1,064 research outputs found
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
CMBPol Mission Concept Study: A Mission to Map our Origins
Quantum mechanical metric fluctuations during an early inflationary phase of
the universe leave a characteristic imprint in the polarization of the cosmic
microwave background (CMB). The amplitude of this signal depends on the energy
scale at which inflation occurred. Detailed observations by a dedicated
satellite mission (CMBPol) therefore provide information about energy scales as
high as GeV, twelve orders of magnitude greater than the highest
energies accessible to particle accelerators, and probe the earliest moments in
the history of the universe. This summary provides an overview of a set of
studies exploring the scientific payoff of CMBPol in diverse areas of modern
cosmology, such as the physics of inflation, gravitational lensing and cosmic
reionization, as well as foreground science and removal .Comment: 6 pages, 3 figure
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
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