低频词预测仍然是现代神经电机翻译（NMT）系统的挑战。最近的自适应培训方法通过强调整体培训目标的重量来促进不频繁词语的产出。尽管召回了低频词的召回，但它们的预测精度意外地受到自适应目标的阻碍。灵感来自观察到低频词形成更紧凑的嵌入空间，我们从代表学习角度解决这一挑战。具体地，我们提出了一种频率感知的令牌级对比度学习方法，其中每个解码步骤的隐藏状态以基于相应的字频率的柔和对比方式从其他目标单词的对应物推开。我们对广泛使用的NIST汉语 - 英语和WMT14英语 - 德语翻译任务进行实验。经验结果表明，我们的提出方法不仅可以显着提高翻译质量，还可以提高词汇分集和优化词表示空间。进一步调查揭示了，与相关的自适应培训策略相比，我们对低频词预测方法的优势在于在不牺牲精度的情况下在不同频率上的令牌级召回的鲁棒性。

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction.

The goal of multimodal abstractive summarization (MAS) is to produce a concise summary given the multimodal data (text and vision). Existing studies on MAS mainly focus on how to effectively use the extracted visual features, having achieved impressive success on the high-resource English dataset. However, less attention has been paid to the quality of the visual features to the summary, which may limit the model performance especially in the low- and zero-resource scenarios. In this paper, we propose to improve the summary quality through summary-oriented visual features. To this end, we devise two auxiliary tasks including \emph{vision to summary task} and \emph{masked image modeling task}. Together with the main summarization task, we optimize the MAS model via the training objectives of all these tasks. By these means, the MAS model can be enhanced by capturing the summary-oriented visual features, thereby yielding more accurate summaries. Experiments on 44 languages, covering mid-high-, low-, and zero-resource scenarios, verify the effectiveness and superiority of the proposed approach, which achieves state-of-the-art performance under all scenarios.

This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT'22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we utilize the pre-training-then-fine-tuning paradigm. In the first pre-training stage, we employ data filtering and synthetic data generation (i.e., back-translation, forward-translation, and knowledge distillation). In the second fine-tuning stage, we investigate speaker-aware in-domain data generation, speaker adaptation, prompt-based context modeling, target denoising fine-tuning, and boosted self-COMET-based model ensemble. Our systems achieve 0.810 and 0.946 COMET scores. The COMET scores of English-German and German-English are the highest among all submissions.

自然语言处理（NLP）通过分析社交媒体或新闻媒体的文本来证明支持财务决策的巨大潜力。在这项工作中，我们建立了一个平台，可以系统地研究NLP股票自动交易算法。与以前的工作相反，我们的平台具有三个功能：（1）我们为每个特定股票提供财务新闻。 （2）我们为每种股票提供各种股票因素。 （3）我们评估了更多与财务相关的指标的绩效。这样的设计使我们能够在更现实的环境中开发和评估NLP库存自动交易算法。除了设计评估平台和数据集集合外，我们还通过提出一个系统来自动从各种输入信息中学习良好的功能表示形式来做出技术贡献。我们算法的关键是一种称为语义角色标签池（SRLP）的方法，该方法利用语义角色标签（SRL）来创建每个新闻段的紧凑表示。基于SRLP，我们进一步纳入了其他股票因素以进行最终预测。此外，我们提出了一种基于SRLP的自我监督的学习策略，以增强系统的分布概括性能。通过我们的实验研究，我们表明所提出的方法可以实现更好的性能，并胜过所有基本线的年度回报率，以及CSI300指数和XIN9指数的最大减收率。我们的ASTOCK数据集和代码可在https://github.com/jinanzou/astock上找到。

基于方面的情绪分析（ABSA）主要涉及三个子任务：方面术语提取，意见术语提取和方面思维分类，其通常以单独的或联合方式处理。然而，以前的方法并没有很好地利用三个子任务之间的互动关系，并不完全利用易于使用的文档级标记的域/情绪知识，这限制了他们的性能。为解决这些问题，我们提出了一种用于端到端ABSA的新型迭代多知识转移网络（IMKTN）。首先，通过ABSA子组织之间的交互式相关性，我们的IMKTN通过利用精心设计的路由算法将来自三个子任务中的任意两个子组织中的任意两个子组织中的任务特定知识传输到另一个，即任何两个这三个子组织将有助于第三个子任务。对于另一个，我们的IMKTN无疑将文档级知识，即特定于域和情绪相关的知识传输到方面级别子特派团，以进一步提高相应的性能。三个基准数据集的实验结果证明了我们方法的有效性和优越性。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.