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。

机器学习方法最近被用作替代品或用于动态系统的物理/数学建模方法的帮助。为了开发一种用于建模和预测多尺度动力学的有效机器学习方法，我们通过使用异质性泄漏积分器（LI）神经元的复发网络提出了具有不同时间尺度的储层计算（RC）模型。我们在两个时间序列的预测任务中评估了所提出模型的计算性能，该任务与四个混乱的快速动力学系统有关。在仅从快速子系统提供输入数据的一步预测任务中，我们表明，所提出的模型比具有相同LI神经元的标准RC模型产生的性能更好。我们的分析表明，通过模型训练，适当，灵活地从储层动力学中选择了产生目标多尺度动力学的每个组件所需的时间尺度。在长期的预测任务中，我们证明了所提出的模型的闭环版本可以实现长期的预测，而与与参数相同的LI神经元相比，它可以实现长期预测。

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.

In this paper, we propose a novel architecture called Composition Attention Grammars (CAGs) that recursively compose subtrees into a single vector representation with a composition function, and selectively attend to previous structural information with a self-attention mechanism. We investigate whether these components -- the composition function and the self-attention mechanism -- can both induce human-like syntactic generalization. Specifically, we train language models (LMs) with and without these two components with the model sizes carefully controlled, and evaluate their syntactic generalization performance against six test circuits on the SyntaxGym benchmark. The results demonstrated that the composition function and the self-attention mechanism both play an important role to make LMs more human-like, and closer inspection of linguistic phenomenon implied that the composition function allowed syntactic features, but not semantic features, to percolate into subtree representations.

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

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。