The effect of temperature evolution on the interior structure of H2{}_{2}O-rich planets

For most planets in the range of radii from 1 to 4 R$_{\oplus}$, water is a major component of the interior composition. At high pressure H${}_{2}$O 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 H${}_{2}$O, 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 H${}_{2}$O. It turns out that the bulk of H${}_{2}$O 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 $\pm$ 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 $\mathcal{O}(K\log N(\tau+1))$ samples to recover $K$ active users whereas standard MUD requires $N(\tau+1)$ samples to process $N$ total users with a maximal delay $\tau$. 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