Variational Autoencoders for Deforming 3D Mesh Models

3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D meshes are flexible to represent 3D animation sequences as well as collections of objects of the same category, allowing diverse shapes with large-scale non-linear deformations. We propose a novel framework which we call mesh variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy to train, and requires very few training examples. We also propose an extended model which allows flexibly adjusting the significance of different latent variables by altering the prior distribution. Extensive experiments demonstrate that our general framework is able to learn a reasonable representation for a collection of deformable shapes, and produce competitive results for a variety of applications, including shape generation, shape interpolation, shape space embedding and shape exploration, outperforming state-of-the-art methods.Comment: CVPR 201

Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Rolling element bearings play a crucial role in determining the overall health condition of a rotating machine. An effective condition-monitoring program on bearing operation can improve a machine’s operation efficiency, reduce the maintenance/replacement cost, and prolong the useful lifespan of a machine. This chapter presents a general overview of various condition-monitoring and fault diagnosis techniques for rolling element bearings in the current practice and discusses the pros and cons of each technique. The techniques introduced in the chapter include data acquisition techniques, major parameters used for bearing condition monitoring, signal analysis techniques, and bearing fault diagnosis techniques using either statistical features or artificial intelligent tools. Several case studies are also presented in the chapter to exemplify the application of these techniques in the data analysis as well as bearing fault diagnosis and pattern recognition

TM-NET: Deep Generative Networks for Textured Meshes

We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in a part-aware manner. Once trained, the network can generate novel textured meshes from scratch or predict textures for a given 3D mesh, without image guidance. Plausible and diverse textures can be generated for the same mesh part, while texture compatibility between parts in the same shape is achieved via conditional generation. Specifically, our method produces texture maps for individual shape parts, each as a deformable box, leading to a natural UV map with minimal distortion. The network separately embeds part geometry (via a PartVAE) and part texture (via a TextureVAE) into their respective latent spaces, so as to facilitate learning texture probability distributions conditioned on geometry. We introduce a conditional autoregressive model for texture generation, which can be conditioned on both part geometry and textures already generated for other parts to achieve texture compatibility. To produce high-frequency texture details, our TextureVAE operates in a high-dimensional latent space via dictionary-based vector quantization. We also exploit transparencies in the texture as an effective means to model complex shape structures including topological details. Extensive experiments demonstrate the plausibility, quality, and diversity of the textures and geometries generated by our network, while avoiding inconsistency issues that are common to novel view synthesis methods

Traumatic asphyxia combined with diffuse axonal injury

AbstractTraumatic asphyxia, a rare, blunt chest trauma-related condition, indicates severe injury and is characterized by subconjunctival hemorrhage, facial edema, cyanosis, and petechiae. This condition mostly appears on the upper chest and face. Rapid oxygen administration with effective ventilation is essential in the treatment of traumatic asphyxia. Prognosis depends on rescue time and associated injuries. Most neurologic symptoms resolve within 24–48 hours and have relatively satisfactory results over a long-term follow-up. We herein report the case of severe and complicated thoracoabdominal compression with a delayed change in consciousness. Susceptibility-weighted magnetic resonance imaging revealed diffuse axonal injury with multifocal microhemorrhages in the brain stem, basal ganglia, internal capsules, and the genu and splenium of the corpus callosum. The patient was in the intensive care unit for more than 21 days

Internet Privacy Information Propagation Model

With the rapid growth of information and communication technology (ICT), the violation of information privacy has increased in recent years. The privacy concerns now re-emerge right because people perceives a threat from new ICT that are equipped with enhanced capabilities for surveillance, storage, retrieval, and diffusion of personal information. With the trend in the prevalence and the easy use of ICT, it is of necessary to pay much attention to the issue how the ICT can threaten the privacy of individuals on the Internet. While the Email and P2P tools are the most popular ICT, this paper aims at understanding their respectively dissemination patterns in spreading of personal private information. To this purpose, this paper using dynamic model technique to simulate the pattern of sensitive or personal private information propagating situation. In this study, an Email propagation model and a Susceptible-Infected-Removed (SIR) model are proposed to simulate the propagation patterns of Email and P2P network respectively. Knowing their dissemination patterns would be helpful for system designers, ICT manager, corporate IT personnel, educators, policy makers, and legislators to incorporate consciousness of social and ethical information issues into the protection of information privacy

AdaFocus: Towards End-to-end Weakly Supervised Learning for Long-Video Action Understanding

Developing end-to-end models for long-video action understanding tasks presents significant computational and memory challenges. Existing works generally build models on long-video features extracted by off-the-shelf action recognition models, which are trained on short-video datasets in different domains, making the extracted features suffer domain discrepancy. To avoid this, action recognition models can be end-to-end trained on clips, which are trimmed from long videos and labeled using action interval annotations. Such fully supervised annotations are expensive to collect. Thus, a weakly supervised method is needed for long-video action understanding at scale. Under the weak supervision setting, action labels are provided for the whole video without precise start and end times of the action clip. To this end, we propose an AdaFocus framework. AdaFocus estimates the spike-actionness and temporal positions of actions, enabling it to adaptively focus on action clips that facilitate better training without the need for precise annotations. Experiments on three long-video datasets show its effectiveness. Remarkably, on two of datasets, models trained with AdaFocus under weak supervision outperform those trained under full supervision. Furthermore, we form a weakly supervised feature extraction pipeline with our AdaFocus, which enables significant improvements on three long-video action understanding tasks