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1. Mechanistic and Data-Driven Agent-Based Models to Explain Human Behavior in Online Networked Group Anagram Games

   https://doi.org/10.1145/3341161.3342965  

   Cedeno‑Mieles, Vanessa; Hu, Zhihao; Ren, Yihui; Deng, Xinwei; Adiga, Abhigin; Barrett, Christopher; Contractor, Noshir; Ekanayake, Saliya; Epstein, Joshua M.; Goode, Brian J.; et al (January 2019, Proceedings of the International Conference on Advances in Social Network Analysis and Mining)

   null (Ed.)

   In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone. 

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2. Kinetic turbulence in shining pair plasma: intermittent beaming and thermalization by radiative cooling

   https://doi.org/10.1093/mnras/staa284  

   Zhdankin, Vladimir; Uzdensky, Dmitri A; Werner, Gregory R; Begelman, Mitchell C (March 2020, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT High-energy astrophysical systems frequently contain collision-less relativistic plasmas that are heated by turbulent cascades and cooled by emission of radiation. Understanding the nature of this radiative turbulence is a frontier of extreme plasma astrophysics. In this paper, we use particle-in-cell simulations to study the effects of external inverse Compton radiation on turbulence driven in an optically thin, relativistic pair plasma. We focus on the statistical steady state (where injected energy is balanced by radiated energy) and perform a parameter scan spanning from low magnetization to high magnetization (0.04 ≲ σ ≲ 11). We demonstrate that the global particle energy distributions are quasi-thermal in all simulations, with only a modest population of non-thermal energetic particles (extending the tail by a factor of ∼2). This indicates that non-thermal particle acceleration (observed in similar non-radiative simulations) is quenched by strong radiative cooling. The quasi-thermal energy distributions are well fit by analytic models in which stochastic particle acceleration (due to, e.g. second-order Fermi mechanism or gyroresonant interactions) is balanced by the radiation reaction force. Despite the efficient thermalization of the plasma, non-thermal energetic particles do make a conspicuous appearance in the anisotropy of the global momentum distribution as highly variable, intermittent beams (for high magnetization cases). The beamed high-energy particles are spatially coincident with intermittent current sheets, suggesting that localized magnetic reconnection may be a mechanism for kinetic beaming. This beaming phenomenon may explain rapid flares observed in various astrophysical systems (such as blazar jets, the Crab nebula, and Sagittarius A*). 

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3. Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals

   https://doi.org/10.1038/s41524-024-01200-1  

   Mahata, A.; Mukhopadhyay, T.; Chakraborty, S.; Asle Zaeem, M. (January 2024, npj Computational Materials)

   Abstract Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification of stochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary. The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization, causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo (MC) simulations. In this work, we present a machine learning (ML) approach, as a data-driven surrogate to MD simulations, which only needs a few MD simulations. This efficient yet high-fidelity ML approach enables MC simulations for full-scale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate. Unlike conventional ML models, the proposed hybrid polynomial correlated function expansion here, being a Bayesian ML approach, is data efficient. Further, it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations, which in principle addresses the issue of repeatability in stochastic simulations with low variance. Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus, yield strength and ultimate strength, illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency. 

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4. Hierarchically embedded scales of movement shape the social networks of vampire bats

   https://doi.org/10.1098/rspb.2023.2880  

   Hartman, C_Raven A; Wilkinson, Gerald S; Razik, Imran; Hamilton, Ian M; Hobson, Elizabeth A; Carter, Gerald G (April 2024, Proceedings of the Royal Society B: Biological Sciences)

   Social structure can emerge fromhierarchically embedded scales of movement, where movement at one scale is constrained within a larger scale (e.g. among branches, trees, forests). In most studies of animal social networks, some scales of movement are not observed, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (Desmodus rotundus), we created an agent-based model of how three nested scales of movement—among roosts, clusters and grooming partners—each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically derived rates that were either fixed or individually variable and either independent or correlated across scales. We found that numbers of partners groomed per bat were driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks. 

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5. On the Estimation of Internal Climate Variability During the Preindustrial Past Millennium

   https://doi.org/10.1029/2021GL096596  

   Mann, Michael E.; Steinman, Byron A.; Brouillette, Daniel J.; Fernandez, Alejandro; Miller, Sonya K. (January 2022, Geophysical Research Letters)

   Abstract We use an ensemble of simulations of a coupled model (NCAR Community Earth System Model) driven by natural radiative forcing estimates over the pre‐industrial past millennium to test the efficacy of methods designed to remove forced variability from proxy‐based climate reconstructions and estimate residual internal variability (e.g., a putative “Atlantic Multidecadal Oscillation”). Within the framework of these experiments, the forced component of surface temperature change can be estimated accurately from the ensemble mean, and the internal variability of each of the independent realizations can be accurately assessed by subtracting off that estimate. We show in this case, where the true internal variability is known, that regression‐based methods of removing the forced component from proxy reconstructions will, in the presence of uncertainties in the underlying natural radiative forcing, fail to yield accurate estimates thereof, incorrectly attributing unresolved forced features (and multidecadal spectral peaks associated with them) to internal variability. 

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