This paper develops a model predictive flocking control scheme for second-order multi-agent systems with input constraints. By penalizing both the control effort and the irregularity of the position distribution to a desired lattice formation, a decentralized controller is designed based only on neighboring measurements. Geometric properties of the optimal path are used to provide conditions guaranteeing convergence to a rigid -lattice flock avoiding inter-agent collision. Finally, numerical simulation is carried out to demonstrate the effectiveness of the proposed design.Index Terms-Flocking, model predictive control, multi-agent system.
We propose a novel domain-specific generative pre-training (DS-GPT) method for text generation and apply it to the product title and review summarization problems on E-commerce mobile display. First, we adopt a decoder-only transformer architecture, which fits well for fine-tuning tasks by combining input and output all together. Second, we demonstrate utilizing only small amount of pretraining data in related domains is powerful. Pre-training a language model from a general corpus such as Wikipedia or the Common Crawl requires tremendous time and resource commitment, and can be wasteful if the downstream tasks are limited in variety. Our DSGPT is pre-trained on a limited dataset, the Chinese short text summarization dataset (LCSTS). Third, our model does not require product-related human-labeled data. For title summarization task, the state of art explicitly uses additional background knowledge in training and predicting stages. In contrast, our model implicitly captures this knowledge and achieves significant improvement over other methods, after fine-tuning on the public Taobao.com dataset. For review summarization task, we utilize JD.com in-house dataset, and observe similar improvement over standard machine translation methods which lack the flexibility of fine-tuning. Our proposed work can be simply extended to other domains for a wide range of text generation tasks.
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