Modelos predictivos competitivos de morosidad crediticia para entidades argentinas Análisis descriptivo y predictivo con datos públicos

Una importante característica del mercado de créditos en la Argentina es la marcada diferencia que existe en el acceso a la información entre las entidades grandes (mayoritariamente bancos) y las entidades chicas (sociedades anónimas, mutuales y cooperativas), a lo que se suma una menor capacidad analítica de estas últimas (generalmente por no disponer de equipos internos plenamente desarrollados y abocados a la tarea). Esto lleva a que en su operatoria sea común que entidades pequeñas deban recurrir a costosos servicios externos, lo que no sólo impacta en su rentabilidad, sino también en los clientes que efectivamente pueden atender. El objetivo de esta tesis es desarrollar y evaluar una herramienta que, utilizando algoritmos de aprendizaje automático y datos enteramente públicos, prediga morosidad futura en personas que hasta el momento tienen todas sus deudas en situación regular. Una herramienta de estas características permitiría, principalmente a entidades pequeñas, aumentar sus ingresos, reducir sus costos operativos y proyectar mejor sus flujos de fondos. Los resultados obtenidos sugieren que, tomando como insumo datos de la Central de Deudores del Banco Central de la República Argentina y haciendo uso de metodologías modernas de aprendizaje automático, se pueden desarrollar modelos predictivos de detección de mora, los cuales alcanzan resultados competitivos cuando se los compara con la literatura previa. En este trabajo se detalla las diferencias entre ambos tipos de entidades, se presenta en detalle las decisiones metodológicas detrás de los modelos desarrollados, se analiza el efecto marginal que genera la incorporación de variables de tendencias, se evalúa la performance de los mismos utilizando datos reales, y se lleva adelante un ejercicio de interpretación de modelos; finalmente, se discute cómo estos modelos pueden ser aplicados para generar valor en una entidad crediticia.One of the most important characteristics of the Argentine credit market is the strong difference between large entities (mostly banks) and small entities (limited companies, mutuals and cooperatives) in their capability to obtain information. In addition to this, the smaller ones usually have fewer resources to analyse data, mostly because of their lack of internal analytical skills. The result is such that small entities are forced to incur in costly external services, affecting not only their earnings, but also the type and amount of customers they can serve. The purpose of this thesis project is to develop and test a tool, using machine learning algorithms with public data, in order to predict future credit loans default in people that, at the moment, have met all their debt obligations. This tool would allow both types of entities, but mostly smaller ones, to raise their revenue, reduce operating costs and project more accurately future cash flows. The final results suggest it is possible to create competitive and marketable default predictive models using modern machine learning techniques and public data from the Central Bank of Argentina. In this thesis, differences between both types of entities are studied. Moreover, the methodological decisions and the performance behind the created models are exhibited. Also, the marginal effects of using tendency variables in the models are calculated. Finally, a discussion on model interpretation and on how this tool can create value to a company are included

Thalidomide-induced Teratogenesis : History and Mechanisms

Peer reviewedPublisher PD

Disaggregating the Relative Influence of Genetic, Environmental and Individual Factors on LCL and HDL Cholesterols and BMI for a Sample of African American (AA) Mothers and Daughters

There are many reports about the associations between blood lipids, body mass index (BMI) and dietary cholesterol intakes both within the individual and between related individuals. The purpose of this descriptive research project was to investigate the relationships between LDL and HDL cholesterols, body mass index and dietary cholesterol intakes for a sample of African American (AA) mothers and their daughters and to attempt to separate the contribution of genetic versus environmental factors. Mother and daughter participants (n =42 and 66, respectively) were 12-14-hours fasted when blood samples were drawn, heights and weights measured, and 24 hour food recalls completed

Sensory Neurons and Schwann Cells Respond to Oxidative Stress by Increasing Antioxidant Defense Mechanisms

Abstract Elevated blood glucose is a key initiator of mechanisms leading to diabetic neuropathy. Increases in glucose induce acute mitochondrial oxidative stress in dorsal root ganglion (DRG) neurons, the sensory neurons normally affected in diabetic neuropathy, whereas Schwann cells are largely unaffected. We propose that activation of an antioxidant response in DRG neurons would prevent glucose-induced injury. In this study, mild oxidative stress (1 μM H2O2) leads to the activation of the transcription factor Nrf2 and expression of antioxidant (phase II) enzymes. DRG neurons are thus protected from subsequent hyperglycemia-induced injury, as determined by activation of caspase 3 and the TUNEL assay. Schwann cells display high basal antioxidant enzyme expression and respond to hyperglycemia and mild oxidative stress via further increases in these enzymes. The botanical compounds resveratrol and sulforaphane activate the antioxidant response in DRG neurons. Other drugs that protect DRG neurons and block mitochondrial superoxide, identified in a compound screen, have differential ability to activate the antioxidant response. Multiple cellular targets exist for the prevention of hyperglycemic oxidative stress in DRG neurons, and these form the basis for new therapeutic strategies against diabetic neuropathy. Antioxid. Redox Signal. 11, 425-438.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78129/1/ars.2008.2235.pd

