11 research outputs found
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Content-Style Decomposition: Representation Discovery and Applications
Content-style decompositions, or CSDs, decompose entities into content, defined by the entity's class, and style, defined as the remaining within-class variation. Content is typically defined in terms of some task. For example, in a face recognition task, identity is the content; in an emotion recognition task, expression is the content. CSDs have many applications: they can provide insight into domains where we have little prior knowledge of the sources of within- and between-class variation, and content-style recombinations are interesting as a creative exercise or for data set augmentation. Our approach is to decompose CSD discovery into two sub-problems: (1) to find useful representations of content that capture the class structure of the domain, and (2) to use those content-representations to discover CSDs. We make contributions to both sub-problems. First, we propose the F-statistic loss, a new method for discovering content representations that uses statistics of class separation on isolated embedding dimensions within a minibatch to determine when to terminate training. In addition to state-of-the-art performance on few-shot learning, we find that the method leads to factorial (also known as disentangled) representations of content when applied with a novel form of weak supervision. Previous work on disentangling is either unsupervised or uses a factor-aware oracle, which provides similar/dissimilar judgments with respect to a named attribute/factor. We explore an intermediate form of supervision, an unnamed-factor oracle, which provides similarity judgments with respect to a random unnamed factor. We demonstrate that the F-statistic loss leads to better disentangling when compared with other deep-embeddings losses and β-VAE, a state-of-the-art unsupervised disentangling method. Second, we introduce a new loss for discovering CSDs that can arbitrarily recombine content and style, called leakage filtering. In contrast to previous research which attempts to separate content and style in two different representation vectors, leakage filtering allows for imperfectly disentangled representations but ensures that residual content information will not leak out of the style representation and vice versa. Leakage filtering is also distinguished by its ability to operate on novel content-classes and by its lack of dependency on style labels for training. The recombined images produced are of high quality and can be used to augment datasets for few-shot learning tasks, yielding significant generalization improvements
O debate teórico-metodológico no campo do secretariado: pluralismos e singularidades
Neste artigo analisa-se a proposta de uma ciência do Secretariado, abordam-se referenciais teóricos sobre epistemologia e práxis, bem como se explora o universo da área nos aspectos de formação, gênero, condutas legais e cultura organizacional. Busca-se identificar os elementos correspondentes à cientificidade da área do secretariado e o grau de autonomia que se revela na ciência própria que lhe é atribuída. As conclusões confirmam o Secretariado como constituindo um campo interdisciplinar de conhecimentos, que se utiliza de várias ciências tanto com respeito à formação quanto à atuação profissional
Environmental Forcing of Nitrogen Fixation in the Eastern Tropical and Sub-Tropical North Atlantic Ocean
During the winter of 2006 we measured nifH gene abundances, dinitrogen (N2) fixation rates and carbon fixation rates in the eastern tropical and sub-tropical North Atlantic Ocean. The dominant diazotrophic phylotypes were filamentous cyanobacteria, which may include Trichodesmium and Katagnymene, with up to 106 L?1 nifH gene copies, unicellular group A cyanobacteria with up to 105 L?1 nifH gene copies and gamma A proteobacteria with up to 104 L?1 nifH gene copies. N2 fixation rates were low and ranged between 0.032–1.28 nmol N L?1 d?1 with a mean of 0.30±0.29 nmol N L?1 d?1 (1?, n = 65). CO2-fixation rates, representing primary production, appeared to be nitrogen limited as suggested by low dissolved inorganic nitrogen to phosphate ratios (DIN:DIP) of about 2±3.2 in surface waters. Nevertheless, N2 fixation rates contributed only 0.55±0.87% (range 0.03–5.24%) of the N required for primary production. Boosted regression trees analysis (BRT) showed that the distribution of the gamma A proteobacteria and filamentous cyanobacteria nifH genes was mainly predicted by the distribution of Prochlorococcus, Synechococcus, picoeukaryotes and heterotrophic bacteria. In addition, BRT indicated that multiple a-biotic environmental variables including nutrients DIN, dissolved organic nitrogen (DON) and DIP, trace metals like dissolved aluminum (DAl), as a proxy of dust inputs, dissolved iron (DFe) and Fe-binding ligands as well as oxygen and temperature influenced N2 fixation rates and the distribution of the dominant diazotrophic phylotypes. Our results suggest that lower predicted oxygen concentrations and higher temperatures due to climate warming may increase N2 fixation rates. However, the balance between a decreased supply of DIP and DFe from deep waters as a result of more pronounced stratification and an enhanced supply of these nutrients with a predicted increase in deposition of Saharan dust may ultimately determine the consequences of climate warming for N2 fixation in the North Atlantic