准确的交通预测对于智能运输系统至关重要。尽管许多深度学习模型已经达到了最新的1小时交通预测，但长期交通预测跨越多小时仍然是一个重大挑战。此外，大多数现有的深度学习流量预测模型都是黑匣子，提出了与解释性和解释性有关的其他挑战。我们开发了图形金字塔自动构造（X-GPA），这是一种基于注意力的空间 - 速率图神经网络，使用了新型金字塔自相关注意机制。它可以从图表上的长时间序列中学习，并提高长期流量预测准确性。与几种最先进的方法相比，我们的模型可以实现高达35％的长期流量预测准确性。 X-GPA模型的基于注意力的分数提供了基于交通动态的空间和时间解释，这些解释会改变正常与高峰时段的流量以及工作日与周末流量的变化。

使用来自环路检测器传感器的数据，用于高速公路中交通事故的近实时检测对避免主要交通拥堵至关重要。虽然最近的监督机器学习方法通​​过利用人类标记的入射数据提供事件检测的解决方案，但误报率通常太高而无法在实践中使用。具体而言，事故的人类标签的不一致显着影响了监督学习模型的表现。为此，我们专注于一种以数据为中心的方法来提高准确性，降低高速公路上交通事故检测的误报率。我们开发了一个弱监管学习工作流程，为没有地面真理标签的入射数据生成高质量的训练标签，我们在监督学习设置中使用这些生成的标签进行最终检测。这种方法包括三个阶段。首先，我们介绍一个数据预处理和策策流水线，用于处理流量传感器数据，通过利用标签函数来生成高质量的培训数据，这可以是域知识相关或简单的启发式规则。其次，我们使用三个监督学习模型 - 随机森林，k最近邻居和支持向量机集合和长短期内存分类器来评估由弱监管生成的培训数据。结果表明，在使用受弱监管生成的培训数据后，所有模型的准确性都会显着提高。第三，我们开发了一种在线实时事件检测方法，在检测事件时利用模型集合和不确定性量化。总体而言，我们表明，我们提出的弱监管学习工作流程实现了高事件检测率（0.90）和低误报率（0.08）。

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.

Facial action units (FAUs) are critical for fine-grained facial expression analysis. Although FAU detection has been actively studied using ideally high quality images, it was not thoroughly studied under heavily occluded conditions. In this paper, we propose the first occlusion-robust FAU recognition method to maintain FAU detection performance under heavy occlusions. Our novel approach takes advantage of rich information from the latent space of masked autoencoder (MAE) and transforms it into FAU features. Bypassing the occlusion reconstruction step, our model efficiently extracts FAU features of occluded faces by mining the latent space of a pretrained masked autoencoder. Both node and edge-level knowledge distillation are also employed to guide our model to find a mapping between latent space vectors and FAU features. Facial occlusion conditions, including random small patches and large blocks, are thoroughly studied. Experimental results on BP4D and DISFA datasets show that our method can achieve state-of-the-art performances under the studied facial occlusion, significantly outperforming existing baseline methods. In particular, even under heavy occlusion, the proposed method can achieve comparable performance as state-of-the-art methods under normal conditions.

We develop a wall model for large-eddy simulation (LES) that takes into account various pressure-gradient effects using multi-agent reinforcement learning (MARL). The model is trained using low-Reynolds-number flow over periodic hills with agents distributed on the wall along the computational grid points. The model utilizes a wall eddy-viscosity formulation as the boundary condition, which is shown to provide better predictions of the mean velocity field, rather than the typical wall-shear stress formulation. Each agent receives states based on local instantaneous flow quantities at an off-wall location, computes a reward based on the estimated wall-shear stress, and provides an action to update the wall eddy viscosity at each time step. The trained wall model is validated in wall-modeled LES (WMLES) of flow over periodic hills at higher Reynolds numbers, and the results show the effectiveness of the model on flow with pressure gradients. The analysis of the trained model indicates that the model is capable of distinguishing between the various pressure gradient regimes present in the flow.

