自动基于图像的疾病严重程度估计通常使用离散（即量化）严重性标签。由于图像含糊不清，因此通常很难注释离散标签。一个更容易的替代方法是使用相对注释，该注释比较图像对之间的严重程度。通过使用带有相对注释的学习对框架，我们可以训练一个神经网络，该神经网络估计与严重程度相关的等级分数。但是，所有可能对的相对注释都是过敏的，因此，适当的样品对选择是强制性的。本文提出了深层贝叶斯的主动学习与级别，该级别训练贝叶斯卷积神经网络，同时自动选择合适的对进行相对注释。我们通过对溃疡性结肠炎的内窥镜图像进行实验证实了该方法的效率。此外，我们确认我们的方法即使在严重的类失衡中也很有用，因为它可以自动从次要类中选择样本。

本文提出了一种用于内窥镜仿真系统中的可视化的现实图像生成方法。在许多医院进行内窥镜诊断和治疗。为了减少与内窥镜插入相关的并发症，内窥镜仿真系统用于内窥镜插入的训练或排练。然而，电流模拟系统产生非现实的虚拟内窥镜图像。为了提高仿真系统的值，需要提高其生成的图像的现实。我们提出了一种用于内窥镜仿真系统的现实图像生成方法。通过使用来自患者的CT体积的体积渲染方法来生成虚拟内窥镜图像。我们使用虚拟到实图像域翻译技术改善虚拟内窥镜图像的现实。图像域转换器实现为完全卷积网络（FCN）。我们通过最小化循环一致性损失函数来训练FCN。使用未配对的虚拟和真实内窥镜图像训练FCN。为了获得高质量的图像域翻译结果，我们执行将图像清理到真实内窥镜图像集。我们测试了使用浅U-Net，U-Net，Deep U-Net和U-Net作为图像域转换器的剩余单元。具有剩余单位的深U-Net和U-Net产生了非常现实的图像。

我们通过使用多尺度边缘损耗，通过使用域改性和深度估计来提出一种从单次单眼镜片图像中的深度估计方法。我们采用了两步估计过程，包括来自未配对数据和深度估计的兰伯语表面平移。器官表面上的纹理和镜面反射降低了深度估计的准确性。我们将Lambertian表面翻译应用于内窥镜图像以消除这些纹理和反射。然后，我们通过使用完全卷积网络（FCN）来估计深度。在FCN的训练期间，改善估计图像和地面真理深度图像之间的对象边缘相似性对于获得更好的结果是重要的。我们介绍了一个Muti-Scale边缘损耗功能，以提高深度估计的准确性。我们定量评估了使用真实的结肠镜片图像的所提出的方法。估计的深度值与真实深度值成比例。此外，我们将估计的深度图像应用于使用卷积神经网络自动解剖学位置识别的结肠镜图像。通过使用估计的深度图像，网络的识别精度从69.2％提高到74.1％。

溃疡性结肠炎（UC）分类，是内窥镜诊断的重要任务，涉及两个主要困难。首先，具有关于UC（正或负）注释的内窥镜图像通常是有限的。其次，由于冒号中的位置，它们在外观上显示出大的变化。特别是，第二个困难阻止了我们使用现有的半监督学习技术，这是第一个难度的常见补救措施。在本文中，我们通过新利用两个附加特征，提出了一种用于UC分类的实际半监督学习方法，结肠中的位置（例如，左冒号）和图像捕获顺序，两者通常都附加到内窥镜中的各个图像图像序列。该方法可以通过与这些功能有效地提取UC分类的基本信息。实验结果表明，所提出的方法在分类任务中优于若干现有的半监督学习方法，即使具有少量注释的图像。

When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.

Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. One representative example of such tasks is text-based games, where players need to make decisions based on both description text previously shown in the game, and their own background knowledge about the language and common sense. In this work, we investigate not simply giving common sense, as can be seen in prior research, but also its effective usage. We assume that a part of the environment states different from common sense should constitute one of the grounds for action selection. We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder. DiffG-RL also contains a framework for extracting the appropriate amount and representation of common sense from the source to support the construction of the graph. We validate DiffG-RL in experiments with text-based games that require common sense and show that it outperforms baselines by 17% of scores. The code is available at https://github.com/ibm/diffg-rl

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

本文档描述了Spotify出于学术研究目的发布的葡萄牙语播客数据集。我们概述了如何采样数据，有关集合的一些基本统计数据，以及有关巴西和葡萄牙方言的分发信息的简要信息。

深度神经网络（DNN）众所周知，很容易受到对抗例子的影响（AES）。此外，AE具有对抗性可传递性，这意味着为源模型生成的AE可以以非平凡的概率欺骗另一个黑框模型（目标模型）。在本文中，我们首次研究了包括Convmixer在内的模型之间的对抗性转移性的属性。为了客观地验证可转让性的属性，使用称为AutoAttack的基准攻击方法评估模型的鲁棒性。在图像分类实验中，Convmixer被确认对对抗性转移性较弱。

场景中光的极化信息对于各种图像处理和计算机视觉任务很有价值。平面偏光仪是一种有前途的方法，可以一次性地捕获不同方向的极化图像，而它需要颜色极化的表现。在本文中，我们提出了一个两步的颜色偏振化学网络〜（TCPDNET），该网络由两个颜色的表演和极化演示组成。我们还引入了YCBCR颜色空间中的重建损失，以提高TCPDNET的性能。实验比较表明，TCPDNET在极化图像的图像质量和Stokes参数的准确性方面优于现有方法。