Densification and residual stress induced by CO2 laserbased mitigation of SiO2 surfaces

ABSTRACT Knowing the ultimate surface morphology resulting from CO 2 laser mitigation of induced laser damage is important both for determining adequate treatment protocols, and for preventing deleterious intensification upon subsequent illumination of downstream optics. Physical effects such as evaporation, viscous flow and densification can strongly affect the final morphology of the treated site. Evaporation is a strong function of temperature and will play a leading role in determining pit shapes when the evaporation rate is large, both because of material loss and redeposition. Viscous motion of the hot molten material during heating and cooling can redistribute material due to surface tension gradients (Marangoni effect) and vapor recoil pressure effects. Less well known, perhaps, is that silica can densify as a result of structural relaxation, to a degree depending on the local thermal history. The specific volume shrinkage due to structural relaxation can be mistaken for material loss due to evaporation. Unlike evaporation, however, local density change can be reversed by post annealing. All of these effects must be taken into account to adequately describe the final morphology and optical properties of single and multiple-pass mitigation protocols. We have investigated, experimentally and theoretically, the significance of such densification on residual stress and under what circumstances it can compete with evaporation in determining the ultimate post treatment surface shape. In general, understanding final surface configurations requires taking all these factors including local structural relaxation densification, and therefore the thermal history, into account. We find that surface depressions due to densification can dominate surface morphology in the non-evaporative regime when peak temperatures are below 2100K

Manipulating Managed Execution Runtimes to Support Self-Healing Systems

Self-healing systems require that repair mechanisms are available to resolve problems that arise while the system executes. Managed execution environments such as the Common Language Runtime (CLR) and Java Virtual Machine (JVM) provide a number of application services (application isolation, security sandboxing, garbage collection and structured exception handling) which are geared primarily at making managed applications more robust. However, none of these services directly enables applications to perform repairs or consistency checks of their components. From a design and implementation standpoint, the preferred way to enable repair in a self-healing system is to use an externalized repair/adaptation architecture rather than hardwiring adaptation logic inside the system where it is harder to analyze, reuse and extend. We present a framework that allows a repair engine to dynamically attach and detach to/from a managed application while it executes essentially adding repair mechanisms as another application service provided in the execution environment

First results of site testing program at Mt. Shatdzhatmaz in 2007 - 2009

We present the first results of the site testing performed at Mt.~Shatdzhatmaz at Northern Caucasus, where the new Sternberg astronomical institute 2.5-m telescope will be installed. An automatic site monitor instrumentation and functionality are described together with the methods of measurement of the basic astroclimate and weather parameters. The clear night sky time derived on the basis of 2006 -- 2009 data amounts to 1340 hours per year. Principle attention is given to the measurement of the optical turbulence altitude distribution which is the most important characteristic affecting optical telescopes performance. For the period from November 2007 to October 2009 more than 85\,000 turbulence profiles were collected using the combined MASS/DIMM instrument. The statistical properties of turbulent atmosphere above the summit are derived and the median values for seeing $\beta_0 = 0.93$~arcsec and free-atmosphere seeing $\beta_{free} = 0.51$~arcsec are determined. Together with the estimations of isoplanatic angle $\theta_0 = 2.07$~arcsec and time constant \tau_0 = 2.58 \mbox{ ms}, these are the first representative results obtained for Russian sites which are necessary for development of modern astronomical observation techniques like adaptive optics.Comment: Accepted for publication in MNRAS, 17 pages, 15 figure

An Assessment of Molecular Dynamic Force Fields for Silica for Use in Simulating Laser Damage Mitigation

We compare force fields (FF's) that have been used in molecular dynamic (MD) simulations of silica in order to assess their applicability for use in simulating IR-laser damage mitigation. Although pairwise FF?s obtained by fitting quantum mechanical calculations such as the BKS and CHIK potentials have been shown to reproduce many of the properties of silica including the stability of silica polymorphs and the densification of the liquid, we show that melting temperatures and fictive temperatures are much too high. Softer empirical force fields give liquid and glass properties at experimental temperatures but may not predict all properties important to laser mitigation experiments

The Use of Large Transparent Ceramics in a High Powered, Diode Pumped Solid State Laser

The advent of large transparent ceramics is one of the key enabling technological advances that have shown that the development of very high average power compact solid state lasers is achievable. Large ceramic neodymium doped yttrium aluminum garnet (Nd:YAG) amplifier slabs are used in Lawrence Livermore National Laboratory's (LLNL) Solid State Heat Capacity Laser (SSHCL), which has achieved world record average output powers in excess of 67 kilowatts. We will describe the attributes of using large transparent ceramics, our present system architecture and corresponding performance; as well as describe our near term future plans