一个由许多移动计算实体组成的自动移动机器人系统（称为机器人）吸引了研究人员的广泛关注，并阐明机器人的能力与问题的可溶性之间的关系是近几十年来的新兴问题。通常，只要没有任何机器人的数量，每个机器人都可以观察所有其他机器人。在本文中，我们提供了关于机器人观察的新观点。机器人不一定要观察所有其他机器人，而不管距离距离如何。我们称此新的计算模型瑕疵视图模型。在该模型下，在本文中，我们考虑了需要所有机器人在同一时刻收集的收集问题，并提出了两种算法来解决对抗性（$ n $，$ n-2 $）中的收集问题 - 违法模型对于$ n \ geq 5 $（每个机器人最多观察$ n-2 $机器人在对手身上选择）和基于距离的（4,2）的模型（每个机器人在最接近的机器人最接近的机器人中分别观察到）分别，其中$ n $是机器人的数量。此外，我们提出了一个不可能的结果，表明在对抗性或基于距离（3,1）的模型中没有（确定性的）收集算法。此外，我们在放松的（$ n $，$ n-2 $）中的聚会中表现出了不可能的结果。

Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To develop such an algorithm, here, we propose DP-GLOW, a hybrid of a local differential privacy (LDP) algorithm and one of the flow-based deep generative models (GLOW). By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images onto the latent vector of the GLOW model, each element of which follows an independent normal distribution, and we apply the Laplace mechanism to the latent vector. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies.

This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in this study, is a population-based stochastic search method that samples solution candidates from a multivariate Gaussian distribution (MGD), which shows excellent performance in continuous BBO. The parameters of MGD, mean and (co)variance, are updated based on the evaluation value of candidate solutions in the CMA-ES. If the CMA-ES is applied to the MI-BBO with straightforward discretization, however, the variance corresponding to the integer variables becomes much smaller than the granularity of the discretization before reaching the optimal solution, which leads to the stagnation of the optimization. In particular, when binary variables are included in the problem, this stagnation more likely occurs because the granularity of the discretization becomes wider, and the existing modification to the CMA-ES does not address this stagnation. To overcome these limitations, we propose a simple extension of the CMA-ES based on lower-bounding the marginal probabilities associated with the generation of integer variables in the MGD. The numerical experiments on the MI-BBO benchmark problems demonstrate the efficiency and robustness of the proposed method. Furthermore, in order to demonstrate the generality of the idea of the proposed method, in addition to the single-objective optimization case, we incorporate it into multi-objective CMA-ES and verify its performance on bi-objective mixed-integer benchmark problems.

We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights. The existing centralized methods assume the number of robots and tasks to be fixed, which is inapplicable to scenarios that differ from the learning environment. Meanwhile, the existing distributed methods limit the minimum number of robots and tasks to a constant value, making them applicable to various numbers of robots and tasks. However, they cannot transport an object whose weight exceeds the load capacity of robots observing the object. To make it applicable to various numbers of robots and objects with different and unknown weights, we propose a framework using multi-agent reinforcement learning for task allocation. First, we introduce a structured policy model consisting of 1) predesigned dynamic task priorities with global communication and 2) a neural network-based distributed policy model that determines the timing for coordination. The distributed policy builds consensus on the high-priority object under local observations and selects cooperative or independent actions. Then, the policy is optimized by multi-agent reinforcement learning through trial and error. This structured policy of local learning and global communication makes our framework applicable to various numbers of robots and objects with different and unknown weights, as demonstrated by numerical simulations.

In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the number of agents is the same as that in the training environment, the number might differ in reality considering that the robots' batteries may completely discharge, or additional robots may be introduced to reduce the time required to complete a task. Therefore, it is crucial that the learned strategy be applicable to scenarios wherein the number of agents differs from that in the training environment. In this paper, we propose a novel multi-agent reinforcement learning framework of event-triggered communication and consensus-based control for distributed cooperative transport. The proposed policy model estimates the resultant force and torque in a consensus manner using the estimates of the resultant force and torque with the neighborhood agents. Moreover, it computes the control and communication inputs to determine when to communicate with the neighboring agents under local observations and estimates of the resultant force and torque. Therefore, the proposed framework can balance the control performance and communication savings in scenarios wherein the number of agents differs from that in the training environment. We confirm the effectiveness of our approach by using a maximum of eight and six robots in the simulations and experiments, respectively.

