Probabilistic Boolean Networks have been proposed for estimating the behaviour of dynamical systems as they combine rule-based modelling with uncertainty principles. Inferring PBNs directly from gene data is challenging however, especially when data is costly to collect and/or noisy, e.g., in the case of gene expression profile data. In this paper, we present a reproducible method for inferring PBNs directly from real gene expression data measurements taken when the system was at a steady state. The steady-state dynamics of PBNs is of special interest in the analysis of biological machinery. The proposed approach does not rely on reconstructing the state evolution of the network, which is computationally intractable for larger networks. We demonstrate the method on samples of real gene expression profiling data from a well-known study on metastatic melanoma. The pipeline is implemented using Python and we make it publicly available.

360-degree panoramic videos have gained considerable attention in recent years due to the rapid development of head-mounted displays (HMDs) and panoramic cameras. One major problem in streaming panoramic videos is that panoramic videos are much larger in size compared to traditional ones. Moreover, the user devices are often in a wireless environment, with limited battery, computation power, and bandwidth. To reduce resource consumption, researchers have proposed ways to predict the users' viewports so that only part of the entire video needs to be transmitted from the server. However, the robustness of such prediction approaches has been overlooked in the literature: it is usually assumed that only a few models, pre-trained on past users' experiences, are applied for prediction to all users. We observe that those pre-trained models can perform poorly for some users because they might have drastically different behaviors from the majority, and the pre-trained models cannot capture the features in unseen videos. In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction. This paradigm uses two machine learning models, where the first model predicts the viewing direction, and the second model predicts the minimum video prefetch size that can include the actual viewport. We first train two meta models so that they are sensitive to new training data, and then quickly adapt them to users while they are watching the videos. Evaluation results reveal that the meta models can adapt quickly to each user, and can significantly increase the prediction accuracy, especially for the worst-performing predictions.

负责将数据从存储转移到GPU的同时，在培训机器学习模型的同时，数据加载器可能会大大提高培训工作的绩效。最近的进步不仅通过大大减少训练时间，而且还提供了新功能，例如从远程存储（如S3）加载数据，这表明了希望。在本文中，我们是第一个将数据加载器区分为深度学习（DL）工作流程中的单独组件并概述其结构和功能的组件。最后，我们提供了可用的不同数据库，其功能，可用性和性能方面的权衡以及从中获得的见解的全面比较。

图表上的交通流量预测在许多字段（例如运输系统和计算机网络）中具有现实世界应用。由于复杂的时空相关性和非线性交通模式，交通预测可能是高度挑战的。现有的作品主要是通过分别考虑空间相关性和时间相关性来模拟此类时空依赖性的模型，并且无法对直接的时空相关性进行建模。受到图形域中变形金刚最近成功的启发，在本文中，我们建议使用局部多头自我攻击直接建模时空图上的跨空间相关性。为了降低时间的复杂性，我们将注意力接收场设置为空间相邻的节点，还引入了自适应图以捕获隐藏的空间范围依赖性。基于这些注意机制，我们提出了一种新型的自适应图形时空变压器网络（ASTTN），该网络堆叠了多个时空注意层以在输入图上应用自我注意力，然后是线性层进行预测。公共交通网络数据集，Metr-La PEMS-Bay，PEMSD4和PEMSD7的实验结果证明了我们模型的出色性能。

图形内核是历史上最广泛使用的图形分类任务的技术。然而，由于图的手工制作的组合特征，这些方法具有有限的性能。近年来，由于其性能卓越，图形神经网络（GNNS）已成为与下游图形相关任务的最先进的方法。大多数GNN基于消息传递神经网络（MPNN）框架。然而，最近的研究表明，MPNN不能超过Weisfeiler-Lehman（WL）算法在图形同构术中的力量。为了解决现有图形内核和GNN方法的限制，在本文中，我们提出了一种新的GNN框架，称为\ Texit {内核图形神经网络}（Kernnns），该框架将图形内核集成到GNN的消息传递过程中。通过卷积神经网络（CNNS）中的卷积滤波器的启发，KERGNNS采用可训练的隐藏图作为绘图过滤器，该绘图过滤器与子图组合以使用图形内核更新节点嵌入式。此外，我们表明MPNN可以被视为Kergnns的特殊情况。我们将Kergnns应用于多个与图形相关的任务，并使用交叉验证来与基准进行公平比较。我们表明，与现有的现有方法相比，我们的方法达到了竞争性能，证明了增加GNN的表现能力的可能性。我们还表明，KERGNNS中的训练有素的图形过滤器可以揭示数据集的本地图形结构，与传统GNN模型相比，显着提高了模型解释性。

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。