Inconscient collectif | EPFL Graph Search

thumb|upright=1.5|Carte de l'Enfer dans la Divine Comédie de Dante Alighieri, par Sandro Botticelli (vers 1480-1495). Selon Carl Gustav Jung, les enfers représentent dans toutes les cultures l'aspect inquiétant de l'inconscient collectif. L'inconscient collectif est un concept de la psychologie analytique s'attachant à désigner les fonctionnements humains liés à l'imaginaire, communs ou partagés, quels que soient les époques et les lieux, et qui influencent et conditionnent les représentations individuelles et collectives. Selon le psychiatre suisse Carl Gustav Jung (1875–1961), créateur du concept, l'inconscient collectif constitue . Toujours selon lui, Jung donne en effet l'épithète de « collectif » à cette partie transpersonnelle de la psyché inconsciente, car ces matériaux se distinguent par leur récurrence d'apparition dans l'histoire humaine et parce qu'ils se manifestent au moyen des archétypes, autre concept central de la psychologie analytique. Si pour Sigmund Freud

Psychophysical magic: rendering the "invisible" visible

Adriana De Pesters

Conscious and unconscious processing are two of the most hot topics in neuroscience. In unconscious processing research, usually a direct measure is used to show that a target is not consciously perceived. An indirect measure is used to show that still the target can influence visual processing. This unconscious processing is usually related to fast motor priming in a short period before conscious processing takes over. Here, I show that features of unconscious elements can influence visual information processing and are not related to motor priming. Interestingly, these features can become conscious even though the carriers of the features are not

2009

Variational Methods for Human Modeling

Timur Bagautdinov

A large part of computer vision research is devoted to building models and algorithms aimed at understanding human appearance and behaviour from images and videos. Ultimately, we want to build automated systems that are at least as capable as people when it comes to interpreting humans. Most of the tasks that we want these systems to solve can be posed as a problem of inference in probabilistic models. Although probabilistic inference in general is a very hard problem of its own, there exists a very powerful class of inference algorithms, variational inference, which allows us to build efficient solutions for a wide range of problems. In this thesis, we consider a variety of computer vision problems targeted at modeling human appearance and behaviour, including detection, activity recognition, semantic segmentation and facial geometry modeling. For each of those problems, we develop novel methods that use variational inference to improve the capabilities of the existing systems. First, we introduce a novel method for detecting multiple potentially occluded people in depth images, which we call DPOM. Unlike many other approaches, our method does probabilistic reasoning jointly, and thus allows to propagate knowledge about one part of the image evidence to reason about the rest. This is particularly important in crowded scenes involving many people, since it helps to handle ambiguous situations resulting from severe occlusions. We demonstrate that our approach outperforms existing methods on multiple datasets. Second, we develop a new algorithm for variational inference that works for a large class of probabilistic models, which includes, among others, DPOM and some of the state-of-the-art models for semantic segmentation. We provide a formal proof that our method converges, and demonstrate experimentally that it brings better performance than the state-of-the-art on several real-world tasks, which include semantic segmentation and people detection. Importantly, we show that parallel variational inference in discrete random fields can be seen as a special case of proximal gradient descent, which allows us to benefit from many of the advances in gradient-based optimization. Third, we propose a unified framework for multi-human scene understanding which simultaneously solves three tasks: multi-person detection, individual action recognition and collective activity recognition. Within our framework, we introduce a novel multi-person detection scheme, which relies on variational inference and jointly refines detection hypotheses instead of relying on suboptimal post-processing. Ultimately, our model takes as an inputs a frame sequence and produces a comprehensive description of the scene. Finally, we experimentally demonstrate that our method brings better performance than the state-of-the-art. Fourth, we propose a new approach for learning facial geometry with deep probabilistic models and variational methods. Our model is based on a variational autoencoder with multiple sets of hidden variables, which are capturing various levels of deformations, ranging from global to local, high-frequency ones. We experimentally demonstrate the power of the model on a variety of fitting tasks. Our model is completely data-driven and can be learned from a relatively small number of individuals.

EPFL2018

Enteric infection induces Lark-mediated intron retention at the 5 ' end of Drosophila genes

Tommaso Andreani, Maroun Bou Sleiman, Bart Deplancke, Michael Vincent Frochaux, Dani Osman

Background RNA splicing is a key post-transcriptional mechanism that generates protein diversity and contributes to the fine-tuning of gene expression, which may facilitate adaptation to environmental challenges. Here, we employ a systems approach to study alternative splicing changes upon enteric infection in females from classical Drosophila melanogaster strains as well as 38 inbred lines. Results We find that infection leads to extensive differences in isoform ratios, which results in a more diverse transcriptome with longer 5 ' untranslated regions (5 ' UTRs). We establish a role for genetic variation in mediating inter-individual splicing differences, with local splicing quantitative trait loci (local-sQTLs) being preferentially located at the 5 ' end of transcripts and directly upstream of splice donor sites. Moreover, local-sQTLs are more numerous in the infected state, indicating that acute stress unmasks a substantial number of silent genetic variants. We observe a general increase in intron retention concentrated at the 5 ' end of transcripts across multiple strains, whose prevalence scales with the degree of pathogen virulence. The length, GC content, and RNA polymerase II occupancy of these introns with increased retention suggest that they have exon-like characteristics. We further uncover that retained intron sequences are enriched for the Lark/RBM4 RNA binding motif. Interestingly, we find that lark is induced by infection in wild-type flies, its overexpression and knockdown alter survival, and tissue-specific overexpression mimics infection-induced intron retention. Conclusion Our collective findings point to pervasive and consistent RNA splicing changes, partly mediated by Lark/RBM4, as being an important aspect of the gut response to infection.

2020

Variational Methods for Human Modeling

Timur Bagautdinov

EPFL2018