1,086 research outputs found
Essays on the economics of networks
Networks (collections of nodes or vertices and graphs capturing their linkages) are a common object of study across a range of fields includ- ing economics, statistics and computer science. Network analysis is often based around capturing the overall structure of the network by some reduced set of parameters. Canonically, this has focused on the notion of centrality. There are many measures of centrality, mostly based around statistical analysis of the linkages between nodes on the network. However, another common approach has been through the use of eigenfunction analysis of the centrality matrix. My the- sis focuses on eigencentrality as a property, paying particular focus to equilibrium behaviour when the network structure is fixed. This occurs when nodes are either passive, such as for web-searches or queueing models or when they represent active optimizing agents in network games. The major contribution of my thesis is in the applica- tion of relatively recent innovations in matrix derivatives to centrality measurements and equilibria within games that are function of those measurements. I present a series of new results on the stability of eigencentrality measures and provide some examples of applications to a number of real world examples
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
In this paper, we address semantic segmentation of road-objects from 3D LiDAR
point clouds. In particular, we wish to detect and categorize instances of
interest, such as cars, pedestrians and cyclists. We formulate this problem as
a point- wise classification problem, and propose an end-to-end pipeline called
SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a
transformed LiDAR point cloud as input and directly outputs a point-wise label
map, which is then refined by a conditional random field (CRF) implemented as a
recurrent layer. Instance-level labels are then obtained by conventional
clustering algorithms. Our CNN model is trained on LiDAR point clouds from the
KITTI dataset, and our point-wise segmentation labels are derived from 3D
bounding boxes from KITTI. To obtain extra training data, we built a LiDAR
simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize
large amounts of realistic training data. Our experiments show that SqueezeSeg
achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per
frame), highly desirable for autonomous driving applications. Furthermore,
additionally training on synthesized data boosts validation accuracy on
real-world data. Our source code and synthesized data will be open-sourced
Starburst and post-starburst high-redshift protogalaxies: The feedback impact of high energy cosmic rays
Quenching of star-formation has been identified in many starburst and
post-starburst galaxies, indicating burst-like star-formation histories (SFH)
in the primordial Universe. We have investigated the role of high energy cosmic
rays (CRs) in such environments, particularly how they could contribute to this
burst-like SFH via quenching and feedback. These high energy particles interact
with the baryon and radiation fields of their host via hadronic processes to
produce secondary leptons. The secondary particles then also interact with
ambient radiation fields to generate X-rays through inverse-Compton scattering.
In addition, they can thermalise directly with the semi-ionised medium via
Coulomb processes. Heating at a rate of can be attained by Coulomb processes
in a star-forming galaxy with one core-collapse SN event per decade, and this
is sufficient to cause quenching of star-formation. At high-redshift, a
substantial amount of CR secondary electron energy can be diverted into
inverse-Compton X-ray emission. This yields an X-ray luminosity of above
by redshift which drives a further
heating effect, operating over larger scales. This would be able to halt
inflowing cold gas filaments, strangulating subsequent star-formation. We
selected a sample of 16 starburst and post-starburst galaxies at and determine the star-formation rates they could have sustained.
We applied a model with CR injection, propagation and heating to calculate
energy deposition rates in these 16 sources. Our calculations show that CR
feedback cannot be neglected as it has the strength to suppress star-formation
in these systems. We also show that their currently observed quiescence is
consistent with the suffocation of cold inflows, probably by a combination of
X-ray and CR heating.Comment: 30 pages, 14 figures, 4 tables, accepted for publication in A&A;
abstract abridged. V2: updates to match published version (minor typo
corrections
Preliminary design and optimization of toroidally-wound limited angle servo motor based on a generalized magnetic circuit model
This paper proposes a new generalized equivalent magnetic circuit model for the preliminary design of a toroidally-wound limited angle servo motor (LASM). In the model, the magnetic networks are formulated as a function of the pole number and geometric dimensions. Nonlinear saturation effect of the ferromagnetic material is also taken into consideration. A multi-objective optimization function involving the torque requirement, the mass, the time constant, and magnetic saturations of ferromagnetic material is introduced. Based on the proposed model, six design cases with different objectives have been carried by the particle swarm optimization (PSO) method. The comparisons of different optimization cases demonstrate the effectiveness and computation efficiency of the proposed method, and hence its suitability in preliminary design. Moreover, the generalized model can be readily applied in the other electromagnetic modelling
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
3D LiDAR scanners are playing an increasingly important role in autonomous
driving as they can generate depth information of the environment. However,
creating large 3D LiDAR point cloud datasets with point-level labels requires a
significant amount of manual annotation. This jeopardizes the efficient
development of supervised deep learning algorithms which are often data-hungry.
We present a framework to rapidly create point clouds with accurate point-level
labels from a computer game. The framework supports data collection from both
auto-driving scenes and user-configured scenes. Point clouds from auto-driving
scenes can be used as training data for deep learning algorithms, while point
clouds from user-configured scenes can be used to systematically test the
vulnerability of a neural network, and use the falsifying examples to make the
neural network more robust through retraining. In addition, the scene images
can be captured simultaneously in order for sensor fusion tasks, with a method
proposed to do automatic calibration between the point clouds and captured
scene images. We show a significant improvement in accuracy (+9%) in point
cloud segmentation by augmenting the training dataset with the generated
synthesized data. Our experiments also show by testing and retraining the
network using point clouds from user-configured scenes, the weakness/blind
spots of the neural network can be fixed
Watermarking Graph Neural Networks by Random Graphs
Many learning tasks require us to deal with graph data which contains rich
relational information among elements, leading increasing graph neural network
(GNN) models to be deployed in industrial products for improving the quality of
service. However, they also raise challenges to model authentication. It is
necessary to protect the ownership of the GNN models, which motivates us to
present a watermarking method to GNN models in this paper. In the proposed
method, an Erdos-Renyi (ER) random graph with random node feature vectors and
labels is randomly generated as a trigger to train the GNN to be protected
together with the normal samples. During model training, the secret watermark
is embedded into the label predictions of the ER graph nodes. During model
verification, by activating a marked GNN with the trigger ER graph, the
watermark can be reconstructed from the output to verify the ownership. Since
the ER graph was randomly generated, by feeding it to a non-marked GNN, the
label predictions of the graph nodes are random, resulting in a low false alarm
rate (of the proposed work). Experimental results have also shown that, the
performance of a marked GNN on its original task will not be impaired.
Moreover, it is robust against model compression and fine-tuning, which has
shown the superiority and applicability.Comment: https://hzwu.github.io
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