高保真，基于AI的模拟课堂系统使教师能够排练有效的教学策略。但是，对话导向的开放式对话，例如教学关于规模因素的教学可能难以模仿。本文建立了一个基于文本的互动会话代理，以帮助教师根据着名的教学质量评估来练习数学质疑技能。我们采取了一种以人为本的设计来设计我们的系统，依靠深度学习，不确定量化和自然语言处理的进步，同时承认对会话代理的局限性进行特定的教学需求。在模拟期间直接使用专家输入，我们展示了如何实现谈话成功率和高用户满意度。

大型研究展示了教师质疑策略如何改善学生学习结果。然而，开发新方案是挑战，因为缺乏特定情景的培训数据以及与标签相关的成本。本文介绍了基于AI的高保真度，级教室模拟器，帮助教师排练基于研究的数学质疑技巧。使用人类循环方法，我们收集了一个高质量的训练数据集，用于数学质疑方案。利用最近的不确定性量化的进步，我们评估了我们的可用性的会话代理，并分析了纳入人类循环方法进行数据收集和系统评估的实用性，以获得数学质疑场景。

Humans are spectacular reinforcement learners, constantly learning from and adjusting to experience and feedback. Unfortunately, this doesn't necessarily mean humans are fast learners. When tasks are challenging, learning can become unacceptably slow. Fortunately, humans do not have to learn tabula rasa, and learning speed can be greatly increased with learning aids. In this work we validate a new type of learning aid -- reward shaping for humans via inverse reinforcement learning (IRL). The goal of this aid is to increase the speed with which humans can learn good policies for specific tasks. Furthermore this approach compliments alternative machine learning techniques such as safety features that try to prevent individuals from making poor decisions. To achieve our results we first extend a well known IRL algorithm via kernel methods. Afterwards we conduct two human subjects experiments using an online game where players have limited time to learn a good policy. We show with statistical significance that players who receive our learning aid are able to approach desired policies more quickly than the control group.

Heteroscedastic regression models a Gaussian variable's mean and variance as a function of covariates. Parametric methods that employ neural networks for these parameter maps can capture complex relationships in the data. Yet, optimizing network parameters via log likelihood gradients can yield suboptimal mean and uncalibrated variance estimates. Current solutions side-step this optimization problem with surrogate objectives or Bayesian treatments. Instead, we make two simple modifications to optimization. Notably, their combination produces a heteroscedastic model with mean estimates that are provably as accurate as those from its homoscedastic counterpart (i.e.~fitting the mean under squared error loss). For a wide variety of network and task complexities, we find that mean estimates from existing heteroscedastic solutions can be significantly less accurate than those from an equivalently expressive mean-only model. Our approach provably retains the accuracy of an equally flexible mean-only model while also offering best-in-class variance calibration. Lastly, we show how to leverage our method to recover the underlying heteroscedastic noise variance.

The GLOM architecture proposed by Hinton [2021] is a recurrent neural network for parsing an image into a hierarchy of wholes and parts. When a part is ambiguous, GLOM assumes that the ambiguity can be resolved by allowing the part to make multi-modal predictions for the pose and identity of the whole to which it belongs and then using attention to similar predictions coming from other possibly ambiguous parts to settle on a common mode that is predicted by several different parts. In this study, we describe a highly simplified version of GLOM that allows us to assess the effectiveness of this way of dealing with ambiguity. Our results show that, with supervised training, GLOM is able to successfully form islands of very similar embedding vectors for all of the locations occupied by the same object and it is also robust to strong noise injections in the input and to out-of-distribution input transformations.

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.

