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Found 23 papers, 5 papers with code
Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation
Ridge detection is a classical tool to extract curvilinear features in image processing.
Non-fungible token transactions: data and challenges
Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes.
K-ARMA Models for Clustering Time Series Data
We then apply our method first with an AR($p$) clustering example and show how the clustering algorithm can be made robust to outliers using a least-absolute deviations criteria.
Bayesian Spillover Graphs for Dynamic Networks
We present Bayesian Spillover Graphs (BSG), a novel method for learning temporal relationships, identifying critical nodes, and quantifying uncertainty for multi-horizon spillover effects in a dynamic system.
Interpretable Latent Variables in Deep State Space Models
We introduce a new version of deep state-space models (DSSMs) that combines a recurrent neural network with a state-space framework to forecast time series data.
Analysis of animal-related electric outages using species distribution models and community science data
These flexible, species-specific estimates can allow future animal-indicators of grid reliability to be investigated in more diverse regions and ecological communities, providing a better understanding of the variation that exists in animal-outage relationship.
Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility Across Independent System Operators in the United States
After correcting for temporal effects, we found an increase in VRE penetration is associated with decrease in system electricity price in all ISOs studied.
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance
Classification of large multivariate time series with strong class imbalance is an important task in real-world applications.
Classifying Contaminated Cell Cultures using Time Series Features
We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism.
Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects
We also provide relevant critical risk indicators (CRIs) across diverse domains that may influence electric power grid risks, including climate, ecology, hydrology, finance, space weather, and agriculture.
Applications
Developing and Evaluating Deep Neural Network-based Denoising for Nanoparticle TEM Images with Ultra-low Signal-to-Noise
This shows that the network exploits global and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface.
Denoising
Materials Science
Image and Video Processing
Extended Missing Data Imputation via GANs for Ranking Applications
We prove that the optimal GAN imputation is achieved for Extended Missing At Random (EMAR) and Extended Always Missing At Random (EAMAR) mechanisms, beyond the naive MCAR.
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
SBD outperforms existing techniques by a wide margin on a simulated benchmark dataset, as well as on real data.
Modeling a Nonlinear Biophysical Trend Followed by Long-Memory Equilibrium with Unknown Change Point
In this paper, we assume that measurements during the trend period are independent deviations from a smooth nonlinear function of time, and that measurements during the equilibrium period are characterized by a simple long memory model.
Applications Quantitative Methods
Graph-Based Continual Learning
Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions.
Factor Analysis of Mixed Data for Anomaly Detection
Anomaly detection aims to identify observations that deviate from the typical pattern of data.
Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures
We apply both distance-based (Jin and Matteson, 2017) and kernel-based (Pfister et al., 2016) mutual dependence measures to independent component analysis (ICA), and generalize dCovICA (Matteson and Tsay, 2017) to MDMICA, minimizing empirical dependence measures as an objective function in both deflation and parallel manners.
Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence
As a crucial problem in statistics is to decide whether additional variables are needed in a regression model.
Optimization and Testing in Linear Non-Gaussian Component Analysis
Linear non-Gaussian component analysis (LNGCA) generalizes the ICA model to a linear latent factor model with any number of both non-Gaussian components (signals) and Gaussian components (noise), where observations are linear combinations of independent components.
Interpretable Vector AutoRegressions with Exogenous Time Series
The Vector AutoRegressive (VAR) model is fundamental to the study of multivariate time series.
Generalizing Distance Covariance to Measure and Test Multivariate Mutual Dependence
We propose three measures of mutual dependence between multiple random vectors.
High Dimensional Forecasting via Interpretable Vector Autoregression
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series.
A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
The divisive method is shown to provide consistent estimates of both the number and location of change points under standard regularity assumptions.
Methodology
