4,493 research outputs found
The effect of temperature evolution on the interior structure of HO-rich planets
For most planets in the range of radii from 1 to 4 R, water is a
major component of the interior composition. At high pressure HO can be
solid, but for larger planets, like Neptune, the temperature can be too high
for this. Mass and age play a role in determining the transition between solid
and fluid (and mixed) water-rich super-Earth. We use the latest high-pressure
and ultra-high-pressure phase diagrams of HO, and by comparing them
with the interior adiabats of various planet models, the temperature evolution
of the planet interior is shown, especially for the state of HO. It
turns out that the bulk of HO in a planet's interior may exist in
various states such as plasma, superionic, ionic, Ice VII, Ice X, etc.,
depending on the size, age and cooling rate of the planet. Different regions of
the mass-radius phase space are also identified to correspond to different
planet structures. In general, super-Earth-size planets (isolated or without
significant parent star irradiation effects) older than about 3 Gyr would be
mostly solid.Comment: Accepted by ApJ, in print for March 2014 (14 pages, 3 colored
figures, 1 table
A Dynamic General Equilibrium Framework of Investment with Financing Constraint
In this paper, we provide a dynamic general equilibrium framework with an explicit investment-financing constraint. The constraint is intended as a reduced form to capture the balance sheet effects that have been widely regarded as an important determinant of financial crises. We derive a link between the value of a firm and social welfare. Using this link, we show the somewhat surprising possibility that the value of a firm can be greater with the constraint. Our model also sheds light on how the effects of productivity shocks and investors' misperception of productivity shocks may be amplified by the financing constraint. Copyright 2003, International Monetary Fund
A Dynamic General Equilibrium Framework of Investment with Financing Constraint
In this paper, we provide a dynamic general equilibrium framework with an explicit investment-financing constraint. The constraint is intended as a reduced form to capture the balance sheet effects that have been widely regarded as an important determinant of financial crises. We derive a link between the value of the firm and social welfare. We find that the value of the firm can be greater with the constraint. Our model also sheds light on how the effects of productivity shocks and investors' misperception of productivity shocks may be amplified by the financing constraint.Investment Constraint, Value of the Firm
Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks
The identification of pulmonary lobes is of great importance in disease
diagnosis and treatment. A few lung diseases have regional disorders at lobar
level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this
work, we propose an automated segmentation of pulmonary lobes using
coordination-guided deep neural networks from chest CT images. We first employ
an automated lung segmentation to extract the lung area from CT image, then
exploit volumetric convolutional neural network (V-net) for segmenting the
pulmonary lobes. To reduce the misclassification of different lobes, we
therefore adopt coordination-guided convolutional layers (CoordConvs) that
generate additional feature maps of the positional information of pulmonary
lobes. The proposed model is trained and evaluated on a few publicly available
datasets and has achieved the state-of-the-art accuracy with a mean Dice
coefficient index of 0.947 0.044.Comment: ISBI 2019 (Oral
Compressive Demodulation of Mutually Interfering Signals
Multi-User Detection is fundamental not only to cellular wireless
communication but also to Radio-Frequency Identification (RFID) technology that
supports supply chain management. The challenge of Multi-user Detection (MUD)
is that of demodulating mutually interfering signals, and the two biggest
impediments are the asynchronous character of random access and the lack of
channel state information. Given that at any time instant the number of active
users is typically small, the promise of Compressive Sensing (CS) is the
demodulation of sparse superpositions of signature waveforms from very few
measurements. This paper begins by unifying two front-end architectures
proposed for MUD by showing that both lead to the same discrete signal model.
Algorithms are presented for coherent and noncoherent detection that are based
on iterative matching pursuit. Noncoherent detection is all that is needed in
the application to RFID technology where it is only the identity of the active
users that is required. The coherent detector is also able to recover the
transmitted symbols. It is shown that compressive demodulation requires
samples to recover active users whereas
standard MUD requires samples to process total users with a
maximal delay . Performance guarantees are derived for both coherent and
noncoherent detection that are identical in the way they scale with number of
active users. The power profile of the active users is shown to be less
important than the SNR of the weakest user. Gabor frames and Kerdock codes are
proposed as signature waveforms and numerical examples demonstrate the superior
performance of Kerdock codes - the same probability of error with less than
half the samples.Comment: submitted for journal publicatio
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