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}

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。

由于其在提高培训数据质量方面的重要性，标签噪声检测已被广泛研究。通过采用分类器的集合来实现令人满意的噪声检测。在这种方法中，如果池中的池中的高比例成员分配错误，则将实例分配为误标定。以前的作者已经经验评估了这种方法;然而，它们主要假设在数据集中随机生成标签噪声。这是一个强烈的假设，因为其他类型的标签噪声在实践中是可行的并且可以影响噪声检测结果。这项工作调查了两个不同噪声模型下集合噪声检测的性能：随机（nar）的嘈杂，其中标签噪声的概率取决于实例类，与在随机模型中完全嘈杂相比，其中概率标签噪声完全独立。在此设置中，我们研究了类分布对噪声检测性能的影响，因为它在NAR假设下改变了数据集中观察到的总噪声水平。此外，对集合投票阈值进行评估以与文献中最常见的方法形成对比。在许多执行的实验中，在考虑不同类别中的类别不平衡和噪声水平比等方面时，选择噪声产生模型可以导致不同的结果。

我们从一组稀疏的光谱时间序列中构建了一个物理参数化的概率自动编码器（PAE），以学习IA型超新星（SNE IA）的内在多样性。 PAE是一个两阶段的生成模型，由自动编码器（AE）组成，该模型在使用归一化流（NF）训练后概率地解释。我们证明，PAE学习了一个低维的潜在空间，该空间可捕获人口内存在的非线性特征范围，并且可以直接从数据直接从数据中准确地对整个波长和观察时间进行精确模拟SNE IA的光谱演化。通过引入相关性惩罚项和多阶段训练设置以及我们的物理参数化网络，我们表明可以在训练期间分离内在和外在的可变性模式，从而消除了需要进行额外标准化的其他模型。然后，我们在SNE IA的许多下游任务中使用PAE进行越来越精确的宇宙学分析，包括自动检测SN Outliers，与数据分布一致的样本的产生以及在存在噪音和不完整数据的情况下解决逆问题限制宇宙距离测量。我们发现，与以前的研究相一致的最佳固有模型参数数量似乎是三个，并表明我们可以用$ 0.091 \ pm 0.010 $ mag标准化SNE IA的测试样本，该样本对应于$ 0.074 \ pm。 0.010 $ mag如果删除了特殊的速度贡献。训练有素的模型和代码在\ href {https://github.com/georgestein/supaernova} {github.com/georgestein/supaernova}上发布

基于事件的视觉传感器基于视觉场景的变化产生具有高时间分辨率的异步事件流。随着事件的生成，这些传感器的特性允许精确快速地计算光学流量。对于从事件数据计算光学流的现有解决方案未能由于孔径问题而无法捕获真正的运动方向，请勿使用传感器的高时间分辨率，或者在嵌入式平台上实时运行太昂贵。在这项研究中，我们首先提供了我们之前的算法，武器（光圈稳健的多尺度流）的更快版本。新的优化软件版本（农场）显着提高了传统CPU的吞吐量。此外，我们呈现危害，一种农场算法的硬件实现，允许实时计算低功耗，嵌入式平台上的真实流量。建议的危害架构针对混合系统的片上器件，旨在最大限度地提高可配置性和吞吐量。硬件架构和农场算法是用异步的神经形态处理而开发的，放弃了事件帧的常用使用，而是仅使用不同事件的小历史运行，允许独立于传感器分辨率进行缩放。与现有方法相比，处理范例的这种变化将流量方向的估计变为高达73％，并在选择的基准配置上显示出危害最高为1.21 Mevent / s的危害。此吞吐量使实时性能能够实现迄今为止迄今为止最快速的基于活动的事件的光流的实现。

高流量鼻腔插管（HFNC）为批判性儿童提供了非侵入性呼吸支持，这些儿童可能比其他非侵入性（NIV）技术更容易耐受。及时预测HFNC故障可以提供增加呼吸支持的指示。这项工作开发并比较了机器学习模型来预测HFNC故障。从2010年1月到2月20日至2月的患者EMR进行了患者EMR进行了回顾性研究。培训了长期内记忆（LSTM）模型，以产生连续预测HFNC故障。在HFNC启动后的各个时间使用接收器操作曲线（AUROC）下的区域评估性能。还评估了HFNC启动后2小时后预测的敏感性，特异性，正面和消极预测值（PPV，NPV）。这些指标也以主要呼吸诊断的群组计算。 834 HFNC试验[455培训，173次验证，206检验]符合纳入标准，其中175 [103,30,42]（21.0％）升级至NIV或插管。具有转移学习的LSTM模型通常比LR模型更好地执行，最佳LSTM模型在启动后2小时实现0.78，VS 0.66的AUTOC。使用EMR数据培训的机器学习模型能够在发起24小时内识别出现在HFNC中失败的风险的风险。 LSTM模型结合了转移学习，输入数据持久性和合奏显示的性能提高了LR和标准LSTM模型。

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.

The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded seven, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18 /100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the six common SDOH as covariates, NLP-extracted SDOH, on average, covered 84.38% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.67, 95% CI=2.46-2.89), followed by violence (aOR=2.26, 95% CI=2.11-2.43). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.

Dry Eye Disease (DED) is one of the most common ocular diseases: over five percent of US adults suffer from DED. Tear film instability is a known factor for DED, and is thought to be regulated in large part by the thin lipid layer that covers and stabilizes the tear film. In order to aid eye related disease diagnosis, this work proposes a novel paradigm in using computer vision techniques to numerically analyze the tear film lipid layer (TFLL) spread. Eleven videos of the tear film lipid layer spread are collected with a micro-interferometer and a subset are annotated. A tracking algorithm relying on various pillar computer vision techniques is developed. Our method can be found at https://easytear-dev.github.io/.