Zero-shot cross-lingual named entity recognition (NER) aims at transferring knowledge from annotated and rich-resource data in source languages to unlabeled and lean-resource data in target languages. Existing mainstream methods based on the teacher-student distillation framework ignore the rich and complementary information lying in the intermediate layers of pre-trained language models, and domain-invariant information is easily lost during transfer. In this study, a mixture of short-channel distillers (MSD) method is proposed to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently. Concretely, a multi-channel distillation framework is designed for sufficient information transfer by aggregating multiple distillers as a mixture. Besides, an unsupervised method adopting parallel domain adaptation is proposed to shorten the channels between the teacher and student models to preserve domain-invariant features. Experiments on four datasets across nine languages demonstrate that the proposed method achieves new state-of-the-art performance on zero-shot cross-lingual NER and shows great generalization and compatibility across languages and fields.

Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language. CLS has attracted wide research attention due to its practical significance in the multi-lingual world. Though great contributions have been made, existing CLS works typically focus on short documents, such as news articles, short dialogues and guides. Different from these short texts, long documents such as academic articles and business reports usually discuss complicated subjects and consist of thousands of words, making them non-trivial to process and summarize. To promote CLS research on long documents, we construct Perseus, the first long-document CLS dataset which collects about 94K Chinese scientific documents paired with English summaries. The average length of documents in Perseus is more than two thousand tokens. As a preliminary study on long-document CLS, we build and evaluate various CLS baselines, including pipeline and end-to-end methods. Experimental results on Perseus show the superiority of the end-to-end baseline, outperforming the strong pipeline models equipped with sophisticated machine translation systems. Furthermore, to provide a deeper understanding, we manually analyze the model outputs and discuss specific challenges faced by current approaches. We hope that our work could benchmark long-document CLS and benefit future studies.

随着区块链技术的开发，基于区块链技术的加密货币越来越受欢迎。这给出了一个巨大的加密货币交易网络，引起了广泛关注。网络的链接预测学习结构有助于了解网络的机制，因此在加密货币网络中也广泛研究了网络的机制。但是，过去研究中忽略了加密货币交易网络的动态。我们使用图形正则方法将过去的交易记录与未来交易联系起来。基于此，我们提出了一种潜在因子依赖性，非负因子，乘法和图形正规化的已归合性更新（SLF-NMGRU）算法，并进一步提出了图形正则化的非负潜在因子分析（GRNLFA）模型。最后，在真实加密货币交易网络上进行的实验表明，提出的方法提高了准确性和计算效率

本文提出了一种新颖的统一特征优化（UFO）范式，用于训练和在现实世界和大规模场景下进行深层模型，这需要集合多个AI功能。不明飞行物的目标是通过对所有任务进行大规模预修。与众所周知的基础模型相比，UFO具有两个不同的重点，即相对较小的模型大小，没有适应性成本：1）UFO以多任务学习方式将广泛的任务挤入中等尺寸的统一模型中并在转移到下游任务时进一步修剪模型大小。 2）不明飞行物不强调转移到新任务。相反，它旨在使修剪模型专门用于一个或多个已经看到的任务。有了这两个特征，UFO为灵活的部署提供了极大的便利，同时保持了大规模预处理的好处。 UFO的一个关键优点是修剪过程不仅可以减少模型的大小和推理消耗，而且还提高了某些任务的准确性。具体而言，UFO考虑了多任务培训，并对统一模型产生了两倍的影响：一些密切相关的任务具有相互利益，而某些任务相互冲突。不明飞行物设法通过新颖的网络体系结构搜索（NAS）方法来减少冲突并保留相互利益。对各种深度表示学习任务（即面部识别，人重新识别，车辆重新识别和产品检索）的实验表明，从UFO中修剪的模型比单件任务训练的对应物更高，但却具有更高的准确性较小的型号大小，验证不明飞行物的概念。此外，UFO还支持发布170亿个参数计算机视觉（CV）基础模型，该模型是该行业中最大的CV模型。

