El espacio narrativo en Revelaciones de un manuscrito, de Bernabé Demaría

Bernabé Demaría (1824-1910), escritor, político y pintor argentino, es autor de una única novela, Revelaciones de un manuscrito (1869). En sus páginas, el espacio geográfico -Europa en la primera parte; la Argentina, en la segunda- funciona como elemento estructurador, pues la novela está concebida como un Bildungsroman, donde el motivo del viaje articula tanto el desplazamiento horizontal (espacial) como el vertical (espiritual y social) del protagonista, Florencio Indarte. Junto a los tópicos del más definido romanticismo, se descubren rasgos realistas: una cuidadosa localización espacial de la acción, la acumulación de detalles tendientes a reforzar el efecto de realidad, el discurso didáctico, de registro aparentemente objetivo, portador de una copiosa enciclopedia científica. La olvidada novela de Bernabé Demaría debe ser tenida en cuenta muy especialmente en toda indagación de los orígenes del realismo en la novela argentina.Bernabé Demaría (1824-1910), Argentine writer, politician and painter, has written an only novel, Revelaciones de un manuscrito (1869). On its pages, geographic space -Europe in the first part, Argentine in the second one- becomes structural agent, having in account that the novel is a Bildungsroman, where the motive of travel articulates not only the horizontal (spatial) movement but the vertical one (spiritual and social) of the protagonist, Florencio Indarte. Beside romantic topics, realistic traces appear: a careful spatial location of the plot, accumulate details through which is reinforced the "reality effect", didactical discourse which encloses a copious scientific encyclopaedia. The forgotten novel of Bernabé Demaría has to be specially taken in account in any research on the origins of realism in Argentine novel.Fil: Curia, Beatriz. Consejo Nacional de Investigaciones Científicas y Técnica

Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study

Breast cancer is one of the most widespread in the female population, being able to predict its developments and capturing the inputs of the onset of the disease is one of the main objectives that science is pursuing. Clinical Decision Support Systems (CDSS) in recent decades are extensively using these technological tools, such as Machine Learning (ML) and Deep Learning (DL). In this paper, two of the main methods of these subset of AI are compared: an ensemble-type algorithm, XGBoost (or Extreme Gradient Boosting) and a deep neural network (DNN) are applied to the data of a study conducted on an Indonesian population. The results obtained are very interesting as despite being tabular, binary categorical and multiclass data, the DNN model achieves performance and results much higher than the well-known XGB used in literature for data of this type

Viaggio nelle dune: esperienza nelle aree rurali

L’autrice, a lungo cooperante in Somalia in un progetto di salute mentale, racconta il suo incontro con la cultura, in via di disgregazione ma ancora viva in alcune aree remote del Paese, dei pastori nomadi del nord della Somalia e il suo tentativo di reinterpretare, con uno sguardo etno-antropologico, il significato della malattia mentale.Qoraaga oo ah haweenay mudda badan Soomaaliya ka wadday mashruuca caafimaadka dhimirka, ayaa wuxay ka sheekaynaysaa wixii ay kala culantay dhaqan baabi'i rabo, laakiin weli ka jira degaannada woqooyi ee raacatada reerguuraaga ah, waxayna isku deyday, iyadoo ka eegeyso xagga culuunta ethnological-anthropological, in ay fasirto micnaha cudurka dhimirka.The author, who has been working as aid-worker in a mental health project inSomalia for a long time, relates her meeting with north Somalia nomadic shepherds’culture – which is disintegrating but is still alive in some remote areasof the country – and her attempt to reinterpret the meaning of mental healthfrom an ethnological-anthropological standpoint

Viaggio nelle dune: esperienza nelle aree rurali

L’autrice, a lungo cooperante in Somalia in un progetto di salute mentale, racconta il suo incontro con la cultura, in via di disgregazione ma ancora viva in alcune aree remote del Paese, dei pastori nomadi del nord della Somalia e il suo tentativo di reinterpretare, con uno sguardo etno-antropologico, il significato della malattia mentale.Qoraaga oo ah haweenay mudda badan Soomaaliya ka wadday mashruuca caafimaadka dhimirka, ayaa wuxay ka sheekaynaysaa wixii ay kala culantay dhaqan baabi'i rabo, laakiin weli ka jira degaannada woqooyi ee raacatada reerguuraaga ah, waxayna isku deyday, iyadoo ka eegeyso xagga culuunta ethnological-anthropological, in ay fasirto micnaha cudurka dhimirka.The author, who has been working as aid-worker in a mental health project inSomalia for a long time, relates her meeting with north Somalia nomadic shepherds’culture – which is disintegrating but is still alive in some remote areasof the country – and her attempt to reinterpret the meaning of mental healthfrom an ethnological-anthropological standpoint

Nosotros y los de extranjis. La identidad como programa : homenaje a Esteban Echeverría en el bicentenario de su nacimiento (1805-2005)

