Search algorithms for the bandit problems are applicable in materials discovery. However, the objectives of the conventional bandit problem are different from those of materials discovery. The conventional bandit problem aims to maximize the total rewards, whereas materials discovery aims to achieve breakthroughs in material properties. The max K-armed bandit (MKB) problem, which aims to acquire the single best reward, matches with the discovery tasks better than the conventional bandit. Thus, here, we propose a search algorithm for materials discovery based on the MKB problem using a pseudo-value of the upper confidence bound of expected improvement of the best reward. This approach is pseudo-guaranteed to be asymptotic oracles that do not depends on the time horizon. In addition, compared with other MKB algorithms, the proposed algorithm has only one hyperparameter, which is advantageous in materials discovery. We applied the proposed algorithm to synthetic problems and molecular-design demonstrations using a Monte Carlo tree search. According to the results, the proposed algorithm stably outperformed other bandit algorithms in the late stage of the search process when the optimal arm of the MKB could not be determined based on its expectation reward.

In recent years, various service robots have been introduced in stores as recommendation systems. Previous studies attempted to increase the influence of these robots by improving their social acceptance and trust. However, when such service robots recommend a product to customers in real environments, the effect on the customers is influenced not only by the robot itself, but also by the social influence of the surrounding people such as store clerks. Therefore, leveraging the social influence of the clerks may increase the influence of the robots on the customers. Hence, we compared the influence of robots with and without collaborative customer service between the robots and clerks in two bakery stores. The experimental results showed that collaborative customer service increased the purchase rate of the recommended bread and improved the impression regarding the robot and store experience of the customers. Because the results also showed that the workload required for the clerks to collaborate with the robot was not high, this study suggests that all stores with service robots may show high effectiveness in introducing collaborative customer service.

We present a lightweight post-processing method to refine the semantic segmentation results of point cloud sequences. Most existing methods usually segment frame by frame and encounter the inherent ambiguity of the problem: based on a measurement in a single frame, labels are sometimes difficult to predict even for humans. To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method. The network, which we call the P2Net, learns the consistency constraints between coincident points from consecutive frames after registration. We evaluate the proposed post-processing method both qualitatively and quantitatively on the SemanticKITTI dataset that consists of real outdoor scenes. The effectiveness of the proposed method is validated by comparing the results predicted by two representative networks with and without the refinement by the post-processing network. Specifically, qualitative visualization validates the key idea that labels of the points that are difficult to predict can be corrected with P2Net. Quantitatively, overall mIoU is improved from 10.5% to 11.7% for PointNet [1] and from 10.8% to 15.9% for PointNet++ [2].

We construct a corpus of Japanese a cappella vocal ensembles (jaCappella corpus) for vocal ensemble separation and synthesis. It consists of 35 copyright-cleared vocal ensemble songs and their audio recordings of individual voice parts. These songs were arranged from out-of-copyright Japanese children's songs and have six voice parts (lead vocal, soprano, alto, tenor, bass, and vocal percussion). They are divided into seven subsets, each of which features typical characteristics of a music genre such as jazz and enka. The variety in genre and voice part match vocal ensembles recently widespread in social media services such as YouTube, although the main targets of conventional vocal ensemble datasets are choral singing made up of soprano, alto, tenor, and bass. Experimental evaluation demonstrates that our corpus is a challenging resource for vocal ensemble separation. Our corpus is available on our project page (https://tomohikonakamura.github.io/jaCappella_corpus/).

Wireless ad hoc federated learning (WAFL) is a fully decentralized collaborative machine learning framework organized by opportunistically encountered mobile nodes. Compared to conventional federated learning, WAFL performs model training by weakly synchronizing the model parameters with others, and this shows great resilience to a poisoned model injected by an attacker. In this paper, we provide our theoretical analysis of the WAFL's resilience against model poisoning attacks, by formulating the force balance between the poisoned model and the legitimate model. According to our experiments, we confirmed that the nodes directly encountered the attacker has been somehow compromised to the poisoned model but other nodes have shown great resilience. More importantly, after the attacker has left the network, all the nodes have finally found stronger model parameters combined with the poisoned model. Most of the attack-experienced cases achieved higher accuracy than the no-attack-experienced cases.

