Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 26 for “"Graph Laplacian"”.
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Interpretable Deep Image Denoiser by Unrolling Graph Laplacian Regularizer
… (MAP) optimization problem regularized using a graph Laplacian prior. To guarantee a minimum level of performance, we initialize the network to a known (pseudo-)linear denoiser, which is mapped to a corresponding graph Laplacian matrix specifying the MAP problem, leveraging a previous linear …
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Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Learning a suitable graph is an important precursor to many graph signal processing (GSP) tasks, such as graph signal compression and denoising. Previous graph learning algorithms either make assumptions on graph connectivity (e.g., graph sparsity), or make individual edge weight assumptions such …
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Minimal Labels, Maximum Gain. Image Classification with Graph-Based Semi-Supervised Learning
… Our approaches are centred around graph-based learning, and we apply them to a range of real-world problems including hyperspectral, natural and medical imaging. Firstly, we propose and design a superpixel contracted semi-supervised learning framework to classify hyperspectral …
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Direct Computations of Spatially Resolved Viscoelastic Moduli of Biomolecular Condensates
… In this study, we develop a modified graph Laplacian-based collective model to characterize viscoelastic heterogeneity within condensates based on results of lattice-based Metropolis Monte Carlo (MMC) simulations. By integrating random graph models and simulations of A1-LCD, a type of …
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Robust graph transduction
Given a weighted graph, graph transduction aims to assign unlabeled examples explicit class labels rather than build a general decision function based on the available labeled examples. Practically, a dataset usually contains many noisy data, such as the “bridge points” located across different …
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Air Ingress in HTGRs: the process, effects, and experimental methods relating to its investigation and consequences
Helium-cooled, graphite moderated reactors have been considered for a future fleet of high temperature and high efficiency nuclear power plants. Nuclear-grade graphite is used in these reactors for structural strength, neutron moderation, heat transfer and, within a helium environment, has …
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On Some Geometry of Graphs
… this thesis we study the intrinsic geometry of graphs via the constants that appear in discretized partial differential equations associated to those graphs. By studying the behavior of a discretized version of Bochner's inequality for smooth manifolds at the cone point for a cone over the set …
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Geometric Conditions for the Recovery of Sparse Signals on Graphs from Measurements Generated with Heat Kernels
… establishes results on signal recovery for graphs, when the signals are functions with small support and what is observed is a noisy version of the signal smoothed by evolving it under the heat equation governed by the graph Laplacian. The results discussed here are in close analogy to the …
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Data parallel algebraic multigrid
… dissertation is also concerned with irregular graph operations necessary to partition sparse matrices into disjoint sets for parallel processing. We apply our solver to accelerate eigenvector computations necessary during spectral partitioning methods and find that performance is limited by …
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Active flows and networks
… systems, the spectrum of the underlying graph Laplacian plays a key role in controlling the flow. Spectral graph theory has traditionally prioritized analyzing Laplacians of unweighted networks with specified adjacency properties. For the second part of the thesis, we introduce a …
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Distributed graph decomposition algorithms on Apache Spark
… analysis and mining of large and complex graphs for describing the characteristics of a vertex or an edge in the graph have widespread use in graph clustering, classification, and modeling. There are various methods for structural analysis of graphs including the discovery of frequent …
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Stable configurations for population and social dynamics
… Lotka--Volterra model when induced by a cycle graph food web network. Results such as orbits, chaos and the probability of stability are given. A result showing convexity of the weighted connections of the food web is sufficient for global stability is given as well. Stability results of food …
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Multimedia Big Data Analytics and Fusion for Data Science
… optimization strategy. First, a hierarchical graph fusion network is presented to capture the inter-modality correlations among modalities. The network hierarchy models the modality-wise combinations with gradually increased complexity to explore all n-modality interactions. Next, an adaptive …
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Dense Optical Flow Estimation using Diffusion Distances
… by considering the overall connectivity of the graph. This report outlines how we can apply the diffusion framework to dense optical flow estimation where diffusion maps are used to embed distributions of local spatial gradients. We review the problem of dense optical flow estimation and several …
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0-1 graph partitioning and image segmentation
Graph partitioning is the grouping of all the nodes in a graph into two or more partitions based on certain criteria. Graph cut techniques are used to partition a graph. The Minimum Cut method gives imbalanced partitions. To overcome the imbalanced partitioning, the Normalized Cut method is used. …
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Graph-Based Acoustic Clustering and Classification
… of each data point. We then develop a graph-based approach for analyzing these signals, representing the data using a similarity graph. Following methods used successfully in image processing and problems on networks, we apply a spectral embedding to project the high-dimensional graph …
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Distributed Newton-type algorithms for network resource allocation
… limit of an iterative procedure involving the graph Laplacian, which can be implemented based only on local information. Using standard Lipschitz conditions, we provide analysis for the convergence properties of our algorithm and show that the method converges superlinearly to an explicitly …
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Graph Neural Networks: Techniques and Applications
… to the geometry of the data represented by a graph. Typical applications include social networks, transportation networks, the spread of epidemic disease, brain's neuronal networks, gene data on biological regulatory networks, telecommunication networks, knowledge graph, which are lying on the …
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Stability thresholds for signed Laplacians on locally-connected networks
… bifurcations of the dynamical systems defined on graphs, and we use signed graph Laplacians as our tool. In chapter 1, we give the formal definition of the Laplacian matrix for a graph, and point out several references on it. In chapter 2, we give the main result from one of the references, along …
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Projection methods for clustering and semi-supervised classification
… on a different approach to clustering based on graph cuts. The minimum normalised graph cut objective has gained considerable attention as relaxations of the objective have been developed, which make them solvable for reasonably well sized problems. This has been adopted by the highly popular …
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