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}

可见的红外人员重新识别（REID）旨在认识到RGB和IR摄像机网络中的同一个人。一些深度学习（DL）模型已直接纳入了两种模式，以在联合表示空间中区分人。但是，由于RGB和IR模式之间数据分布的较大域转移，因此这个跨模式的REID问题仍然具有挑战性。 ％本文引入了一种新的方法，用于创建中间虚拟域，该域在训练过程中充当两个主要领域（即RGB和IR模式）之间的桥梁。该中间域被视为在测试时间无法获得的特权信息（PI），并允许将此跨模式匹配任务制定为在特权信息（LUPI）下学习的问题。我们设计了一种新方法，以在可见的和红外域之间生成图像，这些方法提供了其他信息，以通过中间域的适应来训练深层REID模型。特别是，通过在训练过程中采用无色和多步三重态损失目标，我们的方法提供了通用的特征表示空间，这些空间对大型可见的红外域移动具有牢固的功能。 ％关于挑战性可见红外REID数据集的实验结果表明，我们提出的方法始终提高匹配的准确性，而在测试时没有任何计算开销。该代码可在：\ href {https://github.com/alehdaghi/cross-modal-re-id-iid-via-lupi} {https://github.com/alehdaghi/alehdaghi/cross-modal-re-re-id-i-id--i- id-i--i- id-id-i--i--via-lupi} { Via-Lupi}

阶级失衡是一种以使学习对分类模型更具挑战性的特征，因为它们可能最终会偏向多数级别。在不平衡学习的背景下，基于整体的方法中的一种有希望的方法是动态选择（DS）。 DS技术根据整体中的分类器的一个子集，根据其在查询周围区域中的估计能力标记每个给定的样本。由于在选择方案中只考虑了一个小区域，因此全球类别不成比例可能对系统的性能产生较小的影响。但是，本地类重叠的存在可能会严重阻碍DS技术的性能，而不是分布不平衡，因为它不仅加剧了代表不足的影响，而且还引入了能力估计过程中模棱两可且可能不可靠的样本。因此，在这项工作中，我们提出了一种DS技术，该技术试图最大程度地减少分类器选择过程中本地类别重叠的影响。所提出的方法迭代从目标区域中删除了实例被认为是最难分类的实例，直到分类器被认为有能力标记查询样品为止。使用实例硬度度量量化本地类重叠的实例硬度度量来表征已知样品。实验结果表明，该提出的技术可以显着胜过基线以及其他几种DS技术，这表明其适合处理类别不足的班级和重叠的适用性。此外，当使用标记的集合的重新采样，重叠版本较少的版本时，该技术仍会产生竞争结果，特别是在重叠区域中少数少数族类样本的问题上。可在https://github.com/marianaasouza/lords上找到代码。

尽管深度学习架构最近取得了成功，但在现实词应用程序中，人重新识别（REID）仍然是一个具有挑战性的问题。最近，已经提出了几种无监督的单目标域适应性（STDA）方法，以限制源和目标视频数据之间通常发生的域移位引起的REID准确性下降。鉴于人REID数据的多模式性质（由于跨摄像头观点和捕获条件的变化），训练常见的CNN主链来解决跨多个目标域的域移动，可以为实时REID应用程序提供有效的解决方案。尽管在REID文献中尚未广泛解决多目标域的适应性（MTDA），但一种直接的方法包括混合不同的目标数据集，并在混合物上执行STDA以训练公共CNN。但是，这种方法可能导致概括不佳，尤其是在融合越来越多的不同目标域来训练较小的CNN时。为了减轻此问题，我们基于知识蒸馏（KD-REID）引入了一种新的MTDA方法，该方法适用于实时人员REID应用。我们的方法通过从多个专业的教师CNN中蒸馏出来，适应了目标域上常见的轻型学生骨干CNN，每个CNN都适用于特定目标域的数据。对几个具有挑战性的人REID数据集进行的广泛实验表明，我们的方法优于MTDA的最先进方法，包括混合方法，尤其是在训练像OSNET这样的紧凑型CNN骨架时。结果表明，我们的灵活MTDA方法可用于设计成本效益的REID系统，以实时视频监视应用程序。

