231 research outputs found

    Comparison of post-Newtonian templates for extreme mass ratio inspirals

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    Extreme mass ratio inspirals (EMRIs), the inspirals of compact objects into supermassive black holes, are important gravitational wave sources for the Laser Interferometer Space Antenna (LISA). We study the performance of various post-Newtonian (PN) template families relative to the high precision numerical waveforms in the context of EMRI parameter estimation with LISA. Expressions for the time domain waveforms TaylorT1, TaylorT2, TaylorT3, TaylorT4 and TaylorEt are derived up to 22PN order, i.e O(v44)\mathcal{O}(v^{44}) (vv is the characteristic velocity of the binary) beyond the Newtonian term, for a test particle in a circular orbit around a Schwarzschild black hole. The phase difference between the above 22PN waveform families and numerical waveforms are evaluated during two-year inspirals for two prototypical EMRI systems with mass ratios 10410^{-4} and 10510^{-5}. We find that the dephases (in radians) for TaylorT1 and TaylorT2, respectively, are about 10910^{-9} (10210^{-2}) and 10910^{-9} (10310^{-3}) for mass ratio 10410^{-4} (10510^{-5}). This suggests that using 22PN TaylorT1 or TaylorT2 waveforms for parameter estimation of EMRIs will result in accuracies comparable to numerical waveform accuracy for most of the LISA parameter space. On the other hand, from the dephase results, we find that TaylorT3, TaylorT4 and TaylorEt fare relatively poorly as one approaches the last stable orbit. This implies that, as for comparable mass binaries using the 3.5PN phase of waveforms, the 22PN TaylorT3 and TaylorEt approximants do not perform well enough for the EMRIs. The reason underlying the poor performance of TaylorT3, TaylorT4 and TaylorEt relative to TaylorT1 and TaylorT2 is finally examined.Comment: 10 page

    A Cellular Resolution Map of Barrel Cortex Activity during Tactile Behavior

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    SummaryComprehensive measurement of neural activity remains challenging due to the large numbers of neurons in each brain area. We used volumetric two-photon imaging in mice expressing GCaMP6s and nuclear red fluorescent proteins to sample activity in 75% of superficial barrel cortex neurons across the relevant cortical columns, approximately 12,000 neurons per animal, during performance of a single whisker object localization task. Task-related activity peaked during object palpation. An encoding model related activity to behavioral variables. In the column corresponding to the spared whisker, 300 layer (L) 2/3 pyramidal neurons (17%) each encoded touch and whisker movements. Touch representation declined by half in surrounding columns; whisker movement representation was unchanged. Following the emergence of stereotyped task-related movement, sensory representations showed no measurable plasticity. Touch direction was topographically organized, with distinct organization for passive and active touch. Our work reveals sparse and spatially intermingled representations of multiple tactile features.Video Abstrac

    Remedy: Network-Aware Steady State VM Management for Data Centers

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    Abstract. Steady state VM management in data centers should be network-aware so that VM migrations do not degrade network performance of other flows in the network, and if required, a VM migration can be intelligently orchestrated to decongest a network hotspot. Recent research in network-aware management of VMs has focused mainly on an optimal network-aware initial placement of VMs and has largely ignored steady state management. In this context, we present the design and implementation of Remedy. Remedy ranks target hosts for a VM migration based on the associated cost of migration, available bandwidth for mi-gration and the network bandwidth balance achieved by a migration. It models the cost of migration in terms of additional network traffic generated during mi-gration. We have implemented Remedy as an OpenFlow controller application that detects the most congested links in the network and migrates a set of VMs in a network-aware manner to decongest these links. Our choice of target hosts ensures that neither the migration traffic nor the flows that get rerouted as a result of migration cause congestion in any part of the network. We validate our cost of migration model on a virtual software testbed using real VM migrations. Our simulation results using real data center traffic data demonstrate that selective network aware VM migrations can help reduce unsatisfied bandwidth by up to 80-100%
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