69 research outputs found
Traffic Speed Prediction and Mobility Behavior Analysis Using On-Demand Ride-Hailing Service Data
Providing accurate traffic speed prediction is essential for the success of Intelligent Transportation Systems (ITS) deployments. Accurate traffic speed prediction allows traffic managers take proper countermeasures when emergent changes happen in the transportation network. In this thesis, we present a computationally less expensive machine learning approach XGBoost to predict the future travel speed of a selected sub-network in Beijing\u27s transportation network. We perform different experiments for predicting speed in the network from future 1 min to 20 min. We compare the XGBoost approach against other well-known machine learning and statistical models such as linear regression and decision tree, gradient boosting tree, and random forest regression models. Three metrics MAE, MAPE, and RMSE are used to evaluate the performance of the selected models. Our results show that XGBoost outperforms other models across different experiment conditions. Based on the prediction accuracy of different links, we find that the number of vehicles operating in a network also affect prediction performance. In addition, understanding individual mobility behavior is critical for modeling urban dynamics. It provides deeper insights on the generative mechanisms of human movements. Recently, different types of emerging data sources such as mobile phone call detail records, social media posts, GPS observations, and smart card transactions have been used to analyze individual mobility behavior. In this thesis, we report the spatio-temporal mobility behaviors using large-scale data collected from a ride-hailing service platform. Based on passenger-level travel data, we develop an algorithm to identify users\u27 visited places and the functions of those places. To characterize temporal movement patterns, we reveal the differences in trip generation characteristics between commuting and non-commuting trips and the distribution of gap time between consecutive trips. To understand spatial mobility patterns, we observe the distribution of the number of visited place and their rank, the spatial distribution of residences and workplaces, and the distribution of travel distance and travel time. Our analysis highlights the differences in mobility patterns of the users of ride-hailing services, compared to the findings of existing mobility studies based on other data sources. Our study shows a tremendous potential of developing high-resolution individual-level mobility model that can predict the demand of emerging mobility services with high accuracy
Modeling Individual Activity and Mobility Behavior and Assessing Ridesharing Impacts Using Emerging Data Sources
Predicting individual mobility behavior is one of the major steps of transportation planning models. Accurate prediction of individual mobility behavior will be beneficial for transportation planning. Although previous studies have used different data sources to model individual mobility behaviors, they have several limitations such as the lack of complete mobility sequences and travel mode information, limiting our ability to accurately predict individual movements. In recent years, the emergence of GPS-based floating car data (FCD) and on-demand ride-hailing service platforms can provide innovative data sources to understand and model individual mobility behavior. Compared to the previously used data sources such as mobile phone and social media data, mobility data extracted of the new data sources contain more specific, detailed, and longitudinal information of individual travel mode and coordinates of the visited locations. This dissertation explores the potential of using GPS-based FCD and on-demand ride-hailing service data with different modeling techniques towards understanding and predicting individual mobility and activity behaviors and assessing the ridesharing impacts through three studies
Learning Riemannian Stable Dynamical Systems via Diffeomorphisms
Dexterous and autonomous robots should be capable of executing elaborated
dynamical motions skillfully. Learning techniques may be leveraged to build
models of such dynamic skills. To accomplish this, the learning model needs to
encode a stable vector field that resembles the desired motion dynamics. This
is challenging as the robot state does not evolve on a Euclidean space, and
therefore the stability guarantees and vector field encoding need to account
for the geometry arising from, for example, the orientation representation. To
tackle this problem, we propose learning Riemannian stable dynamical systems
(RSDS) from demonstrations, allowing us to account for different geometric
constraints resulting from the dynamical system state representation. Our
approach provides Lyapunov-stability guarantees on Riemannian manifolds that
are enforced on the desired motion dynamics via diffeomorphisms built on neural
manifold ODEs. We show that our Riemannian approach makes it possible to learn
stable dynamical systems displaying complicated vector fields on both
