联合学习（FL）是以隐私性的方式从分散数据培训全球模型的重要范例。现有的FL方法通常假定可以对任何参与客户端进行培训。但是，在实际应用中，客户的设备通常是异质的，并且具有不同的计算能力。尽管像伯特这样的大型模型在AI中取得了巨大的成功，但很难将它们应用于弱客户的异质FL。直接的解决方案（例如删除弱客户端或使用小型模型适合所有客户端）将带来一些问题，例如由于数据丢失或有限的模型表示能力而导致的掉落客户端的代表性不足和劣等精度。在这项工作中，我们提出了一种包含客户的联合学习方法，以解决此问题。包容性FL的核心思想是将不同尺寸的模型分配给具有不同计算功能的客户，为功能强大的客户提供的较大模型以及针对弱客户的较小客户。我们还提出了一种有效的方法，可以在多个具有不同大小的本地模型之间共享知识。这样，所有客户都可以参与FL中的模型学习，最终模型可以足够大。此外，我们提出了一种动量知识蒸馏方法，以更好地转移强大客户的大型模型中的知识，向弱客户的小型模型。在许多实际基准数据集上进行的广泛实验证明了该方法在FL框架下使用异质设备的客户学习准确模型的有效性。

套索和山脊是机器学习和统计数据中重要的最小化问题。它们是线性回归的版本，具有平方损耗，其中$ \ theta \ in \ mathbb {r}^d $ of系数的$ \ ell_1 $ -norm（对于lasso）或$ \ ell_2 $ norm（in $ \ ell_2 $ norm）（对于山脊）。我们研究了针对这些最小化问题的$ \ varepsilon $ - 二聚体的量子算法的复杂性。我们表明，对于拉索，我们可以通过加快弗兰克 - 沃尔夫算法的每题来获得$ d $的二次量子加速，而对于ridge来说，最好的量子算法是$ d $的线性，就像$ d $一样最好的古典算法。作为套索的量子下限的副产品，我们还证明了套索的第一个经典下限，该结构紧密地属于polyg因子。

Explainable AI, in the context of autonomous systems, like self-driving cars, has drawn broad interests from researchers. Recent studies have found that providing explanations for autonomous vehicles' actions has many benefits (e.g., increased trust and acceptance), but put little emphasis on when an explanation is needed and how the content of explanation changes with driving context. In this work, we investigate which scenarios people need explanations and how the critical degree of explanation shifts with situations and driver types. Through a user experiment, we ask participants to evaluate how necessary an explanation is and measure the impact on their trust in self-driving cars in different contexts. Moreover, we present a self-driving explanation dataset with first-person explanations and associated measures of the necessity for 1103 video clips, augmenting the Berkeley Deep Drive Attention dataset. Our research reveals that driver types and driving scenarios dictate whether an explanation is necessary. In particular, people tend to agree on the necessity for near-crash events but hold different opinions on ordinary or anomalous driving situations.

The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structured logical forms is now formulated as a Seq2Seq task. Despite the promising performance, previous PLM-based approaches often suffer from hallucination problems due to their negligence of the structural information contained in the sentence, which essentially constitutes the key semantics of the logical forms. Furthermore, most works treat PLM as a black box in which the generation process of the target logical form is hidden beneath the decoder modules, which greatly hinders the model's intrinsic interpretability. To address these two issues, we propose to incorporate the current PLMs with a hierarchical decoder network. By taking the first-principle structures as the semantic anchors, we propose two novel intermediate supervision tasks, namely Semantic Anchor Extraction and Semantic Anchor Alignment, for training the hierarchical decoders and probing the model intermediate representations in a self-adaptive manner alongside the fine-tuning process. We conduct intensive experiments on several semantic parsing benchmarks and demonstrate that our approach can consistently outperform the baselines. More importantly, by analyzing the intermediate representations of the hierarchical decoders, our approach also makes a huge step toward the intrinsic interpretability of PLMs in the domain of semantic parsing.

图形神经网络（GNN）是用于建模图数据的流行机器学习方法。许多GNN在同质图上表现良好，同时在异质图上表现不佳。最近，一些研究人员将注意力转移到设计GNN，以通过调整消息传递机制或扩大消息传递的接收场来设计GNN。与从模型设计的角度来减轻异性疾病问题的现有作品不同，我们建议通过重新布线结构来从正交角度研究异质图，以减少异质性并使传统GNN的表现更好。通过全面的经验研究和分析，我们验证了重新布线方法的潜力。为了充分利用其潜力，我们提出了一种名为Deep Hertophilly Graph Rewiring（DHGR）的方法，以通过添加同粒子边缘和修剪异质边缘来重新线图。通过比较节点邻居的标签/特征 - 分布的相似性来确定重新布线的详细方法。此外，我们为DHGR设计了可扩展的实现，以确保高效率。 DHRG可以轻松地用作任何GNN的插件模块，即图形预处理步骤，包括同型和异性的GNN，以提高其在节点分类任务上的性能。据我们所知，这是研究图形的第一部重新绘图图形的作品。在11个公共图数据集上进行的广泛实验证明了我们提出的方法的优势。

图形神经网络（GNN）通过汇总邻居的信息在图表中显示出表达性能。最近，一些研究讨论了在图上建模邻域分布的重要性。但是，大多数现有的GNN通过单个统计量（例如，均值，最大，sum）汇总了邻居的特征，该特征失去了与邻居特征分布相关的信息，因此会降低模型性能。在本文中，受统计理论的力矩方法的启发，我们建议用多阶矩对邻居的特征分布进行建模。我们设计了一种新型的GNN模型，即混合矩图神经网络（MM-gnn），其中包括一个多阶矩嵌入（MME）模块和一个基于元素的注意力矩适配器模块。 MM-gnn首先将每个节点的邻居的多阶矩作为签名计算，然后使用基于元素的注意力矩适配器将较大的权重分配给每个节点的重要矩和更新节点表示。我们对15个真实图表（包括社交网络，引文网络和网页网络等）进行了广泛的实验，以评估我们的模型，结果证明了MM-GNN优于现有最先进模型的优势。

基于神经影像数据的自闭症谱系障碍（ASD）的准确诊断具有重要意义，因为从ASD检测中提取有用的神经影像学数据很具有挑战性。即使已经利用了机器学习技术来改善从神经影像数据中提取信息，但由不同的元数据条件（即数据收集策略）引起的不同数据质量限制了可以提取的有效信息，从而导致数据依赖于数据ASD检测中的预测精度，在某些情况下可能比随机猜测要糟糕。在这项工作中，我们系统地研究了三种元数据对基于从20个不同位点收集的结构MRI分类的预测准确性的影响，其中元数据条件有所不同。

在本文中，我们在短PCCC中呈现点云颜色恒定，这是利用点云的照明色度估计算法。我们利用飞行时间（TOF）传感器捕获的深度信息与RGB传感器刚性安装，并形成一个6D云，其中每个点包含坐标和RGB强度，指出为（x，y，z，r，g，b）。PCCC将注意力架构应用于色彩恒定问题，导出照明矢量点明智，然后制定关于全局照明色度的全局决定。在两个流行的RGB-D数据集上，我们使用照明信息以及新颖的基准延伸，PCCC比最先进的算法获得更低的错误。我们的方法简单且快速，仅需要16 * 16尺寸的输入和超过500 FPS的速度，包括建立点云和净推理的成本。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.