我们在随机多臂匪徒问题中使用固定预算和上下文（协变）信息研究最佳武器识别。在观察上下文信息之后，在每一轮中，我们使用过去的观察和当前上下文选择一个治疗臂。我们的目标是确定最好的治疗组，这是一个在上下文分布中被边缘化的最大预期奖励的治疗组，而错误识别的可能性最小。首先，我们为此问题得出半参数的下限，在这里我们将最佳和次优的治疗臂的预期奖励之间的差距视为感兴趣的参数，以及所有其他参数，例如在上下文中的预期奖励，作为滋扰参数。然后，我们开发“上下文RS-AIPW策略”，该策略由随机采样（RS）规则组成，跟踪目标分配比和使用增强反向概率加权（AIPW）估算器的建议规则。我们提出的上下文RS-AIPW策略是最佳的，因为错误识别概率的上限与预算到Infinity时的半参数下限相匹配，并且差距趋于零。

从出生到死亡，由于老化，我们都经历了令人惊讶的无处不在的变化。如果我们可以预测数字领域的衰老，即人体的数字双胞胎，我们将能够在很早的阶段检测病变，从而提高生活质量并延长寿命。我们观察到，没有一个先前开发的成年人体数字双胞胎在具有深层生成模型的体积医学图像之间明确训练的纵向转换规则，可能导致例如心室体积的预测性能不佳。在这里，我们建立了一个新的成人人体的数字双胞胎，该数字双胞胎采用纵向获得的头部计算机断层扫描（CT）图像进行训练，从而从一个当前的体积头CT图像中预测了未来的体积头CT图像。我们首次采用了三维基于流动的深层生成模型之一，以实现这种顺序的三维数字双胞胎。我们表明，我们的数字双胞胎在相对较短的程度上优于预测心室体积的最新方法。

变压器的令人印象深刻的性能归因于自我注意力，在每个位置都考虑了整个输入之间的依赖性。在这项工作中，我们改革了神经$ n $ gram模型，该模型仅着眼于每个位置的几个周围表示，其多头机制如Vaswani等人（2017年）。通过对序列到序列任务的实验，我们表明，用多头神经$ n $ gram在变压器中替换自我注意力可以比变压器实现可比性或更好的性能。从对我们提出的方法的各种分析中，我们发现多头神经$ n $ gram是互补的，它们的组合可以进一步提高香草变压器的性能。

城市河流提供了影响住宅生活的水环境。河流表面监测对于决定在哪里确定清洁工作以及何时自动开始清洁处理至关重要。我们专注于有机泥浆或“浮渣”，该泥浆积聚在河流的表面上，并给予其特殊的气味和对景观的外部经济影响。由于其具有稀疏分布和不稳定的有机形状模式的特征，因此很难自动进行监测。我们建议使用混合图像增强物进行斑块分类管道，以检测河流表面上的浮渣特征，以增加漂浮在河流上的浮渣与附近建筑物，例如建筑物，桥梁，杆子和障碍物（如建筑物，桥梁和障碍物）所反映的河流背景的多样性。此外，我们建议在河流上覆盖的浮渣索引，以帮助在线监视较差的等级，收集浮渣并决定化学处理政策。最后，我们展示了如何在每十分钟的时间序列数据集中使用框架的时间序列数据集录制河流浮渣事件。我们讨论管道的价值及其实验发现。

多模式学习是建立模型的框架，这些模型可以根据不同类型的方式进行预测。多模式学习中的重要挑战是通过这些表示从任意模式和跨模式产生的共同表示形式推断；但是，实现这一目标需要考虑多模式数据的异质性质。近年来，深层生成模型，即通过深层神经网络参数化的生成模型引起了很多关注，尤其是变异自动编码器，这些自动编码器适合于实现上述挑战，因为它们可以考虑异质性并推断出数据的良好表示。。因此，近年来已经提出了基于变异自动编码器的各种多模式生成模型，称为多模式深生成模型。在本文中，我们提供了对多模式深生成模型研究的分类调查。