本文提出了第二版的头部和颈部肿瘤（Hecktor）挑战的概述，作为第24届医学图像计算和计算机辅助干预（Miccai）2021的卫星活动。挑战由三个任务组成与患有头颈癌（H＆N）的患者的PET / CT图像的自动分析有关，专注于oropharynx地区。任务1是FDG-PET / CT图像中H＆N主肿瘤肿瘤体积（GTVT）的自动分割。任务2是来自同一FDG-PET / CT的进展自由生存（PFS）的自动预测。最后，任务3与任务2的任务2与参与者提供的地面真理GTVT注释相同。这些数据从六个中心收集，总共325个图像，分为224个培训和101个测试用例。通过103个注册团队和448个结果提交的重要参与，突出了对挑战的兴趣。在第一任务中获得0.7591的骰子相似度系数（DSC），分别在任务2和3中的0.7196和0.6978的一致性指数（C-Index）。在所有任务中，发现这种方法的简单性是确保泛化性能的关键。 PFS预测性能在任务2和3中的比较表明，提供GTVT轮廓对于实现最佳结果，这表明可以使用完全自动方法。这可能避免了对GTVT轮廓的需求，用于可重复和大规模的辐射瘤研究的开头途径，包括千元潜在的受试者。

Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professionals by diagnosing whether or not a patient has pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to extract relevant regions of the video and a 3D sparse coding model to represent video features. Given the difficulty in acquiring positive training videos, we trained a small-data classifier with a maximum of 15 positive and 32 negative examples. To counteract this limitation, we leveraged subject matter expert (SME) knowledge to limit the hypothesis space, thus reducing the cost of data collection. We present results using two lung ultrasound datasets and demonstrate that our model is capable of achieving performance on par with SMEs in pneumothorax identification. We then developed an iOS application that runs our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide interpretable diagnoses.

Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up.

转移学习（TL）利用以前获得的知识有效地学习新任务，并且已被用于培训具有有限数量的数据的深度学习（DL）模型。当TL应用于DL时，佩带的预押（教师）模型是微调的，以构建特定域（学生）模型。这种微调依赖于DL模型可以分解到分类器和特征提取器，并且一系列研究表明，相同的特征提取器可用于培训多个任务上的分类器。此外，最近的研究提出了多种算法，可以进行微调教师模型的特征提取器，以更有效地培训学生模型。我们注意到，无论特征提取器的微调如何，学生模型的分类器都接受了特征提取器的最终输出（即倒数第二层的输出）。然而，最近的一项研究表明，跨层中的Resnet中的特征映射可能是在功能上等同的，提高要素提取器内的特征映射的可能性也可用于训练学生模型的分类器。灵感来自这项研究，我们测试了教师模型隐藏层中的特征映射，可用于提高学生模型的准确性（即，TL的效率）。具体而言，我们开发了“自适应传输学习（ATL）”，可以选择用于TL的最佳特征映射，并在几次拍摄的学习设置中测试。我们的实证评估表明，ATL可以帮助DL模型更有效地学习，特别是当可用示例有限时。

人工智能（AI）启用的自主实验为加速科学发现提供了新的范式。非平衡材料合成是复杂，资源密集型实验的象征性，其加速将是物料发现和发展的流域。最近通过高吞吐量实验加速了非平衡合成相图的映射，但仍然限制了材料研究，因为参数空间太大而无法彻底探索。我们通过科学自主推理代理（SARA）管辖的分层自主实验，证明了加速的合成和促进亚稳材料。 SARA将机器人材料合成和表征与AI方法的层次集成，有效地揭示了处理相图的结构。 SARA设计横向梯度激光尖峰退火（LG-LSA）实验，用于平行材料合成，采用光学光谱速度迅速识别相转变。利用嵌套的主动学习（AL）周期实现了多维参数空间的高效探索，该嵌套主动学习模型包括实验的底层物理以及端到端的不确定性量化。有了这个，萨拉在多种尺度处的协调体现了复杂的科学任务的AI利用。我们通过自主映射综合映射_3 $ System的综合相位边界来展示其性能，导致幅度加速度，即建立一个合成相图，其中包括动力学稳定$ \ delta $ -bi $的条件_2 $ o $ _3 $在室温下，用于氧化固体氧化物燃料电池等电化学技术的关键开发。

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.

Restless multi-armed bandits are often used to model budget-constrained resource allocation tasks where receipt of the resource is associated with an increased probability of a favorable state transition. Prior work assumes that individual arms only benefit if they receive the resource directly. However, many allocation tasks occur within communities and can be characterized by positive externalities that allow arms to derive partial benefit when their neighbor(s) receive the resource. We thus introduce networked restless bandits, a novel multi-armed bandit setting in which arms are both restless and embedded within a directed graph. We then present Greta, a graph-aware, Whittle index-based heuristic algorithm that can be used to efficiently construct a constrained reward-maximizing action vector at each timestep. Our empirical results demonstrate that Greta outperforms comparison policies across a range of hyperparameter values and graph topologies.

Prior work has looked at applying reinforcement learning and imitation learning approaches to autonomous driving scenarios, but either the safety or the efficiency of the algorithm is compromised. With the use of control barrier functions embedded into the reinforcement learning policy, we arrive at safe policies to optimize the performance of the autonomous driving vehicle. However, control barrier functions need a good approximation of the model of the car. We use probabilistic control barrier functions as an estimate of the model uncertainty. The algorithm is implemented as an online version in the CARLA (Dosovitskiy et al., 2017) Simulator and as an offline version on a dataset extracted from the NGSIM Database. The proposed algorithm is not just a safe ramp merging algorithm but a safe autonomous driving algorithm applied to address ramp merging on highways.

Facial analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade. Many existing algorithmic audits examine the performance of these systems on later stage elements of facial analysis systems like facial recognition and age, emotion, or perceived gender prediction; however, a core component to these systems has been vastly understudied from a fairness perspective: face detection, sometimes called face localization. Since face detection is a pre-requisite step in facial analysis systems, the bias we observe in face detection will flow downstream to the other components like facial recognition and emotion prediction. Additionally, no prior work has focused on the robustness of these systems under various perturbations and corruptions, which leaves open the question of how various people are impacted by these phenomena. We present the first of its kind detailed benchmark of face detection systems, specifically examining the robustness to noise of commercial and academic models. We use both standard and recently released academic facial datasets to quantitatively analyze trends in face detection robustness. Across all the datasets and systems, we generally find that photos of individuals who are $\textit{masculine presenting}$, $\textit{older}$, of $\textit{darker skin type}$, or have $\textit{dim lighting}$ are more susceptible to errors than their counterparts in other identities.