Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Deborah Meier, Wolfgang Tschacher, José F. F. Mendes
Measuring interpersonal synchrony is a promising approach to assess the complexity of social interaction, which however has been mostly limited to dyads. In this study, we introduce multivariate Surrogate Synchrony (mv-SUSY) to extend the current set of computational methods. Methods: mv-SUSY was applied to eight…
Ethan R. Deyle, Gerald Pao, George Sugihara
The foundation of Empirical dynamic modeling (EDM) is in representing time-series data as the trajectory of a dynamic system in a multidimensional state space rather than as a collection of traces of individual variables changing through time. Takens’s theorem provides a rigorous basis for adopting this state-space…
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…
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…
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…
Conor Heins, Beren Millidge, Lancelot Da Costa, Richard P. Mann + 2 more
'Karl J. Friston' 'Iain D. Couzin'] Title: Significance We introduce a model of collective behavior, proposing that individual members within a group, such as a school of fish or a flock of birds, act to minimize surprise. This active inference approach naturally generates well-known collective phenomena such as…
Franco Orsucci, Wolfgang Tschacher
Complexity and entropy prevail in human behavior and social interaction because the systems underlying behavior and interaction are, without a doubt, highly complex. The human brain, body, language, society, and culture consist of vast numbers of components, and the degrees of freedom in behavior, cognition, 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…
Fred Hasselman
The detection of Early Warning Signals (EWS) of imminent phase transitions, such as sudden changes in symptom severity could be an important innovation in the treatment or prevention of disease or psychopathology. Recurrence-based analyses are known for their ability to detect differences in behavioral modes and order…
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…
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…
Yulong Wang, Yushuo Liu, Xiaoyi Duan, Kai Wang
Multivariate time series forecasting is crucial across various industries, where accurate extraction of complex periodic and trend components can significantly enhance prediction performance. However, existing models often struggle to capture these intricate patterns. To address these challenges, we propose FilterTS, a…
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…
Weijun Gao, Cheng Zhou
Robotic Systems in Dynamic Environments Authors: ['Weijun Gao' 'Cheng Zhou'] Abstract— Accurate prediction of human behavior is crucial for effective human-robot interaction (HRI) systems, especially in dynamic environments where real-time decisions are essential. This paper addresses the challenge of forecasting…
Yuji Okano, Tetsuo Ishikawa, Yasunori Sato, Hideyuki Okano + 1 more
Design of experiments (DOE) principles are increasingly applied to biological assays, yet it remains unclear whether their foundational assumption—orthogonal decomposition—holds in nonlinear biological systems. We addressed this question using Perturb-seq as a case study. By benchmarking a design commonly used in…
Jaume Anguera Peris, Songtao Cheng, Hanzhao Zhang, Wei Ouyang + 1 more
High-content screening microscopy generates large amounts of live-cell imaging data, yet its potential remains constrained by the inability to determine when and where to image most effectively. Optimally balancing acquisition time, computational capacity, and photobleaching budgets across thousands of dynamically…
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…
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…