2,414 research outputs found
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
Word contexts enhance the neural representation of individual letters in early visual cortex
Visual context facilitates perception, but how this is neurally implemented remains unclear. One example of contextual facilitation is found in reading, where letters are more easily identified when embedded in a word. Bottom-up models explain this word advantage as a post-perceptual decision bias, while top-down models propose that word contexts enhance perception itself. Here, we arbitrate between these accounts by presenting words and nonwords and probing the representational fidelity of individual letters using functional magnetic resonance imaging. In line with top-down models, we find that word contexts enhance letter representations in early visual cortex. Moreover, we observe increased coupling between letter information in visual cortex and brain activity in key areas of the reading network, suggesting these areas may be the source of the enhancement. Our results provide evidence for top-down representational enhancement in word recognition, demonstrating that word contexts can modulate perceptual processing already at the earliest visual regions
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