Learning-Based Intrinsic Reflectional Symmetry Detection.

Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this article, we propose a learning-based approach to intrinsic reflectional symmetry detection.

Synthesizing Mesh Deformation Sequences With Bidirectional LSTM.

Synthesizing realistic 3D mesh deformation sequences is a challenging but important task in computer animation. To achieve this, researchers have long been focusing on shape analysis to develop new interpolation and extrapolation techniques. However, such techniques have limited learning capabilities and therefore often produce unrealistic deformation.

Learning on 3D Meshes With Laplacian Encoding and Pooling.

3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is in great need. Unlike images, 3D meshes have irregular connectivity, requiring careful design to capture relations in the data.

Promoter hypermethylation-mediated downregulation of miR-770 and its host gene MEG3, a long non-coding RNA, in the development of gastric cardia adenocarcinoma.

Maternally expressed gene 3 (MEG3) is an imprinted gene located at 14q32 which encodes an lncRNA and is downregulated in an expanding list of cancer cell lines and primary human cancers. The miR-770 is transcribed from the intronic sequence of MEG3 and MEG3 may be the host gene for miR-770. However, the biological role of MEG3 and miR-770 in gastric cardia adenocarcinoma (GCA) development and prognosis is poorly defined.