由于应用程序可用的数据越来越多，因此需要更有能力的学习模型来进行数据处理。我们遇到的数据通常具有某些嵌入式稀疏结构。也就是说，如果它们以适当的基础表示，则它们的能量可以集中于少数基础函数。本文致力于通过深层神经网络（DNN）具有稀疏的正则化具有多个参数的非线性偏微分方程解的自适应近似。指出DNN具有固有的多尺度结构，通过使用多个参数的惩罚来有利于自适应表达功能，我们开发具有多尺度稀疏正则化（SDNN）的DNN，用于有效地表示具有一定单调的功能。然后，我们将提出的SDNN应用于汉堡方程和schr \“ odinger方程的数值解。数值示例确认提出的SDNN生成的溶液稀疏而准确。

由于网络的深度倾向于无穷大，我们探讨了深神经网络与流行的Relu激活函数的收敛。为此，我们介绍了Relu网络的激活域和激活矩阵的概念。通过用激活域上的激活矩阵替换Relu激活函数的应用，我们获得了Relu网络的显式表达。然后，我们将Relu网络的收敛性确定为一类无限矩阵产物的收敛性。研究了这些无限矩阵产物的足够和必要条件。结果，我们为Relu网络建立了必要的条件，即使权重矩阵的顺序收敛到身份矩阵，并且随着Relu网络的深度增加到无穷大，偏置向量的序列会收敛到零。此外，我们从隐藏层的重量矩阵和偏置向量方面获得了足够的条件，以便在深度relu网络的点上收敛。这些结果为图像分类中众所周知的深残留网络的设计策略提供了数学见解。

虽然现在几个月有多个Covid-19疫苗，但疫苗犹豫不决在美国的高水平。部分内容也已成为政治化，特别是自11月总统选举以来。在包括Twitter的社交媒体背景下，在此期间理解疫苗犹豫不决，可以为计算社会科学家和决策者提供有价值的指导。本文通过相对研究两个不同的时间段（选举前的一个，另一个月之后的另一个月，另一个月）采用相对研究的两个Twitter数据集，而不是研究单一的Twitter语料库，而不是研究单个Twitter语料库。数据收集和过滤方法。我们的研究结果表明，从2020年到2021年秋天的政治到Covid-19疫苗的讨论中讨论了重大转变。通过使用基于集群和机器学习的方法与采样和定性分析，我们发现了几种细粒度疫苗犹豫不决的原因，其中一些随着时间的推移而变得更加（或更少）。我们的结果还强调了去年这个问题的强烈极化和政治化。

最近在图像染色的作品表明，结构信息在恢复视觉上令人愉悦的结果方面发挥着重要作用。在本文中，我们提出了由基于两个并行发射机的流组成的端到端架构：主流（MS）和结构流（SS）。在SS的帮助下，MS可以产生具有合理结构和现实细节的合理结果。具体地，MS通过同时推断丢失的结构和纹理来重建详细图像，并且SS仅通过从MS的编码器处理分层信息来恢复丢失的结构。通过在培训过程中与SS进行互动，可以暗示MS可以暗示利用结构性提示。为了帮助SS专注于结构并防止MS中的纹理受到影响，提出了一种门控单元来抑制MS和SS之间的信息流中的结构无关激活。此外，SS中的多尺度结构特征映射用于明确指导通过融合块的MS的解码器中的结构合理的图像重建。在Celeba，Paris Streetview和Parume2数据集上进行了广泛的实验表明我们所提出的方法优于最先进的方法。

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

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.

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.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.