这项工作探讨了物理驱动的机器学习技术运算符推理（IMIPF），以预测混乱的动力系统状态。 OPINF提供了一种非侵入性方法来推断缩小空间中多项式操作员的近似值，而无需访问离散模型中出现的完整订单操作员。物理系统的数据集是使用常规数值求解器生成的，然后通过主成分分析（PCA）投影到低维空间。在潜在空间中，设置了一个最小二乘问题以适合二次多项式操作员，该操作员随后在时间整合方案中使用，以便在同一空间中产生外推。解决后，将对逆PCA操作进行重建原始空间中的外推。通过标准化的根平方误差（NRMSE）度量评估了OPINF预测的质量，从中计算有效的预测时间（VPT）。考虑混乱系统Lorenz 96和Kuramoto-Sivashinsky方程的数值实验显示，具有VPT范围的OPINF降低订单模型的有希望的预测能力，这些模型均超过了最先进的机器学习方法，例如返回和储层计算循环新的Neural网络[1 ]，以及马尔可夫神经操作员[2]。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques, i.e., stacks of simple architectures that end up in more complex models. Although both factors produce outstanding results, they also pose drawbacks regarding the learning process as training complex models over large datasets are expensive and time-consuming. Such a problem is even more evident when dealing with video analysis. Some works have considered transfer learning or domain adaptation, i.e., approaches that map the knowledge from one domain to another, to ease the training burden, yet most of them operate over individual or small blocks of frames. This paper proposes a novel approach to map the knowledge from action recognition to event recognition using an energy-based model, denoted as Spectral Deep Belief Network. Such a model can process all frames simultaneously, carrying spatial and temporal information through the learning process. The experimental results conducted over two public video dataset, the HMDB-51 and the UCF-101, depict the effectiveness of the proposed model and its reduced computational burden when compared to traditional energy-based models, such as Restricted Boltzmann Machines and Deep Belief Networks.

社会机器人的快速发展刺激了人类运动建模，解释和预测，主动碰撞，人类机器人相互作用和共享空间中共同损害的积极研究。现代方法的目标需要高质量的数据集进行培训和评估。但是，大多数可用数据集都遭受了不准确的跟踪数据或跟踪人员的不自然的脚本行为。本文试图通过在语义丰富的环境中提供运动捕获，眼睛凝视跟踪器和板载机器人传感器的高质量跟踪信息来填补这一空白。为了诱导记录参与者的自然行为，我们利用了松散的脚本化任务分配，这使参与者以自然而有目的的方式导航到动态的实验室环境。本文介绍的运动数据集设置了高质量的标准，因为使用语义信息可以增强现实和准确的数据，从而使新算法的开发不仅依赖于跟踪信息，而且还依赖于移动代理的上下文提示，还依赖于跟踪信息。静态和动态环境。

使用机器学习算法从未标记的文本中提取知识可能很复杂。文档分类和信息检索是两个应用程序，可以从无监督的学习（例如文本聚类和主题建模）中受益，包括探索性数据分析。但是，无监督的学习范式提出了可重复性问题。初始化可能会导致可变性，具体取决于机器学习算法。此外，关于群集几何形状，扭曲可能会产生误导。在原因中，异常值和异常的存在可能是决定因素。尽管初始化和异常问题与文本群集和主题建模相关，但作者并未找到对它们的深入分析。这项调查提供了这些亚地区的系统文献综述（2011-2022），并提出了共同的术语，因为类似的程序具有不同的术语。作者描述了研究机会，趋势和开放问题。附录总结了与审查的作品直接或间接相关的文本矢量化，分解和聚类算法的理论背景。

通常，基于生物谱系的控制系统可能不依赖于各个预期行为或合作适当运行。相反，这种系统应该了解未经授权的访问尝试的恶意程序。文献中提供的一些作品建议通过步态识别方法来解决问题。这些方法旨在通过内在的可察觉功能来识别人类，尽管穿着衣服或配件。虽然该问题表示相对长时间的挑战，但是为处理问题的大多数技术存在与特征提取和低分类率相关的几个缺点，以及其他问题。然而，最近的深度学习方法是一种强大的一组工具，可以处理几乎任何图像和计算机视觉相关问题，为步态识别提供最重要的结果。因此，这项工作提供了通过步态认可的关于生物识别检测的最近作品的调查汇编，重点是深入学习方法，强调他们的益处，暴露出弱点。此外，它还呈现用于解决相关约束的数据集，方法和体系结构的分类和表征描述。

我们的目标是讨论其在其理论和实践术语中讨论了强化的计划，指出了在讨论计算模拟的优势的同时实施这些时间表的实际限制。在本文中，我们展示了一个名为喙的R脚本，建立了模拟与加固时间表交互的行为速率。使用喙，我们已经模拟了允许评估不同强化反馈功能（RFF）的数据。这是通过无与伦比的精确度制作的，因为模拟提供了巨大的数据样本，更重要的是，它产生的加强不会改变模拟行为。因此，我们可以系统地改变它。我们将不同的RFF与RI​​时间表进行了比较，用作标准：意义，精确，分析和一般性。我们的结果表明，RI计划的最佳反馈函数由BAUM（1981）公布。我们还建议Killeen（1975）使用的模型是RDRL计划的可行反馈函数。我们认为喙铺平了更多了解加强时间表，解决了关于时间表的定量特征的开放问题。此外，他们可以指导将来使用时间表作为理论和方法工具的实验。

深度学习（DL）是各种计算机视觉任务中使用的主要方法，因为它在许多任务上取得了相关结果。但是，在具有部分或没有标记数据的实际情况下，DL方法也容易出现众所周知的域移位问题。多源无监督的域适应性（MSDA）旨在通过从一袋源模型中分配弱知识来学习未标记域的预测指标。但是，大多数作品进行域适应性仅利用提取的特征并从损失函数设计的角度降低其域的转移。在本文中，我们认为仅基于域级特征处理域移动不足，但是在功能空间上对此类信息进行对齐也是必不可少的。与以前的工作不同，我们专注于网络设计，并建议将多源版本的域对齐层（MS-DIAL）嵌入预测变量的不同级别。这些层旨在匹配不同域之间的特征分布，并且可以轻松地应用于各种MSDA方法。为了显示我们方法的鲁棒性，我们考虑了两个具有挑战性的情况：数字识别和对象分类，进行了广泛的实验评估。实验结果表明，我们的方法可以改善最新的MSDA方法，从而在其分类精度上获得 +30.64％的相对增长。

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.

Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model's sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.\footnote{https://github.com/amorimlb/scaling\_matters}