Surrogate data
View article
Generative modeling of brain maps with spatial autocorrelation Open
Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectati…
View article
Surrogate data for hypothesis testing of physical systems Open
The availability of time series of the evolution of the properties of physical systems is increasing, stimulating the development of many novel methods for the extraction of information about their behaviour over time, including whether or…
View article
In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators Open
Surrogate indicators are meant to be alternatives or complements of safety analyses based on accident records. These indicators are used to study critical traffic events that occur more frequently, making such incidents easier to analyse. …
View article
Global and Local Surrogate-Assisted Differential Evolution for Expensive Constrained Optimization Problems With Inequality Constraints Open
For expensive constrained optimization problems (ECOPs), the computation of objective function and constraints is very time-consuming. This paper proposes a novel global and local surrogate-assisted differential evolution (DE) for solving …
View article
Determining synchrony between behavioral time series: An application of surrogate data generation for establishing falsifiable null-hypotheses. Open
Synchrony between interacting systems is an important area of nonlinear dynamics in physical systems. Recently psychological researchers from multiple areas of psychology have become interested in nonverbal synchrony (i.e., coordinated mot…
View article
A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots Open
Nonlinear time series analysis gained prominence from the late 1980s on, primarily because of its ability to characterize, analyze, and predict nontrivial features in data sets that stem from a wide range of fields such as finance, music, …
View article
Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it? Open
Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its’ presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain …
View article
Human photoplethysmogram: new insight into chaotic characteristics Open
The photoplethysmogram is widely used in medical settings and sports equipment to measure biological signals. The photoplethysmogram, which is measured noninvasively, can provide valuable information about cardiovascular system performance…
View article
Changes in functional connectivity dynamics with aging: A dynamical phase synchronization approach Open
The dynamics of the human brain network has attracted broad attention, in recognition of the concept that functional connectivity is not static, but changes its pattern over time, even in the resting state. We hypothesized that analysis of…
View article
Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia Open
Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions - such as their multifractality or information content -, that otherwise re…
View article
System Reliability Analysis With Autocorrelated Kriging Predictions Open
When limit-state functions are highly nonlinear, traditional reliability methods, such as the first-order and second-order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient …
View article
Error modeling for surrogates of dynamical systems using machine learning Open
Summary A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high‐dimensional regression techniques (eg, random forests, a…
View article
Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques Open
Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a…
View article
Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data Open
Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series for which we are interested in information flows come in the form of (near) instantaneo…
View article
Regenerating time series from ordinal networks Open
Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this p…
View article
Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study Open
Surrogate models play a vital role in overcoming the computational challenge in designing and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This paper presents a comparative study of different surrogate mo…
View article
Validity of Surrogate Endpoints and Their Impact on Coverage Recommendations: A Retrospective Analysis across International Health Technology Assessment Agencies Open
Background Surrogate endpoints (i.e., intermediate endpoints intended to predict for patient-centered outcomes) are increasingly common. However, little is known about how surrogate evidence is handled in the context of health technology a…
View article
Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies Open
Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion. This paper advocates for the training of surrogates that are consistent…
View article
Reporting of surrogate endpoints in randomised controlled trial reports (CONSORT-Surrogate): extension checklist with explanation and elaboration Open
Randomised controlled trials commonly use surrogate endpoints to substitute for a target outcome (outcome of direct interest and relevance to trial participants, clinicians, and other stakeholders—eg, all cause mortality) to improve their …
View article
Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales Open
Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might f…
View article
Estimation of the Optimal Surrogate Based on a Randomized Trial Open
Summary A common scientific problem is to determine a surrogate outcome for a long-term outcome so that future randomized studies can restrict themselves to only collecting the surrogate outcome. We consider the setting that we observe n i…
View article
Synchronization, non-linear dynamics and low-frequency fluctuations: Analogy between spontaneous brain activity and networked single-transistor chaotic oscillators Open
In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based …
View article
How Does the Driver’s Perception Reaction Time Affect the Performances of Crash Surrogate Measures? Open
With the merit on representing traffic conflict through examining the crash mechanism and causality proactively, crash surrogate measures have long been proposed and applied to evaluate the traffic safety. However, the driver's Perception-…
View article
On trend estimation and significance testing for non-Gaussian and serially dependent data: quantifying the urbanization effect on trends in hot extremes in the megacity of Shanghai Open
Quantifying the urbanization effect on trends in climate extremes is important both for detection and attribution studies and for human adaptation; however, a fundamental problem is how to accurately estimate a trend and its statistical si…
View article
A Spectral Method for Generating Surrogate Graph Signals Open
The increasing availability of network data is leading to a growing interest in processing of signals on graphs. One notable tool for extending conventional signal-processing operations to networks is the graph Fourier transform that can b…
View article
Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity Open
Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed mult…
View article
Interpreting Temporal Fluctuations in Resting-State Functional Connectivity MRI Open
Resting-state functional connectivity is a powerful tool for studying human functional brain networks. Temporal fluctuations in functional connectivity, i.e., dynamic functional connectivity (dFC), are thought to reflect dynamic changes in…
View article
Power-law dynamics in neuronal and behavioral data introduce spurious correlations Open
Relating behavioral and neuroimaging measures is essential to understanding human brain function. Often, this is achieved by computing a correlation between behavioral measures, e.g., reaction times, and neurophysiological recordings, e.g.…
View article
Generative modeling of brain maps with spatial autocorrelation Open
Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectati…
View article
Evaluating surrogate marker information using censored data Open
Given the long follow‐up periods that are often required for treatment or intervention studies, the potential to use surrogate markers to decrease the required follow‐up time is a very attractive goal. However, previous studies have shown …