Instituto Tecnológico Autónomo de México

About: Instituto Tecnológico Autónomo de México is a education organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Politics & Population. The organization has 1098 authors who have published 2532 publications receiving 39083 citations. The organization is also known as: Instituto Tecnologico Autonomo de Mexico & Mexico Autonomous Institute of Technology.

Evaluation of the effect of isocyanate modification on the thermal and rheological properties of poly(ε-caprolactone)/cellulose composites

Abstract: Nanocellulose obtained with acid hydrolysis method using microcrystalline cellulose (MCC) was chemically modified with n-octadecyl isocyanate. Nanocellulose (NC), isocyanate modified nanocellulose (ISO-NC) and MCC were added to poly(e-caprolactone) (PCL) in order to produce composites by solvent casting method. Transmission electron microscopy (TEM) images showed that diameter of nanofibers ranged from 10 to 20 nm. The surface chemical modification of nanocellulose was characterized by Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). In order to understand the effect of cellulose content and modification on the performance of PCL/cellulose composites, surface morphologies, contact angle, thermal and rheological properties of the composites were investigated. The surface chemical modification with n-octadecyl isocyanate improved distribution of NC in PCL matrix. The dynamic mechanical and rheological properties of neat PCL were greatly improved by the addition of ISO-NC with respect to untreated NC and MCC due to the excellent interfacial compatibility between PCL and ISO-NC.

The Effects of Automated Redistricting and Partisan Strategic Interaction on Representation: The Case of Mexico

Abstract: In the U.S. redistricting is deeply politicized and often synonymous with gerrymandering -- the manipulation of boundaries to promote the goals of parties, incumbents, and racial groups. In contrast, Mexico’s federal redistricting has been implemented nationwide since 1996 through automated algorithms devised by the electoral management body (EMB) in consultation with political parties. In this setting, parties interact strategically and generate counterproposals to the algorithmically generated plans in a closed-door process that is not revealed outside the bureaucracy. Applying geospatial statistics and large-scale optimization to a novel dataset that has never been available outside of the EMB, we analyze the effects of automated redistricting and partisan strategic interaction on representation. Our dataset comprises the entire set of plans generated by the automated algorithm, as well as all the counterproposals made by each political party during the 2013 redistricting process. Additionally, we inspect the 2006 map with new data and two proposals to replace it towards 2015 in search for partisan effects and political distortions. Our analysis offers a unique insight into the internal workings of a purportedly autonomous EMB and the partisan effects of automated redistricting on representation.

Common Factors in Equity Option Returns

Abstract: This paper studies the factor structure of the cross-section of delta-hedged equity option returns. We find that a four-factor model explains the cross-section and time-series of equity option returns. Out of the four factors, three are characteristic based factors from the long-short option portfolios based on firm size, idiosyncratic volatility, and the difference between implied and historical volatilities. The fourth factor is the market volatility risk factor proxied by the delta-hedged option return of the the S&P 500 index.

Análisis discriminante para predecir el diagnóstico clínico de inmunodeficiencias primarias: reporte preliminar

Abstract: Background: The features in a clinical history from a patient with suspected primary immunodeficiency (PID) direct the differential diagnosis through pattern recognition. PIDs are a heterogeneous group of more than 250 congenital diseases with increased susceptibility to infection, inflammation, autoimmunity, allergy and malignancy. Linear discriminant analysis (LDA) is a multivariate supervised classification method to sort objects of study into groups by finding linear combinations of a number of variables. Objective: To identify the features that best explain membership of pediatric PID patients to a group of defect or disease. Material and method: An analytic cross-sectional study was done with a pre-existing database with clinical and laboratory records from 168 patients with PID, followed at the National Institute of Pediatrics during 1991-2012, it was used to build linear discriminant models that would explain membership of each patient to the different group defects and to the most prevalent PIDs in our registry. After a preliminary run only 30 features were included (4 demographic, 10 clinical, 10 laboratory, 6 germs), with which the training models were developed through a stepwise regression algorithm. We compared the automatic feature selection with a selection made by a human expert, and then assessed the diagnostic usefulness of the resulting models (sensitivity, specificity, prediction accuracy and kappa coefficient), with 95% confidence intervals. Results: The models incorporated 6 to 14 features to explain membership of PID patients to the five most abundant defect groups (combined, antibody, well-defined, dysregulation and phagocytosis), and to the four most prevalent PID diseases (X-linked agammaglobulinemia, chronic granulomatous disease, common variable immunodeficiency and ataxia-telangiectasia). In practically all cases of feature selection the machine outperformed the human expert. Diagnosis prediction using the equations created had a global accuracy of 83 to 94%, with sensitivity of 60 to 100%, specificity of 83 to 95% and kappa coefficient of 0.37 to 0.76. Conclusions: In general, the selection of features has clinical plausibility, and the practical advantage of utilizing only clinical attributes, infecting germs and routine lab results (blood cell counts and serum immunoglobulins). The performance of the model as a diagnostic tool was acceptable. The study’s main limitations are a limited sample size and a lack of cross validation. This is only the first step in the construction of a machine learning system, with a wider approach that includes a larger database and different methodologies, to assist the clinical diagnosis of primary immunodeficiencies.

A new representation of the local volatility surface

Abstract: In this paper, we address the problem of recovering the local volatility surface from option prices consistent with observed market data. We revisit the implied volatility problem and derive an explicit formula for the implied volatility together with bounds for the call price and its derivative with respect to the strike price. The analysis of the implied volatility problem leads to the development of an ansatz approach, which is employed to obtain a semi-explicit solution of Dupire's forward equation. This solution, in turn, gives rise to a new expression for the volatility surface in terms of the price of a European call or put. We provide numerical simulations to demonstrate the robustness of our technique and its capability of accurately reproducing the volatility function.