600 research outputs found
Breakdown Characteristics of Varistor Ceramics
Breakdown characteristics are of great importance for varistor ceramics, which largely depend on Schottky barriers at grain boundaries. In order to enhance breakdown performance for meeting the requirement of device miniaturization, different doping methods are introduced to not only restrict grain size from additional phase but also manipulate defect structure of Schottky barrier at grain boundaries from substitution. Distribution of barriers is another key point affecting breakdown characteristics in varistor ceramics. Dimensional effect, which is detected in not only ZnO ceramics but also CaCu3Ti4O12 ceramics, is practically and theoretically found to be closely correlated with uniformity of grains. As a result, breakdown characteristics of varistors are dominated by combination effect of single barrier performance and spatial barrier distribution. In this chapter, enhanced breakdown field in CaxSr1−xCu3Ti4O12 ceramics, in situ synthesized CaCu3Ti4O12-CuAl2O4 ceramics, and CaCu3Ti4O12-Y2/3Cu3Ti4O12 composite ceramics are investigated from the aspect of Schottky barriers at grain boundaries. In addition, dimensional effect is found in both ZnO and CaCu3Ti4O12 ceramics, which are investigated from grain size distribution through theoretical and experimental analysis
Explicit Contextual Semantics for Text Comprehension
Who did what to whom is a major focus in natural language understanding,
which is right the aim of semantic role labeling (SRL) task. Despite of sharing
a lot of processing characteristics and even task purpose, it is surprisingly
that jointly considering these two related tasks was never formally reported in
previous work. Thus this paper makes the first attempt to let SRL enhance text
comprehension and inference through specifying verbal predicates and their
corresponding semantic roles. In terms of deep learning models, our embeddings
are enhanced by explicit contextual semantic role labels for more fine-grained
semantics. We show that the salient labels can be conveniently added to
existing models and significantly improve deep learning models in challenging
text comprehension tasks. Extensive experiments on benchmark machine reading
comprehension and inference datasets verify that the proposed semantic learning
helps our system reach new state-of-the-art over strong baselines which have
been enhanced by well pretrained language models from the latest progress.Comment: Proceedings of the 33nd Pacific Asia Conference on Language,
Information and Computation (PACLIC 33
Neural 3D Scene Reconstruction from Multiple 2D Images without 3D Supervision
Neural 3D scene reconstruction methods have achieved impressive performance
when reconstructing complex geometry and low-textured regions in indoor scenes.
However, these methods heavily rely on 3D data which is costly and
time-consuming to obtain in real world. In this paper, we propose a novel
neural reconstruction method that reconstructs scenes using sparse depth under
the plane constraints without 3D supervision. We introduce a signed distance
function field, a color field, and a probability field to represent a scene. We
optimize these fields to reconstruct the scene by using differentiable ray
marching with accessible 2D images as supervision. We improve the
reconstruction quality of complex geometry scene regions with sparse depth
obtained by using the geometric constraints. The geometric constraints project
3D points on the surface to similar-looking regions with similar features in
different 2D images. We impose the plane constraints to make large planes
parallel or vertical to the indoor floor. Both two constraints help reconstruct
accurate and smooth geometry structures of the scene. Without 3D supervision,
our method achieves competitive performance compared with existing methods that
use 3D supervision on the ScanNet dataset.Comment: 10 pages, 6 figure
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