Anti-TSN (C-terminal) polyclonal antibody

Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident. This paper aims to discover the principles to design effective ConvNet architectures for action recognition in videos and learn these models given limited training samples. Our first contribution is temporal segment network (TSN), a novel framework for video-based action recognition. which is based on the idea of long-range temporal structure modeling. It combines a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video. The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network. Our approach obtains the state-the-of-art performance on the datasets of HMDB51 (69.4%) and UCF101 (94.2%). We also visualize the learned ConvNet models, which qualitatively demonstrates the effectiveness of temporal segment network and the proposed good practices

Reaction of bis(p-tolyl-propargyl)-tosylamide with [(cod)RhCl](2) in the presence of CO gives the cyclopentadienone complex [(Cpd')Rh(CO)Cl](n) (Cpd' = TsN(CH2)(2)C-4(Tol)(2)CO). Its dissolution in DMSO or pyridine leads to decarbonylation and formation of the adducts (Cpd')Rh(DMSO)(2)Cl and (Cpd')Rh(Py)(2)Cl. The reaction of [(Cpd')Rh(CO)Cl](n) with AgPF6 and p-xylene produces the sandwich arene complex [(Cpd') Rh(p-xylene)]PF6, in which the arene can be substituted by (BuNC)-Bu-t ligand to give [(cpd')Rh((BuNC)-Bu-t)(3)]PF6. The arene complex acts as an efficient catalyst for the reductive amination of carbonyl compounds in the presence of CO as deoxygenation agent, producing various amines in 75-85% yields. (C) 2017 Elsevier B.V. All rights reserved.