近年来，在各种应用程序中，在减轻决策中的不公平或歧视方面，公平感知机器学习的迅速发展。但是，对公平意识的多目标优化的关注要少得多，这确实是在现实生活中通常看到的，例如公平资源分配问题和数据驱动的多目标优化问题。本文旨在从公平的角度阐明和扩大我们对多目标优化的理解。为此，我们首先讨论多目标优化中的用户偏好，然后探索其与机器学习和多目标优化的公平关系。在上述讨论之后，提出了公平意识的多目标优化的代表性案例，进一步阐述了在传统的多目标优化，数据驱动的优化和联合优化中公平性的重要性。最后，解决了公平意识的多目标优化方面的挑战和机遇。我们希望本文在优化的背景下朝着理解公平迈出一步，并促进对公平意识的多目标优化的研究兴趣。

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei Cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods.

深度监督或称为“中间监督”或“辅助监督”是在神经网络的隐藏层上增加监督。最近，该技术越来越多地应用于深层神经网络学习系统中，以用于各种计算机视觉应用。人们达成共识，即深层监督有助于通过减轻梯度消失的问题来改善神经网络的性能，这是深层监督的众多优势之一。此外，在不同的计算机视觉应用程序中，可以以不同的方式应用深度监督。如何最大程度地利用深度监督来改善不同应用程序中的网络性能。在本文中，我们对理论和应用程序中的深入监督进行了全面的深入审查。我们建议对不同深度监督网络进行新的分类，并讨论计算机视觉应用程序中当前深层监督网络的优势和局限性。

Roadheader是一款在地下工程和采矿行业中广泛使用的工程机器人。 Roadheader的交互式动力学模拟是无人发掘和虚拟现实训练中的一个基本问题。但是，当前的研究仅基于传统的动画技术或商业游戏引擎。很少有研究将计算机图形的实时物理模拟应用于Roadheader机器人领域。本文旨在介绍一个基于物理的式型型式机器人的模拟系统。为此，提出了基于广义坐标的改进的多体模拟方法。首先，我们的仿真方法描述了基于广义坐标的机器人动力学。与最新方法相比，我们的方法更稳定和准确。数值仿真结果表明，在相同数量的迭代中，我们的方法的错误明显少于游戏引擎。其次，我们对动态迭代采用符号欧盟积分器，而不是传统的四阶runge-kutta（RK4）方法。与其他集成剂相比，在长期模拟过程中，我们的方法在能量漂移方面更加稳定。测试结果表明，我们的系统达到了每秒60帧（FPS）的实时交互性能。此外，我们提出了一种模型格式，用于实施该系统的路障机器人建模。我们的Roadheader的交互式模拟系统满足了交互，准确性和稳定性的要求。

联合学习（FL）算法通常在每个圆数（部分参与）大并且服务器的通信带宽有限时对每个轮子（部分参与）进行分数。近期对FL的收敛分析的作品专注于无偏见的客户采样，例如，随机均匀地采样，由于高度的系统异质性和统计异质性而均匀地采样。本文旨在设计一种自适应客户采样算法，可以解决系统和统计异质性，以最小化壁时钟收敛时间。我们获得了具有任意客户端采样概率的流动算法的新的遗传融合。基于界限，我们分析了建立了总学习时间和采样概率之间的关系，这导致了用于训练时间最小化的非凸优化问题。我们设计一种高效的算法来学习收敛绑定中未知参数，并开发低复杂性算法以大致解决非凸面问题。硬件原型和仿真的实验结果表明，与几个基线采样方案相比，我们所提出的采样方案显着降低了收敛时间。值得注意的是，我们的硬件原型的方案比均匀的采样基线花费73％，以达到相同的目标损失。

介绍了一种名为VMagent的新型模拟器，以帮助RL研究人员更好地探索新方法，特别是对于虚拟机调度。VMagent由实用虚拟机（VM）调度任务的启发，并提供了一个有效的仿真平台，可以反映云计算的实际情况。从实际云计算结束了三种情况（衰落，恢复和扩展），对应于许多强化学习挑战（高维度和行动空间，高于寿命和终身需求）。VMagent为RL研究人员提供了灵活的配置，以设计考虑不同的问题特征的定制调度环境。从VM调度角度来看，VMagent还有助于探索更好的基于学习的调度解决方案。

Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM problem encounters a low utility threshold or large-scale data, it may be time-consuming and memory-costly to address the HUSPM problem. Several algorithms have been proposed for addressing this problem, but they still cost a lot in terms of running time and memory usage. In this paper, to further solve this problem efficiently, we design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely discovering high-utility sequential patterns with the seqPro structure (HUSP-SP). HUSP-SP utilizes the compact seq-array to store the necessary information in a sequence database. The seqPro structure is designed to efficiently calculate candidate patterns' utilities and upper bound values. Furthermore, a new upper bound on utility, namely tighter reduced sequence utility (TRSU) and two pruning strategies in search space, are utilized to improve the mining performance of HUSP-SP. Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability.

Graph Neural Networks (GNNs) have become increasingly important in recent years due to their state-of-the-art performance on many important downstream applications. Existing GNNs have mostly focused on learning a single node representation, despite that a node often exhibits polysemous behavior in different contexts. In this work, we develop a persona-based graph neural network framework called PersonaSAGE that learns multiple persona-based embeddings for each node in the graph. Such disentangled representations are more interpretable and useful than a single embedding. Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings. The framework is flexible enough and the general design helps in the wide applicability of the learned embeddings to suit the domain. We utilize publicly available benchmark datasets to evaluate our approach and against a variety of baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a variety of important tasks including link prediction where we achieve an average gain of 15% while remaining competitive for node classification. Finally, we also demonstrate the utility of PersonaSAGE with a case study for personalized recommendation of different entity types in a data management platform.

With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.