磁共振成像（MRI）是中风成像的中心方式。它被用来接受患者的治疗决定，例如选择患者进行静脉溶栓或血管内治疗。随后在住院期间使用MRI来通过可视化梗塞核心大小和位置来预测结果。此外，它可以用来表征中风病因，例如（心脏） - 栓塞和非胚胎中风之间的区分。基于计算机的自动医疗图像处理越来越多地进入临床常规。缺血性中风病变分割（ISLE）挑战的先前迭代有助于生成鉴定急性和急性缺血性中风病变分割的基准方法。在这里，我们介绍了一个专家注册的多中心MRI数据集，以分割急性到亚急性中风病变。该数据集包括400个多供应商MRI案例，中风病变大小，数量和位置的可变性很高。它分为n = 250的训练数据集和n = 150的测试数据集。所有培训数据将公开可用。测试数据集将仅用于模型验证，并且不会向公众发布。该数据集是Isles 2022挑战的基础，目的是找到算法方法，以实现缺血性中风的稳健和准确分割算法的开发和基准测试。

自动生物医学图像分析的领域至关重要地取决于算法验证的可靠和有意义的性能指标。但是，当前的度量使用通常是不明智的，并且不能反映基本的域名。在这里，我们提出了一个全面的框架，该框架指导研究人员以问题意识的方式选择绩效指标。具体而言，我们专注于生物医学图像分析问题，这些问题可以解释为图像，对象或像素级别的分类任务。该框架首先编译域兴趣 - 目标结构 - ，数据集和算法与输出问题相关的属性的属性与问题指纹相关，同时还将其映射到适当的问题类别，即图像级分类，语义分段，实例，实例细分或对象检测。然后，它指导用户选择和应用一组适当的验证指标的过程，同时使他们意识到与个人选择相关的潜在陷阱。在本文中，我们描述了指标重新加载推荐框架的当前状态，目的是从图像分析社区获得建设性的反馈。当前版本是在由60多个图像分析专家的国际联盟中开发的，将在社区驱动的优化之后公开作为用户友好的工具包提供。

尽管自动图像分析的重要性不断增加，但最近的元研究揭示了有关算法验证的主要缺陷。性能指标对于使用的自动算法的有意义，客观和透明的性能评估和验证尤其是关键，但是在使用特定的指标进行给定的图像分析任务时，对实际陷阱的关注相对较少。这些通常与（1）无视固有的度量属性，例如在存在类不平衡或小目标结构的情况下的行为，（2）无视固有的数据集属性，例如测试的非独立性案例和（3）无视指标应反映的实际生物医学领域的兴趣。该动态文档的目的是说明图像分析领域通常应用的性能指标的重要局限性。在这种情况下，它重点介绍了可以用作图像级分类，语义分割，实例分割或对象检测任务的生物医学图像分析问题。当前版本是基于由全球60多家机构的国际图像分析专家进行的关于指标的Delphi流程。

The application of deep learning algorithms to financial data is difficult due to heavy non-stationarities which can lead to over-fitted models that underperform under regime changes. Using the Numerai tournament data set as a motivating example, we propose a machine learning pipeline for trading market-neutral stock portfolios based on tabular data which is robust under changes in market conditions. We evaluate various machine-learning models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and without simple feature engineering, as the building blocks for the pipeline. We find that GBDT models with dropout display high performance, robustness and generalisability with relatively low complexity and reduced computational cost. We then show that online learning techniques can be used in post-prediction processing to enhance the results. In particular, dynamic feature neutralisation, an efficient procedure that requires no retraining of models and can be applied post-prediction to any machine learning model, improves robustness by reducing drawdown in volatile market conditions. Furthermore, we demonstrate that the creation of model ensembles through dynamic model selection based on recent model performance leads to improved performance over baseline by improving the Sharpe and Calmar ratios. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility of results.

Accurate segmentation of live cell images has broad applications in clinical and research contexts. Deep learning methods have been able to perform cell segmentations with high accuracy; however developing machine learning models to do this requires access to high fidelity images of live cells. This is often not available due to resource constraints like limited accessibility to high performance microscopes or due to the nature of the studied organisms. Segmentation on low resolution images of live cells is a difficult task. This paper proposes a method to perform live cell segmentation with low resolution images by performing super-resolution as a pre-processing step in the segmentation pipeline.

