It is agreed that portfolio selection is of great importance for the financial market. Numerous outstanding exact and heuristic algorithms have been proposed in the past decades. However, their development always demands meticulous human ideas and could be time-consuming. Moreover, most of them tend to suffer from performance degradation when exposed to new portfolio selection models and different investment environments. Learning-enabled approaches have recently yielded impressive results, but these methods still grapple with challenges in model design and training. In this paper, we explore the mutual facilitation of large language models (LLMs) and huristic approaches in portfolio selection, and propose a novel LLM-based multi-objective evolutionary algorithm (MOEA) named IlmPC-NSGA-II. In this algorithm, the LLM with carefully-designed well-structured prompts serves as a straightforward yet effective engine for generating new solutions, non-dominated sorting and crowding distance calculation are adopted to enable the LLM and the evolutionary process to mutually guide toward the optimal region of the solution space. Experimental results on various scales of constrained multi-objective portfolio selection models and four benchmark problems demonstrate that our proposed approach can achieve a more competitive performance compared to widely-used MOEAs and the LLM-only method.

Ocean general circulation models (OGCMs) are indispensable for studying the multi-scale oceanic processes and climate change. High-resolution ocean simulations require immense computational power and thus become a challenge in climate science. We present LICOMK++, a performance-portable OGCM using Kokkos, to facilitate global kilometer-scale ocean simulations. The breakthroughs include: (1) we enhance cuttingedge Kokkos with the Sunway architecture, enabling LICOMK++ to become the first performance-portable OGCM on diversified architectures, i.e., Sunway processors, CUDA/HIP-based GPUs, and ARM CPUs. (2) LICOMK++ overcomes the one simulated-years-per-day (SYPD) performance challenge for global realistic OGCM at $1-\mathrm{km}$ resolution. It records $\mathbf{1. 0 5}$ and 1.70 SYPD with a parallel efficiency of 54.8% and 55.6% scaling on almost the entire new Sunway supercomputer and two-thirds of the ORISE supercomputer. (3) LICOMK++ is the first global 1-km-resolution realistic OGCM to generate scientific results. It successfully reproduces mesoscale and submesoscale structures that have considerable climate effects.

Data shortage and real-time demand are two essential characteristics of remote sensing signal processing tasks. Existing works rarely focus on the scenario where all categories data are relatively scarce, which may be necessary in some occasions. To further explore the potential of light-weight deep neural network for scarce data remote sensing image classification, we attempt to improve the model performance in such task with tiny proportion of the training data. Due to the information insufficiency caused by data shortage, we propose a statistically independent Gaussian noise based feature augmentation module. A variational auto-encoder is used to provide noise vectors which come from the same input space with feature vectors for classification, but independent from them. To alleviate the inter-class feature confusion caused by the feature augmentation and enhance decision boundaries, we design a 3Σ1/2area based inter-class mutual exclusion strategy to enlarge distances between samples of different classes in feature space with contrastive loss. Extensive experiments are conducted to prove that our method can significantly improve the performance of light-weight models on scarce data remote sensing classification task.