Search · four archives
Search · four archives
25 papers · ranked by Valyu relevance
Sebastian Wallot, Giuseppe Leonardi
This paper provides a practical, hands-on introduction to cross-recurrence quantification analysis (CRQA), diagonal cross-recurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MdRQA) in R. These methods have enjoyed increasing popularity in the cognitive and social sciences since a…
Meihui Jiang, Xiangyun Gao, Haizhong An, Huajiao Li + 1 more
In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns…
Beichen Wang, Jiazhang Cai, Luyang Fang, Ping Ma + 1 more
Contemporary neurobehavior research often collects multi-dimensional tensor (MDT) data, consisting of time-series measurements for multiple features from multiple animals subjected to various perturbations. Proper analysis of the MDT data can facilitate the dissection of the underlying neural circuitry driving the…
SoYoung Park, HyeWon Lee, Sungsu Lim, Carlos Fernandez-Lozano
Understanding and predicting outcomes in complex real-world systems necessitates robust multivariate time series pattern analysis. Advanced techniques, such as dynamic graph neural networks, have shown significant efficacy for these tasks. However, existing approaches often overlook the inherent periodicity in data…
Dan Hudson, Travis J. Wiltshire, Martin Atzmueller
In order to support the burgeoning field of research into intra- and interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the…
Sebastian Wallot, Andreas Roepstorff, Dan Mønster
As already mentioned in the last section, illustrating the application of MdRQA on skin-conductance measures during teamwork, as well as in the sections relating MdRQA to RQA, CRQA, and JRQA, there are two different, but related interpretations of MdRQA measures. On the one hand, we can interpret the outcome variables…
Tosif Ahamed, Antonio C. Costa, Greg J. Stephens
Animal behavior is often quantified through subjective, incomplete variables that may mask essential dynamics. Here, we develop a behavioral state space in which the full instantaneous state is smoothly unfolded as a combination of short-time posture dynamics. Our technique is tailored to multivariate observations and…
Andraž Matkovič, Alan Anticevic, John D. Murray, Grega Repovš
Functional connectivity (FC) of blood-oxygen-level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research…
Andraž Matkovič, Alan Anticevic, John D. Murray, Grega Repovš
Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research…
Hamed Azami, Alberto Fernández, Javier Escudero
Due to the non-linearity of numerous physiological recordings, non-linear analysis of multi-channel signals has been extensively used in biomedical engineering and neuroscience. Multivariate multiscale sample entropy (MSE-mvMSE) is a popular non-linear metric to quantify the irregularity of multi-channel time series.…
Gautam Sridhar, Massimo Vergassola, João C. Marques, Michael B. Orger + 2 more
Animals chain movements into long-lived motor strategies, resulting in variability that ultimately reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build models that bridges across time scales that enable a quantitative comparison of behavioral…
Gordon J. Berman, Daniel M. Choi, William Bialek, Joshua W. Shaevitz
Most animals possess the ability to actuate a vast diversity of movements, ostensibly constrained only by morphology and physics. In practice, however, a frequent assumption in behavioral science is that most of an animal’s activities can be described in terms of a small set of stereotyped motifs. Here we introduce a…
Hao Si, Xiao Wang, Fan Zhang, Xiaoya Zhou + 4 more
—Multivariate time series analysis has long been one of the key research topics in the field of artificial intelligence. However, analyzing complex time series data remains a challenging and unresolved problem due to its high dimensionality, dynamic nature, and complex interactions among variables. Inspired by the…
Isaac Lavine, Andrew Cron, Mike West
Bayesian computation for filtering and forecasting analysis is developed for a broad class of dynamic models. The ability to scale-up such analyses in non-Gaussian, nonlinear multivariate time series models is advanced through the introduction of a novel copula construction in sequential filtering of coupled sets of…
Tareq Tayeh, Sulaiman Aburakhia, Ryan Myers, Abdallah Shami
—As a substantial amount of multivariate time series data is being produced by the complex systems in Smart Manufacturing (SM), improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as…
John Madrid-Carvajal, Shadi Derakhshan, Peter König
The brain’s ability to transform complex, high-dimensional sensory and motor inputs into coordinated, goal-directed behavior remains a central challenge in neuroscience research. Current research suggests that behavior is generated through patterns within a low-dimensional structure. What visuomotor patterns underlie…
Nayely Vélez-Cruz, Manfred D. Laubichler
Nonlinear Dynamics: An Application to Bayesian Learning under Switching Regimes Authors: ['Nayely Vélez-Cruz' 'Manfred D. Laubichler'] In this work, we introduce a generalized framework for multiscale state-space models that incorporates nested nonlinear dynamics, with a specific focus on Bayesian learning under…
Keisuke Fujii, Takeshi Kawasaki, Yuki Inaba, Yoshinobu Kawahara + 1 more
'Yukio Gunji'] Modeling the complex collective behavior is a challenging issue in several material and life sciences. The collective motion has been usually modeled by simple interaction rules and explained by global statistics. However, it remains difficult to bridge the gap between the dynamic properties of the…
Theresa Ramelot, Roberto Tejero, Gaetano Montelione
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges…
Ru Lei, Lin Li, Rustam Stolkin, Bin Feng
Derivatives for Dynamic Multi-Objective Optimization Authors: ['Ru Lei' 'Lin Li' 'Rustam Stolkin' 'Bin Feng'] Abstract—This paper addresses the challenge of dynamic multi-objective optimization problems (DMOPs) by introducing novel approaches for accelerating prediction strategies within the evolutionary algorithm…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
Gildąs Morvan, Yoann Kubera
This paper tries to answer that question, with an analysis based on a generic model of the temporal dynamics of multi-level simulations. This generic model is then used to build an orthogonal approach to multi-level simulation called SIMI-LAR. In this approach, most time-related issues are explicitly modeled, owing to…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…