最近在组合问题中寻找多样化的解决方案，最近受到了相当大的关注（Baste等人2020; Fomin等人2020; Hanaka等。2021）。在本文中，我们研究了以下类型的问题：给出了整数$ k $，问题询问了$ k $解决方案，使得这些解决方案之间的成对和汉明距离的总和最大化。这种解决方案称为各种解决方案。我们介绍了一种用于查找加权定向图中的多样性最短$ ST $ -Paths的多项式时间算法。此外，我们研究了其他经典组合问题的多样化版本，如不同的加权麦芽碱，不同加权树丛和多样化的双链匹配。我们表明这些问题也可以在多项式时间内解决。为了评估我们寻找多样性最短$ ST $ ST -Paths的算法的实际表现，我们进行了合成和现实世界的计算实验。实验表明，我们的算法在合理的计算时间内成功计算了各种解决方案。

The task of image segmentation is to classify each pixel in the image based on the appropriate label. Various deep learning approaches have been proposed for image segmentation that offers high accuracy and deep architecture. However, the deep learning technique uses a pixel-wise loss function for the training process. Using pixel-wise loss neglected the pixel neighbor relationships in the network learning process. The neighboring relationship of the pixels is essential information in the image. Utilizing neighboring pixel information provides an advantage over using only pixel-to-pixel information. This study presents regularizers to give the pixel neighbor relationship information to the learning process. The regularizers are constructed by the graph theory approach and topology approach: By graph theory approach, graph Laplacian is used to utilize the smoothness of segmented images based on output images and ground-truth images. By topology approach, Euler characteristic is used to identify and minimize the number of isolated objects on segmented images. Experiments show that our scheme successfully captures pixel neighbor relations and improves the performance of the convolutional neural network better than the baseline without a regularization term.

The deep learning technique was used to increase the performance of single image super-resolution (SISR). However, most existing CNN-based SISR approaches primarily focus on establishing deeper or larger networks to extract more significant high-level features. Usually, the pixel-level loss between the target high-resolution image and the estimated image is used, but the neighbor relations between pixels in the image are seldom used. On the other hand, according to observations, a pixel's neighbor relationship contains rich information about the spatial structure, local context, and structural knowledge. Based on this fact, in this paper, we utilize pixel's neighbor relationships in a different perspective, and we propose the differences of neighboring pixels to regularize the CNN by constructing a graph from the estimated image and the ground-truth image. The proposed method outperforms the state-of-the-art methods in terms of quantitative and qualitative evaluation of the benchmark datasets. Keywords: Super-resolution, Convolutional Neural Networks, Deep Learning

Learning-from-Observation (LfO) is a robot teaching framework for programming operations through few-shots human demonstration. While most previous LfO systems run with visual demonstration, recent research on robot teaching has shown the effectiveness of verbal instruction in making recognition robust and teaching interactive. To the best of our knowledge, however, few solutions have been proposed for LfO that utilizes verbal instruction, namely multimodal LfO. This paper aims to propose a practical pipeline for multimodal LfO. For input, an user temporally stops hand movements to match the granularity of human instructions with the granularity of robot execution. The pipeline recognizes tasks based on step-by-step verbal instructions accompanied by demonstrations. In addition, the recognition is made robust through interactions with the user. We test the pipeline on a real robot and show that the user can successfully teach multiple operations from multimodal demonstrations. The results suggest the utility of the proposed pipeline for multimodal LfO.

Robot developers develop various types of robots for satisfying users' various demands. Users' demands are related to their backgrounds and robots suitable for users may vary. If a certain developer would offer a robot that is different from the usual to a user, the robot-specific software has to be changed. On the other hand, robot-software developers would like to reuse their developed software as much as possible to reduce their efforts. We propose the system design considering hardware-level reusability. For this purpose, we begin with the learning-from-observation framework. This framework represents a target task in robot-agnostic representation, and thus the represented task description can be shared with various robots. When executing the task, it is necessary to convert the robot-agnostic description into commands of a target robot. To increase the reusability, first, we implement the skill library, robot motion primitives, only considering a robot hand and we regarded that a robot was just a carrier to move the hand on the target trajectory. The skill library is reusable if we would like to the same robot hand. Second, we employ the generic IK solver to quickly swap a robot. We verify the hardware-level reusability by applying two task descriptions to two different robots, Nextage and Fetch.

