Estimating the efficiency gains of debt restructuring

One rationale for debt reduction operations under the Brady Plan has been, by alleviating the debt overhang, to improve investment efficiency. Brady-type debt and debt-service reduction (within a strong policy framework, where there is a track record of economic adjustment) has been shown to affect development significantly. The principle benefit of eliminating the debt overhang is to improve investment incentives for private investors - direct liquidity relief is secondary. So, evaluating a debt and debt-service reduction operation should involve estimating efficiency gains as well as direct financial savings. The authors present a method (requiring only weak assumptions) for establishing an upper bound on the efficiency impact of debt reductions. The key reference framework for evaluating much more complex Brady-type debt deals is open-market buybacks. Their approach to determining this upper bound hinges on the assumption that efficiency gains on a straight open-market repurchase of debt never exceed the gains to creditors. If an open-market buyback indeed reduces the debt overhang and moves a country toward more (and more efficient) investment, creditors will anticipate this in setting a price for remitting their claims. So, at least part of the efficiency gains are dissipated in additional capital gains to creditors. To give point estimates to efficiency gains, they develop a simple two-period model of debt overhang and investment and discuss assumptions under which it is possible to obtain a closed-form solution to the model. Their empirical estimates indicate that the general bounds derived in the first step tend to overstate substantially the efficiency gains of debt reduction operations. In Mexico's case, for example, the upper-bound estimate of efficiency gains is US $15 billion, but the point estimate is only about US$1 billion. What are the policy implications of their low point estimates? The debt-overhang disincentive may not be as important as the broader problem of debtors'credit constraints in international capital markets. How can new loan packages to developing countries be structured to maximize investment incentives? By using loans rather than outright grants, donors can give a country more funds for current investment at lower present dicounted expense. But grants, unlike loans, do not distort investment incentives. In short, if a credit-constrained country starts with no debt overhang, the first tranche of aid should probably be in hard loans. As total transfers increase, if the borrowing country has not gained access to private capital markets, marginal transfers should be grants. The optimal strategy for new flows can involve both increasing grants and decreasing loans. When transfers are expected to be heavy, a case can be made for using grants exclusively.Strategic Debt Management,Economic Theory&Research,Financial Crisis Management&Restructuring,Banks&Banking Reform,Environmental Economics&Policies

Comparative study of various quasiprobability distributions in different models of correlated-emission lasers

We make a comparative study of various quasiprobability distributions in phase-sensitive quantum-optical systems. Starting from a general, linear master equation for the field, which emerges in different models of correlated-emission lasers, we derive the Fokker-Planck equations in the Glauber-Sudarshan P, the antinormal ordering Q, the Wigner W, the complex P, and the positive P representations and find the steady-state solutions for the five distributions. Simple relations between the complex and positive P functions are discovered for the first time. Various moments calculated by using these distributions are found to be identical, as expected. An application of these distributions to the two-photon correlated-emission laser shows that the intracavity field can be near-perfectly squeezed in the phase quadrature and the maximum quadrature squeezing is reached when the mean laser amplitude vanishes

Neural Network Methods for Radiation Detectors and Imaging

Recent advances in image data processing through machine learning and especially deep neural networks (DNNs) allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration

Bag of Deep Features for Instructor Activity Recognition in Lecture Room

This paper has been presented at : 25th International Conference on MultiMedia Modeling (MMM2019)This research aims to explore contextual visual information in the lecture room, to assist an instructor to articulate the effectiveness of the delivered lecture. The objective is to enable a self-evaluation mechanism for the instructor to improve lecture productivity by understanding their activities. Teacher’s effectiveness has a remarkable impact on uplifting students performance to make them succeed academically and professionally. Therefore, the process of lecture evaluation can significantly contribute to improve academic quality and governance. In this paper, we propose a vision-based framework to recognize the activities of the instructor for self-evaluation of the delivered lectures. The proposed approach uses motion templates of instructor activities and describes them through a Bag-of-Deep features (BoDF) representation. Deep spatio-temporal features extracted from motion templates are utilized to compile a visual vocabulary. The visual vocabulary for instructor activity recognition is quantized to optimize the learning model. A Support Vector Machine classifier is used to generate the model and predict the instructor activities. We evaluated the proposed scheme on a self-captured lecture room dataset, IAVID-1. Eight instructor activities: pointing towards the student, pointing towards board or screen, idle, interacting, sitting, walking, using a mobile phone and using a laptop, are recognized with an 85.41% accuracy. As a result, the proposed framework enables instructor activity recognition without human intervention.Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2014-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander

