Constraints on cosmic-ray propagation models from a global Bayesian analysis

Research in many areas of modern physics such as, e.g., indirect searches for dark matter and particle acceleration in SNR shocks, rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions, The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, that uses astrophysical information, nuclear and particle data as input to self-consistently predict CRs, gamma rays, synchrotron and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP.Comment: 19 figures, 3 tables, emulateapj.sty. A typo is fixed. To be published in the Astrophysical Journal v.728 (February 10, 2011 issue). Supplementary material can be found at http://www.g-vo.org/pub/GALPROP/GalpropBayesPaper

A High-Fidelity Realization of the Euclid Code Comparison NN-body Simulation with Abacus

We present a high-fidelity realization of the cosmological $N$-body simulation from the Schneider et al. (2016) code comparison project. The simulation was performed with our Abacus $N$-body code, which offers high force accuracy, high performance, and minimal particle integration errors. The simulation consists of $2048^3$ particles in a $500\ h^{-1}\mathrm{Mpc}$ box, for a particle mass of $1.2\times 10^9\ h^{-1}\mathrm{M}_\odot$ with $10\ h^{-1}\mathrm{kpc}$spline softening. Abacus executed 1052 global time steps to$z=0$in 107 hours on one dual-Xeon, dual-GPU node, for a mean rate of 23 million particles per second per step. We find Abacus is in good agreement with Ramses and Pkdgrav3 and less so with Gadget3. We validate our choice of time step by halving the step size and find sub-percent differences in the power spectrum and 2PCF at nearly all measured scales, with$<0.3\%$errors at$k<10\ \mathrm{Mpc}^{-1}h$. On large scales, Abacus reproduces linear theory better than$0.01\%$. Simulation snapshots are available at http://nbody.rc.fas.harvard.edu/public/S2016 .Comment: 13 pages, 8 figures. Minor changes to match MNRAS accepted versio

Quantum Artificial Life in an IBM Quantum Computer

We present the first experimental realization of a quantum artificial life algorithm in a quantum computer. The quantum biomimetic protocol encodes tailored quantum behaviors belonging to living systems, namely, self-replication, mutation, interaction between individuals, and death, into the cloud quantum computer IBM ibmqx4. In this experiment, entanglement spreads throughout generations of individuals, where genuine quantum information features are inherited through genealogical networks. As a pioneering proof-of-principle, experimental data fits the ideal model with accuracy. Thereafter, these and other models of quantum artificial life, for which no classical device may predict its quantum supremacy evolution, can be further explored in novel generations of quantum computers. Quantum biomimetics, quantum machine learning, and quantum artificial intelligence will move forward hand in hand through more elaborate levels of quantum complexity

Two Decades of Maude

This paper is a tribute to José Meseguer, from the rest of us in the Maude team, reviewing the past, the present, and the future of the language and system with which we have been working for around two decades under his leadership. After reviewing the origins and the language's main features, we present the latest additions to the language and some features currently under development. This paper is not an introduction to Maude, and some familiarity with it and with rewriting logic are indeed assumed.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Building Teams from a Distance

[Excerpt] Virtual teams are comprised of individuals that are separated geographically or organizationally and that rely primarily on technology to complete tasks (Powell, Piccoli & Ives, 2004). This work arrangement has been found to be advantageous for many firms because it reduces the costs and time associated with employee travel. It also permits organizations to attract and retain top talent because workplace flexibility is increasingly seen as a crucial aspect of job satisfaction for many employees (Bergiel, Bergiel & Balsmeier, 2008). Virtual teams are also valuable to many businesses because team members commonly focus their interests on tasks instead of shared social or cultural environments, which often impact the dynamic within conventional teams (Hamilton & Scandura, 2003). This fosters a working environment that encourages innovation and decreases discrimination by hierarchy, employee impairments, race or age because productivity is more important than other characteristics (Bergiel et al., 2008). While virtual teams have many advantages, they frequently struggle to establish a strong sense of trust between individuals, frequent team member intercommunication, and effective leadership; all of which are necessary for team success

On the recognition of complex structures: Computer software using artificial intelligence applied to pattern recognition

An approach to simultaneous interpretation of objects in complex structures so as to maximize a combined utility function is presented. Results of the application of a computer software system to assign meaning to regions in a segmented image based on the principles described in this paper and on a special interactive sequential classification learning system, which is referenced, are demonstrated