为了在老年人的日常生活中实现连续的虚弱护理，我们向家里的老年人提出Ahobo，一位虚弱的护理机器人。通过AHOBO实施两种类型的支持系统，以支持身体健康和心理方面的老年人。对于身体健康的体力保健，我们专注于血压，并开发了一种用Ahobo血压测量的支持系统。对于心理脆弱的护理，我们将用Ahobo作为与机器人的娱乐活动实施着色的着色。根据日常生活中连续使用的假设，评估系统的可用性。对于血压测量的支持系统，我们对16名受试者的问卷进行了定性评估，包括系统血压测量的老年人。结果证实，该拟议的机器人不会影响血压读数，并且在基于主观评估的易用性方面是可接受的。为了使复兴的着色相互作用，在口头流畅性任务下对两名老年人进行了主观评估，并且已经证实了互动可以在日常生活中不断使用。拟议的机器人作为支持日常生活的AI的界面广泛使用将导致AI机器人支持从摇篮到坟墓的社会。

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.

In various fields of data science, researchers are often interested in estimating the ratio of conditional expectation functions (CEFR). Specifically in causal inference problems, it is sometimes natural to consider ratio-based treatment effects, such as odds ratios and hazard ratios, and even difference-based treatment effects are identified as CEFR in some empirically relevant settings. This chapter develops the general framework for estimation and inference on CEFR, which allows the use of flexible machine learning for infinite-dimensional nuisance parameters. In the first stage of the framework, the orthogonal signals are constructed using debiased machine learning techniques to mitigate the negative impacts of the regularization bias in the nuisance estimates on the target estimates. The signals are then combined with a novel series estimator tailored for CEFR. We derive the pointwise and uniform asymptotic results for estimation and inference on CEFR, including the validity of the Gaussian bootstrap, and provide low-level sufficient conditions to apply the proposed framework to some specific examples. We demonstrate the finite-sample performance of the series estimator constructed under the proposed framework by numerical simulations. Finally, we apply the proposed method to estimate the causal effect of the 401(k) program on household assets.

Background and objective: COVID-19 and its variants have caused significant disruptions in over 200 countries and regions worldwide, affecting the health and lives of billions of people. Detecting COVID-19 from chest X-Ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19 since the common occurrence of radiological pneumonia findings in COVID-19 patients. We present a novel high-accuracy COVID-19 detection method that uses CXR images. Methods: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. Results: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters. Conclusions: The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

Purpose: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation. Methods: In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection. Results: Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability. Conclusions: Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.

Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in finding answers to diverse questions. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. Many studies were conducted on answer prediction in CQA but with limited focus on using the background information of the questionnaires. We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features, such as the textual content or the relationships with other participants. Our answer classification model was trained using the Stack Exchange dataset and validated using the Area Under the Curve (AUC) metric. The experimental results show that the proposed method complements previous methods by pointing out the importance of the relationships between users, particularly throughout the level of involvement in different communities on Stack Exchange. Furthermore, we point out that there is little overlap between user-relation information and the information represented by the shallow text features and the meta-features, such as time differences.

This paper solves a generalized version of the problem of multi-source model adaptation for semantic segmentation. Model adaptation is proposed as a new domain adaptation problem which requires access to a pre-trained model instead of data for the source domain. A general multi-source setting of model adaptation assumes strictly that each source domain shares a common label space with the target domain. As a relaxation, we allow the label space of each source domain to be a subset of that of the target domain and require the union of the source-domain label spaces to be equal to the target-domain label space. For the new setting named union-set multi-source model adaptation, we propose a method with a novel learning strategy named model-invariant feature learning, which takes full advantage of the diverse characteristics of the source-domain models, thereby improving the generalization in the target domain. We conduct extensive experiments in various adaptation settings to show the superiority of our method. The code is available at https://github.com/lzy7976/union-set-model-adaptation.

数据集复杂性评估旨在在训练分类器之前先预测具有复杂性计算的数据集上的分类性能，该分类器也可以用于分类器选择和减少数据集。深卷积神经网络（DCNN）的训练过程是迭代的且耗时的，这是由于高参数的不确定性和不同数据集引入的域移位。因此，通过在培训DCNN模型之前有效评估数据集的复杂性来预测分类性能是有意义的。本文提出了一种新的方法，称为Laplacian Spectrum（CMSAUL）下的累积最大缩放区域，该方法可以在六个数据集上实现最新的复杂性评估性能。

背景和目标：需要分享医疗数据以实现医疗保健信息的跨机构流量并构建高准确的计算机辅助诊断系统。但是，大量的医疗数据集，保存深度卷积神经网络（DCNN）模型的大量记忆以及患者的隐私保护是可能导致医疗数据共享效率低下的问题。因此，本研究提出了一种新型的软标签数据集蒸馏方法，用于医疗数据共享。方法：所提出的方法提炼医疗图像数据的有效信息，并生成几个带有不同数据分布的压缩图像，以供匿名医疗数据共享。此外，我们的方法可以提取DCNN模型的基本权重，以减少保存训练有素的模型以进行有效的医疗数据共享所需的内存。结果：所提出的方法可以将数万张图像压缩为几个软标签图像，并将受过训练的模型的大小减少到其原始大小的几百分之一。蒸馏后获得的压缩图像已在视觉上匿名化；因此，它们不包含患者的私人信息。此外，我们可以通过少量压缩图像实现高检测性能。结论：实验结果表明，所提出的方法可以提高医疗数据共享的效率和安全性。

高级模型的采集取决于许多领域的大型数据集，这使存储数据集和培训模型昂贵。作为解决方案，数据集蒸馏可以合成一个小数据集，以便在其上训练有素的模型在与原始大型数据集的情况下达到高性能。通过匹配网络参数的最近提出的数据集蒸馏方法已被证明对多个数据集有效。但是，蒸馏过程中的一些参数很难匹配，这会损害蒸馏性能。基于此观察结果，本文提出了一种使用参数修剪来解决问题的新方法。提出的方法可以通过在蒸馏过程中修剪难以匹配的参数来合成更强大的蒸馏数据集并改善蒸馏性能。三个数据集的实验结果表明，所提出的方法的表现优于其他SOTA数据集蒸馏方法。