Sparse modelling or model selection with categorical data is challenging even for a moderate number of variables, because one parameter is roughly needed to encode one category or level. The Group Lasso is a well known efficient algorithm for selection continuous or categorical variables, but all estimates related to a selected factor usually differ. Therefore, a fitted model may not be sparse, which makes the model interpretation difficult. To obtain a sparse solution of the Group Lasso we propose the following two-step procedure: first, we reduce data dimensionality using the Group Lasso; then to choose the final model we use an information criterion on a small family of models prepared by clustering levels of individual factors. We investigate selection correctness of the algorithm in a sparse high-dimensional scenario. We also test our method on synthetic as well as real datasets and show that it performs better than the state of the art algorithms with respect to the prediction accuracy or model dimension.

Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand; it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.

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.

我们提出了三种新型的修剪技术，以提高推理意识到的可区分神经结构搜索（DNAS）的成本和结果。首先，我们介绍了DNA的随机双路构建块，它可以通过内存和计算复杂性在内部隐藏尺寸上进行搜索。其次，我们在搜索过程中提出了一种在超级网的随机层中修剪块的算法。第三，我们描述了一种在搜索过程中修剪不必要的随机层的新技术。由搜索产生的优化模型称为Prunet，并在Imagenet Top-1图像分类精度的推理潜伏期中为NVIDIA V100建立了新的最先进的Pareto边界。将Prunet作为骨架还优于COCO对象检测任务的GPUNET和EFIDENENET，相对于平均平均精度（MAP）。

本文介绍了我们对Polval 2021任务2的贡献：翻译质量评估指标的评估。我们描述了使用预先训练的语言模型和最新框架进行的实验，以在任务的非盲和盲版中进行翻译质量评估。我们的解决方案在非蓝色版本中排名第二，在盲版中排名第三。

本文介绍了亚当·米基维奇大学（Adam Mickiewicz University）（AMU）提交的《 WMT 2022一般MT任务》的踪迹。我们参加了乌克兰$ \ leftrightarrow $捷克翻译指示。这些系统是基于变压器（大）体系结构的四个模型的加权合奏。模型使用源因素来利用输入中存在的命名实体的信息。合奏中的每个模型仅使用共享任务组织者提供的数据培训。一种嘈杂的反向翻译技术用于增强培训语料库。合奏中的模型之一是文档级模型，该模型在平行和合成的更长序列上训练。在句子级的解码过程中，集合生成了N最佳列表。 n-最佳列表与单个文档级模型生成的n-最佳列表合并，该列表一次翻译了多个句子。最后，使用现有的质量估计模型和最小贝叶斯风险解码来重新列出N最好的列表，因此根据彗星评估指标选择了最佳假设。根据自动评估结果，我们的系统在两个翻译方向上排名第一。

心脏磁共振成像通常用于评估心脏解剖结构和功能。左心室血池和左心室心肌的描述对于诊断心脏疾病很重要。不幸的是，在CMR采集程序中，患者的运动可能会导致最终图像中出现的运动伪像。这种伪像降低了CMR图像的诊断质量和对程序的重做。在本文中，我们提出了一个多任务SWIN UNET变压器网络，用于在CMRXMOTION挑战中同时解决两个任务：CMR分割和运动伪像分类。我们将细分和分类作为多任务学习方法，使我们能够确定CMR的诊断质量并同时生成口罩。 CMR图像分为三个诊断质量类别，而所有具有非严重运动伪像的样本都被分割。使用5倍交叉验证训练的五个网络的合奏实现了骰子系数为0.871的分割性能，分类精度为0.595。

通过磁共振成像（MRI）评估肿瘤负担对于评估胶质母细胞瘤的治疗反应至关重要。由于疾病的高异质性和复杂性，该评估的性能很复杂，并且与高变异性相关。在这项工作中，我们解决了这个问题，并提出了一条深度学习管道，用于对胶质母细胞瘤患者进行全自动的端到端分析。我们的方法同时确定了肿瘤的子区域，包括第一步的肿瘤，周围肿瘤和手术腔，然后计算出遵循神经符号学（RANO）标准的当前响应评估的体积和双相测量。此外，我们引入了严格的手动注释过程，其随后是人类专家描绘肿瘤子区域的，并捕获其分割的信心，后来在训练深度学习模型时被使用。我们广泛的实验研究的结果超过了760次术前和504例从公共数据库获得的神经胶质瘤后患者（2021 - 2020年在19个地点获得）和临床治疗试验（47和69个地点，可用于公共数据库（在19个地点获得）（47和69个地点）术前/术后患者，2009-2011）并以彻底的定量，定性和统计分析进行了备份，表明我们的管道在手动描述时间的一部分中对术前和术后MRI进行了准确的分割（最高20比人更快。二维和体积测量与专家放射科医生非常吻合，我们表明RANO测量并不总是足以量化肿瘤负担。

近年来，对机器学习算法在电子商务，全渠道营销和销售行业中的应用引起了人们的兴趣。它不仅符合算法的进步，而且还代表数据可用性，代表交易，用户和背景产品信息。以不同方式查找相关的产品，即替代品和补充对于供应商网站和供应商的建议至关重要，以执行有效的分类优化。本文介绍了一种新的方法，用于根据嵌入Cleora算法的图来查找产品的替代品和补充。我们还提供有关最先进的购物者算法的实验评估，研究了建议与行业专家的调查的相关性。结论是，此处提出的新方法提供了适当的推荐产品选择，需要最少的其他信息。该算法可用于各种企业，有效地识别替代品和互补产品选项。

非破坏性测试（NDT）被广泛应用于制造和操作过程中涡轮组件的缺陷鉴定。操作效率是燃气轮机OEM（原始设备制造商）的关键。因此，在最小化所涉及的不确定性的同时，尽可能多地自动化检查过程至关重要。我们提出了一个基于视网膜的模型，以识别涡轮叶片X射线图像中的钻孔缺陷。该应用程序是由于大图分辨率而具有挑战性的，在这种分辨率上，缺陷非常小，几乎没有被常用的锚尺寸捕获，并且由于可用数据集的尺寸很小。实际上，所有这些问题在将基于深度学习的对象检测模型应用于工业缺陷数据中非常普遍。我们使用开源模型克服了此类问题，将输入图像分成图块并将其扩展，应用重型数据增强，并使用差分进化器求解器优化锚固尺寸和宽高比。我们用$ 3 $倍的交叉验证验证该模型，显示出非常高的精度，可以识别缺陷的图像。我们还定义了一组最佳实践，可以帮助其他从业者克服类似的挑战。