一组点$ x = \ {\ mathbf {x} _1，\ ldots，\ mathbf {x} _m \} \ subseteq [0,1]^n $的大致消失的理想。在所有点上$ 0 $ $ \ mathbf {x} \在x $中，并通过一组有限的多项式（称为生成器）的有效表示。对构建这组发电机的算法进行了广泛的研究，但最终发现几乎没有实际应用，因为它们的计算复杂性被认为是$ m $的样品数量中的超级线性。在本文中，我们专注于扩展Oracle近似消失的理想算法（OAVI），这是这些方法中最强大的一种。我们证明，Oavi的计算复杂性不是超级线性的，而是在样本$ M $和多项式中线性的$ n $数量，这使Oavi成为大型机器学习的有吸引力的预处理技术。为了进一步加速Oavi的训练时间，我们提出了两个更改：首先，顾名思义，Oavi在执行过程中重复进行了Oracle呼叫以凸出求解器。通过替换OAVI中使用的标准求解器之一的成对条件梯度算法，具有更快的混合成对条件梯度算法，我们说明了Oavi如何直接从凸溶液研究中的进步中受益。其次，我们提出了反向黑森的增强（IHB）：IHB利用了这样一个事实，即Oavi反复解决了二次凸优化问题，这些问题只有很小的不同，并且可以使用倒数Hessian信息以封闭形式写出解决方案。通过有效地更新Hessian Matrix的倒数，几乎可以立即解决凸优化问题，从而使Oavi的训练时间最多可加速多个数量级。我们通过广泛的数值实验对我们的样本数量中的数据集进行了广泛的数值实验，对我们的理论分析进行了补充。

对抗性攻击只着眼于改变分类器的预测，但是它们的危险在很大程度上取决于班级的错误方式。例如，当自动驾驶系统将波斯猫误认为是暹罗猫时，这几乎不是问题。但是，如果它以120公里/小时的最低速度标志误认为猫，可能会出现严重的问题。作为对更有威胁性的对抗性攻击的垫脚石，我们考虑了超级阶级的对抗性攻击，这不仅会导致不仅级别的班级，而且会导致超类。我们在准确性，速度和稳定性方面对超级类对抗攻击（现有和19种新方法）进行了首次全面分析，并确定了几种实现更好性能的策略。尽管这项研究旨在超类错误分类，但这些发现可以应用于涉及多个类别的其他问题设置，例如TOP-K和多标签分类攻击。

本研究旨在解决二次多尺寸机器人到执行器故障的容错问题，这对于在远程或极端环境中运行的机器人至关重要。特别地，建立了具有动态随机化（ACDR）的自适应课程增强学习算法。ACDR算法可以在随机执行器故障条件下自适应地培训四足机器人，并制定一个用于容错机器人控制的单一强大策略。值得注意的是，难以使静止的课程比易于2个课程更有效地用于四足机器人机器人。ACDR算法可用于构建机器人系统，该机器人不需要其他模块检测执行器故障和切换策略。实验结果表明，ACDR算法在平均奖励和步行距离方面优于传统算法。

涉及将知识从富含标签的源域传送到未标记的目标域的无监督域适应，可用于大大降低对象检测领域的注释成本。在这项研究中，我们证明了源域的对抗训练可以作为无监督域适应的新方法。具体地，我们建立了普遍训练的探测器在源极域中显着移位的目标域中实现了改进的检测性能。这种现象归因于普遍训练的探测器可用于提取与人类感知的鲁棒特征提取鲁棒特征，并在丢弃特定于域的非鲁棒特征的同时在域中传输域。此外，我们提出了一种结合对抗性训练和特征对准的方法，以确保具有目标域的鲁棒特征的改进对准。我们对四个基准数据集进行实验，并确认我们在大型域转移到艺术图像的大域移位的有效性。与基线模型相比，普遍训练的探测器在结合特征对准时将平均平均精度提高至7.7％，进一步高达11.8％。虽然我们的方法降低了对小型域移位的性能，但基于Frechet距离的域移位的量化允许我们确定是否应该进行抗逆性培训。

多项式的归一化在消失的理想的近似基础计算中起着至关重要的作用。系数归一化，将多项式归一化及其系数规范是计算机代数中最常见的方法。这项研究提出了梯度加权的归一化方法，用于近似边界基础计算的理想，这是受机器学习最新发展的启发。梯度加权归一化的数据依赖性性质可提高稳定性，以抗扰动和输入点缩放的一致性，这无法通过系数归一化来实现。仅需要一个微妙的变化才能在具有系数归一化的现有算法中引入梯度归一化。算法的分析仍适用于较小的修改，并且算法的时间复杂度的数量级保持不变。我们还证明，通过不提供缩放一致性属性的系数归一化，点的比例（例如，作为预处理）可能会导致近似基础计算失败。这项研究在理论上首先强调了近似基础计算缩放的关键效果，并提出了数据依赖性归一化的实用性。

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].

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.