Disordered ferromagnets with a domain structure that exhibit a hysteresis loop when driven by the external magnetic field are essential materials for modern technological applications.Therefore,the understanding and p...Disordered ferromagnets with a domain structure that exhibit a hysteresis loop when driven by the external magnetic field are essential materials for modern technological applications.Therefore,the understanding and potential for controlling the hysteresis phenomenon in thesematerials,especially concerning the disorder-induced critical behavior on the hysteresis loop,have attracted significant experimental,theoretical,and numerical research efforts.We review the challenges of the numerical modeling of physical phenomena behind the hysteresis loop critical behavior in disordered ferromagnetic systems related to the non-equilibriumstochastic dynamics of domain walls driven by external fields.Specifically,using the extended Random Field Ising Model,we present different simulation approaches and advanced numerical techniques that adequately describe the hysteresis loop shapes and the collective nature of the magnetization fluctuations associated with the criticality of the hysteresis loop for different sample shapes and varied parameters of disorder and rate of change of the external field,as well as the influence of thermal fluctuations and demagnetizing fields.The studied examples demonstrate how these numerical approaches reveal newphysical insights,providing quantitativemeasures of pertinent variables extracted from the systems’simulated or experimentally measured Barkhausen noise signals.The described computational techniques using inherent scale-invariance can be applied to the analysis of various complex systems,both quantum and classical,exhibiting non-equilibrium dynamical critical point or self-organized criticality.展开更多
The economy of a country can be modeled as a complex system in which several players buy and sell goods from each other.By analyzing the investment flows,it is possible to reconstruct the supply chain for the producti...The economy of a country can be modeled as a complex system in which several players buy and sell goods from each other.By analyzing the investment flows,it is possible to reconstruct the supply chain for the production of most goods,whose understanding is important to analysts and public officials interested in creating and evaluating strategies for informed and strategic decision making,for instance,adjusting tax policies.Those networks of players and investments,however,tend to be complex and very dense,which leads to over-plotted visualizations that obfuscate precious information such as the dependencies between productive sectors and regions.In this paper,we propose Hermes,a guidanceenriched Visual Analytics environment(named after the Greek God of Commerce)for the exploration of complex economic networks,to uncover supply chains,regions’productivity,and sector-to-sector relationships.With practical knowledge regarding guidance,we designed and implemented a visual sub-graph querying approach to extract patterns from such complex investment graphs obtained from real-world data.We present a three-fold evaluation of the system:we perform a qualitative evaluation of our approach with three domain experts,a separate assessment of the proposed guidance features with an expert researcher in this field,and a case study of Hermes using a bank account network dataset to demonstrate the generalizability of our approach.展开更多
Systemic risk research is gaining traction across diverse disciplinary research communities, but has as yet not been strongly linked to traditional, well-established risk analysis research. This is due in part to the ...Systemic risk research is gaining traction across diverse disciplinary research communities, but has as yet not been strongly linked to traditional, well-established risk analysis research. This is due in part to the fact that systemic risk research focuses on the connection of elements within a system, while risk analysis research focuses more on individual risk to single elements. We therefore investigate how current systemic risk research can be related to traditional risk analysis approaches from a conceptual as well as an empirical point of view. Based on Sklar's Theorem, which provides a one-to-one relationship between multivariate distributions and copulas, we suggest a reframing of the concept of copulas based on a network perspective. This provides a promising way forward for integrating individual risk(in the form of probability distributions) and systemic risk(in the form of copulasdescribing the dependencies among such distributions)across research domains. Copulas can link continuous node states, characterizing individual risks, with a gradual dependency of the coupling strength between nodes on their states, characterizing systemic risk. When copulas are used for describing such refined coupling between nodes,they can provide a more accurate quantification of a system's network structure. This enables more realistic systemic risk assessments, and is especially useful when extreme events(that occur at low probabilities, but have high impacts) affect a system's nodes. In this way, copulas can be informative in measuring and quantifying changes in systemic risk and therefore be helpful in its management. We discuss the advantages and limitations of copulas for integrative risk analyses from the perspectives of modeling, measurement, and management.展开更多
Collective computation is the process by which groups store and share information to arrive at decisions for collective behavior.How societies engage in effective collective computation depends partly on their scale.S...Collective computation is the process by which groups store and share information to arrive at decisions for collective behavior.How societies engage in effective collective computation depends partly on their scale.Social arrangements and technologies that work for small-and mid-scale societies are inadequate for dealing effectively with the much larger communication loads that societies face during the growth in scale that is a hallmark of the Holocene.An important bottleneck for growth may be the development of systems for persistent recording of information(writing),and perhaps also the abstraction of money for generalizing exchange mechanisms.Building on Shin et al.,we identify a Scale Threshold to be crossed before societies can develop such systems,and an Information Threshold which,once crossed,allows more or less unlimited growth in scale.We introduce several additional articles in this special issue that elaborate or evaluate this Thresholds Model for particular types of societies or times and places in the world.展开更多
基金Djordje Spasojevic and Svetislav Mijatovic acknowledge the support from the Ministry of Science,TechnologicalDevelopment and Innovation of the Republic of Serbia(Agreement No.451-03-65/2024-03/200162)S.J.ibid.(Agreement No.451-03-65/2024-03/200122)Bosiljka Tadic from the Slovenian Research Agency(program P1-0044).
