作为史上史上的医学历史之一，放射学，目睹了巨大的技术进步，并彻底改变了我们今天练习医学的方式。在过去的几十年中，医学成像方式产生了地震量的医疗数据。使用此数据的人工智能（AI）应用程序的开发和采用将导致放射学中的下一阶段进化。它将包括自动化诸如注释，报告 - 生成等的费力的手动任务，以及初始放射学评估案件的援助放射科学家在评估工作流程中。我们为放射学自动化进展提出了一项级别的分类，解释了每个级别的AI援助，具有相应的挑战和解决方案。我们希望这样的讨论可以帮助我们以结构化的方式解决挑战，并采取必要的步骤，以确保在放射学中顺利采用新技术。

One of the major errors affecting GNSS signals in urban canyons is GNSS multipath error. In this work, we develop a Gazebo plugin which utilizes a ray tracing technique to account for multipath effects in a virtual urban canyon environment using virtual satellites. This software plugin balances accuracy and computational complexity to run the simulation in real-time for both software-in-the-loop (SITL) and hardware-in-the-loop (HITL) testing. We also construct a 3D virtual environment of Hong Kong and compare the results from our plugin with the GNSS data in the publicly available Urban-Nav dataset, to validate the efficacy of the proposed Gazebo Plugin. The plugin is openly available to all the researchers in the robotics community. https://github.com/kpant14/multipath_sim

With the continued integration of autonomous vehicles (AVs) into public roads, a mixed traffic environment with large-scale human-driven vehicles (HVs) and AVs interactions is imminent. In challenging traffic scenarios, such as emergency braking, it is crucial to account for the reactive and uncertain behavior of HVs when developing control strategies for AVs. This paper studies the safe control of a platoon of AVs interacting with a human-driven vehicle in longitudinal car-following scenarios. We first propose the use of a model that combines a first-principles model (nominal model) with a Gaussian process (GP) learning-based component for predicting behaviors of the human-driven vehicle when it interacts with AVs. The modeling accuracy of the proposed method shows a $9\%$ reduction in root mean square error (RMSE) in predicting a HV's velocity compared to the nominal model. Exploiting the properties of this model, we design a model predictive control (MPC) strategy for a platoon of AVs to ensure a safe distance between each vehicle, as well as a (probabilistic) safety of the human-driven car following the platoon. Compared to a baseline MPC that uses only a nominal model for HVs, our method achieves better velocity-tracking performance for the autonomous vehicle platoon and more robust constraint satisfaction control for a platoon of mixed vehicles system. Simulation studies demonstrate a $4.2\%$ decrease in the control cost and an approximate $1m$ increase in the minimum distance between autonomous and human-driven vehicles to better guarantee safety in challenging traffic scenarios.

The presence of bias in deep models leads to unfair outcomes for certain demographic subgroups. Research in bias focuses primarily on facial recognition and attribute prediction with scarce emphasis on face detection. Existing studies consider face detection as binary classification into 'face' and 'non-face' classes. In this work, we investigate possible bias in the domain of face detection through facial region localization which is currently unexplored. Since facial region localization is an essential task for all face recognition pipelines, it is imperative to analyze the presence of such bias in popular deep models. Most existing face detection datasets lack suitable annotation for such analysis. Therefore, we web-curate the Fair Face Localization with Attributes (F2LA) dataset and manually annotate more than 10 attributes per face, including facial localization information. Utilizing the extensive annotations from F2LA, an experimental setup is designed to study the performance of four pre-trained face detectors. We observe (i) a high disparity in detection accuracies across gender and skin-tone, and (ii) interplay of confounding factors beyond demography. The F2LA data and associated annotations can be accessed at http://iab-rubric.org/index.php/F2LA.

This work proposes Multi-task Meta Learning (MTML), integrating two learning paradigms Multi-Task Learning (MTL) and meta learning, to bring together the best of both worlds. In particular, it focuses simultaneous learning of multiple tasks, an element of MTL and promptly adapting to new tasks with fewer data, a quality of meta learning. It is important to highlight that we focus on heterogeneous tasks, which are of distinct kind, in contrast to typically considered homogeneous tasks (e.g., if all tasks are classification or if all tasks are regression tasks). The fundamental idea is to train a multi-task model, such that when an unseen task is introduced, it can learn in fewer steps whilst offering a performance at least as good as conventional single task learning on the new task or inclusion within the MTL. By conducting various experiments, we demonstrate this paradigm on two datasets and four tasks: NYU-v2 and the taskonomy dataset for which we perform semantic segmentation, depth estimation, surface normal estimation, and edge detection. MTML achieves state-of-the-art results for most of the tasks. Although semantic segmentation suffers quantitatively, our MTML method learns to identify segmentation classes absent in the pseudo labelled ground truth of the taskonomy dataset.

