El derecho humano a la ciencia: Un viejo derecho con un gran futuro

When we talk about the human right to science, many may think that we are speaking about a new right, recently created to face the challenges that science and technology generate in our society of the 21st century. However, the right to science is already enshrined in the Universal Declaration of Human Rights (1948) and in the International Covenant on Economic, Social and Cultural Rights (1966). We can find it even earlier in the inter-American regional system, particularly, in the Charter of the Organization of American States (1948) and in the American Declaration of the Rights and Duties of Man (1948). Few know that, in fact, the Latin American countries in general and Chile in particular played a crucial role in the universal recognition of this right. The first part of the article explains the history of the right to science and its international legal recognition. In a second chapter, we will study its current institutional situation within the United Nations and, finally, in the third chapter, we will analyze the characteristics of this right, its normative content, elements and type of obligations that it creates.Cuando hablamos del derecho humano a la ciencia se puede pensar que nos referimos a un derecho nuevo, creado recientemente para hacer frente a los desafíos que la ciencia y la tecnología generan en nuestra sociedad del siglo XXI. Sin embargo, el derecho a la ciencia es un derecho ya recogido en la Declaración Universal de los Derechos Humanos de 1948 y en el Pacto Internacional de Derechos Económicos, Sociales y Culturales de 1966. Lo podemos encontrar incluso antes, en el sistema regional interamericano, en particular, en la Carta de la Organización de los Estados Americanos y en la Declaración Americana de Derechos y Deberes del Hombre. Pocos saben que, de hecho, los países lationamericanos, en general, y Chile, muy en particular, tuvieron un papel crucial en el reconocimiento universal de este derecho. La primera parte del artículo está dedica a explicar la historia de este derecho y su reconocimiento jurídico internacional. En una segunda parte estudiaremos su situación institucional actual en el ámbito de las Naciones Unidas y, finalmente, en la tercera parte indagaremos en las características de este derecho, su contenido normativo, elementos y tipo de obligaciones que crea

Female old age in historical perspective: Vetulae of ancient Rome

Las sociedades humanas tienden a dividir el ciclo vital en varias etapas según criterios culturales concretos. En la antigua Roma, al igual que en las sociedades occidentales actuales, la etapa que precede a la muerte del sujeto, parte de la edad adulta pero al mismo tiempo distinta a ésta, era la vejez. La ancianidad ha sido tradicionalmente silenciados en los trabajos historiográficos sobre la Antigüedad, los cuales han tenido como protagonista al hombre adulto, especialmente el perteneciente a la élite. Si bien en las últimas décadas han proliferado las investigaciones relativas a otros sujetos históricos, como las mujeres o los individuos infantiles, no ocurre lo mismo con la vejez, sobre todo en el caso de las vetulae. El objetivo del presente artículo es subrayar la importancia de la vetula como sujeto histórico, presentar las diversas fuentes a través de las cuales podemos acceder a su conocimiento, y definir qué era en época romana la vejez femenina.Human societies tend to divide the life cycle into several stages according to specific cultural criteria. In ancient Rome, as in contemporary Western societies, the stage that precedes death, part of adulthood but at the same time different from it, was old age. Traditionally elders have been silenced in the historiographical works about Antiquity, which have the adult man, especially the one belonging to the elite, as the main subject. Even if the investigations about other historical agents, such as women or children, have proliferated in recent decades, it is not the case of old age, especially of vetulae. In this paper we highlight the importance of the vetula as a historical subject, we present various sources through which we can know them, and we define what female old age was in ancient Rome

Deep Generative Models for Reject Inference in Credit Scoring

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we adopt the Variational Autoencoder (VAE), which has the ability to learn latent representations that contain useful information. We show that it is possible to steer the latent representations in the latent space of the VAE using the Weight of Evidence and forming a specific grouping of the data that reflects the customers' creditworthiness. Our proposed method learns a latent representation of the data, which shows a well-defied clustering structure capturing the customers' creditworthiness. These clusters are well suited for the aforementioned banks' activities. Further, our methodology generalizes to new customers, captures high-dimensional and complex financial data, and scales to large data sets.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0253

Establiment d’un mètode d’anàlisi de glicans biomarcadors de càncer de pàncrees per cromatografia de líquids acoblada a l’espectrometria de masses

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2023, Tutora: Estela Giménez LópezGlycoproteins are proteins that have oligosaccharides covalently attached to the peptide backbone. These carbohydrates are also known as glycans. Protein glycans play a significant role in various cellular processes, including cell-cell recognition, signaling, and adhesion on cell surfaces. However, glycans undergo structural changes in many diseases, such as cancer. Therefore, there is a need to establish new analytical methods that aid in identifying and quantifying glycans to detect the presence of diseases. In the present study, a reference method for the separation and identification of labelled glycans using capillary liquid chromatography (capLC) coupled with ultraviolet (UV) and mass spectrometry (MS) detection will be developed. First, standard glycans selected as model will be derivatized with aniline (AN) and procainamide (ProA) labels. To assess their degree of derivatization, they will be analysed by matrix assisted laser desorption ionization mass spectrometry (MALDI-MS). Finally, these labelled glycans will serve for the development of a reference analytical method by capLC-UV and capLC-MS. This reference method will be used in the future to test the status of the chromatographic system in glycoprotein biomarker research. For instance, for the analysis of the N-glycans of human alpha-1-acid glycoprotein (hAGP) under study as a potential biomarker of pancreatic cancer

Multimodal Generative Models for Bankruptcy Prediction Using Textual Data

Textual data from financial filings, e.g., the Management's Discussion & Analysis (MDA) section in Form 10-K, has been used to improve the prediction accuracy of bankruptcy models. In practice, however, we cannot obtain the MDA section for all public companies, which limits the use of MDA data in traditional bankruptcy models, as they need complete data to make predictions. The two main reasons for the lack of MDA are: (i) not all companies are obliged to submit the MDA and (ii) technical problems arise when crawling and scrapping the MDA section. To solve this limitation, this research introduces the Conditional Multimodal Discriminative (CMMD) model that learns multimodal representations that embed information from accounting, market, and textual data modalities. The CMMD model needs a sample with all data modalities for model training. At test time, the CMMD model only needs access to accounting and market modalities to generate multimodal representations, which are further used to make bankruptcy predictions and to generate words from the missing MDA modality. With this novel methodology, it is realistic to use textual data in bankruptcy prediction models, since accounting and market data are available for all companies, unlike textual data. The empirical results of this research show that if financial regulators, or investors, were to use traditional models using MDA data, they would only be able to make predictions for 60% of the companies. Furthermore, the classification performance of our proposed methodology is superior to that of a large number of traditional classifier models, taking into account all the companies in our sample

El piearcts en querétaro - tercera etapa

Se reporta el avance de la tercera etapa del PIEARCTS en Querétaro, México: análisis estadístico e interpretación de datos de las once instituciones en donde se aplicó el COCTS a un total de 1374 sujetos. Se establece un horizonte de interpretación que integra los datos cuantitativos con la reflexión cualitativa, la cual está amparada en el contexto socio-histórico de cada institución y en la caracterización de cada grupo muestral. Los datos se analizan de acuerdo con el tipo de institución: tecnológica o humanística; nivel educativo: licenciatura, bachillerato, técnico o normal y con los grupos encuestados: estudiantes o profesores, considerando los datos sociodemográficos incluidos en la encuesta. Se calculan los índices de plausibilidad, adecuación, ingenuidad y cultura científica para cada reactivo y para cada sujeto. Con ellos se hacen comparaciones entre grupos