本文旨在通过介绍第一个中国数学预训练的语言模型〜（PLM）来提高机器的数学智能，以有效理解和表示数学问题。与其他标准NLP任务不同，数学文本很难理解，因为它们在问题陈述中涉及数学术语，符号和公式。通常，它需要复杂的数学逻辑和背景知识来解决数学问题。考虑到数学文本的复杂性质，我们设计了一种新的课程预培训方法，用于改善由基本和高级课程组成的数学PLM的学习。特别是，我们首先根据位置偏见的掩盖策略执行令牌级预训练，然后设计基于逻辑的预训练任务，旨在分别恢复改组的句子和公式。最后，我们介绍了一项更加困难的预训练任务，该任务强制执行PLM以检测和纠正其生成的解决方案中的错误。我们对离线评估（包括九个与数学相关的任务）和在线$ A/B $测试进行了广泛的实验。实验结果证明了与许多竞争基线相比，我们的方法的有效性。我们的代码可在：\ textColor {blue} {\ url {https://github.com/rucaibox/jiuzhang}}}中获得。

Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs. Distance-based methods show promising performance on link prediction task, which predicts the result by the distance between two entity representations. However, most of these methods represent the head entity and tail entity separately, which limits the model capacity. We propose two novel distance-based methods named InterHT and InterHT+ that allow the head and tail entities to interact better and get better entity representation. Experimental results show that our proposed method achieves the best results on ogbl-wikikg2 dataset.

体育比赛摘要旨在从实时评论产生体育新闻。但是，现有数据集全部通过自动收集和清洁过程构建，导致大量噪音。此外，目前的作品忽视了现场评论和体育新闻之间的知识差距，这限制了体育比赛摘要的表现。在本文中，我们介绍了K-Sportssum，一个具有两个特征的新数据集：（1）K-Sportssum从大规模游戏中收集大量数据。它有7,854个评论新闻性对。为了提高质量，K-Sportssum采用手动清洁过程; （2）与现有数据集不同，为了缩小知识缺口，K-Sportssum进一步提供了一个大型知识语料库，其中包含523名运动队和14,724名体育运动者的信息。此外，我们还介绍了一个知识增强的摘要，它利用实时评论和知识来生成体育新闻。关于K-Sportssum和Sportssum数据集的广泛实验表明，我们的模型实现了新的最先进的表演。定性分析和人类研究进一步验证我们的模型产生更具信息丰富的体育新闻。

磁共振光谱（MRS）是揭示代谢信息的无创工具。 1H-MRS的一个挑战是低信号噪声比（SNR）。为了改善SNR，一种典型的方法是用M重复样品进行信号平均（SA）。但是，数据采集时间相应地增加了M次，并且在公共环境M = 128时，完整的临床MRS SCAN大约需要10分钟。最近，引入了深度学习以改善SNR，但大多数人将模拟数据用作培训集。这可能会阻碍MRS应用程序，因为某些潜在差异（例如获取系统的缺陷）以及模拟和体内数据之间可能存在生理和心理条件。在这里，我们提出了一种新方案，该方案纯粹使用了现实数据的重复样本。深度学习模型，拒绝长期记忆（RELSTM），旨在学习从低SNR时间域数据（24 SA）到高SNR ONE（128 SA）的映射。对7个健康受试者，2名脑肿瘤患者和1名脑梗塞患者的体内脑光谱进行实验表明，仅使用20％的重复样品，RelstM的DeNoed Spectra可以为128 SA提供可比的代谢物。与最先进的低级别去核法相比，RELSTM在量化某些重要的生物标志物时达到了较低的相对误差和cram \'er-rao下限。总而言之，RELSTM可以在快速获取（24 SA）下对光谱进行高保真降级，这对MRS临床研究很有价值。

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through multi-head attention based intention fusion module to capture the speaker's intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human's psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multi-task learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression.