Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes

Unsupervised monocular depth estimation techniques have demonstrated encouraging results but typically assume that the scene is static. These techniques suffer when trained on dynamical scenes, where apparent object motion can equally be explained by hypothesizing the object's independent motion, or by altering its depth. This ambiguity causes depth estimators to predict erroneous depth for moving objects. To resolve this issue, we introduce Dynamo-Depth, an unifying approach that disambiguates dynamical motion by jointly learning monocular depth, 3D independent flow field, and motion segmentation from unlabeled monocular videos. Specifically, we offer our key insight that a good initial estimation of motion segmentation is sufficient for jointly learning depth and independent motion despite the fundamental underlying ambiguity. Our proposed method achieves state-of-the-art performance on monocular depth estimation on Waymo Open and nuScenes Dataset with significant improvement in the depth of moving objects. Code and additional results are available at https://dynamo-depth.github.io.Comment: NeurIPS 202

Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model

Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Therefore, we formulate amodal segmentation as an out-of-task and out-of-distribution generalization problem. Specifically, we replace the fully connected classifier in neural networks with a Bayesian generative model of the neural network features. The model is trained from non-occluded images using bounding box annotations and class labels only, but is applied to generalize out-of-task to object segmentation and to generalize out-of-distribution to segment occluded objects. We demonstrate how such Bayesian models can naturally generalize beyond the training task labels when they learn a prior that models the object's background context and shape. Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries. Our algorithm outperforms alternative methods that use the same supervision by a large margin, and even outperforms methods where annotated amodal segmentations are used during training, when the amount of occlusion is large. Code is publically available at https://github.com/YihongSun/Bayesian-Amodal

Six-month adherence to Statin use and subsequent risk of major adverse cardiovascular events (MACE) in patients discharged with acute coronary syndromes

Acknowledgements: The authors thank all participants who contributed to the study. Funding: CPACS-1 was funded by unrestricted educational grants from Guidant and Sanofi-Aventis, and grants from The Royal Australasian College of Physicians. AP is supported by an Australian National Heart Foundation Career Development Award. CPACS-2 was funded by an unrestricted grant from Sanofi-Aventis China. The George Institute for Global Health at Peking University Health Science Center sponsored the study and owns the data. Data analyses and reports were supported by Beijing Science and Technology Key Research Plan (D151100002215001). However, the authors are solely responsible for the design, analyses, the drafting and editing of the manuscript, and its final contents.Peer reviewedPublisher PD

A delay induced nonlocal free boundary problem

We study the dynamics of a population with an age structure whose population range expands with time, where the adult population is assumed to satisfy a reaction– diffusion equation over a changing interval determined by a Stefan type free boundary condition, while the juvenile population satisfies a reaction–diffusion equation whose evolving domain is determined by the adult population. The interactions between the adult and juvenile populations involve a fixed time-delay, which renders the model nonlocal in nature. After establishing the well-posedness of the model, we obtain a rather complete description of its long-time dynamical behaviour, which is shown to follow a spreading–vanishing dichotomy. When spreading persists, we show that the population range expands with an asymptotic speed, which is uniquely determined by an associated nonlocal elliptic problem over the half line. We hope this work will inspire further research on age-structured population models with an evolving population range

Effects of Using Aluminum Sulfate as an Accelerator and Acrylic Acid, Aluminum Fluoride, or Alkanolamine as a Regulator in Early Cement Setting

Aluminum sulfate was employed as the main accelerator in order to explore new non-chloride and alkali-free cement accelerators. Acrylic acid, aluminum fluoride, or alkanolamine were used as regulators to further accelerate cement setting. The setting time, compressive, and flexural strengths in cement early strength progress were detected, and both the cement (raw material) and hydrated mortar were fully characterized. The cement setting experiments revealed that only loading acrylic acid as the regulator would decrease the setting time of cement and increase the compressive and flexural strengths of mortar, but further introduction of aluminum fluoride or alkanolamine improved this process drastically. In the meantime, structural characterizations indicated that the raw material (cement) used in this work was composed of C3S (alite), while hydrated mortar consisted of quartz and C3A (tricalcium aluminate). During this transformation, the coordination polyhedron of Al3+ was changed from a tetrahedron to octahedron. This work puts forward a significant strategy for promoting the activity of aluminum sulfate in cement setting and would contribute to the future design of new non-chloride and alkali-free cement accelerators