While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting.

Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not millennia.

In this paper, we propose AutoPalette, a framework that minimizes color redundancy at the individual image and overall dataset levels, respectively. At the image level, we employ a palette network, a specialized neural network, to dynamically allocate colors from a reduced color space to each pixel. The palette network identifies essential areas in synthetic images for model training and consequently assigns more unique colors to them. At the dataset level, we develop a color-guided initialization strategy to minimize redundancy among images.

Moreover, there remains a considerable gap between the ability to answer exam questions and the application of this knowledge in real-world situations. To bridge the gap and thoroughly assess LLMs in supporting the crop science field, we introduce CROP.

The CROP benchmark includes 5,045 multiple-choice questions covering three difficulty levels.