Duchamp within and against Lacan

Critical reception of Marcel Duchamp since the 1970s has tended to elevate him into the very figure of the Artist he sought to attack. One aspect of this domestication has involved neglecting Duchamp’s fin de siècle ‘eroticism’ with its sexual innuendos and double-entendres. Yet this very readymade vulgarity allows us to recover a Duchamp still capable of disrupting the genres of Art and the gendered Artist, by revealing a theory embedded in his work which continually reverses and displaces phallocentrism in a game consisting of the confusion of genders and genres. We argue that Duchamp’s disruption of the discursive typologies of the genre of art can be profitably read through this apparently trivial sexualized wordplay, particularly in the transgender passage into Rrose Sélavy. Reading this aspect of Duchamp after, i.e. within and against, Lacan demonstrates how Duchamp’s singular regime of signs governed by equivocity and indetermination subverts the ‘phallic function’ of the signifier

DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions, since nowadays multiple source and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over the time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches

Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement

The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application. In this paper, we design an algorithm to address all three optimization goals simultaneously. The user specifies desired bounds on approximation error {\delta}, minimal interior angle {\theta} and maximum mesh complexity N (number of vertices). Since such a desired mesh might not even exist, our optimization framework treats only the approximation error bound {\delta} as a hard constraint and the other two criteria as optimization goals. More specifically, we iteratively perform carefully prioritized local operators, whenever they do not violate the approximation error bound and improve the mesh otherwise. In this way our optimization framework greedily searches for the coarsest mesh with minimal interior angle above {\theta} and approximation error bounded by {\delta}. Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that our approach delivers high-quality meshes with implicitly preserved features and better balances between geometric fidelity, mesh complexity and element quality than the state-of-the-art.Comment: 14 pages, 20 figures. Submitted to IEEE Transactions on Visualization and Computer Graphic

Interactive Geometry Remeshing

We present a novel technique, both flexible and efficient, for interactive remeshing of irregular geometry. First, the original (arbitrary genus) mesh is substituted by a series of 2D maps in parameter space. Using these maps, our algorithm is then able to take advantage of established signal processing and halftoning tools that offer real-time interaction and intricate control. The user can easily combine these maps to create a control map – a map which controls the sampling density over the surface patch. This map is then sampled at interactive rates allowing the user to easily design a tailored resampling. Once this sampling is complete, a Delaunay triangulation and fast optimization are performed to perfect the final mesh. As a result, our remeshing technique is extremely versatile and general, being able to produce arbitrarily complex meshes with a variety of properties including: uniformity, regularity, semiregularity, curvature sensitive resampling, and feature preservation. We provide a high level of control over the sampling distribution allowing the user to interactively custom design the mesh based on their requirements thereby increasing their productivity in creating a wide variety of meshes

Desfazer/refazer a condição pós-conceitual

Este artigo visa mostrar como os trabalhos de Daniel Buren e de Gordon Matta-Clark uma desconstrução ao mesmo tempo dos limites da autonomia da arte e da arquitetura. Mas se, por um lado, a crítica da autonomia da arte, que incorpora o nível exclusivo/inclusivo da crítica da arte conceitual - feita pelo “trabalho in situ” de Daniel Buren -, acaba por provocar uma situação de não- -arquitetura na medida em que ele trabalha sob e sobre a arquitetura; por outro, a radicalização desta situação só se dará efetivamente nas operações feitas por Matta-Clark quando este ataca os fundamentos mesmos da arquitetura em nome de uma “anarquitetura”. Comunicação apresentada na Jornada de estudos La condition postconceptuelle — De l’art contemporain que aconteceu na Universidade Paris 8, no dia 9 de maio de 2014

A rare case of severe craniocerebral trauma with penetrating head injury

Penetrating head injury remains an important issue even in modern neurosurgery. Less frequent than other neurosurgical diseases, they may still pose some management problems. The authors present one extremely rare case of suicide attempt by penetrating head injury with harpoon at a male middle aged patient associated with iatrogenous pneumothorax. Operated with a simple occipital craniectomy, the patient had a pretty good recovery with minimal neurological deficit (facial paresis)

StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization

Domain adaptation for semantic segmentation has recently been actively studied to increase the generalization capabilities of deep learning models. The vast majority of the domain adaptation methods tackle single-source case, where the model trained on a single source domain is adapted to a target domain. However, these methods have limited practical real world applications, since usually one has multiple source domains with different data distributions. In this work, we deal with the multi-source domain adaptation problem. Our method, namely StandardGAN, standardizes each source and target domains so that all the data have similar data distributions. We then use the standardized source domains to train a classifier and segment the standardized target domain. We conduct extensive experiments on two remote sensing data sets, in which the first one consists of multiple cities from a single country, and the other one contains multiple cities from different countries. Our experimental results show that the standardized data generated by StandardGAN allow the classifiers to generate significantly better segmentation.Comment: Accepted at CVPR EarthVision Workshop 202