文摘Disordered ferromagnets with a domain structure that exhibit a hysteresis loop when driven by the external magnetic field are essential materials for modern technological applications.Therefore,the understanding and potential for controlling the hysteresis phenomenon in thesematerials,especially concerning the disorder-induced critical behavior on the hysteresis loop,have attracted significant experimental,theoretical,and numerical research efforts.We review the challenges of the numerical modeling of physical phenomena behind the hysteresis loop critical behavior in disordered ferromagnetic systems related to the non-equilibriumstochastic dynamics of domain walls driven by external fields.Specifically,using the extended Random Field Ising Model,we present different simulation approaches and advanced numerical techniques that adequately describe the hysteresis loop shapes and the collective nature of the magnetization fluctuations associated with the criticality of the hysteresis loop for different sample shapes and varied parameters of disorder and rate of change of the external field,as well as the influence of thermal fluctuations and demagnetizing fields.The studied examples demonstrate how these numerical approaches reveal newphysical insights,providing quantitativemeasures of pertinent variables extracted from the systems’simulated or experimentally measured Barkhausen noise signals.The described computational techniques using inherent scale-invariance can be applied to the analysis of various complex systems,both quantum and classical,exhibiting non-equilibrium dynamical critical point or self-organized criticality.
基金This work was partially supported by the Research Cluster"Smart Communities and Technologies(SmartCT)"at TU Wien and the Austrian Science Fund(FWF),grant P31419-N31 Knowledge-Assisted Visual Analytics(KnoVA).
文摘The economy of a country can be modeled as a complex system in which several players buy and sell goods from each other.By analyzing the investment flows,it is possible to reconstruct the supply chain for the production of most goods,whose understanding is important to analysts and public officials interested in creating and evaluating strategies for informed and strategic decision making,for instance,adjusting tax policies.Those networks of players and investments,however,tend to be complex and very dense,which leads to over-plotted visualizations that obfuscate precious information such as the dependencies between productive sectors and regions.In this paper,we propose Hermes,a guidanceenriched Visual Analytics environment(named after the Greek God of Commerce)for the exploration of complex economic networks,to uncover supply chains,regions’productivity,and sector-to-sector relationships.With practical knowledge regarding guidance,we designed and implemented a visual sub-graph querying approach to extract patterns from such complex investment graphs obtained from real-world data.We present a three-fold evaluation of the system:we perform a qualitative evaluation of our approach with three domain experts,a separate assessment of the proposed guidance features with an expert researcher in this field,and a case study of Hermes using a bank account network dataset to demonstrate the generalizability of our approach.
文摘Systemic risk research is gaining traction across diverse disciplinary research communities, but has as yet not been strongly linked to traditional, well-established risk analysis research. This is due in part to the fact that systemic risk research focuses on the connection of elements within a system, while risk analysis research focuses more on individual risk to single elements. We therefore investigate how current systemic risk research can be related to traditional risk analysis approaches from a conceptual as well as an empirical point of view. Based on Sklar's Theorem, which provides a one-to-one relationship between multivariate distributions and copulas, we suggest a reframing of the concept of copulas based on a network perspective. This provides a promising way forward for integrating individual risk(in the form of probability distributions) and systemic risk(in the form of copulasdescribing the dependencies among such distributions)across research domains. Copulas can link continuous node states, characterizing individual risks, with a gradual dependency of the coupling strength between nodes on their states, characterizing systemic risk. When copulas are used for describing such refined coupling between nodes,they can provide a more accurate quantification of a system's network structure. This enables more realistic systemic risk assessments, and is especially useful when extreme events(that occur at low probabilities, but have high impacts) affect a system's nodes. In this way, copulas can be informative in measuring and quantifying changes in systemic risk and therefore be helpful in its management. We discuss the advantages and limitations of copulas for integrative risk analyses from the perspectives of modeling, measurement, and management.
基金the National Science Foundation(No.SMA-1620462)T.A.Kohler further acknowledges support from the Cluster of Excellence ROOTS,EXC 2150the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy.
文摘Collective computation is the process by which groups store and share information to arrive at decisions for collective behavior.How societies engage in effective collective computation depends partly on their scale.Social arrangements and technologies that work for small-and mid-scale societies are inadequate for dealing effectively with the much larger communication loads that societies face during the growth in scale that is a hallmark of the Holocene.An important bottleneck for growth may be the development of systems for persistent recording of information(writing),and perhaps also the abstraction of money for generalizing exchange mechanisms.Building on Shin et al.,we identify a Scale Threshold to be crossed before societies can develop such systems,and an Information Threshold which,once crossed,allows more or less unlimited growth in scale.We introduce several additional articles in this special issue that elaborate or evaluate this Thresholds Model for particular types of societies or times and places in the world.