神经肌肉疾病，例如脊柱肌肉萎缩（SMA）和Duchenne肌肉营养不良症（DMD），导致6,000名儿童中有1例的渐进性肌肉变性和运动功能丧失。传统的上肢运动功能评估不能定量测量患者的性能，这使得很难跟踪进度的增量变化。评估神经肌肉疾病儿童的运动功能特别具有挑战性，因为他们在实验过程中可能会紧张或兴奋，或者简直太年轻而无法遵循精确的说明。这些挑战转化为混杂因素，例如执行臂卷曲的不同部分较慢或更快（相位变异性），从而影响评估的运动质量。本文使用曲线注册和形状分析来暂时对齐轨迹，同时提取平均参考形状。距这种平均形状的距离用于评估运动质量。所提出的指标是混杂因素（例如相位变异性）的不变性，同时提出了几种临床相关的见解。首先，控制和患者人群的功能分数在统计上存在显着差异（p $ = $ 0.0213 $ \ le $ 0.05）。接下来，患者队列中的几名患者能够与健康队列进行运动，反之亦然。我们的指标是根据可穿戴设备计算的，与Brooke的分数有关（（P $ = $ 0.00063 $ \ le $ $ 0.05））以及基于功能测定法的电动机功能评估（（P $ = $ = $ 0.0006 $ \ le $ 0.05）） 。这些结果表明了日常生活中无处不在的运动质量评估的希望。

近年来，深度学习算法在地球观察（EO）中的应用使依赖远程感知数据的领域取得了重大进展。但是，鉴于EO中的数据量表，创建具有专家使用像素级注释的大型数据集是昂贵且耗时的。在这种情况下，先验被视为一种有吸引力的方法，可以减轻在训练EO的深度学习方法时手动标签的负担。对于某些应用，这些先验很容易获得。本研究以许多计算机视觉任务中的自我监督特征表示学习的对比学习方法取得了巨大成功的动机，本研究提出了一种使用作物标签比例的在线深度聚类方法，作为研究基于政府作物的样本级别的先验者 - 整个农业地区的比例数据。我们使用来自巴西两个不同农业地区的两个大数据集评估了该方法。广泛的实验表明，该方法对不同的数据类型（合成句子雷达和光学图像）具有鲁棒性，考虑到目标区域中主要的作物类型，报告了更高的精度值。因此，它可以减轻EO应用中大规模图像注释的负担。

美国和全球的两个主要死亡原因是中风和心肌梗塞。两者的根本原因是由破裂或侵蚀的不稳定的动脉粥样硬化斑块释放的，这些斑块阻塞了心脏（心肌梗塞）或大脑（中风）的血管。临床研究表明，在斑块破裂或侵蚀事件中，斑块组成比病变大小更重要。为了确定斑块组成，计算了3D心血管免疫荧光图像的各种细胞类型的斑块病变。但是，手动计算这些细胞是昂贵的，耗时的，并且容易发生人为错误。手动计数的这些挑战激发了对自动化方法进行定位和计算图像中细胞的需求。这项研究的目的是开发一种自动方法，以最少的注释工作在3D免疫荧光图像中准确检测和计数细胞。在这项研究中，我们使用弱监督的学习方法使用点注释来训练悬停网络分割模型，以检测荧光图像中的核。使用点注释的优点是，与像素的注释相比，它们需要更少的精力。为了使用点注释训练悬停的网络模型，我们采用了一种普遍使用的群集标记方法，将点注释转换为精确的细胞核二进制掩模。传统上，这些方法从点注释产生了二进制面具，使该物体周围的区域未标记（通常在模型训练中被忽略）。但是，这些区域可能包含重要信息，有助于确定细胞之间的边界。因此，我们在这些区域使用了熵最小化的损失函数，以鼓励模型在未标记区域上输出更自信的预测。我们的比较研究表明，使用我们的弱训练的悬停网络模型...

我们提出了一种用于超声心动图视频的新型异常检测方法。引入的方法利用心脏周期的周期性来学习各种潜在轨迹模型（TVAE）的不同变体。对这些模型进行了对婴儿超声心动图视频内部数据集的健康样本的培训，这些数据集由多个室内视图组成，以了解健康人群的规范性。在推断期间，最大值基于后验（MAP）的异常检测以检测我们数据集中的分布样品。所提出的方法可靠地识别出严重的先天性心脏缺陷，例如Ebstein的异常或Shonecomplex。此外，它在检测肺动脉高压和右心室扩张的任务方面，通过标准变异自动编码器实现了优于基于地图的异常检测。最后，我们证明了所提出的方法通过热图提供了对其输出的可解释解释，该图突出了与异常心脏结构相对应的区域。