Investigation of magnetopause reconnection models using two colocated, low‐altitude satellites: A unifying reconnection geometry

Ion precipitation data from two co-orbiting Defense Meteorological Satellite Program satellites (F6 and F8) are used to investigate magnetopause reconnection models. We examine differential fluxes between 30 eV and 30 keV, from a Southern Hemisphere, prenoon pass during the morning of January 10, 1990. Data from the first satellite to pass through the region (F6) show two distinct ion energy dispersions •-1 ø of latitude apart, between 76 ø and 79 ø magnetic latitude. The electron data exhibit similar features at around the same region but with no or little energy dispersion, consistent with their high velocities. We suggest that the two energy dispersions can be explained by two separate injections resulting from two bursts of magnetopause reconnection. Data from the second satellite (F8), which moved through the same region I rain later, reveal the same energy-dispersed structures, only further poleward and with less overall flux. This temporal evolution is consistent with two recently reconnected flux tubes releasing their plasma as they move antisunward away from dayside merging sites. However, an observed overlap between the two ion energy dispersions suggests a more complex reconnection geometry than usual models can accommodate. We propose a generalized reconnection scenario that unifies the Bursty Single X-Line and the Multiple X-Line Reconnection models. A simple time-of-flight particle precipitation model is constructed to reproduce the ion dispersions and their overlap. The modeling results suggest that for time-dependent reconnection the dispersion overlap is observed clearly at low altitudes only for a short period compared with the evolution timescale of the ion precipitation

Separation of spatial and temporal structure of auroral particle precipitation

[1] Knowledge of the dominant temporal and spatial scales of auroral features is instrumental in understanding the various mechanisms responsible for auroral particle precipitation. Single spacecraft data always suffer from temporal/spatial ambiguity. In an effort to separate the temporal and spatial variations of the aurora, we use electron and ion precipitation data from two co-orbiting satellites, F6 and F8 of the Defense Meteorological Satellite Program (DMSP). The two spacecraft have almost identical polar orbits with a small difference in period. As a result the time difference between the two measurements varies with time. We use two statistical tools in order to determine the most probable lifetimes and spatial dimensions of the prevalent auroral features. The first tool is cross-correlation analysis between the magnetic latitude series of electron and ion, number and energy fluxes measured by the two DMSP spacecraft. As one spacecraft overtakes the other, the variable time lag between the two measurements results in different cross-correlation of the two series. We explore the dependence of this variation on the time lag between the satellites. We find that the electron precipitation exhibits a decreasing correlation between the two spacecraft with increasing time lag, whereas there is only a small similar effect for the ion precipitation data. The second statistical tool is cross-spectral analysis, for which we compute the so-called coherence function as a function of frequency (or inverse wavelength) and hence size of the auroral features. The coherence function is a measure of the stability of auroral features of different sizes. We investigate its variation as a function of the time separation between the two measurements. We show that the coherence function of both electrons and ions remains high for up to 1.5 min spacecraft separations for all features larger than about 100 km in width. For smaller features the coherence is lower even for time lags of a few seconds. The results are discussed in the context of characteristic temporal and spatial auroral scales deduced from complementary studies and expected from theory

Wave-like spread of Ebola Zaire

In the past decade the Zaire strain of Ebola virus (ZEBOV) has emerged repeatedly into human populations in central Africa and caused massive die-offs of gorillas and chimpanzees. We tested the view that emergence events are independent and caused by ZEBOV variants that have been long resident at each locality. Phylogenetic analyses place the earliest known outbreak at Yambuku, Democratic Republic of Congo, very near to the root of the ZEBOV tree, suggesting that viruses causing all other known outbreaks evolved from a Yambuku-like virus after 1976. The tendency for earlier outbreaks to be directly ancestral to later outbreaks suggests that outbreaks are epidemiologically linked and may have occurred at the front of an advancing wave. While the ladder-like phylogenetic structure could also bear the signature of positive selection, our statistical power is too weak to reach a conclusion in this regard. Distances among outbreaks indicate a spread rate of about 50 km per year that remains consistent across spatial scales. Viral evolution is clocklike, and sequences show a high level of small-scale spatial structure. Genetic similarity decays with distance at roughly the same rate at all spatial scales. Our analyses suggest that ZEBOV has recently spread across the region rather than being long persistent at each outbreak locality. Controlling the impact of Ebola on wild apes and human populations may be more feasible than previously recognized

Echo State Queueing Network: a new reservoir computing learning tool

In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a recurrent part (the reservoir) that does not participate in the learning process, and the rest of the system where no recurrence (no neural circuit) occurs. This approach has grown rapidly due to its success in solving learning tasks and other computational applications. Some success was also observed with another recently proposed neural network designed using Queueing Theory, the Random Neural Network (RandNN). Both approaches have good properties and identified drawbacks. In this paper, we propose a new RC model called Echo State Queueing Network (ESQN), where we use ideas coming from RandNNs for the design of the reservoir. ESQNs consist in ESNs where the reservoir has a new dynamics inspired by recurrent RandNNs. The paper positions ESQNs in the global Machine Learning area, and provides examples of their use and performances. We show on largely used benchmarks that ESQNs are very accurate tools, and we illustrate how they compare with standard ESNs.Comment: Proceedings of the 10th IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, USA, 201