These results suggest that point clouds derived from the PDBbind datasets are useful to evaluate the performance of 3D point clouds-centered deep learning algorithms, which could learn critical protein-ligand interactions from natural evolution or medicinal chemistry and have wide applications in studying protein-ligand interactions.
At the same time, we evaluated the performance of a variety of existing scoring functions in combination with ResAtom-Score in the absence of experimentally-determined conformations.
Eye gaze estimation has become increasingly significant in computer vision. In this paper, we systematically study the mainstream of eye gaze estimation methods, propose a novel methodology to estimate eye gaze points and eye gaze directions simultaneously. First, we construct a local sharing network for feature extraction of gaze points and gaze directions estimation, which can reduce network computational parameters and converge quickly;Second, we propose a Multiview Multitask Learning (MTL) framework, for gaze directions, a coplanar constraint is proposed for the left and right eyes, for gaze points, three views data input indirectly introduces eye position information, a cross-view pooling module is designed, propose joint loss which handle both gaze points and gaze directions estimation. Eventually, we collect a dataset to use of gaze points, which have three views to exist public dataset. The experiment show our method is state-of-the-art the current mainstream methods on two indicators of gaze points and gaze directions.
Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds.
Here, we introduce Multi-Modal Multitask MIMIC-III (M3) — a dataset and benchmark for evaluating machine learning algorithms in the healthcare domain.
We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method and demonstrate the remarkable performance of the method taking advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed.
Considering the prior human knowledge that these structures are in conformity to regular spatial layouts in terms of components, a learning-based topology-aware 3D reconstruction method which can obtain high-level structural graph layouts and low-level 3D shapes from images is proposed in this paper.
The vascular branch was described using a vascular centerline extraction method with multi-probability fusion-based topology optimization.
The focus in this paper is Bayesian system identification based on noisy incomplete modal data where we can impose spatially-sparse stiffness changes when updating a structural model.
The application of compressive sensing (CS) to structural health monitoring is an emerging research topic.
In this paper, we improve the theory of our previously proposed sparse Bayesian learning approach by eliminating an approximation and, more importantly, incorporating a constraint on stiffness increases.