Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. The objective is to maximize the predictors ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of gradient-based learning models, including both regression and classification tasks.

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney, unseen in training, using between approximately 40\% to 60\% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6\% and 10.2\% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

本报告提出了微型航空车辆（MAV）自主导航的组合最优控制和感知框架在新颖的室内封闭环境中，专门用于车载传感器数据。我们使用模拟器的特权信息来为我们的感知系统生成3D空间中的最佳航点，以便我们学会模仿。培训的基于学习的感知模块又能够单独生成类似障碍避免从传感器数据（RGB + IMU）的航点。我们展示了框架跨IGIBSON模拟环境中的新颖场景的功效。

异常气道扩张，称为牵引支气管扩张，是特发性肺纤维化（IPF）的典型特征。体积计算断层扫描（CT）成像捕获IPF中逐渐变细的丢失。我们假设气道异常的自动化量化可以提供IPF疾病程度和严重程度的估算。我们提出了一种自动化计算管道，系统地将气道树木从基于深度学习的气道分割中划分到其裂片和世代分支，从而从胸部CT获得气道结构措施。重要的是，透气阻止通过厚波传播的杂散气道分支的发生，并通过图表搜索去除气道树中的环，克服现有气道骨架算法的限制。在14名健康参与者和14名IPF患者之间比较了透气段（跨空间）和透气曲线曲线之间的逐渐变化。 IPF患者中，Airway interberering显着降低，与健康对照相比，Airway曲线曲调显着增加。差异在下叶中最大标记，符合IPF相关损伤的典型分布。透气是一种开源管道，避免了现有的气道定量算法的限制，并具有临床解释性。自动化气道测量可能具有作为IPF严重程度和疾病程度的新型成像生物标志物。

在本文中，我们使用基于视觉的图形聚合和推理（VGAI）呈现了一种感知 - 动作通信环路设计。这种多代理分散的学习 - 控制框架将原始的视觉观测映射到代理操作，并通过相邻代理之间的本地通信提供帮助。我们的框架是由圆形卷积和图形神经网络（CNN / GNN）的级联实现，寻址代理级视觉感知和特征学习，以及群级通信，本地信息聚合和代理动作推断。通过联合训练CNN和GNN，结合了解图像特征和通信消息以更好地解决特定任务。我们使用模仿学习在离线阶段训练VGAI控制器，依赖于集中式专家控制器。这导致学习的VGAI控制器可以以分布式方式部署以进行在线执行。此外，控制器展示了良好的缩放性质，在较大的团队中具有较小的团队和应用程序的培训。通过多代理植入应用程序，我们证明VGAI产生与其他分散的控制器相当或更好地使用视觉输入模态，而不访问精确的位置或运动状态信息。

由于无人驾驶航空公司（无人机）随着越来越多的应用程序变得越来越多，因此无人机中断的潜在风险增加。深度学习的最新发展允许基于视觉的柜台 - 无人机系统用单个相机检测和跟踪无人机。然而，单个摄像机的覆盖范围是有限的，需要需要多色配置以跨越电机匹配UAV - 一个称为重新识别（Reid）的问题。虽然对人和车辆REID已经进行了广泛的研究，以争取时间和观点，但据我们所知，在我们的知识中，在无人机雷德没有研究。无人机挑战重新识别：它们比行人和车辆要小得多，并且它们通常在空气中检测到，所以出现在更大范围内的角度。由于没有UAV数据集目前使用多个摄像机，因此我们提出了第一个新的UAV重新识别数据集，无人机REID，这有助于在该新兴区域开发机器学习解决方案。 UAV-REID有两个设置：临时靠近评估视图的性能，以帮助跟踪框架，并且大到小，以评估跨越规模的REID性能，并在从长途距离检测到无人机时允许早期的REID。我们通过广泛评估不同的REID骨干和损失功能来进行基准研究。我们证明，通过正确的设置，深度网络足够强大，以了解无人机的良好陈述，在时间近的环境中实现81.9％的地图，并在挑战大到小的环境下实现46.5％。此外，我们发现视觉变形金刚是最强大的尺度方差。

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training data pairs for compositing requires substantial manual effort from professionals, and is hardly scalable. Thus, with the recent advances in generative models, in this work, we propose a self-supervised framework for object compositing by leveraging the power of conditional diffusion models. Our framework can hollistically address the object compositing task in a unified model, transforming the viewpoint, geometry, color and shadow of the generated object while requiring no manual labeling. To preserve the input object's characteristics, we introduce a content adaptor that helps to maintain categorical semantics and object appearance. A data augmentation method is further adopted to improve the fidelity of the generator. Our method outperforms relevant baselines in both realism and faithfulness of the synthesized result images in a user study on various real-world images.

我们介绍了Twhin-Bert，这是一种多语言语言模型，该模型在流行的社交网络Twitter上训练了内域数据。Twhin-bert与先前的预训练的语言模型有所不同，因为它不仅接受了基于文本的自学训练，而且还具有基于Twitter异质信息网络（TWHIN）中丰富社交活动的社会目标。我们的模型接受了70亿条推文的培训，涵盖了100多种不同的语言，为简短，嘈杂，用户生成的文本提供了有价值的表示形式。我们对各种多语言社会建议和语义理解任务进行评估，并证明了对既定的预训练的语言模型的大幅改进。我们将自由开放源代码Twhin-Bert和我们为研究社区提供的精心策划标签预测和社会参与基准数据集。

我们提出Dave Aquatic Virtual Environals（Dave），这是用于水下机器人，传感器和环境的开源仿真堆栈。传统的机器人模拟器并非旨在应对海洋环境带来的独特挑战，包括但不限于在空间和时间上变化的环境条件，受损或具有挑战性的感知以及在通常未探索的环境中数据的不可用。考虑到各种传感器和平台，对于不可避免地抵制更广泛采用的特定用例，车轮通常会重新发明。在现有模拟器的基础上，我们提供了一个框架，以帮助加快算法的开发和评估，否则这些算法需要在海上需要昂贵且耗时的操作。该框架包括基本的构建块（例如，新车，水跟踪多普勒速度记录仪，基于物理的多微型声纳）以及开发工具（例如，动态测深的产卵，洋流），使用户可以专注于方法论，而不是方法。比软件基础架构。我们通过示例场景，测深数据导入，数据检查的用户界面和操纵运动计划以及可视化来演示用法。