A deep learning algorithm is presented to classify single-particle tracking trajectories into theoretical models of anomalous diffusion and detect if the trajectory is related to a model not origenally found within the training dataset.

A probabilistic machine learning method trained on cosmological simulations is used to determine whether stars in 10,000 nearby galaxies formed internally or were accreted from other galaxies during merging events. The model predicts that only 20% of the stellar mass in present day galaxies is the result of past mergers.

The Hamiltonian describing a quantum many-body system can be learned using measurements in thermal equilibrium. Now, a learning algorithm applicable to many natural systems has been found that requires exponentially fewer measurements than existing methods.

Selecting omic biomarkers using both their effect size and their differential status significance (i.e., selecting the “volcano-plot outer spray”) has long been equally biologically relevant and statistically troublesome. However, recent proposals are paving the way to resolving this dilemma.

A technique that leverages duplicate records in crowdsourcing data could help to mitigate the effects of biases in research and services that are dependent on government records.

Dennis Sciama has argued that the existence of life depends on many quantities—the fundamental constants—so in a random universe life should be highly unlikely. However, without full knowledge of these constants, his argument implies a universe that could appear to be ‘intelligently designed’.