Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.

Deep latent variable models have achieved significant empirical successes in model-based reinforcement learning (RL) due to their expressiveness in modeling complex transition dynamics. On the other hand, it remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of RL. In this paper, we provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle in the face of uncertainty for exploration. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models. Theoretically, we establish the sample complexity of the proposed approach in the online and offline settings. Empirically, we demonstrate superior performance over current state-of-the-art algorithms across various benchmarks.

Intelligent mesh generation (IMG) refers to a technique to generate mesh by machine learning, which is a relatively new and promising research field. Within its short life span, IMG has greatly expanded the generalizability and practicality of mesh generation techniques and brought many breakthroughs and potential possibilities for mesh generation. However, there is a lack of surveys focusing on IMG methods covering recent works. In this paper, we are committed to a systematic and comprehensive survey describing the contemporary IMG landscape. Focusing on 110 preliminary IMG methods, we conducted an in-depth analysis and evaluation from multiple perspectives, including the core technique and application scope of the algorithm, agent learning goals, data types, targeting challenges, advantages and limitations. With the aim of literature collection and classification based on content extraction, we propose three different taxonomies from three views of key technique, output mesh unit element, and applicable input data types. Finally, we highlight some promising future research directions and challenges in IMG. To maximize the convenience of readers, a project page of IMG is provided at \url{https://github.com/xzb030/IMG_Survey}.

背景：基于其可变的历史视觉记录，对青少年的球形等效物进行定量预测。方法：从2019年10月到2022年3月，我们检查了来自中国成都成都6-20岁的37,586名青少年的双眼未校正视力，轴向长度，角膜曲率和轴向75,172眼。 80 \％样品由训练集和剩余的20 \％组成测试集。时间感知的长期短期记忆被用来定量预测青少年在两年半内的球形当量。结果：球形当量的测试集的平均绝对预测误差为0.273-0.257，如果我们考虑不同的历史记录和不同的预测持续时间，则从0.189-0.160到0.596-0.473。结论：时间感知时间长的短期记忆被应用于不规则采样时间序列中的时间特征，这更符合实际数据的特征，因此具有更高的适用性，并有助于较早地识别近视的进展。总体误差0.273远小于临床上可接受预测的标准，例如0.75。

在本文中，我们设计了一个基于信息的多机器人来源，以寻求算法，其中一组移动传感器仅使用基于局部范围的测量值就本地化并移动靠近单个源。在算法中，移动传感器执行源标识/本地化以估计源位置；同时，他们移至新位置，以最大程度地提高有关传感器测量中包含的源的Fisher信息。在这样做的过程中，它们改善了源位置估计，并更靠近源。与传统的攀登算法相比，我们的算法在收敛速度方面具有优越性，在测量模型和信息指标的选择中是灵活的，并且对测量模型误差非常强大。此外，我们提供了算法的完全分布式版本，每个传感器都决定自己的动作，并且仅通过稀疏的通信网络与邻居共享信息。我们进行密集的仿真实验，以测试带有光传感器的小型地面车辆上的大规模系统和物理实验的算法，这表明在寻求光源方面取得了成功。

多臂强盗（MAB）问题是一个简单而强大的框架，在不确定性下的决策背景下进行了广泛的研究。在许多实际应用程序（例如机器人应用程序）中，选择ARM对应于限制下一个可用臂（动作）选择的物理动作。在此激励的情况下，我们研究了一个称为图形匪徒的mAb的扩展，在该图形上，试图从不同节点收集的奖励来传播图形。该图定义了代理在每个步骤中选择下一个可用节点时的自由度。我们假设图形结构完全可用，但是奖励分布未知。我们建立在基于脱机图的计划算法和乐观原则的基础上，我们设计了一种在线学习算法，该算法可以使用乐观原则来平衡长期探索 - 探索。我们表明我们提出的算法达到$ o（| s | \ sqrt {t} \ log（t）+d | s | s | \ log t）$学习后悔，其中$ | s | $是节点的数量和$ d $是该图的直径，与在类似设置下的最著名的增强学习算法相比，这是优越的。数值实验证实，我们的算法优于几个基准。最后，我们提出了一个由图形匪徒框架建模的合成机器人应用程序，其中机器人在农村/郊区位置网络上移动，使用我们建议的算法提供高速Internet访问。

跨核心联合学习（FL）已成为医疗保健机器学习应用程序中有前途的工具。它允许医院/机构在数据私有时使用足够的数据培训模型。为了确保FL模型在FL客户之间面对异质数据时，大多数努力都集中在为客户个性化模型上。但是，客户数据之间的潜在关系被忽略了。在这项工作中，我们专注于一个特殊的非IID FL问题，称为域混合FL，其中每个客户的数据分布都被认为是几个预定域的混合物。认识到域的多样性和域内的相似性，我们提出了一种新颖的方法Feddar，该方法以脱钩的方式学习了域共享表示形式和域名个性化的预测头。对于简化的线性回归设置，我们从理论上证明了Feddar具有线性收敛速率。对于一般环境，我们对合成和现实世界医学数据集进行了深入的经验研究，这些研究表明了其优越性比先前的FL方法。

混乱是一种由认知不平衡触发的精神状态，可以在许多类型的以任务为导向的相互作用（包括人类机器人相互作用（HRI））中发生。由于交流甚至以任务为中心的挑战，人们在与机器人互动时可能会感到困惑。为了建立平稳而引人入胜的HRI，代理人简单地发现混乱是不足的。相反，该系统应旨在减轻情况。鉴于此，在本文中，我们介绍了对话政策的语言设计方法，以建立对话框架以减轻对话者的混乱。我们还概述了草图，并讨论了有关其操作的挑战。

病理学家需要结合不同染色病理切片的信息，以获得准确的诊断结果。可变形图像配准是融合多模式病理切片的必要技术。本文提出了一个基于混合特征的基于特征的可变形图像登记框架，用于染色的病理样品。我们首先提取密集的特征点，并通过两个深度学习功能网络执行匹配点。然后，为了进一步减少虚假匹配，提出了一种结合隔离森林统计模型和局部仿射校正模型的异常检测方法。最后，插值方法基于上述匹配点生成用于病理图像注册的DVF。我们在非刚性组织学图像注册（ANHIR）挑战的数据集上评估了我们的方法，该挑战与IEEE ISBI 2019会议共同组织。我们的技术的表现使传统方法的平均水平注册目标误差（RTRE）达到0.0034。所提出的方法实现了最先进的性能，并在评估测试数据集时将其排名1。提出的基于特征的混合特征的注册方法可能会成为病理图像注册的可靠方法。