SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments

Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving and the generalization of learning-based algorithms on different environmental effects is still an open problem. Although monocular depth prediction has been well studied recently, there is few work focusing on the robust learning-based depth prediction across different environments, e.g. changing illumination and seasons, owing to the lack of such a multi-environment real-world dataset and benchmark. To this end, the first cross-season monocular depth prediction dataset and benchmark SeasonDepth is built based on CMU Visual Localization dataset. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset, the influence of multiple environments on performance and robustness is analyzed qualitatively and quantitatively, showing that the long-term monocular depth prediction is still challenging even with fine-tuning. We further give promising avenues that self-supervised training and stereo geometry constraint help to enhance the robustness to changing environments. The dataset is available on https://seasondepth.github.io, and benchmark toolkit is available on https://github.com/SeasonDepth/SeasonDepth.Comment: 19 pages, 13 figure

Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling

Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data. Rotation, as an important quantity in computer vision, graphics, and robotics, can exhibit many ambiguities when occlusion and symmetry occur and thus demands such probabilistic models. Though much progress has been made for NFs in Euclidean space, there are no effective normalizing flows without discontinuity or many-to-one mapping tailored for SO(3) manifold. Given the unique non-Euclidean properties of the rotation manifold, adapting the existing NFs to SO(3) manifold is non-trivial. In this paper, we propose a novel normalizing flow on SO(3) by combining a Mobius transformation-based coupling layer and a quaternion affine transformation. With our proposed rotation normalizing flows, one can not only effectively express arbitrary distributions on SO(3), but also conditionally build the target distribution given input observations. Extensive experiments show that our rotation normalizing flows significantly outperform the baselines on both unconditional and conditional tasks.Comment: CVPR 202

Methyl-CpG binding protein 2 is associated with the prognosis and mortality of elderly patients with hip fractures

Objectives: To investigate the expression level and clinical significance of Methyl-CpG binding Protein 2 (MECP2) in elderly patients with hip fractures. Methods: This prospective observational study included 367 elderly patients with hip fractures between April 2016 and December 2018. All the patients were treated with internal fixation or joint replacement. In addition, 50 healthy elderly individuals were enrolled as healthy controls. The serum levels of MECP2 and inflammatory factors Interleukin (IL)-1β, IL-6, IL-8, and Tumor Necrosis Factor (TNF)-α was determined by enzyme-linked immunosorbent assay. Data on patients' basic characteristics and postoperative complications were collected. The Harris score was used to assess hip function at 1-month, 3-months, and 6-months after surgery. Patient quality of life was measured using the Barthel Index (BI) score 3-months after surgery. The 1-year mortality was analyzed using the Kaplan-Meier curve, and logical regression was used to analyze the risk factors for mortality. Results: No significant differences were observed in the basic clinical characteristics of all patients. The serum MECP2 levels were remarkably high in patients with hip fractures and negatively correlated with serum IL-1β, IL-6, and TNF-α levels. Patients with higher MECP2 predicted higher dynamic Harris scores, lower postoperative complications, lower 1-year mortality, and higher BI scores. Logical regression showed that age was the only independent risk factor for postoperative 1-year mortality in elderly patients with hip fractures. Conclusion: Lower MECP2 predicted poor prognosis and higher 1-year mortality in elderly patients with hip fractures

A BIM-LCA approach for estimating the greenhouse gas emissions of large-scale public buildings : a case study

Exiting green building assessment standards sometimes cannot work well for large-scale public buildings due to insufficient attention to the operation and maintenance stage. This paper combines the theory of life cycle assessment (LCA) and building information modeling (BIM) technology, thereby proposing a green building assessment method by calculating the greenhouse gas emissions (GGE) of buildings from cradle to grave. Life cycle GGE (LCGGE) can be divided into three parts, including the materialization stage, the operation and maintenance stage, and the demolition stage. Two pieces of BIM software (Revit and Designbuilder) are applied in this study. A museum in Guangdong, China, with a hot summer and warm winter is selected for a case study. The results show that BIM can provide a rich source of needed engineering information for LCA. In addition, the operation and maintenance stage plays the most important role in the GGE reduction of a building throughout the whole life cycle. This research contributes to the knowledge body concerning green buildings and sustainable construction. It helps to achieve the reduction of GGE over the whole life cycle of a building. This is pertinent to contractors, homebuyers, and governments who are constantly seeking ways to achieve a low-carbon econom

Improving Utility of GPU in Accelerating Industrial Applications with User-centred Automatic Code Translation

SMEs (Small and medium-sized enterprises), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck on the speed of computation in many applications. The recent developments in GPUs have been the marked increase in their versatility in many computational areas. But due to the lack of specialist GPU (Graphics processing units) programming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and hard-to-use. Little attentions have been paid to the applicability, usability and learnability of these tools for normal users. In this paper, we present an online automated CPU-to-GPU source translation system, (GPSME) for inexperienced users to utilize GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with new kernel generation scheme and memory management hierarchy to optimize its performance. A web-service based interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with non-expert GPU users in 4 SMEs reflect that GPSME system can efficiently accelerate real-world applications with at least 4x and have a better applicability, usability and learnability than existing automatic CPU-to-GPU source translators