机器学习（ML）方法已成为解决车辆路由问题的有用工具，可以与流行的启发式方法或独立模型结合使用。但是，当解决不同大小或不同分布的问题时，当前的方法的概括不佳。结果，车辆路由中的ML见证了一个扩展阶段，为特定问题实例创建了新方法，这些方法在较大的问题大小上变得不可行。本文旨在通过理解和改善当前现有模型，即Kool等人的注意模型来鼓励该领域的整合。我们确定了VRP概括的两个差异类别。第一个是基于问题本身固有的差异，第二个与限制模型概括能力的建筑弱点有关。我们的贡献变成了三倍：我们首先通过适应Kool等人来靶向模型差异。方法及其基于alpha-entmax激活的稀疏动态注意力的损耗函数。然后，我们通过使用混合实例训练方法来靶向固有的差异，该方法已被证明在某些情况下超过了单个实例培训。最后，我们介绍了推理水平数据增强的框架，该框架通过利用模型缺乏旋转和扩张变化的不变性来提高性能。

这项工作通过调整适合常规TSP的最新方法，使用深入的加固学习（DRL）提出了使用优先限制（TSPPC）的解决方案。这些方法共有的是基于多头注意（MHA）层的图形模型的使用。解决拾取和交付问题（PDP）的一个想法是使用异质注意来嵌入每个节点可以扮演的不同可能的角色。在这项工作中，我们将这种异质注意的概念推广到TSPPC。此外，我们适应了最近的想法，以使注意力稀疏以获得更好的可扩展性。总体而言，我们通过对解决TSPPC的最新DRL方法的应用和评估为研究界做出了贡献。

组合优化问题在许多实际情况（例如物流和生产）中遇到，但是精确的解决方案尤其难以找到，通常对于大量的问题大小而言，通常是NP-HARD。为了计算近似解决方案，通常使用局部搜索的通用和特定问题的动物园。但是，哪种变体适用于哪种特定问题，即使对于专家来说也很难决定。在本文中，我们确定了这种本地搜索算法的三个独立算法方面，并将其在优化过程中正式选择为马尔可夫决策过程（MDP）。我们将深图神经网络设计为该MDP的策略模型，为当地搜索提供了一个名为Neurols的局部搜索控制器。充分的实验证据表明，神经元能够胜过操作研究和最新基于机器学习的方法的众所周知的通用本地搜索控制器。

学习解决组合优化问题，例如车辆路径问题，提供古典运营研究求解器和启发式的巨大计算优势。最近开发的深度加强学习方法迭代或顺序地构建一组个别旅游的最初给定的解决方案。然而，大多数现有的基于学习的方法都无法为固定数量的车辆工作，从而将客户的复杂分配问题绕过APRIORI给定数量的可用车辆。另一方面，这使得它们不太适合真实应用程序，因为许多物流服务提供商依赖于提供的解决方案提供了特定的界限船队规模，并且无法适应车辆数量的短期更改。相比之下，我们提出了一个强大的监督深度学习框架，在尊重APRiori固定数量的可用车辆的同时构建完整的旅游计划。与高效的后处理方案结合，我们的监督方法不仅要快得多，更容易训练，而且还实现了包含车辆成本的实际方面的竞争结果。在彻底的控制实验中，我们将我们的方法与我们展示稳定性能的多种最先进的方法进行比较，同时利用较少的车辆并在相关工作的实验协议中存在一些亮点。

我们介绍了ThreedWorld（TDW），是交互式多模态物理模拟的平台。 TDW能够模拟高保真感官数据和富裕的3D环境中的移动代理和对象之间的物理交互。独特的属性包括：实时近光 - 真实图像渲染;对象和环境库，以及他们定制的例程;有效构建新环境课程的生成程序;高保真音频渲染;各种材料类型的现实物理相互作用，包括布料，液体和可变形物体;可定制的代理体现AI代理商;并支持与VR设备的人类交互。 TDW的API使多个代理能够在模拟中进行交互，并返回一系列表示世界状态的传感器和物理数据。我们在计算机视觉，机器学习和认知科学中的新兴的研究方向上提供了通过TDW的初始实验，包括多模态物理场景理解，物理动态预测，多代理交互，像孩子一样学习的模型，并注意研究人类和神经网络。

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.