illustrative examples and real-world manipulation tasks, where Euclidean
approximations fail.Comment: To appear at CoRL 202
Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation
Semi-supervised learning has become increasingly popular in medical image
segmentation due to its ability to leverage large amounts of unlabeled data to
extract additional information. However, most existing semi-supervised
segmentation methods only focus on extracting information from unlabeled data,
disregarding the potential of labeled data to further improve the performance
of the model. In this paper, we propose a novel Correlation Aware Mutual
Learning (CAML) framework that leverages labeled data to guide the extraction
of information from unlabeled data. Our approach is based on a mutual learning
strategy that incorporates two modules: the Cross-sample Mutual Attention
Module (CMA) and the Omni-Correlation Consistency Module (OCC). The CMA module
establishes dense cross-sample correlations among a group of samples, enabling
the transfer of label prior knowledge to unlabeled data. The OCC module
constructs omni-correlations between the unlabeled and labeled datasets and
regularizes dual models by constraining the omni-correlation matrix of each
sub-model to be consistent. Experiments on the Atrial Segmentation Challenge
dataset demonstrate that our proposed approach outperforms state-of-the-art
methods, highlighting the effectiveness of our framework in medical image
segmentation tasks. The codes, pre-trained weights, and data are publicly
available.Comment: MICCAI2023 early accepted, camera ready versio
High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Objective: Bloch simulation constitutes an essential part of magnetic
resonance imaging (MRI) development. However, even with the graphics processing
unit (GPU) acceleration, the heavy computational load remains a major
challenge, especially in large-scale, high-accuracy simulation scenarios. This
work aims to develop a deep learning-based simulator to accelerate Bloch
simulation. Approach: The simulator model, called Simu-Net, is based on an
end-to-end convolutional neural network and is trained with synthetic data
generated by traditional Bloch simulation. It uses dynamic convolution to fuse
spatial and physical information with different dimensions and introduces
position encoding templates to achieve position-specific labeling and overcome
the receptive field limitation of the convolutional network. Main Results:
Compared with mainstream GPU-based MRI simulation software, Simu-Net
successfully accelerates simulations by hundreds of times in both traditional
and advanced MRI pulse sequences. The accuracy and robustness of the proposed
framework were verified qualitatively and quantitatively. Besides, the trained
Simu-Net was applied to generate sufficient customized training samples for
deep learning-based T2 mapping and comparable results to conventional methods
were obtained in the human brain. Significance: As a proof-of-concept work,
Simu-Net shows the potential to apply deep learning for rapidly approximating
the forward physical process of MRI and may increase the efficiency of Bloch
simulation for optimization of MRI pulse sequences and deep learning-based
methods.Comment: 18 pages, 8 figure
Phosphoproteins regulated by heat stress in rice leaves
<p>Abstract</p> <p>Background</p> <p>High temperature is a critical abiotic stress that reduces crop yield and quality. Rice (<it>Oryza sativa </it>L.) plants remodel their proteomes in response to high temperature stress. Moreover, phosphorylation is the most common form of protein post-translational modification (PTM). However, the differential expression of phosphoproteins induced by heat in rice remains unexplored.</p> <p>Methods</p> <p>Phosphoprotein in the leaves of rice under heat stress were displayed using two-dimensional electrophoresis (2-DE) and Pro-Q Diamond dye. Differentially expressed phosphoproteins were identified by MALDI-TOF-TOF-MS/MS and confirmed by Western blotting.</p> <p>Results</p> <p>Ten heat-phosphoproteins were identified from twelve protein spots, including ribulose bisphos-phate carboxylase large chain, 2-Cys peroxiredoxin BAS1, putative mRNA binding protein, Os01g0791600 protein, OSJNBa0076N16.12 protein, putative H(+)-transporting ATP synthase, ATP synthase subunit beta and three putative uncharacterized proteins. The identification of ATP synthase subunit beta was further validated by Western-blotting. Four phosphorylation site predictors were also used to predict the phosphorylation sites and the specific kinases for these 10 phosphoproteins.</p> <p>Conclusion</p> <p>Heat stress induced the dephosphorylation of RuBisCo and the phosphorylation of ATP-β, which decreased the activities of RuBisCo and ATP synthase. The observed dephosphorylation of the mRNA binding protein and 2-Cys peroxiredoxin may be involved in the transduction of heat-stress signaling, but the functional importance of other phosphoproteins, such as H<sup>+</sup>-ATPase, remains unknown.</p
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