Algorithms, Automation, and News

This special issue examines the growing importance of algorithms and automation in the gathering, composition, and distribution of news. It connects a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these articles share some of the noble ambitions of the pioneering publications on ‘reporting algorithms’, such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems, to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematize computational journalism by, for example, pointing out some of the challenges inherent in applying AI to investigative journalism and in trying to preserve public service values. Fourthly, they offer suggestions for future research and practice, including by presenting a framework for developing democratic news recommenders and another that may help us think about computational journalism in a more integrated, structured manner

Sensitivity studies for r-process nucleosynthesis in three astrophysical scenarios

In rapid neutron capture, or r-process, nucleosynthesis, heavy elements are built up via a sequence of neutron captures and beta decays that involves thousands of nuclei far from stability. Though we understand the basics of how the r-process proceeds, its astrophysical site is still not conclusively known. The nuclear network simulations we use to test potential astrophysical scenarios require nuclear physics data (masses, beta decay lifetimes, neutron capture rates, fission probabilities) for all of the nuclei on the neutron-rich side of the nuclear chart, from the valley of stability to the neutron drip line. Here we discuss recent sensitivity studies that aim to determine which individual pieces of nuclear data are the most crucial for r-process calculations. We consider three types of astrophysical scenarios: a traditional hot r-process, a cold r-process in which the temperature and density drop rapidly, and a neutron star merger trajectory.Comment: 8 pages, 4 figures, submitted to the Proceedings of the International Nuclear Physics Conference (INPC) 201

Sn-modification of Pt7/alumina model catalysts: Suppression of carbon deposition and enhanced thermal stability.

An atomic layer deposition process is used to modify size-selected Pt7/alumina model catalysts by Sn addition, both before and after Pt7 cluster deposition. Surface science methods are used to probe the effects of Sn-modification on the electronic properties, reactivity, and morphology of the clusters. Sn addition, either before or after cluster deposition, is found to strongly affect the binding properties of a model alkene, ethylene, changing the number and type of binding sites, and suppressing decomposition leading to carbon deposition and poisoning of the catalyst. Density functional theory on a model system, Pt4Sn3/alumina, shows that the Sn and Pt atoms are mixed, forming alloy clusters with substantial electron transfer from Sn to Pt. The presence of Sn also makes all the thermally accessible structures closed shell, such that ethylene binds only by π-bonding to a single Pt atom. The Sn-modified catalysts are quite stable in repeated ethylene temperature programmed reaction experiments, suggesting that the presence of Sn also reduces the tendency of the sub-nano-clusters to undergo thermal sintering

A combinatorial approach to geometric rough paths and their controlled paths

We develop the structure theory for transformations of weakly geometric rough paths of bounded $1 < p$-variation and their controlled paths. Our approach differs from existing approaches as it does not rely on smooth approximations. We derive an explicit combinatorial expression for the rough path lift of a controlled path, and use it to obtain fundamental identities such as the associativity of the rough integral, the adjunction between pushforwards and pullbacks, and a change of variables formula for rough differential equations (RDEs). As applications we define rough paths, rough integration and RDEs on manifolds, extending the results of [CDL15] to the case of arbitrary $p$