A percolation model for the emergence of the Bitcoin Lightning Network

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. Read more

Approximate Uncertain Programs

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. Read more

Escape from model-land

Policy makers in areas ranging from public health to weather forecasting or economics rely on mathematical models to inform their decisions. As models grow more complex and powerful, one might expect, they should contribute to better decisions. Read more

Bifurcations on Fully Inhomogeneous Networks

One of the most powerful methods of bifurcation theory is centre manifold reduction, in which a judicious coordinate change greatly simplifies the analysis of dynamical systems in the vicinity of a bifurcation point. Read more

Superextreme Waves Generation in the Linear Regime

So-called extreme or rogue waves are large amplitude waves which appear unpredictably in optical and acoustic systems, in plasmas, as well as in quantum physics and in hydrodynamics. Read more

Should We Be Afraid of Artificial Intelligence?

Economists worry about the impact that artificial intelligence (AI) technology could have as it begins to displace human employees, especially those in entry-level jobs such as data entry, customer service or retail. Read more

Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America

Extreme weather events emerge out of the interaction of many physical processes, and understanding how is a key challenge in atmospheric science. Read more

Sampling hyperspheres via extreme value theory: implications for measuring attractor dimensions

Advancing computational power has encouraged the analysis of large, high-dimensional data sets with machine learning and data mining techniques, as well as the use of algorithms to compute dynamical indicators such as Lyapunov exponents or generalized dimensions. Read more

Forecasting the magnitude of the largest expected earthquake

Geophysicists still lack a comprehensive understanding of the mechanisms and stochastic dynamics behind the earthquake generation process, and so also lack an ability to make reliable predictions of the likelihood of extreme events. Read more

Epidemic spreading with awareness and different timescales in multiplex networks

Efforts to control epidemics rely on mathematical and computational models of how infectious agents spread. Such models help to find ways to deter transmission – through vaccination and quarantine, for example, or information campaigns to alter human behaviour. Read more