Identifying the production dates of historical manuscripts is one of the main goals for paleographers when studying ancient documents. Automatized methods can provide paleographers with objective tools to estimate dates more accurately. Previously, statistical features have been used to date digitized historical manuscripts based on the hypothesis that handwriting styles change over periods. However, the sparse availability of such documents poses a challenge in obtaining robust systems. Hence, the research of this article explores the influence of data augmentation on the dating of historical manuscripts. Linear Support Vector Machines were trained with k-fold cross-validation on textural and grapheme-based features extracted from historical manuscripts of different collections, including the Medieval Paleographical Scale, early Aramaic manuscripts, and the Dead Sea Scrolls. Results show that training models with augmented data improve the performance of historical manuscripts dating by 1% - 3% in cumulative scores. Additionally, this indicates further enhancement possibilities by considering models specific to the features and the documents' scripts.

在本文中，我们提出了一种有效且有效的单级框架（Divergan），根据自然语言描述产生多样化，可粘性和语义一致的图像。 Divergan采用两种新颖的单词级注意模块，即通道关注模块（CAM）和像素 - 注意模块（PAM），这在允许网络允许将较大的权重分配给定句子中的每个单词的重要性与突出字，语义对齐的重要通道和像素。之后，引入了条件自适应实例层归一化（CADailn）以使语言提示嵌入的句子中的语言线索灵活地操纵形状和纹理的变化量，进一步改善视觉语义表示和帮助稳定训练。此外，开发了双剩余结构以保持更多原始的视觉功能，同时允许更深的网络，从而产生更快的收敛速度和更生动的细节。此外，我们建议将完全连接的层插入管道以解决缺乏多样性问题，因为我们观察到致密层会显着提高网络的生成能力，平衡低于之间的权衡尺寸随机潜代码有助于使用高维和文本上下文来强度特征映射的变体和调制模块。在第二个残差块之后插入线性层，实现最佳品种和质量。基准数据集的定性和定量结果都展示了我们的潜水员实现多样性的优越性，而不会损害质量和语义一致性。

标记数据可以是昂贵的任务，因为它通常由域专家手动执行。对于深度学习而言，这是繁琐的，因为它取决于大型标记的数据集。主动学习（AL）是一种范式，旨在通过仅使用二手车型认为最具信息丰富的数据来减少标签努力。在文本分类设置中，在AL上完成了很少的研究，旁边没有涉及最近的最先进的自然语言处理（NLP）模型。在这里，我们介绍了一个实证研究，可以将基于不确定性的基于不确定性的算法与Bert $ _ {base} $相比，作为使用的分类器。我们评估两个NLP分类数据集的算法：斯坦福情绪树木银行和kvk-Front页面。此外，我们探讨了旨在解决不确定性的al的预定问题的启发式;即，它是不可规范的，并且易于选择异常值。此外，我们探讨了查询池大小对al的性能的影响。虽然发现，AL的拟议启发式没有提高AL的表现;我们的结果表明，使用BERT $ _ {Base} $概率使用不确定性的AL。随着查询池大小变大，性能的这种差异可以减少。

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

High-definition (HD) map change detection is the task of determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collect the first dataset for the task, which we entitle the Trust, but Verify (TbV) dataset, by mining thousands of hours of data from over 9 months of autonomous vehicle fleet operations. We present learning-based formulations for solving the problem in the bird's eye view and ego-view. Because real map changes are infrequent and vector maps are easy to synthetically manipulate, we lean on simulated data to train our model. Perhaps surprisingly, we show that such models can generalize to real world distributions. The dataset, consisting of maps and logs collected in six North American cities, is one of the largest AV datasets to date with more than 7.8 million images. We make the data available to the public at https://www.argoverse.org/av2.html#mapchange-link, along with code and models at https://github.com/johnwlambert/tbv under the the CC BY-NC-SA 4.0 license.

