scholarly journals Diffusion profile embedding as a basis for graph vertex similarity

2021 ◽  
Vol 9 (3) ◽  
pp. 328-353
Author(s):  
Scott Payne ◽  
Edgar Fuller ◽  
George Spirou ◽  
Cun-Quan Zhang
Keyword(s):  
Random Walks ◽  
Synthetic Data ◽  
Diffusion Profile ◽  
Mouse Retina ◽  
Community Structures ◽  
Graph Vertex ◽  
C Elegans ◽  
Uncertainty Index ◽  
Vertex Similarity

AbstractWe describe here a notion of diffusion similarity, a method for defining similarity between vertices in a given graph using the properties of random walks on the graph to model the relationships between vertices. Using the approach of graph vertex embedding, we characterize a vertex vi by considering two types of diffusion patterns: the ways in which random walks emanate from the vertex vi to the remaining graph and how they converge to the vertex vi from the graph. We define the similarity of two vertices vi and vj as the average of the cosine similarity of the vectors characterizing vi and vj. We obtain these vectors by modifying the solution to a differential equation describing a type of continuous time random walk.This method can be applied to any dataset that can be assigned a graph structure that is weighted or unweighted, directed or undirected. It can be used to represent similarity of vertices within community structures of a network while at the same time representing similarity of vertices within layered substructures (e.g., bipartite subgraphs) of the network. To validate the performance of our method, we apply it to synthetic data as well as the neural connectome of the C. elegans worm and a connectome of neurons in the mouse retina. A tool developed to characterize the accuracy of the similarity values in detecting community structures, the uncertainty index, is introduced in this paper as a measure of the quality of similarity methods.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

scholarly journals Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data

2021 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Georg Steinbuss ◽  
Klemens Böhm
Keyword(s):  
Outlier Detection ◽  
Synthetic Data ◽  
Real Data ◽  
Detection Methods ◽  
Quality Of Data ◽  
Benchmark Data ◽  
Core Idea ◽  
Generic Process ◽  
Unsupervised Outlier Detection

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic data. In this work, we propose a generic process for the generation of datasets for such benchmarking. The core idea is to reconstruct regular instances from existing real-world benchmark data while generating outliers so that they exhibit insightful characteristics. We propose and describe a generic process for the benchmarking of unsupervised outlier detection, as sketched so far. We then describe three instantiations of this generic process that generate outliers with specific characteristics, like local outliers. To validate our process, we perform a benchmark with state-of-the-art detection methods and carry out experiments to study the quality of data reconstructed in this way. Next to showcasing the workflow, this confirms the usefulness of our proposed process. In particular, our process yields regular instances close to the ones from real data. Summing up, we propose and validate a new and practical process for the benchmarking of unsupervised outlier detection.

Detection of local community structures in complex dynamic networks with random walks

2009 ◽  
Vol 3 (4) ◽  
pp. 266-278 ◽  
Author(s):  
G.S. Thakur ◽  
A.W.M. Dress ◽  
R. Tiwari ◽  
S.-S. Chen ◽  
M.T. Thai
Keyword(s):  
Random Walks ◽  
Local Community ◽  
Dynamic Networks ◽  
Community Structures ◽  
Complex Dynamic

Advances in C. elegans Specimen Preparation for TEM Using Microwave and Fast Freeze Techniques

2001 ◽  
Vol 7 (S2) ◽  
pp. 1196-1197
Author(s):  
D.H. Hall ◽  
T. Starich ◽  
J. Shaw ◽  
V. Gobel ◽  
J. Fleming ◽  
...  
Keyword(s):  
Freeze Substitution ◽  
Microwave Energy ◽  
Specimen Preparation ◽  
Genetic Studies ◽  
Larval Cuticle ◽  
C Elegans ◽  
Larval Stages ◽  
Mechanical Cutting ◽  
Fast Freezing

The nematode C. elegans is a simple model for genetic studies of cell and tissue development. There is a need to improve the preservation of embryonic and early larval stages, during which nematode tissues elaborate and separate. However, these stages are particularly resistant to fixation and embedment due an impenetrable eggshell and larval cuticle. Their small size at these ages precludes mechanical cutting, which has been used successfully for immersion fixation of older stages. Here we compare the quality of preservation under three rather different regimes: using laserholes to permeabilize the eggshell during the primary fixation step, using microwave energy to enhance the first fixation step, or using fast freezing and freeze substitution to circumvent the standard immersion procedure. Vancoppenolle et al (2000) have recently demonstrated very good results through enzymatic weakening of the eggshell prior to immersion fixation. Their data are comparable to what we achieve by either the laserhole or microwave methods.

