Primordial Gravitational Waves and Cosmology

The observation of primordial gravitational waves could provide a new and unique window on the earliest moments in the history of the universe, and on possible new physics at energies many orders of magnitude beyond those accessible at particle accelerators. Such waves might be detectable soon in current or planned satellite experiments that will probe for characteristic imprints in the polarization of the cosmic microwave background (CMB), or later with direct space-based interferometers. A positive detection could provide definitive evidence for Inflation in the early universe, and would constrain new physics from the Grand Unification scale to the Planck scale.Comment: 12 pages. 4 figure

H9N2 Influenza A Viruses from Poultry in Asia Have Human Virus-like Receptor Specificity

AbstractH9N2 influenza A viruses are currently widespread in chickens, quail, and other poultry in Asia and have caused a few cases of influenza in humans. In this study, we found that H9N2 viruses from Hong Kong live bird markets have receptor specificity similar to that of human H3N2 viruses. In addition, the neuraminidase of poultry H9N2 viruses has mutations in its hemadsorbing site, a characteristic resembling that of human H2N2 and H3N2 viruses but differing from that of other avian viruses. Peculiar features of surface glycoproteins of H9N2 viruses from Hong Kong suggest an enhanced propensity for introduction into humans and emphasize the importance of poultry in the zoonotic transmission of influenza viruses

Reassortment and Interspecies Transmission of North American H6N2 Influenza Viruses

AbstractH6N2 influenza viruses were isolated from California chickens in 2000 and 2001. Here we report the characterization of these H6N2 viruses, one of the few descriptions of non-H5, non-H7 subtype influenza viruses in this host. The H6N2 viruses were nonpathogenic in experimentally infected chickens and could be divided into three genotypes. All three genotypes of virus had similar surface glycoproteins and all contained an 18 amino acid deletion in the neuraminidase, a characteristic of other chicken influenza viruses. Differences were apparent, however, in the complement of replicative protein genes between the genotypes. The presence of multiple H6N2 genotypes suggests that independent transmission and/or reassortment events may have taken place between aquatic bird and chicken influenza viruses

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.Comment: 17 pages, 19 figures, in Proceedings of ACM FAT* 2019, dataset & demo available at https://deception.machineintheloop.co

Seismology in the cloud: guidance for the individual researcher

The commercial cloud offers on-demand computational resources that could be revolutionary for the seismological community, especially as seismic datasets continue to grow. However, there are few educational examples for cloud use that target individual seismological researchers. Here, we present a reproducible earthquake detection and association workflow that runs on Microsoft Azure. The Python-based workflow runs on continuous time-series data using both template matching and machine learning. We provide tutorials for constructing cloud resources (both storage and computing) through a desktop portal and deploying the code both locally and remotely on the cloud resources. We report on scaling of compute times and costs to show that CPU-only processing is generally inexpensive, and is faster and simpler than using GPUs. When the workflow is applied to one year of continuous data from a mid-ocean ridge, the resulting earthquake catalogs suggest that template matching and machine learning are complementary methods whose relative performance is dependent on site-specific tectonic characteristics. Overall, we find that the commercial cloud presents a steep learning curve but is cost-effective. This report is intended as an informative starting point for any researcher considering migrating their own processing to the commercial cloud