在本研究中，我们提出了一种基于病例的新型图像检索（SIR）方法，用于苏木精和曙红（H＆E）染色的恶性淋巴瘤的组织病理学图像。当将整个幻灯片图像（WSI）用作输入查询时，希望能够通过重点关注病理上重要区域（例如肿瘤细胞）中的图像斑块来检索相似情况。为了解决这个问题，我们采用了基于注意力的多个实例学习，这使我们能够在计算案例之间的相似性时专注于肿瘤特异性区域。此外，我们采用对比度距离度量学习将免疫组织化学（IHC）染色模式纳入有用的监督信息，以定义异质性恶性淋巴瘤病例之间的适当相似性。在对249例恶性淋巴瘤患者的实验中，我们证实该方法比基线基于病例的SIR方法表现出更高的评估措施。此外，病理学家的主观评估表明，我们使用IHC染色模式的相似性度量适用于代表恶性淋巴瘤H＆E染色组织图像的相似性。

Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example of reinforcement learning. However, the bandit problem deals with a static environment where the agent's action does not influence the reward probabilities. This study aims to extend the conventional approach to a more general multi-agent reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of agents' actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, discontinuous bandit Q-learning, in view of a potential photonic implementation. Here, state-action pairs in the environment are regarded as slot machines in the context of the bandit problem and an updated amount of Q-value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents. We demonstrate that multi-agent reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.

The long-standing theory that a colour-naming system evolves under the dual pressure of efficient communication and perceptual mechanism is supported by more and more linguistic studies including the analysis of four decades' diachronic data from the Nafaanra language. This inspires us to explore whether artificial intelligence could evolve and discover a similar colour-naming system via optimising the communication efficiency represented by high-level recognition performance. Here, we propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining the accuracy of machine recognition on the quantised images. Given an RGB image, Annotation Branch maps it into an index map before generating the quantised image with a colour palette, meanwhile the Palette Branch utilises a key-point detection way to find proper colours in palette among whole colour space. By interacting with colour annotation, CQFormer is able to balance both the machine vision accuracy and colour perceptual structure such as distinct and stable colour distribution for discovered colour system. Very interestingly, we even observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages. Besides, our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage while maintaining a high performance in high-level recognition tasks such as classification and detection. Extensive experiments demonstrate the superior performance of our method with extremely low bit-rate colours. We will release the source code soon.

Telework "avatar work," in which people with disabilities can engage in physical work such as customer service, is being implemented in society. In order to enable avatar work in a variety of occupations, we propose a mobile sales system using a mobile frozen drink machine and an avatar robot "OriHime", focusing on mobile customer service like peddling. The effect of the peddling by the system on the customers are examined based on the results of video annotation.

Measuring the semantic similarity between two sentences is still an important task. The word mover's distance (WMD) computes the similarity via the optimal alignment between the sets of word embeddings. However, WMD does not utilize word order, making it difficult to distinguish sentences with large overlaps of similar words, even if they are semantically very different. Here, we attempt to improve WMD by incorporating the sentence structure represented by BERT's self-attention matrix (SAM). The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences. Experiments on paraphrase identification and semantic textual similarity show that the proposed method improves WMD and its variants. Our code is available at https://github.com/ymgw55/WSMD.

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.

超级解决全球气候模拟的粗略产出，称为缩减，对于需要长期气候变化预测的系统做出政治和社会决策至关重要。但是，现有的快速超分辨率技术尚未保留气候数据的空间相关性，这在我们以空间扩展（例如运输基础设施的开发）处理系统时尤其重要。本文中，我们展示了基于对抗性的网络的机器学习，使我们能够在降尺度中正确重建区域间空间相关性，并高达五十，同时保持像素统计的一致性。与测量的温度和降水分布的气象数据的直接比较表明，整合气候上重要的物理信息对于准确的缩减至关重要，这促使我们称我们的方法称为$ \ pi $ srgan（物理学知情的超级分辨率生成生成的对手网络）。本方法对气候变化影响的区域间一致评估具有潜在的应用。

本文介绍了一种在线改进的方法，用于考虑可遍历植物的机器人导航的场景识别模型，即机器人在移动时可以将其推开的柔性植物零件。在考虑可穿越的植物到路径上的场景识别系统中，错误分类可能会导致机器人由于被识别为障碍的可穿越的植物而被卡住。然而，在任何估计方法中，错误分类都是不可避免的。在这项工作中，我们提出了一个框架，该框架可以在机器人操作期间即时精制语义分割模型。我们引入了一些基于在线模型完善的重量印迹而无需微调的镜头细分。通过观察人与植物部位的相互作用来收集培训数据。我们提出了新颖的健壮权重，以减轻相互作用产生的面膜中包含的噪声的影响。通过使用现实世界数据进行实验评估了所提出的方法，并显示出胜过普通的权重，并通过模型蒸馏提供竞争性结果，同时需要较少的计算成本。

集体决策对于最近的信息和通信技术至关重要。在我们以前的研究中，我们在数学上得出了无冲突的联合决策，最佳地满足了玩家的概率偏好概况。但是，关于最佳联合决策方法存在两个问题。首先，随着选择的数量的增加，计算最佳关节选择概率矩阵爆炸的计算成本。其次，要得出最佳的关节选择概率矩阵，所有玩家都必须披露其概率偏好。现在，值得注意的是，不一定需要对关节概率分布的明确计算；集体决策的必要条件是抽样。这项研究研究了几种抽样方法，这些方法会融合到满足玩家偏好的启发式关节选择概率矩阵。我们表明，它们可以大大减少上述计算成本和机密性问题。我们分析了每种采样方法的概率分布，以及所需的计算成本和保密性。特别是，我们通过光子的量子干扰引入了两种无冲突的关节抽样方法。第一个系统允许玩家隐藏自己的选择，同时在玩家具有相同的偏好时几乎完美地满足了玩家的喜好。第二个系统，其物理性质取代了昂贵的计算成本，它也掩盖了他们的选择，因为他们拥有可信赖的第三方。

神经领域对3D视觉任务的成功现在是无可争议的。遵循这种趋势，已经提出了几种旨在进行视觉定位的方法（例如，大满贯）使用神经场估算距离或密度场。但是，很难仅通过基于密度字段的方法（例如神经辐射场（NERF））实现较高的定位性能，因为它们在大多数空区域中不提供密度梯度。另一方面，基于距离场的方法，例如神经隐式表面（NEU）在物体表面形状中具有局限性。本文提出了神经密度距离场（NEDDF），这是一种新颖的3D表示，可相互约束距离和密度场。我们将距离场公式扩展到没有明确边界表面的形状，例如皮毛或烟雾，从而可以从距离场到密度场进行显式转换。通过显式转换实现的一致距离和密度字段使稳健性可以符合初始值和高质量的注册。此外，字段之间的一致性允许从稀疏点云中快速收敛。实验表明，NEDDF可以实现较高的定位性能，同时在新型视图合成中提供可比的结果。该代码可在https://github.com/ueda0319/neddf上找到。