Con su obra y con su acción Esteban Echeverría marca un hito en el desarrollo y afianzamiento de la identidad político social y cultural argentina. Formula expresamente un programa de construcción identitaria y toda su obra se encuentra teñida por la voluntad de crear una literatura propia y original, en una lengua castellana enriquecida por el uso americano, con temas provenientes de la realidad del país y con la finalidad de contribuir, trascendiendo lo estético, al engrandecimiento de su patria. Subyace en cada página de Echeverría la necesidad de crear una sociedad democrática que continúe el pensamiento anticolonial de la Revolución de Mayo. Su “Apología del matambre" condensa ejemplarmente estas características.Together with his work and with his action, Esteban Echeverría marks a hit in the development and strengthening of the socio-political and cultural personality of Argentina. He literally formulates a program of identity’s construction, and the whole of his work is affected by the will to create an own and original literature. He writes in Spanish, enriched by the American usage, and his topics come from the country’s reality, and his aim is that of contributing. He transcends the aesthetic so as to magnify his nation. In any of Echeverría’s page, underlies the necessity of creating a democratic society that follows the anti – colonial thought of Mayo Revolution. His “Apología del matambre" condenses in an exemplary way these characteristics.Fil: Curia, Beatriz. Universidad Nacional de Cuyo. Facultad de Filosofía y Letra

Explainable clinical decision support system: opening black-box meta-learner algorithm expert's based

Mathematical optimization methods are the basic mathematical tools of all artificial intelligence theory. In the field of machine learning and deep learning the examples with which algorithms learn (training data) are used by sophisticated cost functions which can have solutions in closed form or through approximations. The interpretability of the models used and the relative transparency, opposed to the opacity of the black-boxes, is related to how the algorithm learns and this occurs through the optimization and minimization of the errors that the machine makes in the learning process. In particular in the present work is introduced a new method for the determination of the weights in an ensemble model, supervised and unsupervised, based on the well known Analytic Hierarchy Process method (AHP). This method is based on the concept that behind the choice of different and possible algorithms to be used in a machine learning problem, there is an expert who controls the decisionmaking process. The expert assigns a complexity score to each algorithm (based on the concept of complexity-interpretability trade-off) through which the weight with which each model contributes to the training and prediction phase is determined. In addition, different methods are presented to evaluate the performance of these algorithms and explain how each feature in the model contributes to the prediction of the outputs. The interpretability techniques used in machine learning are also combined with the method introduced based on AHP in the context of clinical decision support systems in order to make the algorithms (black-box) and the results interpretable and explainable, so that clinical-decision-makers can take controlled decisions together with the concept of "right to explanation" introduced by the legislator, because the decision-makers have a civil and legal responsibility of their choices in the clinical field based on systems that make use of artificial intelligence. No less, the central point is the interaction between the expert who controls the algorithm construction process and the domain expert, in this case the clinical one. Three applications on real data are implemented with the methods known in the literature and with those proposed in this work: one application concerns cervical cancer, another the problem related to diabetes and the last one focuses on a specific pathology developed by HIV-infected individuals. All applications are supported by plots, tables and explanations of the results, implemented through Python libraries. The main case study of this thesis regarding HIV-infected individuals concerns an unsupervised ensemble-type problem, in which a series of clustering algorithms are used on a set of features and which in turn produce an output used again as a set of meta-features to provide a set of labels for each given cluster. The meta-features and labels obtained by choosing the best algorithm are used to train a Logistic regression meta-learner, which in turn is used through some explainability methods to provide the value of the contribution that each algorithm has had in the training phase. The use of Logistic regression as a meta-learner classifier is motivated by the fact that it provides appreciable results and also because of the easy explainability of the estimated coefficients

Cervical cancer risk prediction with robust ensemble and explainable black boxes method

Clinical decision support systems (CDSS) that make use of algorithms based on intelligent systems, such as machine learning or deep learning, they sufer from the fact that often the methods used are hard to interpret and difcult to understand on how some decisions are made; the opacity ofsome methods, sometimes voluntary due to problems such as data privacy or the techniques used to protect intellectual property, makes these systems very complicated. Besides this series of problems, the results obtained also sufer from the poor possibility of being interpreted; in the clinical context therefore it is required that the methods used are as accurate as possible, transparent techniques and explainable results. In this work the problem of the development of cervical cancer is treated, a disease that mainly afects the female population. In order to introduce advanced machine learning techniques in a clinical decision support system that can be transparent and explainable, a robust, accurate ensemble method is presented, in terms of error and sensitivity linked to the classifcation of possible development of the aforementioned pathology and advanced techniques are also presented of explainability and interpretability (Explanaible Machine Learning) applied to the context of CDSS such as Lime and Shapley. The results obtained, as well as being interesting, are understandable and can be implemented in the treatment of this type of problem