Non-stoichiometry and optical spectra of Nd(III) substituted PbTiO3

The non-stoichiometry of the perovskite (ABO3)-type phase in the system PbO---TiO2---Nd2O3 has been studied. Monophasic compounds of composition Pb1−αxNdxTiO3+x(1.5−α) with x 0.21 and 0.09 α 1.5 were prepared. The ferroelectric Curie temperature (Tc) shows a decrease of 18.5 K/at% Nd with increasing value of x. Tc shows an increase of 3.5 K/mol % PbO with decreasing value of α (increasing content of PbO). The observed effect of α on optical spectra can be interpreted by assuming that Nd(III) ions partly occupy B sites in compounds with α < 1.5

Electron spin resonance of Gd3+ in ceramic PbTio3

The electron spin resonance spectra of Gd3+ in ceramic PbTiO3 and, as a comparison, in powdered Bi3Mg2(NO3)12·24H2O are reported. The interpretation, in terms of crystal field parameters, of the spectrum of the double nitrate is in good agreement with previous single crystal results. It was not possible to interpret the room temperature spectrum of PbTiO3. However, the spectrum of this compound measured above its Curie-temperature (763 K) can be interpreted. This shows that the local crystal field symmetry of a small part of the Gd3+-ions at this temperature is orthorhombic or lower, not-withstanding the cubic lattice symmetry of PbTiO3 under these conditions

Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization

State-of-the-art temporal action detectors inefficiently search the entire video for specific actions. Despite the encouraging progress these methods achieve, it is crucial to design automated approaches that only explore parts of the video which are the most relevant to the actions being searched for. To address this need, we propose the new problem of action spotting in video, which we define as finding a specific action in a video while observing a small portion of that video. Inspired by the observation that humans are extremely efficient and accurate in spotting and finding action instances in video, we propose Action Search, a novel Recurrent Neural Network approach that mimics the way humans spot actions. Moreover, to address the absence of data recording the behavior of human annotators, we put forward the Human Searches dataset, which compiles the search sequences employed by human annotators spotting actions in the AVA and THUMOS14 datasets. We consider temporal action localization as an application of the action spotting problem. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently (observing on average 17.3% of the video) but it also accurately finds human activities with 30.8% mAP.Comment: Accepted to ECCV 201