We discuss an application of Generalized Random Forests (GRF) proposed by Athey et al.(2019) to quantile regression for time series data. We extracted the theoretical results of the GRF consistency for i.i.d. data to time series data. In particular, in the main theorem, based only on the general assumptions for time series data in Davis and Nielsen (2020), and trees in Athey et al.(2019), we show that the tsQRF (time series Quantile Regression Forests) estimator is consistent. Davis and Nielsen (2020) also discussed the estimation problem using Random Forests (RF) for time series data, but the construction procedure of the RF treated by the GRF is essentially different, and different ideas are used throughout the theoretical proof. In addition, a simulation and real data analysis were conducted.In the simulation, the accuracy of the conditional quantile estimation was evaluated under time series models. In the real data using the Nikkei Stock Average, our estimator is demonstrated to be more sensitive than the others in terms of volatility, thus preventing underestimation of risk.

我们提出了一种基于多任务对抗训练的多扬声器神经文本到语音（TTS）模型的新型培训算法。传统的基于基于的训练算法的常规生成对抗网络（GAN）通过减少自然语音和合成语音之间的统计差异来显着提高合成语音的质量。但是，该算法不能保证训练有素的TTS模型的概括性能在综合培训数据中未包括的看不见的说话者的声音中。我们的算法替代训练两个深神经网络：多任务歧视器和多扬声器神经TTS模型（即GAN的生成器）。对歧视者的训练不仅是为了区分自然语音和合成语音，而且还存在验证输入语音的说话者的存在或不存在（即，通过插值可见的说话者的嵌入向量而新生成）。同时，对发电机进行了训练，以最大程度地减少语音重建损失的加权总和和欺骗歧视者的对抗性损失，即使目标扬声器看不见，也可以实现高质量的多演讲者TT。实验评估表明，我们的算法比传统的甘斯多克算法更好地提高了合成语音的质量。

在本文中，我们报告了一项现场研究，在该研究中，我们在面包店使用了两个服务机器人作为促销活动。先前的研究探索了公共公共公众公共应用，例如购物中心。但是，需要更多的证据表明，服务机器人可以为真实商店的销售做出贡献。此外，在促销促销的背景下，客户和服务机器人的行为尚未得到很好的检查。因此，可以认为有效的机器人行为类型，并且客户对这些机器人的反应尚不清楚。为了解决这些问题，我们在面包店安装了两个远程操作的服务机器人将近2周，一个在入口处作为招待员，另一个在商店里推荐产品。结果表明，在应用机器人时，销售额急剧增加。此外，我们注释了机器人和客户行为的视频录制。我们发现，尽管放置在入口处的机器人成功吸引了路人的兴趣，但没有观察到访问商店的客户数量明显增加。但是，我们确认商店内部运行的机器人的建议确实产生了积极影响。我们详细讨论我们的发现，并为未来的研究和应用提供理论和实用建议。

机器人进行深入增强学习（RL）的导航，在复杂的环境下实现了更高的性能，并且表现良好。同时，对深度RL模型的决策的解释成为更多自主机器人安全性和可靠性的关键问题。在本文中，我们提出了一种基于深入RL模型的注意力分支的视觉解释方法。我们将注意力分支与预先训练的深度RL模型联系起来，并通过以监督的学习方式使用受过训练的深度RL模型作为正确标签来训练注意力分支。由于注意力分支经过训练以输出与深RL模型相同的结果，因此获得的注意图与具有更高可解释性的代理作用相对应。机器人导航任务的实验结果表明，所提出的方法可以生成可解释的注意图以进行视觉解释。

人级AI将对人类社会产生重大影响。但是，实现时间的估计值应有争议。为了到达人工通用情报（AGI）的人工AI，而不是专门从事特定任务的AI系统，是技术意义上有意义的长期目标。但是现在，由于深度学习的进步，这一成就越来越近了。考虑到最近的技术发展，通过“综合技术地图方法”讨论人级AI的完成日期是有意义的，其中我们以合理的粒度绘制人类水平的能力，确定当前的技术范围，并讨论并讨论人类水平的能力。穿越未开发领域的技术挑战，并预测何时将克服它们。本文提出了一种新的论证选择来查看本体论六重奏，该选项涵盖了实体，该实体与我们的日常直觉和科学实践一致，作为全面的技术图。因为关于如何解释世界的大多数建模，因此智能主题是对远端实体的认可以及对它们的时间进化的预测，能够处理所有远端实体是一个合理的目标。根据哲学和工程认知技术的发现，我们预测，在相对较远的将来，AI将能够与人类相同的程度识别各种实体。