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

Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments

Visual localization is a crucial problem in mobile robotics and autonomous driving. One solution is to retrieve images with known pose from a database for the localization of query images. However, in environments with drastically varying conditions (e.g. illumination changes, seasons, occlusion, dynamic objects), retrieval-based localization is severely hampered and becomes a challenging problem. In this paper, a novel domain-invariant feature learning method (DIFL) is proposed based on ComboGAN, a multi-domain image translation network architecture. By introducing a feature consistency loss (FCL) between the encoded features of the original image and translated image in another domain, we are able to train the encoders to generate domain-invariant features in a self-supervised manner. To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector. We then preform retrieval by selecting the database image with the most similar domain-invariant feature vector. We validate the proposed approach on the CMU-Seasons dataset, where we outperform state-of-the-art learning-based descriptors in retrieval-based localization for high and medium precision scenarios.Comment: Accepted by 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

The limbic-prefrontal network modulated by electroacupuncture at CV4 and CV12

fMRI studies showed that acupuncture could induce hemodynamic changes in brain networks. Many of these studies focused on whether specific acupoints could activate specific brain regions and were often limited to manual acupuncture at acupoints on the limbs. In this fMRI study, we investigated acupuncture\u27s modulation effects on brain functional networks by electroacupuncture (EA) at acupoints on the midline of abdomen. Acupoints Guanyuan (CV4) and Zhongwan (CV12) were stimulated in 21 healthy volunteers. The needling sensations, brain activation, and functional connectivity were studied. We found that the limbic-prefrontal functional network was deactivated by EA at CV4 and CV12. More importantly, the local functional connectivity was significantly changed during EA stimulation, and the change persisted during the period after the stimulation. Although minor differences existed, both acupoints similarly modulated the limbic-prefrontal functional network, which is overlapped with the functional circuits associated with emotional and cognitive regulation. Copyright © 2012 Jiliang Fang et al

The Limbic-Prefrontal Network Modulated by Electroacupuncture at CV4 and CV12

fMRI studies showed that acupuncture could induce hemodynamic changes in brain networks. Many of these studies focused on whether specific acupoints could activate specific brain regions and were often limited to manual acupuncture at acupoints on the limbs. In this fMRI study, we investigated acupuncture's modulation effects on brain functional networks by electroacupuncture (EA) at acupoints on the midline of abdomen. Acupoints Guanyuan (CV4) and Zhongwan (CV12) were stimulated in 21 healthy volunteers. The needling sensations, brain activation, and functional connectivity were studied. We found that the limbic-prefrontal functional network was deactivated by EA at CV4 and CV12. More importantly, the local functional connectivity was significantly changed during EA stimulation, and the change persisted during the period after the stimulation. Although minor differences existed, both acupoints similarly modulated the limbic-prefrontal functional network, which is overlapped with the functional circuits associated with emotional and cognitive regulation

lncRNA LOC100911717-targeting GAP43-mediated sympathetic remodeling after myocardial infarction in rats

ObjectiveSympathetic remodeling after myocardial infarction (MI) is the primary cause of ventricular arrhythmias (VAs), leading to sudden cardiac death (SCD). M1-type macrophages are closely associated with inflammation and sympathetic remodeling after MI. Long noncoding RNAs (lncRNAs) are critical for the regulation of cardiovascular disease development. Therefore, this study aimed to identify the lncRNAs involved in MI and reveal a possible regulatory mechanism.Methods and resultsM0- and M1-type macrophages were selected for sequencing and screened for differentially expressed lncRNAs. The data revealed that lncRNA LOC100911717 was upregulated in M1-type macrophages but not in M0-type macrophages. In addition, the lncRNA LOC100911717 was upregulated in heart tissues after MI. Furthermore, an RNA pull-down assay revealed that lncRNA LOC100911717 could interact with growth-associated protein 43 (GAP43). Essentially, immunofluorescence assays and programmed electrical stimulation demonstrated that GAP43 expression was suppressed and VA incidence was reduced after lncRNA LOC100911717 knockdown in rat hearts using an adeno-associated virus.ConclusionsWe observed a novel relationship between lncRNA LOC100911717 and GAP43. After MI, lncRNA LOC100911717 was upregulated and GAP43 expression was enhanced, thus increasing the extent of sympathetic remodeling and the frequency of VA events. Consequently, silencing lncRNA LOC100911717 could reduce sympathetic remodeling and VAs

Performance Analysis of the Structures Using Glass-Fiber-Reinforced-Polymer-Produced Hollow Internal Molds

Hollow structures reduce weight without compromising load-bearing capacity and are widely used. The new Glass-Fiber-Reinforced Polymer high-strength thin-walled inner mold simplifies internal cavity construction and boosts structural performance. This study first investigates the influence of a GFRP high-strength thin-walled circular tube on the cross-sectional load-carrying capacity of hollow slabs. Then, a formula for the bending load-carrying capacity of the section under the action of the tube is derived. The results indicate that when the height of the concrete compression zone meets certain conditions, GFRP high-strength thin-walled circular tubes can improve the ultimate load-carrying capacity of the hollow floor slabs. In order to achieve a more economical design, the bending moment modification of a GFRP high-strength thin-walled circular tube of a continuous slab was studied. Research has found that the bending moment modulation limit for a continuous slab is 35.65% when it is subjected to a load of Pu=24 kN. Experimental analysis has shown that the results are generally consistent with the calculations. In practical engineering, the application of a GFRP high-strength thin-walled circular tube of continuous slabs has limitations. Therefore, this study investigated a GFRP high-strength thin-walled honeycomb core slab and found that its ultimate load-bearing capacity is greater compared to waffle slabs. In addition, the stress performance of the GFRP high-strength thin-walled honeycomb core internal mold is superior, making it more promising for practical applications