YuanGongND/uav

Towards End-to-end Unsupervised Speech Recognition

Unsupervised speech recognition has shown great potential to make Automatic Speech Recognition (ASR) systems accessible to every language. However, existing methods still heavily rely on hand-crafted pre-processing. Similar to the trend of making supervised speech recognition end-to-end, we introduce \wvu~which does away with all audio-side pre-processing and improves accuracy through better architecture. In addition, we introduce an auxiliary self-supervised objective that ties model predictions back to the input. Experiments show that \wvu~improves unsupervised recognition results across different languages while being conceptually simpler.Comment: Preprin