scholarly journals 3D Imaging of Buried Dielectric Targets with a Tomographic Microwave Approach Applied to GPR Synthetic Data

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Alessandro Galli ◽  
Davide Comite ◽  
Ilaria Catapano ◽  
Gianluca Gennarelli ◽  
Francesco Soldovieri ◽  
...  
Keyword(s):  
Near Field ◽  
Scattering Problem ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Numerical Data ◽  
Shallow Subsurface ◽  
Different Shapes ◽  
3D Information ◽  
Ground Penetrating

Effective diagnostics with ground penetrating radar (GPR) is strongly dependent on the amount and quality of available data as well as on the efficiency of the adopted imaging procedure. In this frame, the aim of the present work is to investigate the capability of a typical GPR system placed at a ground interface to derive three-dimensional (3D) information on the features of buried dielectric targets (location, dimension, and shape). The scatterers can have size comparable to the resolution limits and can be placed in the shallow subsurface in the antenna near field. Referring to canonical multimonostatic configurations, the forward scattering problem is analyzed first, obtaining a variety of synthetic GPR traces and radargrams by means of a customized implementation of an electromagnetic CAD tool. By employing these numerical data, a full 3D frequency-domain microwave tomographic approach, specifically designed for the inversion problem at hand, is applied to tackle the imaging process. The method is tested here by considering various scatterers, with different shapes and dielectric contrasts. The selected tomographic results illustrate the aptitude of the proposed approach to recover the fundamental features of the targets even with critical GPR settings.

Adaptive focusing window for seismic angle migration

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. S1-S10 ◽  
Author(s):  
Mathias Alerini ◽  
Bjørn Ursin
Keyword(s):  
Synthetic Data ◽  
Main Idea ◽  
Three Dimensions ◽  
User Friendliness ◽  
Depth Migration ◽  
Kirchhoff Migration ◽  
Migration Operator ◽  
Local Geology ◽  
Original Approach

Kirchhoff migration is based on a continuous integral ranging from minus infinity to plus infinity. The necessary discretization and truncation of this integral introduces noise in the migrated image. The attenuation of this noise has been studied by many authors who propose different strategies. The main idea is to limit the migration operator around the specular point. This means that the specular point must be known before migration and that a criterion exists to determine the size of the migration operator. We propose an original approach to estimate the size of the focusing window, knowing the geologic dip. The approach benefits from the use of prestack depth migration in angle domain, which is recognized as the most artifact-free Kirchhoff-type migration. The main advantages of the method are ease of implementation in an existing angle-migration code (two or three dimensions), user friendliness, ability to take into account multiorientation of the local geology as in faulted regions, and flexibility with respect to the quality of the estimated geologic dip field. Common-image gathers resulting from the method are free from migration noise and can be postprocessed in an easier way. We validate the approach and its possibilities on synthetic data examples with different levels of complexity.

scholarly journals Adaptive 2D To 3D Image Conversion Using A Hybrid Graph Cuts And Random Walks Approach

2021 ◽  
Author(s):  
Mohammad Fawaz
Keyword(s):  
Random Walks ◽  
Spatial Information ◽  
Depth Map ◽  
Graph Cuts ◽  
Adaptive Method ◽  
Image Conversion ◽  
2D To 3D Conversion ◽  
Work Done ◽  
2D To 3D

This thesis proposes an adaptive method for 2D to 3D conversion of images using a user-aided process based on Graph Cuts and Random Walks. Given user-defined labelling that correspond to a rough estimate of depth, the system produces a depth map which, combined with a 2D image can be used to synthesize a stereoscopic image pair. The work presented here is an extension of work done previously combining the popular Graph Cuts and Random Walks image segmentation algorithms. Specifically, the previous approach has been made adaptive by removing empirically determined constants; as well the quality of the results has been improved. This is achieved by feeding information from the Graph Cuts result into the Random Walks process in two different ways, and using edge and spatial information to adapt various weights. This thesis also presents a practical application which allows for a user to go through the entire process of 2D to 3D conversion using the method proposed in this work. The application is written using MATLAB, and allows a user to generate and edit depth maps intuitively and also allows a user to synthesize additional views of the image for display on 3D capable devices.

scholarly journals Multidimensional phenotyping predicts lifespan and quantifies health in C. elegans