小型模块化反应堆的概念改变了解决未来能源危机的前景。考虑到其较低的投资要求，模块化，设计简单性和增强的安全功能，这种新的反应堆技术非常有希望。人工智能驱动的多尺度建模（中子，热液压，燃料性能等）在小型模块化反应堆的研究中纳入了数字双胞胎和相关的不确定性。在这项工作中，进行了一项关于耐亡燃料的多尺度建模的全面研究。探索了这些燃料在轻水的小型模块化反应堆中的应用。本章还重点介绍了机器学习和人工智能在设计优化，控制和监视小型模块反应器中的应用。最后，简要评估了有关人工智能在高燃烧复合事故耐受燃料的发展中的研究差距。还讨论了实现这些差距的必要行动。

DeepFake是指量身定制和合成生成的视频，这些视频现在普遍存在并大规模传播，威胁到在线可用信息的可信度。尽管现有的数据集包含不同类型的深击，但它们的生成技术各不相同，但它们并不考虑以“系统发育”方式进展。现有的深层面孔可能与另一个脸交换。可以多次执行面部交换过程，并且可以演变出最终的深层效果，以使DeepFake检测算法混淆。此外，许多数据库不提供应用的生成模型作为目标标签。模型归因通过提供有关所使用的生成模型的信息，有助于增强检测结果的解释性。为了使研究界能够解决这些问题，本文提出了Deephy，这是一种新型的DeepFake系统发育数据集，由使用三种不同的一代技术生成的5040个DeepFake视频组成。有840个曾经交换深击的视频，2520个换两次交换深击的视频和1680个换装深击的视频。使用超过30 GB的大小，使用1,352 GB累积内存的18 GPU在1100多个小时内准备了数据库。我们还使用六种DeepFake检测算法在Deephy数据集上展示了基准。结果突出了需要发展深击模型归因的研究，并将过程推广到各种深层生成技术上。该数据库可在以下网址获得：http：//iab-rubric.org/deephy-database

我们解决了在线顺序决策的问题，即在利用当前知识以最大程度地提高绩效和探索新信息以使用多武器的强盗框架获得长期利益之间的权衡平衡。汤普森采样是选择解决这一探索探索困境的动作的启发式方法之一。我们首先提出了一个通用框架，该框架可帮助启发性地调整汤普森采样中的探索与剥削权衡取舍，并使用后部分布中的多个样本进行调整。利用此框架，我们为多臂匪徒问题提出了两种算法，并为累积遗憾提供了理论界限。接下来，我们证明了拟议算法对汤普森采样的累积遗憾表现的经验改善。我们还显示了所提出的算法在现实世界数据集上的有效性。与现有方法相反，我们的框架提供了一种机制，可以根据手头的任务改变探索/开发量。为此，我们将框架扩展到两个其他问题，即，在土匪中最佳的ARM识别和时间敏感学习，并将我们的算法与现有方法进行比较。

深度学习系统needlargedatafortraining.Datasets的面部验证系统难以获得并容易出现隐私问题。由GAN等生成模型生成的合成数据可以是一个很好的选择。但是，我们表明，甘恩产生的数据容易出现偏见和公平问题。特别是在FFHQ数据集中训练的甘斯表明，在20-29岁年龄段的年龄组中产生白脸。我们还证明，当用于微调面部验证系统时，合成面部面孔会引起不同的影响，特别是针对种族属性的影响。这是使用$ dob_ {fv} $ metric测量的，该公制定义为gar@far far for face验证的标准偏差。

面部行为分析是一个广泛的主题，具有各种类别，例如面部情绪识别，年龄和性别认识，……许多研究都集中在单个任务上，而多任务学习方法仍然开放，需要更多的研究。在本文中，我们为情感行为分析在野外竞争中的多任务学习挑战提供了解决方案和实验结果。挑战是三个任务的组合：动作单元检测，面部表达识别和偶像估计。为了应对这一挑战，我们引入了一个跨集团模块，以提高多任务学习绩效。此外，还应用面部图来捕获动作单元之间的关联。结果，我们在组织者提供的验证数据上实现了1.24的评估度量，这比0.30的基线结果要好。