Interaction Between Supernova Remnant G22.7-0.2 And The Ambient Molecular Clouds

We have carried out 12CO (J=1-0 and 2-1), 13CO (J=1-0), and C18O (J=1-0) observations in the direction of the supernova remnant (SNR) G22.7-0.2. A filamentary molecular gas structure, which is likely part of a larger molecular complex with VLSR~75-79 km/s, is detected and is found to surround the southern boundary of the remnant. In particular, the high-velocity wing (77-110 km/s) in the 12CO (J=1-0 and J=2-1) emission shows convincing evidence of the interaction between SNR G22.7-0.2 and the 75-79 km/s molecular clouds (MCs). Spectra with redshifted profiles, a signature of shocked molecular gas, are seen in the southeastern boundary of the remnant. The association between the remnant and the 77 km/s MCs places the remnant at the near distance of 4.0-4.8 kpc, which agrees with a location on the Scutum-Crux arm. We suggest that SNR G22.7-0.2, SNR W41, and HII region G022.760-0.485 are at the same distance and are associated with GMC G23.0-0.4.Comment: 9 pages, 9 figures, 3 tables, accepted for publication in Ap

From Facial Parts Responses to Face Detection: A Deep Learning Approach

In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.Comment: To appear in ICCV 201

Fast quantum information transfer with superconducting flux qubits coupled to a cavity

We present a way to realize quantum information transfer with superconducting flux qubits coupled to a cavity. Because only resonant qubit-cavity interaction and resonant qubit-pulse interaction are applied, the information transfer can be performed much faster, when compared with the previous proposals. This proposal does not require adjustment of the qubit level spacings during the operation. Moreover, neither uniformity in the device parameters nor exact placement of qubits in the cavity is needed by this proposal.Comment: 6 pages, 3 figure

Standardized maximim D-optimal designs for enzyme kineticinhibition models

Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters.An alternative is the maximin approach that allows the user to specify a range of values for each parameter ofinterest. However, the maximin approach is difficult because we first have to determine the locally optimal designfor each set of nominal values before maximin types of optimal designs can be found via a nested optimizationprocess. We show that particle swarm optimization (PSO) techniques can solve such complex optimizationproblems effectively. We demonstrate numerical results from PSO can help find, for the first time, formulae forstandardized maximin D-optimal designs for nonlinear model with 3 or 4 parameters on the compact andnonnegative design space. Additionally, we show locally and standardized maximin D-optimal designs for inhibitionmodels are not necessarily supported at a minimum number of points. To facilitate use of such designs, wecreate a web-based tool for practitioners to find tailor-made locally and standardized maximin optimal designs