Protein pre-training has emerged as a transformative approach for solving diverse biological tasks. While many contemporary methods focus on sequence-based language models, recent findings highlight that protein sequences alone are insufficient to capture the extensive information inherent in protein structures. Recognizing the crucial role of protein structure in defining function and interactions, we introduce $\mathcal{S}$able, a versatile pre-training model designed to comprehensively understand protein structures. $\mathcal{S}$able incorporates a novel structural encoding mechanism that enhances inter-atomic information exchange and spatial awareness, combined with robust pre-training strategies and lightweight decoders optimized for specific downstream tasks. This approach enables $\mathcal{S}$able to consistently outperform existing methods in tasks such as generation, classification, and regression, demonstrating its superior capability in protein structure representation. The code and models can be accessed via GitHub repository at https://github.com/baaihealth/Sable.

The rich spectral information in hyperspectral images (HSIs) requires effective joint extraction of spectral and spatial features for super-resolution (SR) tasks. To better capture spectral–spatial representations and restore edge textures, we propose the differentiation and cross-domain feature extraction network (DCDFENet) for HSI SR. Its core module enhances reconstruction by leveraging cross-domain interactions and differential features. DCDFE consists of three components: cross-domain and multiscale feature extraction (CDMSFE), which uses separated 3D convolutions and cross-domain connections to extract complementary features at multiple scales; spectral feature differentiation highlighting attention (SFDHA), which emphasizes significant spectral variations via differentiation and attention mechanisms; and edge feature extraction (EFE), which integrates Laplacian filtering and an extreme value feaure extraction (EVFE) to enhance spatial edge textures. Additionally, a loss function based on singular value decomposition further mitigates spectral distortion. Experiments on three benchmark datasets demonstrate that our method achieves superior performance on PSNR, MPSNR, SSIM, and SAM metrics.

Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalization ability of the learning algorithm. Two classic proximal optimization algorithms, i.e., proximal stochastic gradient descent (ProxSGD) and proximal stochastic coordinate descent (ProxSCD) have been widely used to solve the R-ERM problem. Recently, variance reduction technique was proposed to improve ProxSGD and ProxSCD, and the corresponding ProxSVRG and ProxSVRCD have better convergence rate. These proximal algorithms with variance reduction technique have also achieved great success in applications at small and moderate scales. However, in order to solve large-scale R-ERM problems and make more practical impacts, the parallel versions of these algorithms are sorely needed. In this paper, we propose asynchronous ProxSVRG (Async-ProxSVRG) and asynchronous ProxSVRCD (Async-ProxSVRCD) algorithms, and prove that Async-ProxSVRG can achieve near linear speedup when the training data is sparse, while Async-ProxSVRCD can achieve near linear speedup regardless of the sparse condition, as long as the number of block partitions are appropriately set. We have conducted experiments on a regularized logistic regression task. The results verified our theoretical findings and demonstrated the practical efficiency of the asynchronous stochastic proximal algorithms with variance reduction.