通用形态（UNIMORPH）项目是一项合作的努力，可为数百种世界语言实例化覆盖范围的标准化形态拐角。该项目包括两个主要的推力：一种无独立的特征架构，用于丰富的形态注释，并以各种语言意识到该模式的各种语言的带注释数据的类型级别资源。本文介绍了过去几年对几个方面的扩张和改进（自McCarthy等人（2020年）以来）。众多语言学家的合作努力增加了67种新语言，其中包括30种濒危语言。我们已经对提取管道进行了一些改进，以解决一些问题，例如缺少性别和马克龙信息。我们还修改了模式，使用了形态学现象所需的层次结构，例如多肢体协议和案例堆叠，同时添加了一些缺失的形态特征，以使模式更具包容性。鉴于上一个UniMorph版本，我们还通过16种语言的词素分割增强了数据库。最后，这个新版本通过通过代表来自metphynet的派生过程的实例丰富数据和注释模式来推动将衍生物形态纳入UniMorph中。

We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer. The former estimate a set of latent variables that represent the causal factors, and the latter governs their interaction. Causal capsules and tensor transformers may be implemented using shallow autoencoders, but for a scalable architecture we employ block algebra and derive a deep neural network composed of a hierarchy of autoencoders. An interleaved kernel hierarchy preprocesses the data resulting in a hierarchy of kernel tensor factor models. Inverse causal questions are addressed with a neural network that implements multilinear projection and estimates the causes of effects. As an alternative to aggressive bottleneck dimension reduction or regularized regression that may camouflage an inherently underdetermined inverse problem, we prescribe modeling different aspects of the mechanism of data formation with piecewise tensor models whose multilinear projections are well-defined and produce multiple candidate solutions. Our forward and inverse neural network architectures are suitable for asynchronous parallel computation.

Science tests competing theories or models by evaluating the similarity of their predictions against observational experience. Thus, how we measure similarity fundamentally determines what we learn. In machine learning and scientific modeling, similarity metrics are used as objective functions. A classic example being mean squared error, which is the optimal measure of similarity when errors are normally distributed and independent and identically distributed (iid). In many cases, however, the error distribution is neither normal nor iid, so it is left to the scientist to determine an appropriate objective. Here, we review how information theory can guide that selection, then demonstrate the approach with a simple hydrologic model.

For conceptual design, engineers rely on conventional iterative (often manual) techniques. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive. Pure optimisation methods, however, ignore qualitative aspects (e.g. aesthetics or construction methods). This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE), which serves as forward performance predictor for given design features as well as an inverse design feature predictor conditioned on a set of performance requests. The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland. Sensitivity analysis is employed for explainability and informing designers about (i) relations of the model between features and/or performances and (ii) structural improvements under user-defined objectives. A case study proved our framework's potential to serve as a future co-pilot for conceptual design studies of pedestrian bridges and beyond.

Modern statistical learning algorithms are capable of amazing flexibility, but struggle with interpretability. One possible solution is sparsity: making inference such that many of the parameters are estimated as being identically 0, which may be imposed through the use of nonsmooth penalties such as the $\ell_1$ penalty. However, the $\ell_1$ penalty introduces significant bias when high sparsity is desired. In this article, we retain the $\ell_1$ penalty, but define learnable penalty weights $\lambda_p$ endowed with hyperpriors. We start the article by investigating the optimization problem this poses, developing a proximal operator associated with the $\ell_1$ norm. We then study the theoretical properties of this variable-coefficient $\ell_1$ penalty in the context of penalized likelihood. Next, we investigate application of this penalty to Variational Bayes, developing a model we call the Sparse Bayesian Lasso which allows for behavior qualitatively like Lasso regression to be applied to arbitrary variational models. In simulation studies, this gives us the Uncertainty Quantification and low bias properties of simulation-based approaches with an order of magnitude less computation. Finally, we apply our methodology to a Bayesian lagged spatiotemporal regression model of internal displacement that occurred during the Iraqi Civil War of 2013-2017.

深度学习已在许多神经影像应用中有效。但是，在许多情况下，捕获与小血管疾病有关的信息的成像序列的数量不足以支持数据驱动的技术。此外，基于队列的研究可能并不总是具有用于准确病变检测的最佳或必需成像序列。因此，有必要确定哪些成像序列对于准确检测至关重要。在这项研究中，我们旨在找到磁共振成像（MRI）序列的最佳组合，以深入基于学习的肿瘤周围空间（EPV）。为此，我们实施了一个有效的轻巧U-NET，适用于EPVS检测，并全面研究了来自易感加权成像（SWI），流体侵入的反转恢复（FLAIR），T1加权（T1W）和T2的不同信息组合 - 加权（T2W）MRI序列。我们得出的结论是，T2W MRI对于准确的EPV检测最为重要，并且在深神经网络中掺入SWI，FLAIR和T1W MRI可能会使精度的提高无关。