808 research outputs found
4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion
Four-dimensional (4D) radar--visual odometry (4DRVO) integrates complementary
information from 4D radar and cameras, making it an attractive solution for
achieving accurate and robust pose estimation. However, 4DRVO may exhibit
significant tracking errors owing to three main factors: 1) sparsity of 4D
radar point clouds; 2) inaccurate data association and insufficient feature
interaction between the 4D radar and camera; and 3) disturbances caused by
dynamic objects in the environment, affecting odometry estimation. In this
paper, we present 4DRVO-Net, which is a method for 4D radar--visual odometry.
This method leverages the feature pyramid, pose warping, and cost volume (PWC)
network architecture to progressively estimate and refine poses. Specifically,
we propose a multi-scale feature extraction network called Radar-PointNet++
that fully considers rich 4D radar point information, enabling fine-grained
learning for sparse 4D radar point clouds. To effectively integrate the two
modalities, we design an adaptive 4D radar--camera fusion module (A-RCFM) that
automatically selects image features based on 4D radar point features,
facilitating multi-scale cross-modal feature interaction and adaptive
multi-modal feature fusion. In addition, we introduce a velocity-guided
point-confidence estimation module to measure local motion patterns, reduce the
influence of dynamic objects and outliers, and provide continuous updates
during pose refinement. We demonstrate the excellent performance of our method
and the effectiveness of each module design on both the VoD and in-house
datasets. Our method outperforms all learning-based and geometry-based methods
for most sequences in the VoD dataset. Furthermore, it has exhibited promising
performance that closely approaches that of the 64-line LiDAR odometry results
of A-LOAM without mapping optimization.Comment: 14 pages,12 figure
Monitoring wound healing of elastic cartilage using multiphoton microscopy
SummaryObjectiveTo demonstrate the ability of multiphoton microscopy (MPM) for monitoring wound healing of elastic cartilage.MethodIn a rabbit ear model, four cartilage specimen groups at 1-day, 1-, 4-, 20-week healing time points as well as a normal elastic cartilage were examined with MPM without using labeling agents. MPM images at wound margins were obtained from specimens at different healing stages, compared with the Hematoxylin and Eosin (H&E) stained images. Image analysis was performed to characterize the collagen morphology for quantifying the wound healing progression of elastic cartilage.ResultsMPM provided high-resolution images of elastic cartilage at varying depths. Comparisons of the images of specimens at different healing stages show obvious cell growth and matrix deposition. The results are consistent with the histological results. Moreover, quantitative analysis results show significant alteration in the collagen cavity size or collagen orientation index during wound healing of elastic cartilage, indicating the possibility to act as indicators for monitoring wound healing.ConclusionOur results suggested that MPM has the ability to monitor the wound healing progression of elastic cartilage, based on the visualization of cell growth and proliferation and quantitative characterization of collagen morphology during wound healing
C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak
The novel coronavirus disease (COVID-19) has crushed daily routines and is
still rampaging through the world. Existing solution for nonpharmaceutical
interventions usually needs to timely and precisely select a subset of
residential urban areas for containment or even quarantine, where the spatial
distribution of confirmed cases has been considered as a key criterion for the
subset selection. While such containment measure has successfully stopped or
slowed down the spread of COVID-19 in some countries, it is criticized for
being inefficient or ineffective, as the statistics of confirmed cases are
usually time-delayed and coarse-grained. To tackle the issues, we propose
C-Watcher, a novel data-driven framework that aims at screening every
neighborhood in a target city and predicting infection risks, prior to the
spread of COVID-19 from epicenters to the city. In terms of design, C-Watcher
collects large-scale long-term human mobility data from Baidu Maps, then
characterizes every residential neighborhood in the city using a set of
features based on urban mobility patterns. Furthermore, to transfer the
firsthand knowledge (witted in epicenters) to the target city before local
outbreaks, we adopt a novel adversarial encoder framework to learn
"city-invariant" representations from the mobility-related features for precise
early detection of high-risk neighborhoods, even before any confirmed cases
known, in the target city. We carried out extensive experiments on C-Watcher
using the real-data records in the early stage of COVID-19 outbreaks, where the
results demonstrate the efficiency and effectiveness of C-Watcher for early
detection of high-risk neighborhoods from a large number of cities.Comment: 11 pages, accepted by AAAI 2021, appendix is include
Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors
Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model
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