基于培训数据的各种统计特性，对基于统计数据（ML）技术概括或学习的基于统计数据。基础统计数据的假设导致理论或经验性能担保是培训数据的分布代表了生产数据分布。这个假设经常破裂;例如，数据的统计分布可能会改变。我们术语改变会影响ML性能“数据漂移”或“漂移”。许多分类技术对其结果计算了信心的衡量标准。该措施可能不会反映实际的ML表现。一个着名的例子是熊猫图片，正确地归类为距离约60 \％，但是当添加噪音时，它被错误地被归类为长臂猿，置信度高于99 \％。但是，我们在此报告的工作表明，分类器的置信度量可用于检测数据漂移的目的。我们提出了一种完全基于分类器建议标签的方法及其对其的信心，用于警告可能导致数据漂移的数据分布或功能空间变化。我们的方法标识在模型性能下劣化，并且不需要在生产中标记通常缺乏或延迟的生产中的数据。我们的三种不同数据集和分类器的实验证明了这种方法在检测数据漂移方面的有效性。这特别令人鼓舞，因为分类本身可能是或可能不正确，并且不需要模型输入数据。我们进一步探索了顺序变化点测试的统计方法，以便自动确定要识别漂移的数据量，同时控制误率（类型-1错误）。

考虑一个结构化的特征数据集，例如$ \ {\ textrm {sex}，\ textrm {compy}，\ textrm {race}，\ textrm {shore} \} $。用户可能希望在特征空间观测中集中在哪里，并且它稀疏或空的位置。大稀疏或空区域的存在可以提供软或硬特征约束的域知识（例如，典型的收入范围是什么，或者在几年的工作经验中可能不太可能拥有高收入）。此外，这些可以建议用户对稀疏或空区域中的数据输入的机器学习（ML）模型预测可能是不可靠的。可解释的区域是一个超矩形，例如$ \ {\ textrm {rame} \ in \ {\ textrm {black}，\ textrm {white} \} \} \} \＆$ $ \ {10 \ leq \ ：\ textrm {体验} \：\ leq 13 \} $，包含满足约束的所有观察;通常，这些区域由少量特征定义。我们的方法构造了在数据集中观察到的特征空间的基于观察密度的分区。它与其他人具有许多优点，因为它适用于原始域中的混合类型（数字或分类）的特征，也可以分开空区域。从可视化可以看出，所产生的分区符合人眼可能识别的空间分组;因此，结果应延伸到更高的尺寸。我们还向其他数据分析任务展示了一些应用程序，例如推断M1模型误差，测量高尺寸密度可变性以及治疗效果的因果推理。通过分区区域的超矩形形式可以实现许多这些应用。

训练有素的ML模型被部署在另一个“测试”数据集上，其中目标特征值（标签）未知。漂移是培训数据和部署数据之间的分配变化，这是关于模型性能是否改变的。例如，对于猫/狗图像分类器，部署过程中的漂移可能是兔子图像（新类）或具有变化特征（分布变化）的猫/狗图像。我们希望检测这些更改，但没有部署数据标签，无法衡量准确性。相反，我们通过非参数测试模型预测置信度变化的分布间接检测到漂移。这概括了我们的方法，并回避特定于域特异性特征表示。我们使用变更点模型（CPMS；参见Adams and Ross 2012）解决了重要的统计问题，尤其是在顺序测试中类型1误差控制。我们还使用非参数异常方法来显示用户可疑观察结果以进行模型诊断，因为更改置信度分布显着重叠。在证明鲁棒性的实验中，我们在MNIST数字类别的子集上进行训练，然后在各种设置中的部署数据中插入漂移（例如，看不见的数字类）（漂移比例的逐渐/突然变化）。引入了新的损耗函数，以比较不同水平的漂移类污染的漂移检测器的性能（检测延迟，1型和2个误差）。

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.

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.

Topic modeling is widely used for analytically evaluating large collections of textual data. One of the most popular topic techniques is Latent Dirichlet Allocation (LDA), which is flexible and adaptive, but not optimal for e.g. short texts from various domains. We explore how the state-of-the-art BERTopic algorithm performs on short multi-domain text and find that it generalizes better than LDA in terms of topic coherence and diversity. We further analyze the performance of the HDBSCAN clustering algorithm utilized by BERTopic and find that it classifies a majority of the documents as outliers. This crucial, yet overseen problem excludes too many documents from further analysis. When we replace HDBSCAN with k-Means, we achieve similar performance, but without outliers.

With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding to PPG signals can be used to predict different tasks such as activity recognition, sleep stage detection, or more general health status. However, supervised learning is often limited by the amount of available labeled data, which is typically expensive to obtain. To address this problem, we propose a Self-Supervised Learning (SSL) method with a pretext task of signal reconstruction to learn an informative generalized PPG representation. The performance of the proposed SSL framework is compared with two fully supervised baselines. The results show that in a very limited label data setting (10 samples per class or less), using SSL is beneficial, and a simple classifier trained on SSL-learned representations outperforms fully supervised deep neural networks. However, the results reveal that the SSL-learned representations are too focused on encoding the subjects. Unfortunately, there is high inter-subject variability in the SSL-learned representations, which makes working with this data more challenging when labeled data is scarce. The high inter-subject variability suggests that there is still room for improvements in learning representations. In general, the results suggest that SSL may pave the way for the broader use of machine learning models on PPG data in label-scarce regimes.

Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentinel-1 and -2 satellite imagery and develop several extensions toward making it effective in practice. In an application to few-shot Local Climate Zone (LCZ) prediction, we show that these networks outperform multiple common baselines on datasets with a large number of classes. Further, we provide evidence that instance normalization can significantly stabilize the training process and that explicitly shaping the embedding space using supervised contrastive learning can lead to improved performance.

Front-door adjustment is a classic technique to estimate causal effects from a specified directed acyclic graph (DAG) and observed data. The advantage of this approach is that it uses observed mediators to identify causal effects, which is possible even in the presence of unobserved confounding. While the statistical properties of the front-door estimation are quite well understood, its algorithmic aspects remained unexplored for a long time. Recently, Jeong, Tian, and Barenboim [NeurIPS 2022] have presented the first polynomial-time algorithm for finding sets satisfying the front-door criterion in a given DAG, with an $O(n^3(n+m))$ run time, where $n$ denotes the number of variables and $m$ the number of edges of the graph. In our work, we give the first linear-time, i.e. $O(n+m)$, algorithm for this task, which thus reaches the asymptotically optimal time complexity, as the size of the input is $\Omega(n+m)$. We also provide an algorithm to enumerate all front-door adjustment sets in a given DAG with delay $O(n(n + m))$. These results improve the algorithms by Jeong et al. [2022] for the two tasks by a factor of $n^3$, respectively.

The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection inpainting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding methods are investigated. Both methods have improved the performance of the baseline network. Furthermore, by choosing appropriate window size, the model with neighborhood-embedding could achieve the lowest mean absolute error of 0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in CBCT projections. At the end, the efficiency of metal-conscious embedding on both simulated and real cadaver CBCT data has been demonstrated, where the inpainting capability of the baseline network has been enhanced.