Blog
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Giant leaps and long excursions: fluctuation mechanisms in systems with long-range memory
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Many random processes lack memory – that is, the probability distribution for change over any time interval depends on the present state of the process but remains independent of how it behaved previously.
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On the Computation of the Extremal Index for Time Series
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Many insights have recently emerged from applications of extreme value theory to the study of time series generated by dynamical systems.
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What are we weighting for? A mechanistic model for probability weighting
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It’s a common belief in areas such as psychology, economics or finance that people suffer from cognitive biases — systematic deviations from correct or rational behaviour when they make decisions.
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Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation
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Since the outbreak of the coronavirus, modellers from around the world have scrambled to predict the course of its spread, projecting forward from data on past infections and deaths in any region. Such estimates help public health officials and governments to judge […]
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The effect of social balance on social fragmentation
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Nations and communities typically function as relatively coherent groups, yet also occasionally fall apart or fragment into highly polarised sub-groups.
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Phase transitions in atypical systems induced by a condensation transition on graphs
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Random graphs can undergo structural phase transitions as a function of their model parameters. For example, in the well-known percolation transition, a network-spanning cluster of linked nodes abruptly appears at a critical value of the probability, c, for a link to exist […]
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A percolation model for the emergence of the Bitcoin Lightning Network
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The distributed ledger system known as blockchain has triggered a revolution in the payments industry, and is the technological infrastructure underlying cryptocurrencies such as Bitcoin. Even so, the blockchain concept itself faces some limitations which may hinder its future growth and adoption.
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Approximate Uncertain Programs
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Uncertain programs are optimization problems involving uncertainties in constraints or objectives. In the past few decades, such programs have been applied in areas from economics and management to automotive control and machine learning.
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General resource: useful articles on the SARS-CoV-2 coronavirus pandemic (last updated 8 April 2020)
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This blog post lists some further articles and scientific papers which may be of interest in following the unfolding coronavirus epidemic. It has been assembled from suggestions made by LML Fellows and associates.
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General resource: useful articles on the SARS-CoV-2 coronavirus pandemic (last updated 31 March 2020)
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This blog post lists a number of articles and scientific papers which may be of interest in following the unfolding coronavirus epidemic. It has been assembled from suggestions made by LML Fellows and associates.