Creditor discrimination during sovereign debt restructurings

This paper explores patterns of discrimination between residents and foreign creditors during recents sovereign debt restructurings. We analyze 10 recent episodes distinguishing between neutral cases in which the sovereign treated creditors equitably irrespective of their nationality and instances of discrimination against residents and non-residents. We then present evidence in support of the hypothesis that these patterns of discrimination can be explained by the origin of liquidity pressures, the ex ante soundness of the banking system and the extent of the domestic corporate sector’s reliance on international financial markets. On the theoretical side, we present a simple model of a government’s strategic decision to diferentiate between the servicing of its domestic and its external debt. In our model, the basic trade-off facing the authorities is to default on external debt and in so doing restricting private access to international capital markets or to default on domestic debt, thereby curtailing the banking sector’s capacity to lend to domestic firm

Measuring and explaining the volatility of capital flows towards emerging countries

This paper analyzes the determinants of the volatility of different types of capital inflows to emerging countries. After calculating a variable that proxies capital flows volatility, we study its possible causality relations with a set of explanatory variables by type of flow through a panel data model. We show that in recent years the significance of global factors, beyond the control of emerging economies, has increased at the expense of that of country specific factors. In addition, various factors exhibit a non-robust effect on the volatility of the three different categories of capital flows, which poses additional challenges for policy-maker

The role of the IMF in recent sovereign debt restructurings : implication for the policy of lending into arrears

This paper analyzes the role played by the IMF in eight recent sovereign debt restructurings from a comparative perspective: Argentina (2001-2005), the Dominican Republic (2004-2005), Ecuador (1999-2000), Pakistan (1998-2001), the Russian Federation (1998-2001), Serbia (2000-2004), Ukraine (1998-2000) and Uruguay (2004). Our objective is to identify the various dimensions of the IMF's potential involvement during those processes, and to extract some relevant policy implications to reform the Policy of Lending Into Arrears. We find that the IMF can potentially exert a substantial influence on sovereign debt restructurings by influencing countries' decision to restructure when the debt burden is deemed unsustainable, by providing official finance to substitute for a loss of access to international financial markets, by setting a medium-term domestic adjustment path through conditionality, by providing 'independent' information at a time of heightened uncertainty, and by providing incentives both to creditors and debtors. However, a lack of consistency has tended to characterize the role of the IMF in recent sovereign debt restructurings. In part, this reflects the flexibility with which the IMF has adapted its intervention to country-specific factors. However, we argue that this lack of consistency has tended to exacerbate the uncertainty and information asymmetries that are often associated with sovereign debt restructurings, and that a more systematic approach is neede

Recent episodes of sovereign debt restructurings : a case-study approach

Sovereign debt restructurings do constitute a recurrent phenomenon in emerging and developing economies. Consequently, the international community has repeatedly explored options to increase the predictability and orderliness of debt workouts, of which the debate on the Sovereign Debt Restructuring Mechanism (SDRM) proposed by the IMF in 2002 is the most recent example. Eventually, however, the most ambitious reform proposals have been systematically abandoned, thereby consolidating debt restructurings as market-led case-by-case processes. This paper reviews nine recent sovereign debt restructurings: Argentina (2001-2005), Belize (2006-2007), the Dominican Republic (2004-2005), Ecuador (1999-2000), Pakistan (1998-2001), the Russian Federation (1998-2001), Serbia (2000-2004), Ukraine (1998-2000) and Uruguay (2004). Our case study analysis reveals the lack of a single model for sovereign debt restructurings. Indeed, we find significant variations in the roots of the crises, the size of the losses undergone by investors, the speed at which an agreement was reached with creditors, the proportion of creditors accepting the terms of that agreement, or the time needed to restore access to international financial markets. There also appears to be a lack of consistency in the role played by the IMF in the various crises.

Cosmic acceleration in entropic cosmology

In this paper we study the viability of an entropic cosmological model. The effects of entropic gravity are derived from a modified entropy-area relationship with a volumetric entropy term. This model describes a late time limit cosmic acceleration, whose origin is related to a volumetric term in the entropy. Moreover, we analyze the phenomenological implications of the entropic model using the Supernovae Pantheon compilation and the observational Hubble parameter data to find consistency with cosmological observations. Finally, we show the equivalence between the entropic model and a brane world cosmological model, by means of an effective geometrical construction.Comment: 11 pages, 3 figures. Version accepted for Publication in Phys. Lett.

On the entropy of a stealth vector-tensor black hole

We apply Wald's formalism to a Lagrangian within generalised Proca gravity that admits a Schwarzschild black hole with a non-trivial vector field. The resulting entropy differs from that of the same black hole in General Relativity by a logarithmic correction modulated by the only independent charge of the vector field. We find conditions on this charge to guarantee that the entropy is a non-decreasing function of the black hole area, as is the case in GR. If this requirement is extended to black hole mergers, we find that for Planck scale black holes, a non-decreasing entropy is possible only if the area of the final black hole is several times larger than the initial total area of the merger. Finally, we discuss some implications of the vector Galileon entropy from the point of view of entropic gravity

Dual Machine-Learning system to aid Glaucoma Diagnosis using disc and cup feature extraction.

Glaucoma is a degenerative disease that affects vision, causing damage to the optic nerve that ends in vision loss. The classic techniques to detect it have undergone a great change since the intrusion of machine learning techniques into the processing of eye fundus images. Several works focus on training a convolutional neural network (CNN) by brute force, while others use segmentation and feature extraction techniques to detect glaucoma. In this work, a diagnostic aid tool to detect glaucoma using eye fundus images is developed, trained and tested. It consists of two subsystems that are independently trained and tested, combining their results to improve glaucoma detection. The first subsystem applies machine learning and segmentation techniques to detect optic disc and cup independently, combine them and extract their physical and positional features. The second one applies transfer learning techniques to a pre-trained CNN to detect glaucoma through the analysis of the complete eye fundus images. The results of both systems are combined to discriminate positive cases of glaucoma and improve final detection. The results show that this system achieves a higher classification rate than previous works. The system also provides information on the basis for the proposed diagnosis suggestion that can help the ophthalmologist to accept or modify it

Measuring the dark matter equation of state

The nature of the dominant component of galaxies and clusters remains unknown. While the astrophysics community supports the cold dark matter (CDM) paradigm as a clue factor in the current cosmological model, no direct CDM detections have been performed. Faber and Visser 2006 have suggested a simple method for measuring the dark matter equation of state that combines kinematic and gravitational lensing data to test the widely adopted assumption of pressureless dark matter. Following this formalism, we have measured the dark matter equation of state for first time using improved techniques. We have found that the value of the equation of state parameter is consistent with pressureless dark matter within the errors. Nevertheless, the measured value is lower than expected because typically the masses determined with lensing are larger than those obtained through kinematic methods. We have tested our techniques using simulations and we have also analyzed possible sources of error that could invalidate or mimic our results. In the light of this result, we can now suggest that the understanding of the nature of dark matter requires a complete general relativistic analysis.Comment: 4 pages, 2 figures, accepted for publication in Monthly Notices of the Royal Astronomical Society Letters. Minor revision as suggested by refere

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-

Multi-dataset Training for Medical Image Segmentation as a Service

Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.Ministerio de Economía y Competitividad TEC2016-77785-