Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF and body part location refinement results in a substantial increase in both runtime performance and accuracy. We also present the first combined body and foot keypoint detector, based on an internal annotated foot dataset that we have publicly released. We show that the combined detector not only reduces the inference time compared to running them sequentially, but also maintains the accuracy of each component individually. This work has culminated in the release of OpenPose, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints.

Technical indicators use graphic representations of data sets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors: the market in which it operates, the size of the time window, and others. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of technical and financial indicators and propose other applications, such as glucose time series. We propose the combination of several Multi-objective Evolutionary Algorithms (MOEAs). Unlike other approaches, this paper applies a set of different MOEAs, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of non-dominated solutions obtained with different MOEAs simultaneously. Experimental results show that this technique increases the returns of the commonly used Buy \& Hold strategy and other multi-objective strategies, even for daily operations.

嗜睡是驾驶员和交通事故主要原因之一的主要关注点。认知神经科学和计算机科学的进步已通过使用脑部计算机界面（BCIS）和机器学习（ML）来检测驾驶员的嗜睡。然而，几个挑战仍然开放，应该面对。首先，文献中缺少使用一组ML算法的多种ML算法对嗜睡检测性能的全面评估。最后，需要研究适合受试者组的可扩展ML模型的检测性能，并将其与文献中提出的单个模型进行比较。为了改善这些局限性，这项工作提出了一个智能框架，该框架采用了BCIS和基于脑电图（EEG）的功能，以检测驾驶场景中的嗜睡。 SEED-VIG数据集用于喂食不同的ML回归器和三类分类器，然后评估，分析和比较单个受试者和组的表现最佳模型。有关单个模型的更多详细信息，随机森林（RF）获得了78％的F1分数，改善了通过文献中使用的模型（例如支持向量机（SVM））获得的58％。关于可扩展模型，RF达到了79％的F1得分，证明了这些方法的有效性。所学的经验教训可以总结如下：i）不仅SVM，而且文献中未充分探索的其他模型与嗜睡检测有关，ii）ii）适用于受试者组的可伸缩方法也有效地检测嗜睡，即使新受试者也是如此评估模型培训中未包括的。