对话（ERC）任务中的情感识别旨在预测对话中话语的情感标签。由于说话者之间的依赖性是复杂而动态的，这包括言论和言论者间的依赖性，因此说话者特定信息的建模是ERC中的至关重要的作用。尽管现有的研究人员提出了各种说话者互动建模的方法，但他们不能共同探索动态的言论和言论者的依赖性，从而导致对上下文的理解不足并进一步阻碍情绪预测。为此，我们设计了一种新颖的扬声器建模方案，该方案以动态方式共同探索言论和言论者的依赖性。此外，我们为ERC提出了一个演讲者引导的编码编码器（SGED）框架，该框架完全利用了说话者信息来解码情感。我们使用不同的现有方法作为我们框架的对话上下文编码器，显示了提出的框架的高扩展性和灵活性。实验结果证明了SGED的优势和有效性。

情绪原因对提取（ECPE）任务旨在从文档中提取情绪和原因。我们观察到，在典型的ECPE数据集中，情绪和原因的相对距离分布极为不平衡。现有方法设置了一个固定的大小窗口，以捕获相邻子句之间的关系。但是，他们忽略了遥远条款之间的有效语义联系，从而导致对位置不敏感数据的概括能力差。为了减轻问题，我们提出了一种新型的多晶格语义意识图模型（MGSAG），以共同结合细粒度和粗粒语义特征，而无需距离限制。特别是，我们首先探讨从子句和从文档中提取的关键字之间的语义依赖性，这些文档传达了细颗粒的语义特征，从而获得了关键字增强子句表示。此外，还建立了子句图，以模拟条款之间的粗粒语义关系。实验结果表明，MGSAG超过了现有的最新ECPE模型。特别是，MGSAG在不敏感数据的条件下大大优于其他模型。

本文提出了一个新颖的框架，以根据权威的睡眠医学指导自动捕获人睡眠的脑电图（EEG）信号的时间频率。该框架由两个部分组成：第一部分通过将输入EEG频谱图将其划分为一系列时频贴片来提取信息特征。第二部分是由基于注意力的体系结构有效地搜索分配的时频贴片和并行睡眠阶段定义因素之间的相关性构成的。拟议的管道在Sleep Heart Health研究数据集上进行了验证，其阶段唤醒，N2和N3的新最新结果获得了相应的F1分数为0.93、0.88和0.87，仅使用EEG信号。该提出的方法还具有高评分者间可靠性为0.80 kappa。我们还可以看到睡眠分期决策与提出方法提取的特征之间的对应关系，为我们的模型提供了强大的解释性。

文档级别的情感分析（DSA）由于含糊的语义链接并使情感信息复杂化，因此更具挑战性。最近的工作专门用于利用文本摘要，并取得了令人鼓舞的结果。但是，这些基于摘要的方法没有充分利用摘要，包括忽略摘要和文档之间的固有交互。结果，他们将代表限制在文档中表达主要点，这高度表明了关键情绪。在本文中，我们研究了如何有效地产生具有明确的主题模式和情感环境的歧视性表示。提出了一个分层互动网络（HIN），以探索多个粒度的摘要和文档之间的双向交互，并学习以主题为导向的文档表示情感分类。此外，我们通过使用情感标签信息来完善HIN来学习基于情感的重新思考机制（SR），以学习更感知的文档表示。我们在三个公共数据集上广泛评估了我们提出的模型。实验结果始终证明了我们提出的模型的有效性，并表明HIN-SR优于各种最新方法。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

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.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

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.