In this paper we propose a scalable framework for large-scale farm scene modeling that utilizes remote sensing data, specifically satellite images. Our approach begins by accurately extracting and categorizing the distributions of various scene elements from satellite images into four distinct layers: fields, trees, roads, and grasslands. For each layer, we introduce a set of controllable Parametric Layout Models (PLMs). These models are capable of learning layout parameters from satellite images, enabling them to generate complex, large-scale farm scenes that closely reproduce reality across multiple scales. Additionally, our framework provides intuitive control for users to adjust layout parameters to simulate different stages of crop growth and planting patterns. This adaptability makes our model an excellent tool for graphics and virtual reality applications. Experimental results demonstrate that our approach can rapidly generate a variety of realistic and highly detailed farm scenes with minimal inputs.

Protein-protein interactions (PPIs) play a critical role in cellular activities. However, discover them experimentally is exhausted. How to predict the missing PPIs with the known PPI networks (PPINs) is a challenging task considering their large scale and extreme sparsity. To utilize the known data efficiently, this work proposes a Highly-Confident Protein Interactome Prediction (HPIP) model with two-fold ideas: a) employing a variational autoencoder model based on Gaussian distribution as a basic PPI predictor for processing the sparse input PPIN; b) embedding the basic PPI predictor into an elastic architecture which based on a block and probability summing strategy. Experimental results on two real world PPINs from the STRING database indicate that HPIP can effectively predict PPIs with high confidence.