Werkloosheid en Inkomensongelijkheid

Uit een speltheoretische benadering wordt duidelijk, hoe hoge werkloosheid tot een groter loonverschil tussen hoog- en laaggeschoolden leidt

Image retrieval outperforms diffusion models on data augmentation

Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large datasets, often with noisy annotations, and it remains an open question to which extent these models contribute to downstream classification performance. In particular, it remains unclear if they generalize enough to improve over directly using the additional data of their pre-training process for augmentation. We systematically evaluate a range of existing methods to generate images from diffusion models and study new extensions to assess their benefit for data augmentation. Personalizing diffusion models towards the target data outperforms simpler prompting strategies. However, using the pre-training data of the diffusion model alone, via a simple nearest-neighbor retrieval procedure, leads to even stronger downstream performance. Our study explores the potential of diffusion models in generating new training data, and surprisingly finds that these sophisticated models are not yet able to beat a simple and strong image retrieval baseline on simple downstream vision tasks

Robustness of the European power grids under intentional attack

The power grid defines one of the most important technological networks of our times and sustains our complex society. It has evolved for more than a century into an extremely huge and seemingly robust and well understood system. But it becomes extremely fragile as well, when unexpected, usually minimal, failures turn into unknown dynamical behaviours leading, for example, to sudden and massive blackouts. Here we explore the fragility of the European power grid under the effect of selective node removal. A mean field analysis of fragility against attacks is presented together with the observed patterns. Deviations from the theoretical conditions for network percolation (and fragmentation) under attacks are analysed and correlated with non topological reliability measures.Comment: 7 pages, 4 figure

Image retrieval outperforms diffusion models on data augmentation

Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large datasets, often with noisy annotations, and it remains an open question to which extent these models contribute to downstream classification performance. In particular, it remains unclear if they generalize enough to improve over directly using the additional data of their pre-training process for augmentation. We systematically evaluate a range of existing methods to generate images from diffusion models and study new extensions to assess their benefit for data augmentation. Personalizing diffusion models towards the target data outperforms simpler prompting strategies. However, using the pre-training data of the diffusion model alone, via a simple nearest-neighbor retrieval procedure, leads to even stronger downstream performance. Our study explores the potential of diffusion models in generating new training data, and surprisingly finds that these sophisticated models are not yet able to beat a simple and strong image retrieval baseline on simple downstream vision tasks

Lift-Off Characteristics and Flame Base Structure of Coal Seeded Gas Jet Flames

An experimental study of the burner rim stability characteristics and the flame base structure of flames co-fired with pulverized coal and propane gas is presented. Lift-off and reattachment characteristics are examined as functions of propane concentration in the jet stream for lignite, bituminous and anthracite coals. The effects on flame base structure are studied in terms of temperature, product species concentration and radiation profiles. The addition of lignite and anthracite coals favours the lift-off transitions. Bituminous coal, on the other hand, makes the flame more stable. The peak values of temperature and concentrations of major combustion product species in the flame stabilization region strongly depend upon the rank of coal. Among the coals tested, bituminous coal produces the highest peak temperature and its flame emits maximum radiation from the stabilization region. Anthracite and lignite coals produce somewhat comparable stability characteristics and structure of the flame base. The effects of coal rank are explained by the differences in volatile matter, moisture and pyrolysis characteristics of coals.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline