El humor negro frente a la muerte

El sujeto agonizante que utiliza el humor, al cual se le llamará “humorante”, cuestiona. ¿Cómo puede, en ese punto preciso de su existencia, permitirse tal afrenta? Si lo hace, es a través del humor negro, es decir, bajo una forma particular y muy cruda. Apoyándose en las aportaciones de Freud y de Lacan sobre la cuestión de la muerte y en el ejercicio clínico en cuidados paliativos, los autores abordan en el presente artículo la expresión de un goce lenguajero de un sujeto que está en camino de lo que pacificará para siempre sus pulsiones. Ahí donde la influencia conjunta del discurso capitalista y del discurso de la ciencia permite sostener en nuestra modernidad el carácter accidental de la muerte. Es a la perplejidad ansiosa frente al real traumático, en la cual permanecen muchos pacientes agonizantes, que el “humorante” parece ensayar una respuesta.Dying subjects that resort to humor, whom we shall call "humorants", question themselves and especially us. How could they possibly allow themselves such an outrage at this precise moment of their existence?  When they do so, it is through the particular and rather crude form of black humor. On the basis of Freud's and Lacan's contributions regarding the question of death and of the clinical exercise of palliative care, the article addresses the expression of a linguistic jouissance on the part of subjects who are on the way to that which will forever calm their drives - the place in which the joint influence of capitalist and scientific discourse make it possible to affirm the accidental nature of death in our modern world. In this sense, "humorants" seem to be responding to the anxious perplexity of many dying patients when confronted by the traumatic real.Le sujet agonisant se servant de l’humour, à qui on nommera “humourant”, questionne. Comment peut-il, dans ce point précis de son existence, se permettre un tel affront? S’il le fait, c’est au moyen de l’humour noir, c’est-à-dire, sous une forme particulière et très crue. S’appuyant sur les contributions de Freud et de Lacan par rapport à la question de la mort et sur l’exercice clinique en soins palliatifs, les auteurs abordent dans cet article, l’expression d’une jouissance langagière d’un sujet qui est sur la voie de ce qui apaisera à jamais ses pulsions. Là où l’influence conjointe du discours capitaliste et du discours de la science permet d’avancer dans notre modernité le caractère accidentel de la mort. C’est devant la perplexité anxieuse face au réel traumatique, dans laquelle restent plusieurs patients agonisants, que “l’humourant” semble tenter une réponse

El humor negro frente a la muerte

Dying subjects that resort to humor, whom we shall call "humorants", question themselves and especially us. How could they possibly allow themselves such an outrage at this precise moment of their existence? When they do so, it is through the particular and rather crude form of black humor. On the basis of Freud's and Lacan's contributions regarding the question of death and of the clinical exercise of palliative care, the article addresses the expression of a linguistic jouissance on the part of subjects who are on the way to that which will forever calm their drives - the place in which the joint influence of capitalist and scientific discourse make it possible to affirm the accidental nature of death in our modern world. In this sense, "humorants" seem to be responding to the anxious perplexity of many dying patients when confronted by the traumatic real.El sujeto agonizante que utiliza el humor, al cual se le llamará “humorante”, cuestiona. ¿Cómo puede, en ese punto preciso de su existencia, permitirse tal afrenta? Si lo hace, es a través del humor negro, es decir, bajo una forma particular y muy cruda. Apoyándose en las aportaciones de Freud y de Lacan sobre la cuestión de la muerte y en el ejercicio clínico en cuidados paliativos, los autores abordan en el presente artículo la expresión de un goce lenguajero de un sujeto que está en camino de lo que pacificará para siempre sus pulsiones. Ahí donde la influencia conjunta del discurso capitalista y del discurso de la ciencia permite sostener en nuestra modernidad el carácter accidental de la muerte. Es a la perplejidad ansiosa frente al real traumático, en la cual permanecen muchos pacientes agonizantes, que el “humorante” parece ensayar una respuesta

Training Set Class Distribution Analysis for Deep Learning Model - Application to Cancer Detection

International audienceDeep learning models specifically CNNs have been used successfully in many tasks including medical image classification. CNN effectiveness depends on the availability of large training data set to train which is generally costly to obtain for new applications or new cases. However, there is a little concrete recommendation about training set creation. In this research, we analyze the impact of different class distributions in the training data to a CNN model. We consider the case of cancer detection task from histopathological images for cancer diagnosis and derive some useful hypotheses about the distribution of classes in the training data. We found that using all the training data leads to the best recall-precision trade-off, while training with a reduced number of examples from some classes, it is possible to inflect the model toward a desired accuracy on a given class

A Study on the Impact of Class Distribution on Deep Learning - The Case of Histological Images and Cancer Detection

Extended AbstractInternational audienceStudies on deep learning tuning mostly focus on the neural network architectures and algorithms hyperparameters. Another core factor for accurate training is the class distribution of the training dataset. This paper contributes to understanding the optimal class distribution on the case for histological images used in cancer diagnosis. We formulate several hypotheses, which are then tested considering experiments with hundreds of trials. We considered both segmentation and classification tasks considering the U-net and group equivariant CNN (G-CNN). This paper is an extended abstract of another paper published by the authors 1

Finding a Suitable Class Distribution for Building Histological Images Data Sets Used in Deep Model Training - the Case of Cancer Detection

International audienceThe class distribution of a training data set is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be costly to annotate. This is the case for histological images used in cancer diagnosis where image annotation requires domain experts. In this paper we tackle the problem of finding the optimal class distribution of a training set to be able to train an optimal model that detects cancer in histological images. We formulate several hypotheses which are then tested in scores of experiments with hundreds of trials. The experiments have been designed to account for both segmentation and cla

Finding a Suitable Class Distribution for Building Histological Images Data Sets Used in Deep Model Training - the Case of Cancer Detection

International audienceThe class distribution of a training data set is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be costly to annotate. This is the case for histological images used in cancer diagnosis where image annotation requires domain experts. In this paper we tackle the problem of finding the optimal class distribution of a training set to be able to train an optimal model that detects cancer in histological images. We formulate several hypotheses which are then tested in scores of experiments with hundreds of trials. The experiments have been designed to account for both segmentation and cla

Mass spectrometry-based proteomics in clinical practice amyloid typing: state-of-the-art from a French nationwide cohort

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1980

Pour vous, ou dans votre domaine, de quoi l’année 1980 est-elle le nom? Telle est ici la question posée aux chercheuses et chercheurs de la Faculté des lettres de l’Université de Lausanne. Cette aventure interdisciplinaire interroge le possible seuil que représente 1980 tant pour les historiens de la littérature qui retiennent usuellement cette date comme l’an zéro de notre contemporanéité que pour bien des sociologues qui font naître en 1980 les premiers enfants de la «génération Y», celle qui dicte aujourd’hui la norme esthétique et idéologique. La mosaïque des réponses apportées à cette question dessine une sorte de portrait chinois de l’an huitante en une cinquantaine de tesselles littéraires, artistiques, culturelles, sociales, politiques, scientifiques, technologiques, philosophiques et intellectuelles