Diffusion Probabilistic Models (DPMs) have recently been employed for image deblurring. DPMs are trained via a stochastic denoising process that maps Gaussian noise to the high-quality image, conditioned on the concatenated blurry input. Despite their high-quality generated samples, image-conditioned Diffusion Probabilistic Models (icDPM) rely on synthetic pairwise training data (in-domain), with potentially unclear robustness towards real-world unseen images (out-of-domain). In this work, we investigate the generalization ability of icDPMs in deblurring, and propose a simple but effective guidance to significantly alleviate artifacts, and improve the out-of-distribution performance. Particularly, we propose to first extract a multiscale domain-generalizable representation from the input image that removes domain-specific information while preserving the underlying image structure. The representation is then added into the feature maps of the conditional diffusion model as an extra guidance that helps improving the generalization. To benchmark, we focus on out-of-distribution performance by applying a single-dataset trained model to three external and diverse test sets. The effectiveness of the proposed formulation is demonstrated by improvements over the standard icDPM, as well as state-of-the-art performance on perceptual quality and competitive distortion metrics compared to existing methods.

Asteroids are an indelible part of most astronomical surveys though only a few surveys are dedicated to their detection. Over the years, high cadence microlensing surveys have amassed several terabytes of data while scanning primarily the Galactic Bulge and Magellanic Clouds for microlensing events and thus provide a treasure trove of opportunities for scientific data mining. In particular, numerous asteroids have been observed by visual inspection of selected images. This paper presents novel deep learning-based solutions for the recovery and discovery of asteroids in the microlensing data gathered by the MOA project. Asteroid tracklets can be clearly seen by combining all the observations on a given night and these tracklets inform the structure of the dataset. Known asteroids were identified within these composite images and used for creating the labelled datasets required for supervised learning. Several custom CNN models were developed to identify images with asteroid tracklets. Model ensembling was then employed to reduce the variance in the predictions as well as to improve the generalisation error, achieving a recall of 97.67%. Furthermore, the YOLOv4 object detector was trained to localize asteroid tracklets, achieving a mean Average Precision (mAP) of 90.97%. These trained networks will be applied to 16 years of MOA archival data to find both known and unknown asteroids that have been observed by the survey over the years. The methodologies developed can be adapted for use by other surveys for asteroid recovery and discovery.

In peer review systems, reviewers are often asked to evaluate various features of submissions, such as technical quality or novelty. A score is given to each of the predefined features and based on these the reviewer has to provide an overall quantitative recommendation. However, reviewers differ in how much they value different features. It may be assumed that each reviewer has her own mapping from a set of criteria scores (score vectors) to a recommendation, and that different reviewers have different mappings in mind. Recently, Noothigattu, Shah and Procaccia introduced a novel framework for obtaining an aggregated mapping by means of Empirical Risk Minimization based on $L(p,q)$ loss functions, and studied its axiomatic properties in the sense of social choice theory. We provide a body of new results about this framework. On the one hand we study a trade-off between strategy-proofness and the ability of the method to properly capture agreements of the majority of reviewers. On the other hand, we show that dropping a certain unrealistic assumption makes the previously reported results to be no longer valid. Moreover, in the general case, strategy-proofness fails dramatically in the sense that a reviewer is able to make significant changes to the solution in her favor by arbitrarily small changes to their true beliefs. In particular, no approximate version of strategy-proofness is possible in this general setting since the method is not even continuous w.r.t. the data. Finally we propose a modified aggregation algorithm which is continuous and show that it has good axiomatic properties.

Robust 2004是一种信息检索基准，其每个查询的大量判断使其成为可靠的评估数据集。在本文中，我们介绍了Mrobust04，这是一种多语言版本的robust04，使用Google Translate翻译为8种语言。我们还提供了该数据集上三个不同多语言检索器的结果。该数据集可在https://huggingface.co/datasets/unicamp-dl/mrobust上获得

估计空间变化的干预对空间变化结果的因果影响可能会受到非本地混杂（NLC）的影响，这种现象可能会估计给定单位的处理和结果部分由协方差估计。附近的其他单元。特别是，NLC是评估环境政策和气候事件对健康相关结果（例如空气污染暴露）的影响的挑战。本文首先使用潜在结果框架对NLC进行正式化，从而与因果干扰的相关现象进行了比较。然后，它提出了一个称为“ weather2vec”的广泛适用框架，该框架使用平衡分数理论来学习非本地信息的表示形式，以定义为每个观察单元定义的标量或向量使用因果推理方法。该框架在一项仿真研究和两项关于空气污染的案例研究中进行了评估，天气是（本质上是区域）已知的混杂因素。