迄今为止对文本生成的评估主要集中在依次创建的内容上，而不是对文本的改进。但是，写作自然是一个迭代和增量过程，需要在不同的模块化技能上进行专业知识，例如修复过时的信息或使样式更加一致。即便如此，对模型执行这些技能和编辑能力的模型能力的全面评估仍然很少。这项工作介绍了EditeVal：基于指导的，基准和评估套件，该套件利用现有的现有和新数据集自动评估编辑功能，例如使文本更具凝聚力和释义。我们评估了几种预训练的模型，这表明指令和同伴表现最好，但是大多数基准都落在监督的SOTA以下，尤其是在中和和更新信息时。我们的分析还表明，用于编辑任务的常用指标并不总是很好地关联，并且对具有最高性能的提示的优化并不一定带来对不同模型的最强鲁棒性。通过发布此基准和公开可用的排行榜挑战，我们希望在开发能够迭代和更可控制的编辑模型中解锁未来的研究。

我们可以看到这一切吗？我们知道这一切吗？这些是我们当代社会中人类提出的问题，以评估我们解决问题的趋势。最近的研究探索了对象检测中的几种模型。但是，大多数人未能满足对客观性和预测准确性的需求，尤其是在发展中和发达国家中。因此，几种全球安全威胁需要开发有效解决这些问题的方法。本文提出了一种被称为智能监视系统（3S）的网络物理系统的对象检测模型。这项研究提出了一种2阶段的方法，突出了Yolo V3深度学习体系结构在实时和视觉对象检测中的优势。该研究实施了一种转移学习方法，以减少培训时间和计算资源。用于培训模型的数据集是MS COCO数据集，其中包含328,000个注释的图像实例。实施了深度学习技术，例如预处理，数据管道调查和检测，以提高效率。与其他新型研究模型相比，该模型的结果在检测监视镜头中的野生物体方面表现出色。记录了99.71％的精度，改进的地图为61.5。

现代自动驾驶汽车在很大程度上依赖机械激光雷达。当前的感知方法通常需要360 {\ deg}点云，随着激光雷达扫描方位角并获得连续的楔形切片，依次收集。全面扫描（〜100ms）的采集潜伏期可能导致过时的感知，这不利于安全操作。最近提出的流媒体感知作品直接处理LiDAR切片并通过以前的切片重复使用特征来补偿切片的狭窄视野（FOV）。但是，这些作品都是基于单一模式的，并且需要过去的信息可能过时。同时，高频摄像头的图像可以支持流型模型，因为它们提供了更大的FOV与LiDAR片相比。但是，FOV中的这种差异使传感器融合复杂化。为了解决这一研究差距，我们提出了一个创新的摄像头流媒体3D对象检测框架，该框架使用摄像头图像而不是过去的LiDAR切片来提供最新，密集和广泛的上下文，以进行流媒体感知。所提出的方法在挑战性的Nuscenes基准测试上优于先前的流媒体模型。它还胜过强大的全扫描探测器，同时更快。我们的方法证明对缺少相机图像，狭窄的雷达切片和小型摄像机劳动错误校准具有强大的功能。

深度学习通常是饥饿的，并且开发了无监督的域适应性（UDA），以将标记的源域中的知识引入未标记的目标域中。最近，深度自我训练为UDA提供了一种强大的手段，涉及一个预测目标域，然后将自信的预测作为硬伪标记的迭代过程。但是，伪标签通常是不可靠的，因此很容易导致带有传播误差的偏差解决方案。在本文中，我们采用基于能量的模型，并以能量函数最小化目标来限制未标记的目标样品的训练。可以通过简单的附加正规化或基于能量的损失来实现。该框架使我们能够获得基于能量的模型的好处，同时在插件时保持强劲的判别性能。研究了收敛属性及其与分类期望最小化的联系。我们对图像分类的最流行和大规模UDA基准以及语义分割进行了广泛的实验，以证明其通用性和有效性。