Punjab, the most populous province in Pakistan, is currently facing substantial electricity shortages that are adversely affecting both residential and industrial sectors. To address this issue, the Cholistan Desert presents a promising solution due to its high solar irradiance, making it an ideal location for solar energy production. This study aims to identify the most suitable area for solar photovoltaic (PV) power plants in the Cholistan Desert using Geographic Information System (GIS) and machine learning techniques. The analysis included field survey data encompassing 14 conditioning factors such as geophysical, socio-economic, and resource conditions. Three machine learning models were utilized: Random Forest, XGBoost, and Multilayer Perceptron (MLP). The Random Forest model demonstrated superior performance with an AUC of 0.92, and feature importance was measured through SHAP. The resulting suitability map indicates that Bahawalnagar in the eastern region and Bahawalpur in the central region have 10.50% and 11.06% of their areas classified as having a “high” and “very high” probability for solar PV installation, respectively. For stakeholders in the wind industry, these regions also present potential for wind farm feasibility due to favorable wind conditions and flat terrain. The methodology can be adapted to prioritize wind energy sites by incorporating factors such as land availability, wind direction, and other related factors. Co-locating solar and wind farms in these regions could optimize land use, enhance grid stability, and support Pakistan’s renewable energy targets. Future research integrating real-time solar and wind data could further refine site selection and support multi-source renewable energy planning, providing actionable insights for policymakers and investors.

Neural processes (NPs) have recently emerged as a powerful meta-learning framework capable of making predictions based on an arbitrary number of context points. However, the learning of NPs and their variants is hindered by the need for explicit reliance on the log-likelihood of predictive distributions, which complicates the training process. To tackle this problem, we introduce score-based NP (SNP) models, drawing inspiration from recently developed score-based generative models (SGMs) that restore data from noise by reversing a perturbation process. With denoising score matching (DSM) techniques, the SNPs bypass the intractable log-likelihood calculations, learning parameterized score functions instead. We also demonstrate that score functions possess excellent attributes that enable us to represent a wide family of conditional distributions naturally. Moreover, as data points are inherently unordered, it is crucial to incorporate appropriate inductive biases into SNPs. To this end, we propose building blocks for parameterizing permutation equivariant score functions, which induce the SNPs with the desired properties. Through extensive experimentation on both synthetic and real-world datasets, our SNPs exhibit remarkable performance and outperform existing state-of-the-art NP approaches.

Diffusion models (DMs) have recently demonstrated remarkable success in modeling large-scale data distributions. However, many downstream tasks require guiding the generated content based on specific differentiable metrics, typically necessitating backpropagation during the generation process. This approach is computationally expensive, as generating with DMs often demands tens to hundreds of recursive network calls, resulting in high memory usage and significant time consumption. In this paper, we propose a more efficient alternative that approaches the problem from the perspective of parallel denoising. We show that full backpropagation throughout the entire generation process is unnecessary. The downstream metrics can be optimized by retaining the computational graph of only one step during generation, thus providing a shortcut for gradient propagation. The resulting method, which we call Shortcut Diffusion Optimization (SDO), is generic, high-performance, and computationally lightweight, capable of optimizing all parameter types in diffusion sampling. We demonstrate the effectiveness of SDO on several real-world tasks, including controlling generation by optimizing latent and aligning the DMs by fine-tuning network parameters. Compared to full backpropagation, our approach reduces computational costs by $\sim\! 90\%$ while maintaining superior performance. Code is available at https://github.com/deng-ai-lab/SDO.