Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently interact with diverse road users in diverse scenarios remains largely unsolved. Learning methods have much to offer towards solving this problem. But they require a realistic multi-agent simulator that generates diverse and competent driving interactions. To meet this need, we develop a dedicated simulation platform called SMARTS (Scalable Multi-Agent RL Training School). SMARTS supports the training, accumulation, and use of diverse behavior models of road users. These are in turn used to create increasingly more realistic and diverse interactions that enable deeper and broader research on multiagent interaction. In this paper, we describe the design goals of SMARTS, explain its basic architecture and its key features, and illustrate its use through concrete multi-agent experiments on interactive scenarios. We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving. Our code is available at https://github.com/huawei-noah/SMARTS.

Relation extraction is a popular subtask in natural language processing (NLP). In the task of entity relation joint extraction, overlapping entities and multi-type relation extraction in overlapping triplets remain a challenging problem. The classification of relations by sharing the same probability space will ignore the correlation information among multiple relations. A relational-adaptive entity relation joint extraction model based on multi-head self-attention and densely connected graph convolution network (which is called MA-DCGCN) is proposed in the paper. In the model, the multi-head attention mechanism is specifically used to assign weights to multiple relation types among entities so as to ensure that the probability space of multiple relation is not mutually exclusive. This mechanism also predicts the strength of the relationship between various relationship types and entity pairs flexibly. The structure information of deeper level in the text graph is extracted by the densely connected graph convolution network, and the interaction information of entity relation is captured. To demonstrate the superior performance of our model, we conducted a variety of experiments on two widely used public datasets, NYT and WebNLG. Extensive results show that our model achieves state-of-the-art performance. Especially, the detection effect of overlapping triplets is significantly improved compared with the several existing mainstream methods.

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ObjectiveTo elucidate the current situation of breastfeeding in neonates in China and to investigate whether SARS-CoV-2 is transmitted through the mother’s milk.DesignA nationwide cross-sectional surveySettingThree hundred and forty-four member hospitals of the Chinese Neonatologist Association network from 31 provinces in China.SampleNine hundred and fourteen neonatologistsMain outcome measuresThese included (1) breastfeeding practices in the obstetrics ward; (2) breastfeeding implementation for neonates admitted to neonatal intensive care unit (NICU); (3) presence of SARS-CoV-2 in the breast milk of COVID-19 positive mothers based on the real-time reverse transcriptase-polymerase chain reaction (RT-PCT) test results.ResultsBreastfeeding was undermined during the COVID-19 pandemic. Of the 344 hospitals, 153 (44.48%) centers received breast milk from milk banks to feed babies in NICU. Eight (2.33%) Level III centers performed SARS-CoV-2 PCR tests on breast milk from 15 mothers with COVID-19 and found no SARS-CoV-2 RNA presence in breast milk. Moreover, none of the mothers engaged in breastfeeding. Further, only 52 (5.69%) neonatologists supported breastfeeding in mothers with COVID-19.ConclusionsBased on the available evidence, the benefits of breastfeeding for both infants and mothers outweigh the potential risk of SARS-CoV-2 transmission through breast milk. Amidst the COVID-19 pandemic, medical staff should encourage breastfeeding, in keeping with normal infant feeding guidelines, and provide skilled support to all mothers who choose to breastfeed.