2019 ◽  
Author(s):  
Céline N. Martineau ◽  
André E. X. Brown ◽  
Patrick Laurent
Keyword(s):  
Simple Model ◽  
Objective Measure ◽  
Quantitative Model ◽  
Model Organisms ◽  
Wild Type ◽  
Data Set ◽  
C Elegans ◽  
Wide Range ◽  
Initial Hypothesis

AbstractAgeing affects a wide range of phenotypes at all scales, but an objective measure of ageing remains challenging, even in simple model organisms. We assumed that a wide range of phenotypes at the organismal scale rather than a limited number of biomarkers of ageing would best describe the ageing process. Hundreds of morphological, postural and behavioural features are extracted at once from high resolutions videos. A quantitative model using this multi-parametric dataset can predict the biological age and lifespan of individual C. elegans. We show that the quality of predictions on a held-out data set increases with the number of features added to the model, supporting our initial hypothesis. Despite the large diversity of ageing mechanisms, including stochastic insults, our results highlight a robust ageing trajectory, but variable ageing rates along that trajectory. We show that healthspan, which we defined as the range of abilities of the animals, is correlated to lifespan in wild-type worms.

scholarly journals Network meta-analysis and random walks

2021 ◽  
Author(s):  
Annabel Louisa Davies ◽  
Theodoros Papakonstantinou ◽  
Adriani Nikolakopoulou ◽  
Gerta Rucker ◽  
Tobias Galla
Keyword(s):  
Random Walk ◽  
Random Walks ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Graph Representation ◽  
Flow Network ◽  
Treatment Comparison ◽  
The Matrix ◽  
Hat Matrix

Network meta-analysis (NMA) is a central tool for evidence synthesis in clinical research. The results of an NMA depend critically on the quality of evidence being pooled. In assessing the validity of an NMA, it is therefore important to know the proportion contributions of each direct treatment comparison to each network treatment effect. The construction of proportion contributions is based on the observation that each row of the hat matrix represents a so-called 'evidence flow network' for each treatment comparison. However, the existing algorithm used to calculate these values is associated with ambiguity according to the selection of paths. In this work we present a novel analogy between NMA and random walks. We use this analogy to derive closed-form expressions for the proportion contributions. A random walk on a graph is a stochastic process that describes a succession of random 'hops' between vertices which are connected by an edge. The weight of an edge relates to the probability that the walker moves along that edge. We use the graph representation of NMA to construct the transition matrix for a random walk on the network of evidence. We show that the net number of times a walker crosses each edge of the network is related to the evidence flow network. By then defining a random walk on the directed evidence flow network, we derive analytically the matrix of proportion contributions. The random-walk approach, in addition to being computationally more efficient, has none of the associated ambiguity of the existing algorithm.

scholarly journals A comparison of denoising methods in dynamic MRS using pseudo-synthetic data

2021 ◽  
Author(s):  
Benjamin C. Rowland ◽  
Lasya Sreepada ◽  
Alexander P. Lin
Keyword(s):  
Mr Spectroscopy ◽  
Synthetic Data ◽  
Temporal Correlation ◽  
Ground Truth ◽  
Effective Technique ◽  
Tuning Parameters ◽  
Rms Error ◽  
Value Decomposition ◽  
Dynamic Mr

PurposeMR spectroscopy of dynamic systems is limited by low signal to noise. Denoising along a series of acquired spectra exploits their temporal correlation to improve the quality of individual spectra, and reduce errors in fitting metabolite peaks. In this study we compare the performance of several denoising methods.MethodsSix different denoising methods were considered: SIFT (Spectral Improvement by Fourier Thresholding), HSVD (Hankel Singular Value Decomposition), spline, wavelet, sliding window and sliding Gaussian. Pseudo-synthetic data was constructed to mimic 31Phosphorus spectra from exercising muscle. For each method the optimal tuning parameters were determined for SNRs of 2, 5, 10 and 20 using a Monte Carlo approach. Denoised data from each method was then fitted using the AMARES algorithm and the results compared to the pseudo-synthetic ground truth.ResultsAll six methods produced improvements in both fitting accuracy and agreement with the ground truth, compared to unprocessed noisy data. The least effective methods, SIFT and HSVD, achieved around 10-20% reduction in RMS error, while the most effective, Spline, reduced RMS error by 70%. The improvement from denoising was typically greater for lower SNR data.ConclusionsIndirect time domain denoising of dynamic MR spectroscopy data can substantially improve subsequent metabolite fitting. Spline-based denoising was found to be the most flexible and effective technique.

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