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.

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。

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.

In the process of materials discovery, chemists currently need to perform many laborious, time-consuming, and often dangerous lab experiments. To accelerate this process, we propose a framework for robots to assist chemists by performing lab experiments autonomously. The solution allows a general-purpose robot to perform diverse chemistry experiments and efficiently make use of available lab tools. Our system can load high-level descriptions of chemistry experiments, perceive a dynamic workspace, and autonomously plan the required actions and motions to perform the given chemistry experiments with common tools found in the existing lab environment. Our architecture uses a modified PDDLStream solver for integrated task and constrained motion planning, which generates plans and motions that are guaranteed to be safe by preventing collisions and spillage. We present a modular framework that can scale to many different experiments, actions, and lab tools. In this work, we demonstrate the utility of our framework on three pouring skills and two foundational chemical experiments for materials synthesis: solubility and recrystallization. More experiments and updated evaluations can be found at https://ac-rad.github.io/arc-icra2023.

This paper proposes embedded Gaussian Process Barrier States (GP-BaS), a methodology to safely control unmodeled dynamics of nonlinear system using Bayesian learning. Gaussian Processes (GPs) are used to model the dynamics of the safety-critical system, which is subsequently used in the GP-BaS model. We derive the barrier state dynamics utilizing the GP posterior, which is used to construct a safety embedded Gaussian process dynamical model (GPDM). We show that the safety-critical system can be controlled to remain inside the safe region as long as we can design a controller that renders the BaS-GPDM's trajectories bounded (or asymptotically stable). The proposed approach overcomes various limitations in early attempts at combining GPs with barrier functions due to the abstention of restrictive assumptions such as linearity of the system with respect to control, relative degree of the constraints and number or nature of constraints. This work is implemented on various examples for trajectory optimization and control including optimal stabilization of unstable linear system and safe trajectory optimization of a Dubins vehicle navigating through an obstacle course and on a quadrotor in an obstacle avoidance task using GP differentiable dynamic programming (GP-DDP). The proposed framework is capable of maintaining safe optimization and control of unmodeled dynamics and is purely data driven.

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

时间是文档的重要方面，用于一系列NLP和IR任务。在这项工作中，我们研究了在预训练期间合并时间信息的方法，以进一步提高与时间相关的任务的性能。与Bert相比，使用同步文档收集（BooksCorpus和English Wikipedia）作为培训语料库相比，我们使用长跨度的时间新闻文章集合来构建单词表示。我们介绍了Timebert，这是一种新颖的语言表示模型，该模型通过两项新的预训练任务培训了新闻文章的临时收集，这些任务利用了两个不同的时间信号来构建时间认识的语言表示。实验结果表明，TimeBert始终胜过BERT和其他现有的预训练模型，在不同的下游NLP任务或应用程序上，时间很高的时间很重要。

Differentially private federated learning (DP-FL) has received increasing attention to mitigate the privacy risk in federated learning. Although different schemes for DP-FL have been proposed, there is still a utility gap. Employing central Differential Privacy in FL (CDP-FL) can provide a good balance between the privacy and model utility, but requires a trusted server. Using Local Differential Privacy for FL (LDP-FL) does not require a trusted server, but suffers from lousy privacy-utility trade-off. Recently proposed shuffle DP based FL has the potential to bridge the gap between CDP-FL and LDP-FL without a trusted server; however, there is still a utility gap when the number of model parameters is large. In this work, we propose OLIVE, a system that combines the merits from CDP-FL and LDP-FL by leveraging Trusted Execution Environment (TEE). Our main technical contributions are the analysis and countermeasures against the vulnerability of TEE in OLIVE. Firstly, we theoretically analyze the memory access pattern leakage of OLIVE and find that there is a risk for sparsified gradients, which is common in FL. Secondly, we design an inference attack to understand how the memory access pattern could be linked to the training data. Thirdly, we propose oblivious yet efficient algorithms to prevent the memory access pattern leakage in OLIVE. Our experiments on real-world data demonstrate that OLIVE is efficient even when training a model with hundreds of thousands of parameters and effective against side-channel attacks on TEE.

鉴于音乐源分离和自动混合的最新进展，在音乐曲目中删除音频效果是开发自动混合系统的有意义的一步。本文着重于消除对音乐制作中吉他曲目应用的失真音频效果。我们探索是否可以通过设计用于源分离和音频效应建模的神经网络来解决效果的去除。我们的方法证明对混合处理和清洁信号的效果特别有效。与基于稀疏优化的最新解决方案相比，这些模型获得了更好的质量和更快的推断。我们证明这些模型不仅适合倾斜，而且适用于其他类型的失真效应。通过讨论结果，我们强调了多个评估指标的有用性，以评估重建的不同方面的变形效果去除。

本文提出了来自Covid-19患者CT体积的肺部感染区的分段方法。 Covid-19在全球范围内传播，造成许多受感染的患者和死亡。 CT图像的Covid-19诊断可以提供快速准确的诊断结果。肺中感染区的自动分割方法提供了诊断的定量标准。以前的方法采用整个2D图像或基于3D卷的过程。感染区域的尺寸具有相当大的变化。这种过程容易错过小型感染区域。基于补丁的过程对于分割小目标是有效的。然而，在感染区分割中选择适当的贴片尺寸难以。我们利用分段FCN的各种接受场大小之间的规模不确定性以获得感染区域。接收场尺寸可以定义为贴片尺寸和块从斑块的卷的分辨率。本文提出了一种执行基于补丁的分割的感染分段网络（ISNet）和尺度的不确定性感知预测聚合方法，其改进分割结果。我们设计ISNET到具有各种强度值的分段感染区域。 ISNet具有多个编码路径来处理由多个强度范围归一化的修补程序卷。我们收集具有各种接收场尺寸的ISNet产生的预测结果。预测聚合方法提取预测结果之间的规模不确定性。我们使用聚合FCN来在预测之间的规模不确定性来生成精确的分段结果。在我们的实验中，使用199例Covid-19案例，预测聚集方法将骰子相似度评分从47.6％提高到62.1％。