本文提出了一种新型的极化传感器结构和网络结构，以获得高质量的RGB图像和极化信息。常规的极化传感器可以同时获取RGB图像和极化信息，但是传感器上的极化器会降低RGB图像的质量。 RGB图像的质量与极化信息之间存在权衡，因为较少的极化像素减少了RGB图像的降解，但减少了极化信息的分辨率。因此，我们提出了一种方法，该方法通过在传感器上稀疏排列极化像素来解决权衡，并使用RGB图像作为指导来补偿以更高分辨率的低分辨率极化信息。我们提出的网络体系结构由RGB图像改进网络和两极分化信息补偿网络组成。我们通过将其性能与最先进的方法进行比较，确认了我们提出的网络在补偿极化强度的差异成分方面的优势：深度完成。此外，我们确认我们的方法可以同时获得更高质量的RGB图像和极化信息，而不是传统的极化传感器，从而解决了RGB图像质量和极化信息之间的权衡。基线代码以及新生成的真实和合成的大规模极化图像数据集可用于进一步的研究和开发。

机器学习（ML）与高能物理学（HEP）的快速发展的交集给我们的社区带来了机会和挑战。远远超出了标准ML工具在HEP问题上的应用，这两个领域的一代人才素养正在开发真正的新的和潜在的革命性方法。迫切需要支持跨学科社区推动这些发展的需求，包括在这两个领域的交汇处为专门研究提供资金，在大学投资高性能计算以及调整分配政策以支持这项工作，开发社区工具和标准，并为年轻研究人员提供教育和职业道路，从而吸引了机器学习的智力活力，以吸引高能量物理学。

我们提出了一个名为“ Visual配方流”的新的多模式数据集，使我们能够学习每个烹饪动作的结果。数据集由对象状态变化和配方文本的工作流程组成。状态变化表示为图像对，而工作流则表示为食谱流图（R-FG）。图像对接地在R-FG中，该R-FG提供了交叉模式关系。使用我们的数据集，可以尝试从多模式常识推理和程序文本生成来尝试一系列应用程序。

本文提出了一种通过视觉解释3D卷积神经网络（CNN）的决策过程的方法，并具有闭塞灵敏度分析的时间扩展。这里的关键思想是在输入3D时间空间数据空间中通过3D掩码遮住特定的数据，然后测量输出评分中的变更程度。产生较大变化程度的遮挡体积数据被认为是分类的更关键元素。但是，虽然通常使用遮挡敏感性分析来分析单个图像分类，但将此想法应用于视频分类并不是那么简单，因为简单的固定核心无法处理动作。为此，我们将3D遮挡掩模的形状调整为目标对象的复杂运动。通过考虑从输入视频数据中提取的光流的时间连续性和空间共存在，我们的灵活面膜适应性进行了。我们进一步建议通过使用分数的一阶部分导数相对于输入图像来降低其计算成本，以近似我们的方法。我们通过与删除/插入度量的常规方法和UCF-101上的指向度量来证明我们方法的有效性。该代码可在以下网址获得：https：//github.com/uchiyama33/aosa。

鉴于HEP研究的核心，数据科学（DS）和机器学习（ML）在高能量物理学（HEP）中的作用增长良好和相关。此外，利用物理数据固有的对称性激发了物理信息的ML作为计算机科学研究的充满活力的子场。 HEP研究人员从广泛使用的材料中受益匪浅，可用于教育，培训和劳动力开发。他们还为这些材料做出了贡献，并为DS/ML相关的字段提供软件。物理部门越来越多地在DS，ML和物理学的交集上提供课程，通常使用HEP研究人员开发的课程，并涉及HEP中使用的开放软件和数据。在这份白皮书中，我们探讨了HEP研究与DS/ML教育之间的协同作用，讨论了此交叉路口的机会和挑战，并提出了将是互惠互利的社区活动。