无意的行动是罕见的事件，难以精确定义，并且高度依赖于动作的时间背景。在这项工作中，我们探讨了此类行动，并试图确定视频中的观点，这些动作从故意到无意中过渡。我们提出了一个多阶段框架，该框架利用了固有的偏见，例如运动速度，运动方向和为了识别无意的行动。为了通过自我监督的训练来增强表示，我们提出了时间转变，称为时间转变，称为无意义行动固有偏见（T2IBUA）的时间转变。多阶段方法对各个帧和完整剪辑的级别进行了时间信息。这些增强的表示表现出强烈的无意行动识别任务的表现。我们对我们的框架进行了广泛的消融研究，并报告结果对最先进的结果有了显着改善。

In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management. For such specialized domains, previous research has shown that fine-tuning models stemming from broad-coverage checkpoints can largely benefit additional training rounds over large-scale in-domain resources. However, these resources are often unreachable for less-resourced languages like Italian, preventing local medical institutions to employ in-domain adaptation. In order to reduce this gap, our work investigates two accessible approaches to derive biomedical language models in languages other than English, taking Italian as a concrete use-case: one based on neural machine translation of English resources, favoring quantity over quality; the other based on a high-grade, narrow-scoped corpus natively written in Italian, thus preferring quality over quantity. Our study shows that data quantity is a harder constraint than data quality for biomedical adaptation, but the concatenation of high-quality data can improve model performance even when dealing with relatively size-limited corpora. The models published from our investigations have the potential to unlock important research opportunities for Italian hospitals and academia. Finally, the set of lessons learned from the study constitutes valuable insights towards a solution to build biomedical language models that are generalizable to other less-resourced languages and different domain settings.

时间序列分析是自然科学，社会科学和工程中的广泛任务。基本问题是发现输入时间序列的表现力且有效的计算表示，以用作执行任意下游任务的起点。在本文中，我们建立了最近的作品，该作品使用路径的签名作为特征映射，并研究基于线性随机投影来近似这些特征的计算上有效的技术。我们提出了几种理论结果，以证明我们的方法和经验验证，我们的随机预测可以有效地检索路径的底层签名。我们在多个任务中展示了所提出的随机特征的令人惊讶的性能，包括（1）使用随机签名将随机微分方程的控制和（2）映射到相应的解决方案，以及用于分类任务的时间序列表示。与相应的截断签名方法相比，我们的随机签名在高维度上更加计算效率，并且通常会导致更好的准确性和更快的培训。除了提供一个新的工具来提取签名还是进一步验证这些特征的高度表现力，我们相信我们的结果提供了几个现有的研究领域之间有趣的概念联系，这表明未来调查的新的兴趣方向。