55 research outputs found
Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction
Access to quality travel time information for roads in a road network has
become increasingly important with the rising demand for real-time travel time
estimation for paths within road networks. In the context of the Danish road
network (DRN) dataset used in this paper, the data coverage is sparse and
skewed towards arterial roads, with a coverage of 23.88% across 850,980 road
segments, which makes travel time estimation difficult. Existing solutions for
graph-based data processing often neglect the size of the graph, which is an
apparent problem for road networks with a large amount of connected road
segments. To this end, we propose a framework for predicting road segment
travel speed histograms for dataless edges, based on a latent representation
generated by an adversarially regularized convolutional network. We apply a
partitioning algorithm to divide the graph into dense subgraphs, and then train
a model for each subgraph to predict speed histograms for the nodes. The
framework achieves an accuracy of 71.5% intersection and 78.5% correlation on
predicting travel speed histograms using the DRN dataset. Furthermore,
experiments show that partitioning the dataset into clusters increases the
performance of the framework. Specifically, partitioning the road network
dataset into 100 clusters, with approximately 500 road segments in each
cluster, achieves a better performance than when using 10 and 20 clusters.Comment: This thesis was completed 2020-06-12 and defended 2020-06-2
Generating stable molecules using imitation and reinforcement learning
Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted. However, these methods often rely on a graph representation, ignoring 3D information necessary for determining the stability of the molecules. We propose a reinforcement learning (RL) approach for generating molecules in Cartesian coordinates allowing for quantum chemical prediction of the stability. To improve sample-efficiency we learn basic chemical rules from imitation learning (IL) on the GDB-11 database to create an initial model applicable for all stoichiometries. We then deploy multiple copies of the model conditioned on a specific stoichiometry in a RL setting. The models correctly identify low energy molecules in the database and produce novel isomers not found in the training set. Finally, we apply the model to larger molecules to show how RL further refines the IL model in domains far from the training data
Scalable WDM phase regeneration in a single phase-sensitive amplifier through optical time lenses
Scalable solutions for data regeneration of multiple parallel channels are elusive. Here the authors report a scalable wavelength-division multiplexing technique for phase regeneration and demonstrate the highest reported number of regenerated wavelength-division multiplexed channels in a single phase regenerator
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