18,790 research outputs found
Efficient computation of the gravitational wave spectrum emitted by eccentric massive black hole binaries in stellar environments
We present a fast and versatile method to calculate the characteristic
spectrum of the gravitational wave background (GWB) emitted by a
population of eccentric massive black hole binaries (MBHBs). We fit the
spectrum of a reference MBHB with a simple analytic function and show that the
spectrum of any other MBHB can be derived from this reference spectrum via
simple scalings of mass, redshift and frequency. We then apply our calculation
to a realistic population of MBHBs evolving via 3-body scattering of stars in
galactic nuclei. We demonstrate that our analytic prescription satisfactorily
describes the signal in the frequency band relevant to pulsar timing array
(PTA) observations. Finally we model the high frequency steepening of the GWB
to provide a complete description of the features characterizing the spectrum.
For typical stellar distributions observed in massive galaxies, our calculation
shows that 3-body scattering alone is unlikely to affect the GWB in the PTA
band and a low frequency turnover in the spectrum is caused primarily by high
eccentricities.Comment: 12 pages, 9 figures, published in MNRA
Existence of negative differential thermal conductance in one-dimensional diffusive thermal transport
We show that in a finite one-dimensional (1D) system with diffusive thermal
transport described by the Fourier's law, negative differential thermal
conductance (NDTC) cannot occur when the temperature at one end is fixed. We
demonstrate that NDTC in this case requires the presence of junction(s) with
temperature dependent thermal contact resistance (TCR). We derive a necessary
and sufficient condition for the existence of NDTC in terms of the properties
of the TCR for systems with a single junction. We show that under certain
circumstances we even could have infinite (negative or positive) differential
thermal conductance in the presence of the TCR. Our predictions provide
theoretical basis for constructing NDTC-based devices, such as thermal
amplifiers, oscillators and logic devices
Neuron Segmentation Using Deep Complete Bipartite Networks
In this paper, we consider the problem of automatically segmenting neuronal
cells in dual-color confocal microscopy images. This problem is a key task in
various quantitative analysis applications in neuroscience, such as tracing
cell genesis in Danio rerio (zebrafish) brains. Deep learning, especially using
fully convolutional networks (FCN), has profoundly changed segmentation
research in biomedical imaging. We face two major challenges in this problem.
First, neuronal cells may form dense clusters, making it difficult to correctly
identify all individual cells (even to human experts). Consequently,
segmentation results of the known FCN-type models are not accurate enough.
Second, pixel-wise ground truth is difficult to obtain. Only a limited amount
of approximate instance-wise annotation can be collected, which makes the
training of FCN models quite cumbersome. We propose a new FCN-type deep
learning model, called deep complete bipartite networks (CB-Net), and a new
scheme for leveraging approximate instance-wise annotation to train our
pixel-wise prediction model. Evaluated using seven real datasets, our proposed
new CB-Net model outperforms the state-of-the-art FCN models and produces
neuron segmentation results of remarkable qualityComment: miccai 201
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