Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification

Recently, substantial research effort has focused on how to apply CNNs or RNNs to better extract temporal patterns from videos, so as to improve the accuracy of video classification. In this paper, however, we show that temporal information, especially longer-term patterns, may not be necessary to achieve competitive results on common video classification datasets. We investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we propose a local feature integration framework based on attention clusters, and introduce a shifting operation to capture more diverse signals. We carefully analyze and compare the effect of different attention mechanisms, cluster sizes, and the use of the shifting operation, and also investigate the combination of attention clusters for multimodal integration. We demonstrate the effectiveness of our framework on three real-world video classification datasets. Our model achieves competitive results across all of these. In particular, on the large-scale Kinetics dataset, our framework obtains an excellent single model accuracy of 79.4% in terms of the top-1 and 94.0% in terms of the top-5 accuracy on the validation set. The attention clusters are the backbone of our winner solution at ActivityNet Kinetics Challenge 2017. Code and models will be released soon.Comment: The backbone of the winner solution at ActivityNet Kinetics Challenge 201

Testing and Data Reduction of the Chinese Small Telescope Array (CSTAR) for Dome A, Antarctica

The Chinese Small Telescope ARray (hereinafter CSTAR) is the first Chinese astronomical instrument on the Antarctic ice cap. The low temperature and low pressure testing of the data acquisition system was carried out in a laboratory refrigerator and on the 4500m Pamirs high plateau, respectively. The results from the final four nights of test observations demonstrated that CSTAR was ready for operation at Dome A, Antarctica. In this paper we present a description of CSTAR and the performance derived from the test observations.Comment: Accepted Research in Astronomy and Astrophysics (RAA) 1 Latex file and 20 figure

Longitudinal fluctuations and decorrelations of anisotropic flows in relativistic heavy-ion collisions

We study the longitudinal decorrelations of elliptic, triangular and quadrangular flows in heavy-ion collisions at the LHC and RHIC energies. The event-by-event CLVisc (3+1)-dimensional hydrodynamics model, combined with the fully fluctuating AMPT initial conditions, is utilized to simulate the space-time evolution of the strongly-coupled quark-gluon plasma. Detailed analysis is performed for the longitudinal decorrelations of flow vectors, flow magnitudes and flow orientations. We find strong correlations between final-state longitudinal decorrelations of anisotropic flows and initial-state longitudinal structures and collision geometry: while the decorrelation of elliptic flow shows a non-monotonic centrality dependence due to initial elliptic geometry, typically the longitudinal flow decorrelations are larger in lower energy and less central collisions where the mean lengths of the string structure are shorter in the initial states.Comment: 4 pages, 3 figures, Proceedings of Quark Matter 201

2-{(1R,2R)-2-[Bis(4-methyl­benz­yl)amino]­cyclo­hex­yl}isoindoline-1,3-dione

In the title mol­ecule, C30H32N2O2, the two tolyl rings form dihedral angles of 65.8 (1) and 6.6 (1)° with the isoindole-1,3-dione mean plane. The cyclo­hexane ring adopts a chair conformation

catena-Poly[zinc(II)-bis­[μ2-3-(3-pyrid­yl)­benzoato]-κ2 O:N;κ2 N:O]

In the title compound, [Zn(C12H8NO2)2]n, the Zn2+ cation is coordinated by a pair of carboxyl­ate O atoms as well as two pyridyl N atoms to afford a distorted tetra­hedral environment. Adjacent Zn2+ cations, with a separation of 8.807 (2) Å, are linked by two 3-(3-pyrid­yl)benzoate ligand bridges, generating an infinite ribbon extending parallel to [001]