提供有关学习者论证的反馈对于发展批判性思维技能至关重要，但是，它需要大量的时间和精力。为了减轻教师的过载，我们旨在自动化提供反馈的过程，尤其是给出诊断评论，以指出论点固有的弱点。建议给出特定的诊断评论，以便学习者可以识别诊断而不会误解。但是，如何制定提供特定的诊断评论的任务并不明显。我们将任务的表述作为模板选择和插槽填充，以使自动评估变得更加容易，并且模型的行为更加可行。该公式的关键是创建足以实用的模板集的可能性。在本文中，我们定义了三个标准，即模板集应满足：表达性，信息性和唯一性，并验证创建一个满足这些标准作为第一个试验的模板集的可行性。我们将通过一项注释研究证明，将文本中给出的诊断评论转换为模板格式是可行的。注释研究中使用的语料库公开可用。

Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions. For instance, OT is a popular loss function that quantifies the discrepancy between an empirical distribution and a parametric model. Recently, an entropic penalty term and the celebrated Sinkhorn algorithm have been commonly used to approximate the original OT in a computationally efficient way. However, since the Sinkhorn algorithm runs a projection associated with the Kullback-Leibler divergence, it is often vulnerable to outliers. To overcome this problem, we propose regularizing OT with the \beta-potential term associated with the so-called $\beta$-divergence, which was developed in robust statistics. Our theoretical analysis reveals that the $\beta$-potential can prevent the mass from being transported to outliers. We experimentally demonstrate that the transport matrix computed with our algorithm helps estimate a probability distribution robustly even in the presence of outliers. In addition, our proposed method can successfully detect outliers from a contaminated dataset

Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not been tested on common benchmarks that quantify progress in the field. Towards applications on resource-constrained devices, it is important to develop optical flow algorithms that are fast, light-weight and accurate. This work leverages insights from neuroscience, and proposes a novel optical flow estimation scheme based on triplet matching. The experiments on publicly available benchmarks demonstrate its capability to handle complex scenes with comparable results as prior packet-based algorithms. In addition, the proposed method achieves the fastest execution time (> 10 kHz) on standard CPUs as it requires only three events in estimation. We hope that our research opens the door to real-time, incremental motion estimation methods and applications in real-world scenarios.

Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state-of-the-art accuracy on motion estimation using events, may suffer from an overfitting problem called event collapse. Prior works are computationally expensive or cannot alleviate the overfitting, which undermines the benefits of the CMax framework. We propose a novel, computationally efficient regularizer based on geometric principles to mitigate event collapse. The experiments show that the proposed regularizer achieves state-of-the-art accuracy results, while its reduced computational complexity makes it two to four times faster than previous approaches. To the best of our knowledge, our regularizer is the only effective solution for event collapse without trading off runtime. We hope our work opens the door for future applications that unlocks the advantages of event cameras.

Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the graph streams, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (METR-LA and PEMS-BAY) and a large-scale spatio-temporal dataset that contains a variaty of non-stationary phenomena. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle locations and time slots with different patterns and be robustly adaptive to different anomalous situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.

Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traffic stream, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (METR-LA and PEMS-BAY) and a new large-scale traffic speed dataset in which traffic incident information is contained. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle the road links and time slots with different patterns and be robustly adaptive to any anomalous traffic situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.

事件摄像机对场景动态做出响应，并提供了估计运动的优势。遵循最近基于图像的深度学习成就，事件摄像机的光流估计方法急于将基于图像的方法与事件数据相结合。但是，由于它们具有截然不同的属性，因此需要几个改编（数据转换，损失功能等）。我们开发了一种原则性的方法来扩展对比度最大化框架以估算仅事件的光流。我们研究关键要素：如何设计目标函数以防止过度拟合，如何扭曲事件以更好地处理遮挡，以及如何改善与多规模原始事件的收敛性。有了这些关键要素，我们的方法在MVSEC基准的无监督方法中排名第一，并且在DSEC基准上具有竞争力。此外，我们的方法使我们能够在这些基准测试中揭露地面真相流的问题，并在将其转移到无监督的学习环境中时会产生出色的结果。我们的代码可在https://github.com/tub-rip/event_based_optility_flow上找到

上下文最大化（CMAX）是一个框架，可在几个基于事件的计算机视觉任务（例如自我移动或光流估计）上提供最新结果。但是，它可能会遇到一个称为事件崩溃的问题，这是一种不希望的解决方案，其中事件被扭曲成太少的像素。由于先前的工作在很大程度上忽略了这个问题或提议的解决方法，因此必须详细分析这种现象。我们的工作证明了事件以最简单的形式崩溃，并通过使用基于差异几何和物理学的时空变形的第一原理提出了崩溃指标。我们通过实验表明，公开可用的数据集表明，拟议的指标减轻了事件崩溃，并且不会损害良好的扭曲。据我们所知，与其他方法相比，基于提议的指标的正规化器是唯一有效的解决方案，可以防止在考虑的实验环境中发生事件崩溃。我们希望这项工作激发了进一步的研究，以应对更复杂的翘曲模型。

时间序列数据通常仅在观察过程中的中断时仅在有限的时间范围内获得。为了对这样的部分时间序列进行分类，我们需要考虑1）从2）不同时间戳绘制的可变长度数据。为了解决第一个问题，现有的卷积神经网络在卷积层之后使用全球池取消长度差异。这种体系结构遭受了将整个时间相关性纳入长数据和避免用于简短数据的功能崩溃之间的权衡。为了解决这种权衡，我们提出了自适应多尺度合并，该池从自适应数量的层中汇总了功能，即仅用于简短数据的前几层和更多的长数据层。此外，为了解决第二个问题，我们引入了时间编码，将观察时间戳嵌入中间特征中。我们的私有数据集和UCR/UEA时间序列档案中的实验表明，我们的模块提高了分类精度，尤其是在部分时间序列获得的短数据上。

最近的文本到语音（TTS）的质量与人类的质量相当。但是，其在口语对话中的应用尚未得到广泛研究。这项研究旨在实现与人类对话非常相似的TT。首先，我们记录并抄录实际自发对话。然后，提出的对话TTS分为两个阶段：第一阶段，各种自动编码器（VAE） - VITS或高斯混合物变化自动编码器（GMVAE） - 培训了训练，从端到端文本对语音（VIT），最近提出的端到端TTS模型。从语音中提取潜在的口语表示的样式编码器与TTS共同培训。在第二阶段，对风格预测指标进行了训练，以预测从对话历史中综合的说话风格。在推断期间，通过将样式预测器预测的语言样式表示为VAE/gmvae-vits，可以以适合对话背景的样式合成语音。主观评估结果表明，所提出的方法在对话级别的自然性方面优于原始VIT。