组织病理学图像提供了癌症诊断的明确来源，其中包含病理学家用来识别和分类恶性疾病的信息，并指导治疗选择。这些图像包含大量信息，其中大部分目前不可用人类的解释。有监督的深度学习方法对于分类任务非常有力，但它们本质上受注释的成本和质量限制。因此，我们开发了组织形态表型学习，这是一种无监督的方法，它不需要注释，并且通过小图像瓷砖中的歧视性图像特征的自我发现进行操作。瓷砖分为形态上相似的簇，这些簇似乎代表了自然选择下出现的肿瘤生长的复发模式。这些簇具有不同的特征，可以使用正交方法识别。应用于肺癌组织，我们表明它们与患者的结局紧密保持一致，组织病理学识别的肿瘤类型和生长模式以及免疫表型的转录组度量。

关于使用ML模型的一个基本问题涉及其对提高决策透明度的预测的解释。尽管已经出现了几种可解释性方法，但已经确定了有关其解释可靠性的一些差距。例如，大多数方法都是不稳定的（这意味着它们在数据中提供了截然不同的解释），并且不能很好地应对无关的功能（即与标签无关的功能）。本文介绍了两种新的可解释性方法，即Varimp和Supclus，它们通过使用局部回归拟合的加权距离来克服这些问题，以考虑可变重要性。 Varimp生成了每个实例的解释，可以应用于具有更复杂关系的数据集，而Supclus解释了具有类似说明的实例集群，并且可以应用于可以找到群集的较简单数据集。我们将我们的方法与最先进的方法进行了比较，并表明它可以根据几个指标产生更好的解释，尤其是在具有无关特征的高维问题中，以及特征与目标之间的关系是非线性的。

在水下活动期间获得的图像遭受了水的环境特性，例如浊度和衰减。这些现象会导致颜色失真，模糊和对比度减少。另外，不规则的环境光分布会导致色道不平衡和具有高强度像素的区域。最近的作品与水下图像增强有关，并基于深度学习方法，解决了缺乏生成合成基地真相的配对数据集。在本文中，我们提出了一种基于深度学习的水下图像增强的自我监督学习方法，不需要配对的数据集。提出的方法估计了水下图像中存在的降解。此外，自动编码器重建此图像，并使用估计的降解信息降解其输出图像。因此，该策略在训练阶段的损失函数中用降级版本代替了输出图像。此过程\ textIt {Misleads}学会补偿其他降解的神经网络。结果，重建的图像是输入图像的增强版本。此外，该算法还提出了一个注意模块，以减少通过颜色通道不平衡和异常区域在增强图像中产生的高强度区域。此外，提出的方法不需要基本真实。此外，仅使用真实的水下图像来训练神经网络，结果表明该方法在颜色保存，颜色铸造降低和对比度改进方面的有效性。

当使用基于视觉的方法对被占用和空的空地之间的单个停车位进行分类时，人类专家通常需要注释位置，并标记包含目标停车场中收集的图像的训练集，以微调系统。我们建议研究三种注释类型（多边形，边界框和固定尺寸的正方形），提供停车位的不同数据表示。理由是阐明手工艺注释精度和模型性能之间的最佳权衡。我们还调查了在目标停车场微调预训练型号所需的带注释的停车位数。使用PKLOT数据集使用的实验表明，使用低精度注释（例如固定尺寸的正方形），可以将模型用少于1,000个标记的样品微调到目标停车场。

这项工作使用来自建设性模拟的可靠数据比较了监督的机器学习方法，以估算空袭期间发射导弹的最有效时刻。我们采用了重采样技术来改善预测模型，分析准确性，精度，召回和F1得分。的确，我们可以根据决策树以及其他算法对重采样技术的显着敏感性来确定模型的显着性能。最佳F1分数的模型的值分别为0.379和0.465，而没有重新采样技术，这一值分别增加了22.69％。因此，如果理想，重新采样技术可以改善模型的召回率和F1得分，而准确性和精确度略有下降。因此，通过通过建设性模拟获得的数据，可以根据机器学习模型开发决策支持工具，从而可以提高BVR空中战斗的飞行质量，从而提高进攻任务的有效性以达到特定目标。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

The ability to convert reciprocating, i.e., alternating, actuation into rotary motion using linkages is hindered fundamentally by their poor torque transmission capability around kinematic singularity configurations. Here, we harness the elastic potential energy of a linear spring attached to the coupler link of four-bar mechanisms to manipulate force transmission around the kinematic singularities. We developed a theoretical model to explore the parameter space for proper force transmission in slider-crank and rocker-crank four-bar kinematics. Finally, we verified the proposed model and methodology by building and testing a macro-scale prototype of a slider-crank mechanism. We expect this approach to enable the development of small-scale rotary engines and robotic devices with closed kinematic chains dealing with serial kinematic singularities, such as linkages and parallel manipulators.

This paper considers a combination of actuation tendons and measurement strings to achieve accurate shape sensing and direct kinematics of continuum robots. Assuming general string routing, a methodical Lie group formulation for the shape sensing of these robots is presented. The shape kinematics is expressed using arc-length-dependent curvature distributions parameterized by modal functions, and the Magnus expansion for Lie group integration is used to express the shape as a product of exponentials. The tendon and string length kinematic constraints are solved for the modal coefficients and the configuration space and body Jacobian are derived. The noise amplification index for the shape reconstruction problem is defined and used for optimizing the string/tendon routing paths, and a planar simulation study shows the minimal number of strings/tendons needed for accurate shape reconstruction. A torsionally stiff continuum segment is used for experimental evaluation, demonstrating mean (maximal) end-effector absolute position error of less than 2% (5%) of total length. Finally, a simulation study of a torsionally compliant segment demonstrates the approach for general deflections and string routings. We believe that the methods of this paper can benefit the design process, sensing and control of continuum and soft robots.

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.