回答有关知识图（KG）的复杂查询是一项重要但具有挑战性的任务，因为在推理过程中存在KG不完整问题和级联错误。最近的查询嵌入（QE）方法将实体和关系嵌入kg中，并将一阶逻辑（fol）查询纳入一个低维空间，从而通过密集的相似性搜索来回答查询。但是，以前的作品主要集中在目标答案上，忽略了中间实体的实用性，这对于缓解逻辑查询答案中的级联错误问题至关重要。此外，这些方法通常是用自己的几何或分配嵌入设计的，以处理逻辑运算符，例如联合，交叉路口和否定，并牺牲了基本操作员的准确性 - 投影，他们无法吸收其他嵌入方法，以使其吸收其他嵌入方法楷模。在这项工作中，我们提出了一个神经和象征性的纠缠框架（ENESY），以进行复杂的查询答案，这使神经和象征性推理可以相互增强以减轻级联错误和kg不完整。 Enesy中的投影操作员可以是具有链接预测能力的任何嵌入方法，并且其他FOL操作员无需参数处理。随着神经和象征性推理的结果，合奏中的Enesy答案查询。 Enesy在几个基准上实现了SOTA性能，尤其是在培训模型的设置中，仅具有链接预测任务。

基于Xornet的低功耗控制器是一种流行的技术，可以减少基于扫描的测试中的电路过渡。然而，现有解决方案构造Xordet均匀用于扫描链控制，并且可能导致次优溶液而没有任何设计指导。在本文中，我们提出了一种具有进化学习的新型可测试性感知的低功率控制器。从所提出的遗传算法（GA）产生的XorNET可以根据其使用，使扫描链的自适应控制能够显着提高XorNET编码容量，从而减少了ATPG的故障情况的数量和降低测试数据量。实验结果表明，在相同的控制比特下，我们的GA引导的Xornet设计可以将故障覆盖率提高至2.11％。所提出的GA引导的XorNET还允许降低控制比特的数量，并且总测试时间平均降低20.78％，与现有设计相比，在不牺牲测试覆盖的情况下相比，相比，高达47.09％。

Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements. AI segmentation and AI-ROI method were evaluated on 1,014 non-contrast enhanced chest CT images from eight international datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A, RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a mean dice coefficient of 0.957. Attenuations measured by AI-ROI showed no significant differences (p = 0.545) and a reduction of 71% time compared to expert measurements. The area under the curve (AUC) of the steatosis classification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as a routine screening method, our AI protocol could potentially allow early non-invasive, non-pharmacological preventative interventions for hepatic steatosis. 1,014 expert-annotated liver segmentations of patients with hepatic steatosis annotations can be downloaded here: https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX.

In online experimentation, appropriate metrics (e.g., purchase) provide strong evidence to support hypotheses and enhance the decision-making process. However, incomplete metrics are frequently occurred in the online experimentation, making the available data to be much fewer than the planned online experiments (e.g., A/B testing). In this work, we introduce the concept of dropout buyers and categorize users with incomplete metric values into two groups: visitors and dropout buyers. For the analysis of incomplete metrics, we propose a clustering-based imputation method using $k$-nearest neighbors. Our proposed imputation method considers both the experiment-specific features and users' activities along their shopping paths, allowing different imputation values for different users. To facilitate efficient imputation of large-scale data sets in online experimentation, the proposed method uses a combination of stratification and clustering. The performance of the proposed method is compared to several conventional methods in both simulation studies and a real online experiment at eBay.

为了推荐有关季节性零售活动的相关商品，我们依靠市场清单中的项目检索。通过反馈来扩展查询范围，我们使用单词嵌入相似性讨论关键字扩展候选选择，以及增强的TF-IDF公式，用于搜索排名中的扩展单词。

近年来，无线数据传输需求的指数增加增加了准确的光谱传感方法的紧迫性，以提高频谱效率。通过使用单个二级用户（SU）的测量结果，传统频谱传感方法的不可靠性激发了对合作频谱传感（CSS）的研究。在这项工作中，我们提出了一个垂直联合学习（VFL）框架，以利用多个SU的分布式功能，而不会损害数据隐私。但是，VFL的重复培训过程面临着高通信延迟的问题。为了加速培训过程，我们提出了一种截断的垂直联合学习（T-VFL）算法，在该算法中，通过将标准VFL算法与频道意识的用户调度策略集成在一起，可以大大降低培训潜伏期。 T-VFL的收敛性能通过数学分析提供，并通过模拟结果证明。此外，为了确保T-VFL算法的融合性能，我们对VFL框架下使用的神经体系结构进行了三个设计规则，该规则通过模拟证明了其有效性。

强化学习（RL）已见证其培训对话政策代理人以最大限度地提高用户累计奖励的潜力。但是，奖励可以非常稀疏，它通常仅在对话会话结束时提供，这会导致可接受的对话框的无法实现的交互要求。区别于许多致力于优化策略并恢复奖励，替代地恢复了困难的奖励，这些奖励遭受了容易地陷入困境和模型崩溃，我们将对抗训练分解为两个步骤：1）我们将预先训练的语言模型集成为判别员判断当前的系统动作是否足够好，对最后一个用户操作（即，\ texit {下一个操作预测}）; 2）鉴别者给出和额外的本地密集奖励，以指导代理人的探索。实验结果表明，我们的方法显着提高了对话系统的完整速率（〜4.4 \％）和成功率（〜8.0％）。

At eBay, there are thousands of product health metrics for different domain teams to monitor. We built a two-phase alerting system to notify users with actionable alerts based on anomaly detection and alert retrieval. In the first phase, we developed an efficient anomaly detection algorithm, called Moving Metric Detector (MMD), to identify potential alerts among metrics with distribution agnostic criteria. In the second alert retrieval phase, we built additional logic with feedbacks to select valid actionable alerts with point-wise ranking model and business rules. Compared with other trend and seasonality decomposition methods, our decomposer is faster and better to detect anomalies in unsupervised cases. Our two-phase approach dramatically improves alert precision and avoids alert spamming in eBay production.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.