We identify the task of measuring data to quantitatively characterize the composition of machine learning data and datasets. Similar to an object's height, width, and volume, data measurements quantify different attributes of data along common dimensions that support comparison. Several lines of research have proposed what we refer to as measurements, with differing terminology; we bring some of this work together, particularly in fields of computer vision and language, and build from it to motivate measuring data as a critical component of responsible AI development. Measuring data aids in systematically building and analyzing machine learning (ML) data towards specific goals and gaining better control of what modern ML systems will learn. We conclude with a discussion of the many avenues of future work, the limitations of data measurements, and how to leverage these measurement approaches in research and practice.

Deep Learning models are easily disturbed by variations in the input images that were not seen during training, resulting in unpredictable behaviours. Such Out-of-Distribution (OOD) images represent a significant challenge in the context of medical image analysis, where the range of possible abnormalities is extremely wide, including artifacts, unseen pathologies, or different imaging protocols. In this work, we evaluate various uncertainty frameworks to detect OOD inputs in the context of Multiple Sclerosis lesions segmentation. By implementing a comprehensive evaluation scheme including 14 sources of OOD of various nature and strength, we show that methods relying on the predictive uncertainty of binary segmentation models often fails in detecting outlying inputs. On the contrary, learning to segment anatomical labels alongside lesions highly improves the ability to detect OOD inputs.

Generally, microscopy image analysis in biology relies on the segmentation of individual nuclei, using a dedicated stained image, to identify individual cells. However stained nuclei have drawbacks like the need for sample preparation, and specific equipment on the microscope but most importantly, and as it is in most cases, the nuclear stain is not relevant to the biological questions of interest but is solely used for the segmentation task. In this study, we used non-stained brightfield images for nuclei segmentation with the advantage that they can be acquired on any microscope from both live or fixed samples and do not necessitate specific sample preparation. Nuclei semantic segmentation from brightfield images was obtained, on four distinct cell lines with U-Net-based architectures. We tested systematically deep pre-trained encoders to identify the best performing in combination with the different neural network architectures used. Additionally, two distinct and effective strategies were employed for instance segmentation, followed by thorough instance evaluation. We obtained effective semantic and instance segmentation of nuclei in brightfield images from standard test sets as well as from very diverse biological contexts triggered upon treatment with various small molecule inhibitor. The code used in this study was made public to allow further use by the community.

深度神经网络已成为3D医学图像自动分割的金标准方法。然而，由于缺乏对所提供的结果评估可理解的不确定性评估，他们被临床医生的全部接受仍然受到阻碍。量化其不确定性的大多数方法，例如流行的蒙特卡洛辍学物，仅限于在体素水平上预测的某种不确定性度量。除了与真正的医学不确定性无关紧要之外，这在临床上并不令人满意，因为大多数感兴趣的对象（例如，脑部病变）是由素食组成的，其整体相关性可能不会简单地减少其个人不确定性的总和或平均值。在这项工作中，我们建议使用创新的图形神经网络方法超越体素评估，并从蒙特卡洛辍学模型的输出中训练。该网络允许融合体素不确定性的三个估计量：熵，方差和模型的置信度；并且可以应用于任何病变，无论其形状或大小如何。我们证明了我们方法对多发性硬化病变的任务的不确定性估计的优势。

道路事故已成为全世界的八项主要死亡原因。这些事故中有很多是由于驾驶员的注意力不集中或由于疲劳而缺乏专注。各种因素导致驾驶员的疲劳。本文考虑了表现出驾驶员疲劳的所有可测量数据，即在车辆可测量数据中表现出的疲劳以及驾驶员的物理和生理数据。这三个主要因素中的每个因素都进一步细分为较小的细节。例如，车辆的数据由从方向盘的角度，偏航角，车道上的位置以及移动时车辆的速度和加速度获得的值组成。驾驶员疲劳检测的本体论知识和规则将集成到智能系统中，以便在检测到危险疲劳水平的第一个迹象时，将警告通知发送给驾驶员。这项工作旨在为安全的道路驾驶做出贡献。