Ancient genome analyses shed light on kinship organization and mating practice of Late Neolithic society in China

Anthropology began in the late nineteenth century with an emphasis on kinship as a key factor in human evolution. From the 1960s, archaeologists attempted increasingly sophisticated ways of reconstructing prehistoric kinship but ancient DNA analysis has transformed the field, making it possible, to directly examine kin relations from human skeletal remains. Here, we retrieved genomic data from four Late Neolithic individuals in central China associated with the Late Neolithic Longshan culture. We provide direct evidence of consanguineous mating in ancient China, revealing inbreeding among the Longshan populations. By combining ancient genomic data with anthropological and archaeological evidence, we further show that Longshan society household was built based on the extended beyond the nuclear family, coinciding with intensified social complexity during the Longshan period, perhaps showing the transformation of large communities through a new role of genetic kinship-based extended family units.Introduction Results - Archaeological and anthropological insight into the Pingliangtai site - Ancient DNA authentication and uniparental genetic analyses - Neolithic period genetic contribution into the Yellow River basin from southern China - Three Pingliangtai individuals share second-degree relatedness (SDR) to each other - Parental relatedness in the Pingliangtai individuals Discussio

Polarization instabilities in a two-photon laser

We describe the operating characteristics of a new type of quantum oscillator that is based on a two-photon stimulated emission process. This two-photon laser consists of spin-polarized and laser-driven $^{39}$K atoms placed in a high-finesse transverse-mode-degenerate optical resonator, and produces a beam with a power of $\sim$0.2 $\mu$W at a wavelength of 770 nm. We observe complex dynamical instabilities of the state of polarization of the two-photon laser, which are made possible by the atomic Zeeman degeneracy. We conjecture that the laser could emit polarization-entangled twin beams if this degeneracy is lifted.Comment: Accepted by Physical Review Letters. REVTeX 4 pages, 4 EPS figure

Photometric Variability in the CSTAR Field: Results From the 2008 Data Set

The Chinese Small Telescope ARray (CSTAR) is the first telescope facility built at Dome A, Antarctica. During the 2008 observing season, the installation provided long-baseline and high-cadence photometric observations in the i-band for 18,145 targets within 20 deg2 CSTAR field around the South Celestial Pole for the purpose of monitoring the astronomical observing quality of Dome A and detecting various types of photometric variability. Using sensitive and robust detection methods, we discover 274 potential variables from this data set, 83 of which are new discoveries. We characterize most of them, providing the periods, amplitudes and classes of variability. The catalog of all these variables is presented along with the discussion of their statistical properties.Comment: 38 pages, 11 figures, 4 tables; Accepted for publication in ApJ

Pressure effects on the electron-doped high Tc superconductor BaFe(2-x)Co(x)As(2)

Application of pressures or electron-doping through Co substitution into Fe sites transforms the itinerant antiferromagnet BaFe(2)As(2) into a superconductor with the Tc exceeding 20K. We carried out systematic transport measurements of BaFe(2-x)Co(x)As(2) superconductors in pressures up to 2.5GPa, and elucidate the interplay between the effects of electron-doping and pressures. For the underdoped sample with nominal composition x = 0.08, application of pressure strongly suppresses a magnetic instability while enhancing Tc by nearly a factor of two from 11K to 21K. In contrast, the optimally doped x=0.20 sample shows very little enhancement of Tc=22K under applied pressure. Our results strongly suggest that the proximity to a magnetic instability is the key to the mechanism of superconductivity in iron-pnictides.Comment: 5 figure