The performance of discontinuous deformation analysis (DDA) needs to be improved for large-scale analysis. In this study, the contact detection and open-close iteration, as the bottlenecks of DDA computing, are reimplemented on graphics processing units (GPUs). For contact detection, the proposed parallel method accelerates DDA computing by maintaining a high loading balance and data reuse ratio on the GPU. The open-close iteration is divided into two parts, simultaneous equations solver and interpenetration checker. For the simultaneous equations solver, both the parallel Jacobi method and the block Jacobi preconditioned conjugate gradient (BJPCG) methods are implemented on the GPU to substitute the original successive overrelaxation (SOR) method. The parallel interpenetration checker is improved by optimizing conditional branches on the GPU. Two applications of the new parallel methods are introduced. The results showed that the broad and narrow phases of contact detection showed 18 and 5 times ...

Pretrained language models (PLMs) have made significant progress on various NLP tasks recently. However, PLMs encounter challenges when it comes to domain-specific tasks such as legal AI. These tasks often involve intricate expertise, expensive data annotation, and limited training data availability. To tackle this problem, we propose a human-oriented artificial–natural parallel system for organized intelligence (HANOI)-Legal based on the parallel learning (PL) framework. First, by regarding the description in PL as the pretraining process based on a large-scale corpus, we setup an artificial system based on a PLM. Second, to adapt the PLM to legal tasks with limited resources, we propose UniPrompt as a prescription. UniPrompt serves as a unified prompt-based training framework, enabling the utilization of diverse open datasets for these tasks. Third, we labeled a few task-specific legal data through distributed autonomous operations (DAO-II) for further fine-tuning. By combining a scalable unified-task-format reformulation and a unified-prompt-based training pipeline, HANOI-Legal leverages PLMs’ linguistic capabilities acquired from a variety of open datasets to generate task-specific models. Our experiments in two legal domain tasks show that HANOI-Legal achieved an excellent performance in low-resource scenarios compared to the state-of-the-art prompt-based approach.

This paper introduces the applications of Parallel Intelligence to hydraulic engineering. A software is developed based on Blender to support the ACP framework, i.e., construction of Artificial models, carrying out Computational experiments, and Parallel execution. Based on the framework, two case studies in hydraulic engineering on a dam and a slope respectively are descripted briefly, including the main steps of modelling, simulating and executing. The applications verified that the paradigm of parallel intelligence works well in practice.