Gorenstein formats, canonical and Calabi-Yau threefolds

We extend the known classification of threefolds of general type that are complete intersections to various classes of non-complete intersections, and find other classes of polarised varieties, including Calabi-Yau threefolds with canonical singularities, that are not complete intersections. Our methods apply more generally to construct orbifolds described by equations in given Gorenstein formats.Comment: 25 page

Coherent Operation of a Gap-tunable Flux Qubit

We replace the Josephson junction defining a three-junction flux qubit's properties with a tunable direct current superconducting quantum interference devices (DC-SQUID) in order to tune the qubit gap during the experiment. We observe different gaps as a function of the external magnetic pre-biasing field and the local magnetic field through the DC-SQUID controlled by high-bandwidth on chip control lines. The persistent current and gap behavior correspond to numerical simulation results. We set the sensitivity of the gap on the control lines during the sample design stage. With a tuning range of several GHz on a qubit dynamics timescale, we observe coherent system dynamics at the degeneracy point.Comment: 3 pages, 4 figure

H\"older Error Bounds and H\"older Calmness with Applications to Convex Semi-Infinite Optimization

Using techniques of variational analysis, necessary and sufficient subdifferential conditions for H\"older error bounds are investigated and some new estimates for the corresponding modulus are obtained. As an application, we consider the setting of convex semi-infinite optimization and give a characterization of the H\"older calmness of the argmin mapping in terms of the level set mapping (with respect to the objective function) and a special supremum function. We also estimate the H\"older calmness modulus of the argmin mapping in the framework of linear programming.Comment: 25 page

Hidden Two-Stream Convolutional Networks for Action Recognition

Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding, and not end-to-end trainable. In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames. We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. Our end-to-end approach is 10x faster than its two-stage baseline. Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.Comment: Accepted at ACCV 2018, camera ready. Code available at https://github.com/bryanyzhu/Hidden-Two-Strea