最近的深度学习技术和精心设计的DEBIA算法，公正的排名学习（ULTR）问题已大大提高。但是，由于从那些流行的基准数据集中观察到的以下缺点，因此现有基准数据集的有希望的结果可能不会扩展到实际情况：（1）过时的语义功能提取，其中最先进的大规模预训练的预培训的语言由于原始文本的缺失，无法利用像伯特这样的模型；（2）不完整的显示功能，用于深入研究Ultr，例如，缺少显示的文档的摘要，用于分析单击必要的偏见； （3）缺乏现实世界的用户反馈，导致经验研究中合成数据集的普遍性。为了克服上述缺点，我们介绍了Baidu-ultr数据集。它涉及随机采样12亿次搜索会议和7,008个专家注释的查询，该查询比现有的搜索范围大。 Baidu-ultr提供：（1）原始语义功能和一个预先训练的语言模型，以方便使用； （2）足够的显示信息，例如位置，显示高度并显示了抽象，从而可以全面研究具有先进技术的不同偏见，例如因果发现和元学习； （3）搜索结果页面（SERP）等丰富的用户反馈，例如住宅时间，允许用户参与优化并促进ULTR中多任务学习的探索。在本文中，我们介绍了Baidu-Ultr的设计原理以及在此新数据资源上的基准超级算法的性能，有利于探索长尾查询和排名预培训任务的排名。 BAIDU-ULTR数据集和相应的基线实现可在https://github.com/chuxiaokai/baidu_ultr_dataset上获得。

图形神经网络（GNNS）在具有图形结构数据的各种任务中取得了巨大成功，其中节点分类是必不可少的。无监督的图形域适应（UGDA）显示了其降低节点分类标签成本的实用价值。它利用标记图（即源域）的知识来解决另一个未标记的图形（即目标域）的相同任务。大多数现有的UGDA方法严重依赖于源域中的标记图。它们利用来自源域的标签作为监控信号，并在源图和目标图中共同培训。但是，在一些真实的场景中，由于无法使用或隐私问题，源图无法访问。因此，我们提出了一种名为Source Firect Insuperved Graph域适应（SFUGDA）的新颖情景。在这种情况下，我们可以从源域中杠杆的唯一信息是训练有素的源模型，而不会曝光源图和标签。结果，现有的UGDA方法不再可行。为了解决本实际情况的非琐碎的适应挑战，我们提出了一种模型 - 无话学算法，用于域适应，以充分利用源模型的辨别能力，同时保留目标图上的结构接近度的一致性。我们在理论和经验上证明了所提出的算法的有效性。四个跨域任务的实验结果显示了宏F1得分的一致性改进，高达0.17。

正规化可以通过引入感应偏压来减轻训练与推理之间的泛化差距。现有的作品已经提出了各种视角的各种归纳偏见。然而，据我们所知，他们都没有探讨各种神经元的类依赖性响应分布的视角探讨归纳偏见。在本文中，我们对这种分布的特征进行了大量分析。基于分析结果，我们阐明了神经元稳定性假设：具有与同一类别的情况相似的神经元导致更好的概括。因此，我们提出了一种新的正则化方法，称为神经元稳定正则化，以减少神经元内响应方差。我们在多层的Perceptron，卷积神经网络和图形神经网络上进行了广泛的实验，具有不同域的流行基准数据集，这表明我们的神经元稳定性正则化始终优于Vanilla版本的模型，具有显着增益和低额外的开销。

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.

Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network with several modules like CNN, LSTM and Attention. Recent methods combine the Transformer with these modules for better performance. However, it requires tedious optimization skills to train a network composed of mixed modules, making these methods inconvenient to be used in practice. In this paper, we propose to design \emph{pure Transformer-based networks} for deep RL, aiming at providing off-the-shelf backbones for both the online and offline settings. Specifically, the Transformer in Transformer (TIT) backbone is proposed, which cascades two Transformers in a very natural way: the inner one is used to process a single observation, while the outer one is responsible for processing the observation history; combining both is expected to extract spatial-temporal representations for good decision-making. Experiments show that TIT can achieve satisfactory performance in different settings, consistently.

Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.

Pre-trained language models have achieved promising success in code retrieval tasks, where a natural language documentation query is given to find the most relevant existing code snippet. However, existing models focus only on optimizing the documentation code pairs by embedding them into latent space, without the association of external knowledge. In this paper, we propose a generation-augmented query expansion framework. Inspired by the human retrieval process - sketching an answer before searching, in this work, we utilize the powerful code generation model to benefit the code retrieval task. Specifically, we demonstrate that rather than merely retrieving the target code snippet according to the documentation query, it would be helpful to augment the documentation query with its generation counterpart - generated code snippets from the code generation model. To the best of our knowledge, this is the first attempt that leverages the code generation model to enhance the code retrieval task. We achieve new state-of-the-art results on the CodeSearchNet benchmark and surpass the baselines significantly.

We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task. Compared with previous text style transfer tasks that can be mostly addressed by slight token- or phrase-level edits, polite language rewrite requires deep understanding and extensive sentence-level edits over an offensive and impolite sentence to deliver the same message euphemistically and politely, which is more challenging -- not only for NLP models but also for human annotators to rewrite with effort. To alleviate the human effort for efficient annotation, we first propose a novel annotation paradigm by a collaboration of human annotators and GPT-3.5 to annotate \textsc{PoliteRewrite}. The released dataset has 10K polite sentence rewrites annotated collaboratively by GPT-3.5 and human, which can be used as gold standard for training, validation and test; and 100K high-quality polite sentence rewrites by GPT-3.5 without human review. We wish this work (The dataset (10K+100K) will be released soon) could contribute to the research on more challenging sentence rewrite, and provoke more thought in future on resource annotation paradigm with the help of the large-scaled pretrained models.