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.

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。

Many e-commerce marketplaces offer their users fast delivery options for free to meet the increasing needs of users, imposing an excessive burden on city logistics. Therefore, understanding e-commerce users' preference for delivery options is a key to designing logistics policies. To this end, this study designs a stated choice survey in which respondents are faced with choice tasks among different delivery options and time slots, which was completed by 4,062 users from the three major metropolitan areas in Japan. To analyze the data, mixed logit models capturing taste heterogeneity as well as flexible substitution patterns have been estimated. The model estimation results indicate that delivery attributes including fee, time, and time slot size are significant determinants of the delivery option choices. Associations between users' preferences and socio-demographic characteristics, such as age, gender, teleworking frequency and the presence of a delivery box, were also suggested. Moreover, we analyzed two willingness-to-pay measures for delivery, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS), and applied a non-semiparametric approach to estimate their distributions in a data-oriented manner. Although VODT has a large heterogeneity among respondents, the estimated median VODT is 25.6 JPY/day, implying that more than half of the respondents would wait an additional day if the delivery fee were increased by only 26 JPY, that is, they do not necessarily need a fast delivery option but often request it when cheap or almost free. Moreover, VOTS was found to be low, distributed with the median of 5.0 JPY/hour; that is, users do not highly value the reduction in time slot size in monetary terms. These findings on e-commerce users' preferences can help in designing levels of service for last-mile delivery to significantly improve its efficiency.

Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior. We develop a framework algorithm for the problem using various arm selection policies and propose policies called FCB and FTSV. We show a smaller sample complexity upper bound for FCB than that for the existing algorithm of the level set estimation, in which whether f(x) is at least h or not must be decided for every arm's x. Arm selection policies depending on an estimated rate of arms with rewards of at least h are also proposed and shown to improve empirical sample complexity. According to our experimental results, the rate-estimation versions of FCB and FTSV, together with that of the popular active learning policy that selects the point with the maximum variance, outperform other policies for synthetic functions, and the version of FTSV is also the best performer for our real-world dataset.

Color is a critical design factor for web pages, affecting important factors such as viewer emotions and the overall trust and satisfaction of a website. Effective coloring requires design knowledge and expertise, but if this process could be automated through data-driven modeling, efficient exploration and alternative workflows would be possible. However, this direction remains underexplored due to the lack of a formalization of the web page colorization problem, datasets, and evaluation protocols. In this work, we propose a new dataset consisting of e-commerce mobile web pages in a tractable format, which are created by simplifying the pages and extracting canonical color styles with a common web browser. The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements. We present several Transformer-based methods that are adapted to this task by prepending structural message passing to capture hierarchical relationships between elements. Experimental results, including a quantitative evaluation designed for this task, demonstrate the advantages of our methods over statistical and image colorization methods. The code is available at https://github.com/CyberAgentAILab/webcolor.

在深度学习时代，注释的数据集已成为遥感社区的关键资产。在过去的十年中，发表了许多不同的数据集，每个数据集都为特定的数据类型以及特定的任务或应用程序设计。在遥感数据集的丛林中，很难跟踪已经可用的内容。在本文中，我们介绍了EOD -IEEE GRSS地球观察数据库（EOD） - 一个交互式在线平台，用于分类不同类型的数据集利用遥感图像。

自动基于图像的疾病严重程度估计通常使用离散（即量化）严重性标签。由于图像含糊不清，因此通常很难注释离散标签。一个更容易的替代方法是使用相对注释，该注释比较图像对之间的严重程度。通过使用带有相对注释的学习对框架，我们可以训练一个神经网络，该神经网络估计与严重程度相关的等级分数。但是，所有可能对的相对注释都是过敏的，因此，适当的样品对选择是强制性的。本文提出了深层贝叶斯的主动学习与级别，该级别训练贝叶斯卷积神经网络，同时自动选择合适的对进行相对注释。我们通过对溃疡性结肠炎的内窥镜图像进行实验证实了该方法的效率。此外，我们确认我们的方法即使在严重的类失衡中也很有用，因为它可以自动从次要类中选择样本。

自我监督的跨模式超分辨率（SR）可以克服获得配对训练数据的困难，但由于只有低分辨率（LR）源和高分辨率源（HR）指导图像，因此具有挑战性。现有方法利用伪或LR空间中的弱监督，因此提供了模糊或不忠于源方式的结果。为了解决这个问题，我们提出了一个相互调制的SR（MMSR）模型，该模型通过相互调制策略来解决任务，包括源至指南调制和指南对源调制。在这些调制中，我们开发了跨域自适应过滤器，以完全利用跨模式的空间依赖性，并有助于诱导源以模拟指南的分辨率并诱导指南模仿源的模态特征。此外，我们采用周期一致性约束，以完全自我监督的方式训练MMSR。各种任务的实验证明了我们的MMSR的最新性能。

我们提出了一项对基于自我监督的语音表示（S3R）语音转换（VC）的大规模比较研究。在识别合成VC的背景下，S3RS由于其替代昂贵的监督表示的潜力，例如语音后验（PPG），因此很有吸引力，这些表示是由最先进的VC系统采用的。使用先前开发的开源VC软件S3PRL-VC，我们在三种VC设置下提供了一系列深入的目标和主观分析：内部/跨语义的任何一对一（A2O）和任何对象 - 使用语音转换挑战2020（VCC2020）数据集。我们在各个方面研究了基于S3R的VC，包括模型类型，多语言和监督。我们还研究了通过K-均值聚类的滴定过程的效果，并展示了其在A2A设置中的改进。最后，与最先进的VC系统的比较证明了基于S3R的VC的竞争力，并阐明了可能的改进方向。

通常通过过去的选择来告知机器学习中的评估，例如要使用哪些数据集或指标。该标准化可以使用排行榜对平等基础进行比较，但是随着出现更好的替代方案，评估选择变得不佳。这个问题在自然语言生成中尤其相关，该语言需要不断改善的数据集，指标和人类评估以提出确定性的主张。为了使遵循最佳模型评估实践更加容易，我们介绍了GEMV2。新版本的一代，评估和指标基准为数据集，模型和指标开发人员提供了模块化基础架构，以使彼此受益。GEMV2支持40种记录的数据集中51种语言。所有数据集的模型都可以在线评估，我们的交互式数据卡创建和渲染工具使得在Living Benchmark中添加新数据集变得更加容易。