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Showing 1 to 19 of 19 for “"Diffusion maps"”.
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Cryo-electron microscopy image analysis using multi-frequency vector diffusion maps
… a novel approach called multi-frequency vector diffusion maps (MFVDM) to improve the efficiency and accuracy of cryo-EM 2D image classification and denoising. This framework incorporates different irreducible representations of the estimated alignment between similar images. In addition, we …
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A new semantic similarity join method using diffusion maps and long string table attributes
<p>With the rapid increase of the distributed data sources, and in order to make information integration, there is a need to combine the information that refers to the same entity from different sources. However, there are no global conventions that control the format of the data, and it is …
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New tools for Bayesian optimal experimental design and kernel-based generative modeling
… employ kernel-type algorithms based on diffusion maps. First, we propose an interacting particle system for generative modeling, based on diffusion maps and Laplacian-adjusted Wasserstein gradient descent (LAWGD). Diffusion maps are used to approximate the generator of the corresponding …
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Dense Optical Flow Estimation using Diffusion Distances
Diffusion maps have been shown to model relations between points 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 …
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Probabilistic Models on Fibre Bundles
… process of data. The main tool we use is the diffusion kernel and we use it in two ways. First, we build from the diffusion kernel on a fibre bundle a projected kernel that generates robust representations of the data, and we test that it outperforms regular diffusion maps under noise. Second, …
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A Study of the Thermodynamics of Small Systems and Phase Transition in Bulk Square Well-Hard Disk Binary Mixture
… Jones potential. A machine learning technique, Diffusion Maps (DMap), has been employed to the large datasets of thermodynamically small systems from Monte Carlo simulations in order to identify the structural and energetic changes in these systems. DMap suggests at most three dimensions are …
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Constitutionally Confederated: Developing an Interstate Policy Diffusion Framework for K-12 STEM Education
… characteristics to promote more effective policy diffusion. To accomplish that, principal component analysis was applied to 26 variables (representing four different education-related aspects) to reduce the dimensionality of the data at the elementary school, middle school, and the whole K-12 …
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Inverse design of self-assembling colloids via landscape engineering
… focuses on the development of the many-body diffusion map and its application to the study of self-assembly. Diffusion map dimensionality reduction has shown great promise in the study of the low-dimensional folding landscapes inherent to protein folding. We extend this technique to handle …
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Microscale Energy Transport in Lead Halide Perovskites
… and develop a framework to quantify carrier diffusion anisotropy and grain boundary effects in optical microscopy measurements. First, we quantify the enhancement due to photon recycling in the macroscale for state-of-the-art perovskite films. We find that even with finite nonradiative …
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A Geometric Approach to Biomedical Time Series Analysis
… model can be well-recovered by applying the diffusion maps algorithm to the time series' set of oscillatory cycles. We provide several applications of the wave-shape oscillatory model and the associated algorithm for dynamics recovery, including unsupervised and supervised heartbeat …
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QUANTUM SIMULATION OF BOSONIC SYSTEM AND APPLICATION OF MACHINE LEARNING
… use an unsupervised learning algorithm, namely diffusion maps, to differentiate between symmetry-broken phases and topologically ordered phases and between non-trivial topological phases in different classes. Specifically, we show that phase transitions associated with these phases can be …
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Inference of Low-Dimensional Latent Structure in High-Dimensional Data
… to spectral embedding methods, for example, diffusion maps and Isomap, yielding a new statistical spectral framework. The proposed approach allows one to discard the training data when embedding new data, allows synthesis of high-dimensional data from the embedding space, and provides …
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Robust and scalable unsupervised learning via landmark diffusion, from theory to medical application
… Robust and Scalable Embedding via Landmark Diffusion (ROSELAND). The solution is a generic and not limited to analyze physiological waveforms. In short, we measure the affinity between two points via a set of landmarks, which is composed of a small number of points, and ``diffuse'' on the …
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Modelling Nuclear Body Dynamics in Living Cells by 4-D Microscopy, Image Analysis and Simulation
… Analysis shows that VNBs undergo anomalous diffusion in the nuclei, independent of metabolic energy. Individual bodies display either one of the three modes of diffusion -- directed, restricted or simple. The consistency of modes and magnitudes of diffusion constants between VNBs and bona …
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Nonlinear Dimensionality Reduction for the Thermodynamics of Small Clusters of Particles
<p>This work employs tools and methods from computer science to study clusters comprising a small number N of interacting particles, which are of interest in science, engineering, and nanotechnology. Specifically, the thermodynamics of such clusters is studied using techniques from spectral graph …
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Deep Learning for Brain Structural Connectivity Analysis: From Tissue Segmentation to Tractogram Alignment
… in combination with advanced sequences such as diffusion MRI (dMRI) for the computation of the structural connectivity of the brain. In particular, from the processing of dMRI data, it is possible to investigate the structures of WM through tractography techniques, obtaining a virtual …
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Building a robust clinical diagnosis support system for childhood cancer using data mining methods
… (LLE), Stochastic Neighbour Embedding (SNE) and Diffusion Maps. The framework compares these different machine learning methods by tuning different parameters to find the optimal method among them. Area under the curve (AUC) is used to rank the results and SVM is used to classify between relapsed …
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Resolving Cell Fate: Experimental, computational and mathematical methods in single cell transcriptomic analysis
Deeper understanding of the embryological origins of tissues and organs is likely to provide insights into novel clinically relevant preventative and therapeutic strategies. To do so effectively and at a large scale so as to have clinical significance requires an exhaustive and meticulously …