大多数深度学习模型的诊断性能受到模型架构及其普遍参数的影响很大。模型选择方法中的主要挑战是建筑优化器和模型评估策略的设计。在本文中，我们提出了一种进化深神经网络的新颖框架，它使用政策梯度来指导DNN架构的演变实现最大诊断准确性。我们制定了一个基于策略梯度的控制器，它会生成一个动作，以在每一代采样新模型架构。获得的最佳健身用作更新策略参数的奖励。此外，所获得的最佳模型被转移到NSGA-II进化框架中的快速模型评估的下一代。因此，该算法获得了快速非主导排序的好处以及快速模型评估。拟议框架的有效性已在三个数据集中验证：空气压缩机数据集，案例西部储备大学数据集和戴克邦大学数据集。

基于优化的元学习旨在学习初始化，以便在一些梯度更新中可以学习新的看不见的任务。模型不可知的元学习（MAML）是一种包括两个优化回路的基准算法。内部循环致力于学习一项新任务，并且外循环导致元定义。但是，Anil（几乎没有内部环）算法表明，功能重用是MAML快速学习的替代方法。因此，元定义阶段使MAML用于特征重用，并消除了快速学习的需求。与Anil相反，我们假设可能需要在元测试期间学习新功能。从非相似分布中进行的一项新的看不见的任务将需要快速学习，并重用现有功能。在本文中，我们调用神经网络的宽度深度二元性，其中，我们通过添加额外的计算单元（ACU）来增加网络的宽度。 ACUS可以在元测试任务中学习新的原子特征，而相关的增加宽度有助于转发通行证中的信息传播。新学习的功能与最后一层的现有功能相结合，用于元学习。实验结果表明，我们提出的MAC方法的表现优于现有的非相似任务分布的Anil算法，约为13％（5次任务设置）

在本文中，我们开发了多元回归模型和神经网络模型，以预测湍流热对流中的雷诺数（RE）和泡沫编号。我们将他们的预测与早期模型的对流模型进行比较：Grossmann-Lohse〜[物理。rev. lett。\ textbf {86}，3316（2001）]，修订了Grossmann-LoHse〜[phys。Fluids \ TextBF {33}，015113（2021）]和Pandey-Verma [物理。Rev. E \ TextBF {94}，053106（2016）]模型。我们观察到，尽管对所有模型的预测相互接近，但在本工作中开发的机器学习模型提供了与实验性和数值结果的最佳匹配。

本文研究了随机多武装匪徒问题的新变种，其中有关手臂奖励的辅助信息以控制变化的形式可用。在许多应用程序中等队列和无线网络中，ARM奖励是一些外源变量的功能。这些变量的平均值是从历史数据的先验证实，并且可以用作控制变化。利用控制变体理论，我们获得了具有较小方差和更严格的置信度的平均估计。我们开发了一种名为UCB-CV的改进的上置信界限算法，并在奖励和控制之间的相关性遵循多变量正态分布时表征后悔界限。我们还使用像千斤顶和分离等重采样方法扩展UCB-CV到其他分布。合成问题实例实验验证了所提出的算法的性能保证。

目的：我们对颅颌面（CMF）骨骼进行解剖地标，而无需明确分割它们。为此，我们提出了一种新的简单而有效的深层网络体系结构，称为\ textit {关系推理网络（RRN）}，以准确地学习CMF骨骼中地标之间的本地和全球关系；具体而言，下颌骨，上颌和鼻骨。方法：拟议的RRN以端到端的方式工作，利用基于密集块单元的地标的学习关系。对于给定的少数地标作为输入，RRN将地标的过程类似于数据推出问题，而数据插图问题被认为缺少了预测的地标。结果：我们将RRN应用于从250名患者获得的锥束计算机断层扫描扫描。使用4倍的交叉验证技术，我们获得了平均均方根误差，每个地标小于2 mm。我们提出的RRN揭示了地标之间的独特关系，这些关系帮助我们推断了关于地标的信息的几个\ textit {推理}。所提出的系统即使骨骼中存在严重的病理或变形，也可以准确地识别缺失的地标性位置。结论：准确识别解剖标志是CMF手术的变形分析和手术计划的关键步骤。实现这一目标而无需明确的骨骼分割解决了基于分割方法的主要局限性，在这种方法中，分割失败（在具有严重病理或变形的骨骼中通常情况下）很容易导致地标不正确。据我们所知，这是使用深度学习发现对象的解剖学关系的第一种此类算法。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.