An ATC Simulation Platform based Compass Satellite Navigation System

AbstractBased real-time, high precision characteristics of satellite navigation system, the aircraft in flight can be continuous and accurate positioned its location. Therefore the interval width of en-route and the interval separation could be reduced. And the reduced flight time, the increased flights, and the high utilization of airspace could meet the development of airline transportation. Thus the free flight could be achieved in near future. The compass satellite navigation system is established by the Chinese regional navigation and positioning system. The system could provide users around the clock, the clock real-time location services, short message service and precision timing services. According to the compass satellite navigation system, an air traffic control simulation platform based compass satellite navigation system has been proposed in this paper. The structure, functions and the future research fields have been introduced in detail. The research of this paper plays an important role to impel applications of air traffic management based compass navigation satellite system

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

Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition

Energy-based language models (ELMs) parameterize an unnormalized distribution for natural sentences and are radically different from popular autoregressive language models (ALMs). As an important application, ELMs have been successfully used as a means for calculating sentence scores in speech recognition, but they all use less-modern CNN or LSTM networks. The recent progress in Transformer networks and large pretrained models such as BERT and GPT2 opens new possibility to further advancing ELMs. In this paper, we explore different architectures of energy functions and different training methods to investigate the capabilities of ELMs in rescoring for speech recognition, all using large pretrained models as backbones.Comment: Accepted into INTERSPEECH 202

An Improved Transplantation Strategy for Mouse Mesenchymal Stem Cells in an Acute Myocardial Infarction Model

To develop an effective therapeutic strategy for cardiac regeneration using bone marrow mesenchymal stem cells (BM-MSCs), the primary mouse BM-MSCs (1st BM-MSCs) and 5th passage BM-MSCs from β-galactosidase transgenic mice were respectively intramyocardially transplanted into the acute myocardial infarction (AMI) model of wild type mice. At the 6th week, animals/tissues from the 1st BM-MSCs group, the 5th passage BM-MSCs group, control group were examined. Our results revealed that, compared to the 5th passage BM-MSCs, the 1st BM-MSCs had better therapeutic effects in the mouse MI model. The 1st BM-MSCs maintained greater differentiation potentials towards cardiomocytes or vascular endothelial cells in vitro. This is indicated by higher expressions of cardiomyocyte and vascular endothelial cell mature markers in vitro. Furthermore, we identified that 24 proteins were down-regulated and 3 proteins were up-regulated in the 5th BM-MSCs in comparison to the 1st BM-MSCs, using mass spectrometry following two-dimensional electrophoresis. Our data suggest that transplantation of the 1st BM-MSCs may be an effective therapeutic strategy for cardiac tissue regeneration following AMI, and altered protein expression profiles between the 1st BM-MSCs and 5th passage BM-MSCs may account for the difference in their maintenance of stemness and their therapeutic effects following AMI