Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Origins of the Ambient Solar Wind: Implications for Space Weather

The Sun's outer atmosphere is heated to temperatures of millions of degrees, and solar plasma flows out into interplanetary space at supersonic speeds. This paper reviews our current understanding of these interrelated problems: coronal heating and the acceleration of the ambient solar wind. We also discuss where the community stands in its ability to forecast how variations in the solar wind (i.e., fast and slow wind streams) impact the Earth. Although the last few decades have seen significant progress in observations and modeling, we still do not have a complete understanding of the relevant physical processes, nor do we have a quantitatively precise census of which coronal structures contribute to specific types of solar wind. Fast streams are known to be connected to the central regions of large coronal holes. Slow streams, however, appear to come from a wide range of sources, including streamers, pseudostreamers, coronal loops, active regions, and coronal hole boundaries. Complicating our understanding even more is the fact that processes such as turbulence, stream-stream interactions, and Coulomb collisions can make it difficult to unambiguously map a parcel measured at 1 AU back down to its coronal source. We also review recent progress -- in theoretical modeling, observational data analysis, and forecasting techniques that sit at the interface between data and theory -- that gives us hope that the above problems are indeed solvable.Comment: Accepted for publication in Space Science Reviews. Special issue connected with a 2016 ISSI workshop on "The Scientific Foundations of Space Weather." 44 pages, 9 figure

Measurement of the B0-anti-B0-Oscillation Frequency with Inclusive Dilepton Events

The $B^0$-$\bar B^0$ oscillation frequency has been measured with a sample of 23 million \B\bar B pairs collected with the BABAR detector at the PEP-II asymmetric B Factory at SLAC. In this sample, we select events in which both B mesons decay semileptonically and use the charge of the leptons to identify the flavor of each B meson. A simultaneous fit to the decay time difference distributions for opposite- and same-sign dilepton events gives $\Delta m_d = 0.493 \pm 0.012{(stat)}\pm 0.009{(syst)}$ ps$^{-1}$.Comment: 7 pages, 1 figure, submitted to Physical Review Letter

Prophets and loss: how "soft facts" on social media influenced the Brexit campaign and social reactions to the murder of Jo Cox MP

This article examines “soft facts” about security issues in the 2016 Brexit referendum campaign. Soft facts arise when information provenance is uncertain, and are forms of malleable and contingent knowledge, such as rumors, conspiracy theories, and propaganda. There is a growing appreciation that digital communications environments are especially conducive to the dissemination of these kinds of information. Informed by empirical data comprising forty‐five thousand nine hundred and fifty‐seven data points collected by monitoring social media before and after the UK Brexit referendum campaign (June 16–October 12, 2016), the analysis examines how and why a series of soft facts concerning Brexit were mobilized. By developing the concept of “digital prophecy,” the article explores how influence is exerted by online prophets who were connecting current events to past grievances, to advance negative predictions about the future. This starts to capture the tradecraft of digital influencing, in ways that move beyond the structural topologies of communication networks. In policy terms, the analysis reminds us of the need to attend not just to how influence is achieved through fake news (e.g., using social media bots to amplify a message), but also why influence is sought in the first place

Operational experience with the DIRC detector

The {\sc Dirc}, a novel type of Cherenkov ring imaging device, is the primary hadronic particle identification system for the $BABAR$ detector at the asymmetric B-factory, {\sc Pep-II} at SLAC. It is based on total internal reflection and uses long, rectangular bars made from synthetic fused silica as Cherenkov radiators and light guides. $BABAR$ began taking data with colliding beams in late spring 1999. This paper describes the performance of the {\sc Dirc} during the first 2.5 years of operation

The DIRC Particle Identification System for the BABAR Experiment

A new type of ring-imaging Cherenkov detector is being used for hadronic particle identification in the BABAR experiment at the SLAC B Factory (PEP-II). This detector is called DIRC, an acronym for Detection of Internally Reflected Cherenkov (Light). This paper will discuss the construction, operation and performance of the BABAR DIRC in detail

The Origin, Early Evolution and Predictability of Solar Eruptions

Coronal mass ejections (CMEs) were discovered in the early 1970s when space-borne coronagraphs revealed that eruptions of plasma are ejected from the Sun. Today, it is known that the Sun produces eruptive flares, filament eruptions, coronal mass ejections and failed eruptions; all thought to be due to a release of energy stored in the coronal magnetic field during its drastic reconfiguration. This review discusses the observations and physical mechanisms behind this eruptive activity, with a view to making an assessment of the current capability of forecasting these events for space weather risk and impact mitigation. Whilst a wealth of observations exist, and detailed models have been developed, there still exists a need to draw these approaches together. In particular more realistic models are encouraged in order to asses the full range of complexity of the solar atmosphere and the criteria for which an eruption is formed. From the observational side, a more detailed understanding of the role of photospheric flows and reconnection is needed in order to identify the evolutionary path that ultimately means a magnetic structure will erupt