当前对火星（新鲜）影响的库存表现出对低热惯性区域的强烈偏见。这些区域通常在视觉上明亮，影响会产生黑暗的冲浪和射线，从而使它们更易于检测。预计在较高的热惯性区域以类似的速度发生影响，但这些影响不足。这项研究调查了使用训练有素的机器学习分类器，以使用CTX数据来增加对火星新鲜影响的检测。这种方法发现了69种新的新鲜影响，这些影响已通过后续的Hirise图像得到了证实。我们发现，检查由热惯性（TI）值分区的候选物值，仅由于大量的机器学习候选物而可能有助于减少观察偏置并增加已知的高TI影响的数量。

对任何大数据集的初始分析都可以分为两个阶段：（1）识别共同趋势或模式以及（2）识别偏离这些趋势的异常或异常值。我们专注于检测具有新内容的观察结果的目标，这可以提醒我们数据集中的工件，或者可能发现以前未知现象的发现。为了帮助解释和诊断这些选定的观察的新颖方面，我们建议使用产生解释的新颖性检测方法。在大图像数据集的背景下，这些解释应突出显示给定图像的哪个方面是新的（颜色，形状，纹理，内容），以人为易懂的形式。我们提出了Demud-Vis，这是通过使用卷积神经网络（CNN）提取图像特征来提供新图像内容可视化解释的第一种方法，该方法使用重建误差来检测新内容，并转换上的跨跨网络来转换。 CNN功能表示返回图像空间。我们在来自ImageNet，淡水流和火星表面的各种图像上演示了这种方法。

无缝人体机器人互动（HRI）和合作人员（HR）批判性地依靠准确和及时的人类心理工作量（MW）模型。认知负载理论（CLT）表明代表性的物理环境产生代表性的心理过程;物理环境保护程度对应于改进的建模精度。虚拟现实（VR）系统提供能够复制复杂情景的沉重环境，特别是那些与高风险高应力场景相关的复杂情景。被动生物数据建模显示了承诺作为MW建模的非侵入性方法。然而，VR系统很少包括多模式心理生理反馈或大写在线MW建模的生物功能数据。在这里，我们开发了一种新的VR仿真管线，受到NASA多属性任务电池II（MATB-II）任务架构的启发，能够在模拟危险勘探环境中同步地收集客观性能，主观性能和被动人体生物的。我们的系统设计提取并通过机器人操作系统（ROS）提取生物斑点，促进基于心理生理学的MW模型集成到完整的端到端系统中。能够在线评估MWS的VR模拟管道可以通过使这些系统能够以响应于操作者MW自适应地改变其行为来推进人力资源系统和VR经验。

成功的视觉导航取决于捕获包含足够有用信息的图像。在这封信中，我们探索了一种数据驱动的方法来说明环境照明的变化，改善了在视觉探测器（VO）或视觉同时定位和映射（SLAM）中使用的图像质量。我们训练深层卷积神经网络模型，以预测地调整相机增益和曝光时间参数，以便连续图像包含最大数量的可匹配功能。训练过程是完全自我监督的：我们的训练信号来自基础VO或SLAM管道，因此，对模型进行了优化，可以通过该特定管道进行良好的操作。我们通过广泛的现实世界实验证明，我们的网络可以预期并补偿急剧的照明变化（例如，过渡到道路隧道的过渡），比竞争摄像机参数控制算法保持了更高数量的Inlier功能匹配。

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.

The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.