我们在数字世界中采取的每一步都会落后于我们行为的记录;数字足迹。研究表明，算法可以将这些数字足迹转化为精确的心理特征估计，包括人格特质，心理健康或情报。然而，AI产生这些见解的机制通常保持不透明。在本文中，我们展示了如何解释AI（XAI）可以帮助域专家和数据主体验证，问题和改进分类数字足迹的心理特征的模型。我们在来自金融交易数据的大五个人格预测（特征和方面）的范围内，详细说明了两个流行的XAI方法（规则提取和反事实解释）（n = 6,408）。首先，我们展示了全球规则提取在模型中标识的消费模式中如何阐明了最重要的人格，并讨论这些规则如何用于解释，验证和改进模型。其次，我们实施当地规则提取，以表明，由于其独特的财务行为，个人分配给个性课程，并且模型的预测信心与促进预测的特征数量之间存在积极的联系。我们的实验突出了全球和本地XAI方法的重要性。通过更好地了解预测模型如何工作，以及他们如何获得特定人的结果，Xai促进了一个世界的问责制，其中AI影响了世界各地数十亿人的生命。

准确的术中诊断对于在脑肿瘤手术期间提供安全有效的护理至关重要。我们的护理标准诊断方法是时间，资源和劳动密集型，限制了获得最佳手术治疗的机会。为了解决这些局限性，我们提出了一种替代工作流程，该工作流程结合了刺激的拉曼组织学（SRH），一种快速的光学成像方法，以及对SRH图像的深层自动解释，用于术中脑肿瘤诊断和实时手术决策支持。在这里，我们介绍了OpenSRH，这是来自300多名脑肿瘤患者和1300多个独特全幻灯片光学图像的第一个公共数据集。 OPENSRH包含来自最常见的脑肿瘤诊断，完整的病理注释，整个幻灯片肿瘤分割，原始和加工的光学成像数据的数据，用于端到端模型的开发和验证。我们为使用弱（即患者级）诊断标签的基于补丁的整个幻灯片分类和推断提供了一个框架。最后，我们基准了两项计算机视觉任务：多类组织学脑肿瘤分类和基于斑块的对比表示学习。我们希望OpenSRH能够促进快速光学成像和基于ML的手术决策支持的临床翻译，以提高精密医学时代的癌症手术的获取，安全性和功效。数据集访问，代码和基准可在opensrh.mlins.org上找到。

社交媒体越来越多地用于大规模的人口预测，例如估计社区健康统计数据。但是，社交媒体用户通常不是预期人群的代表性样本 - “选择偏见”。在社会科学中，这种偏见通常是通过约束技术解决的，在这种偏见的情况下，根据其社会人口统计学群体的不足或过度采样，将观察结果重新恢复。然而，很少评估约束性以改善预测。在这项两部分的研究中，我们首先评估了标准“现成”的限制技术，发现它们在四个从Twitter中介绍美国县人口健康统计数据的四个任务中没有提供任何改进，甚至通常会退化预测准确性。降级表现的核心原因似乎与他们对每个人群社会人口统计学的稀疏或缩减估计的依赖有关。在研究的第二部分中，我们开发和评估了强大的阶段化后，该方法包括解决这些问题的三种方法：（1）估算器重新分布以说明缩小的缩小，以及（2）自适应式嵌套和（3）告知平滑为处理稀疏的社会人口统计学估计。我们表明，这些方法中的每一种都会导致预测准确性比标准限制方法显着改善。综上所述，强大的后阶段能够实现最先进的预测准确性，在调查的生活满意度的情况下，解释的方差（R^2）增加了53.0％，所有任务的平均平均值增加了17.8％。

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 purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.

The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view Mo- Cap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motiondriven animation accessible to the general public. However, performance of existing HMR methods degrade when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action,and show that NeMo achieves better 3D reconstruction compared to various baselines.

Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.