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/).

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。

本文提出了一种用于多演讲者文本到语音的人类扬声器适应方法。使用常规的说话者适应方法，使用对扬声器歧视任务进行培训的扬声器编码器，从其参考语音中提取目标扬声器的嵌入矢量。但是，当参考语音不可用时，该方法无法获得目标扬声器的嵌入向量。我们的方法基于人类的优化框架，该框架结合了用户来探索扬声器 - 安装空间以查找目标扬声器的嵌入。提出的方法使用顺序线搜索算法，该算法反复要求用户在嵌入空间中的线段上选择一个点。为了有效地从多个刺激中选择最佳的语音样本，我们还开发了一个系统，在该系统中，用户可以在每个音素的声音之间切换在循环发音的同时。实验结果表明，即使不直接将参考语音用作说话者编码器的输入，提出的方法也可以在客观和主观评估中实现与常规评估相当的性能。

我们提出了一个端到端的移情对话言语综合（DSS）模型，该模型既考虑对话历史的语言和韵律背景。同理心是人类积极尝试进入对话中的对话者，而同理心DSS是在口语对话系统中实施此行为的技术。我们的模型以语言和韵律特征的历史为条件，以预测适当的对话环境。因此，可以将其视为传统基于语言 - 基于语言的对话历史建模的扩展。为了有效地培训善解人意的DSS模型，我们研究1）通过大型语音语料库预审预测的一个自我监督的学习模型，2）一种风格引导的培训，使用韵律嵌入对话上下文嵌入的当前话语，3）对结合文本和语音方式的跨模式的关注，以及4）句子的嵌入，以实现细粒度的韵律建模，而不是通过话语建模。评估结果表明，1）仅考虑对话历史的韵律环境并不能提高善解人意的DSS中的语音质量和2）引入样式引导的培训和句子嵌入模型的言语质量比传统方法更高。

我们提出了研究，这是一种新的演讲语料库，用于开发一个可以以友好方式讲话的语音代理。人类自然会控制他们的言语韵律以相互同情。通过将这种“同情对话”行为纳入口语对话系统，我们可以开发一个可以自然响应用户的语音代理。我们设计了研究语料库，以包括一位演讲者，他明确地对对话者的情绪表示同情。我们描述了构建善解人意的对话语音语料库的方法论，并报告研究语料库的分析结果。我们进行了文本到语音实验，以最初研究如何开发更多的自然语音代理，以调整其口语风格，以对应对话者的情绪。结果表明，对话者的情绪标签和对话上下文嵌入的使用可以与使用代理商的情感标签相同的自然性产生语音。我们的研究项目页面是http://sython.org/corpus/studies。

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.

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.

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].

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.