Accurate airway extraction from computed tomography (CT) images is a critical step for planning navigation bronchoscopy and quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). The existing methods are challenging to sufficiently segment the airway, especially the high-generation airway, with the constraint of the limited label and cannot meet the clinical use in COPD. We propose a novel two-stage 3D contextual transformer-based U-Net for airway segmentation using CT images. The method consists of two stages, performing initial and refined airway segmentation. The two-stage model shares the same subnetwork with different airway masks as input. Contextual transformer block is performed both in the encoder and decoder path of the subnetwork to finish high-quality airway segmentation effectively. In the first stage, the total airway mask and CT images are provided to the subnetwork, and the intrapulmonary airway mask and corresponding CT scans to the subnetwork in the second stage. Then the predictions of the two-stage method are merged as the final prediction. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analysis demonstrate that our proposed method extracted much more branches and lengths of the tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.

Purpose: Trans-oral robotic surgery (TORS) using the da Vinci surgical robot is a new minimally-invasive surgery method to treat oropharyngeal tumors, but it is a challenging operation. Augmented reality (AR) based on intra-operative ultrasound (US) has the potential to enhance the visualization of the anatomy and cancerous tumors to provide additional tools for decision-making in surgery. Methods: We propose and carry out preliminary evaluations of a US-guided AR system for TORS, with the transducer placed on the neck for a transcervical view. Firstly, we perform a novel MRI-transcervical 3D US registration study. Secondly, we develop a US-robot calibration method with an optical tracker and an AR system to display the anatomy mesh model in the real-time endoscope images inside the surgeon console. Results: Our AR system reaches a mean projection error of 26.81 and 27.85 pixels for the projection from the US to stereo cameras in a water bath experiment. The average target registration error for MRI to 3D US is 8.90 mm for the 3D US transducer and 5.85 mm for freehand 3D US, and the average distance between the vessel centerlines is 2.32 mm. Conclusion: We demonstrate the first proof-of-concept transcervical US-guided AR system for TORS and the feasibility of trans-cervical 3D US-MRI registration. Our results show that trans-cervical 3D US is a promising technique for TORS image guidance.

垂直协作学习系统也被称为垂直联合学习（VFL）系统最近成为一个概念，以处理在许多个人来源上分布的数据，而无需集中它。多个参与者以隐私保留方式基于其本地数据协作培训模型。迄今为止，VFL已成为一项事实上的解决方案，以便在组织之间安全地学习模型，允许在不影响任何个人组织的隐私的情况下共享知识。尽管VFL系统的发展繁荣发展，但我们发现参与者的某些投入，名叫对抗的主导投入（ADIS），可以将联合推断占主持旨在的意志的方向，并迫使其他（受害者）参与者进行可忽略不计的捐款，失败奖励通常提供他们在合作学习情景中的贡献的重要性。通过首先在典型的VFL系统中证明其存在，我们对ADI进行了系统研究。然后，我们提出基于梯度的方法来综合各种格式的ADI并利用公共VFL系统。我们进一步推出了Greybox Fuzz测试，以“受害者”参与者的弹性分数为指导，以扰乱对抗控制的输入，并以隐私保存方式系统地探索VFL攻击表面。我们对临界参数和环境在合成ADIS中的影响进行了深入的研究。我们的研究揭示了新的VFL攻击机会，在违反之前促进了未知威胁的识别，并建立了更安全的VFL系统。

作为服务的云计算和机器学习的繁荣发展导致媒体软件的广泛使用来处理机密媒体数据。本文探讨了对媒体软件启动侧通道分析（SCA）以重建机密介质输入的侵略性的能力。代表学习和感知学习的最新进展激发了我们考虑从侧通道迹线的媒体输入的重建作为跨模式歧管学习任务，可以以统一的方式通过训练的自动介质框架来寻址，以便学习媒体输入之间的映射和侧沟道观测。我们进一步提升了自动统计学家，注意本地化对SCA的主要贡献的程序点，从而自动查明媒体软件中的信息泄漏点。我们还提出了一种新颖且非常有效的防御技术，称为感知致盲，可以使媒体输入具有感知掩模和减轻基于多种学习的SCA。我们的评估利用三个流行的媒体软件重建图像，音频和文本格式的输入。我们分析了三个常见的侧面通道 - 缓存库，缓存行和页面表 - 以及仅由标准Prime +探针记录的用户空间缓存设置访问。我们的框架成功地从评估的媒体软件重建了高质量的机密输入，并自动查明了他们脆弱的程序点，其中许多是公众所未知的。我们进一步表明，感知致盲可以减轻基于流形的学习的SCA，额外的成本可忽略不计。

Markov决策过程（MDP）为建模顺序决策问题提供了一种数学框架，其中许多是对安全性和安全性至关重要，例如自主驾驶和机器人控制。人工智能研究的快速发展已经创造了解决MDP的有效方法，例如深神经网络（DNN），加固学习（RL）和仿制学习（IL）。然而，这些用于解决MDP的流行模型既不彻底测试也不是严格的可靠性。我们呈现MDPFuzzer，这是求解MDP的模型的第一个Blackbox Fuzz测试框架。 MDPFuzzer通过检查目标模型是否进入异常和危险状态来形成oracelles。在模糊期间，MDPFuzzer通过测量可以减少累积奖励或形成新的状态序列来确定哪个突变状态。我们设计有效的技术来使用高斯混合模型（GMM）和动态期望 - 最大化（Dynem）来量化状态序列的“新鲜度”。我们还通过估计各种目标模型的局部敏感度，优先考虑具有泄露崩溃的高潜力。 MDPFuzzer在五种最先进的模型中进行了评估，用于解决MDP，包括监督DNN，RL，IL和多代理RL。我们的评估包括自动驾驶，飞机碰撞避免和经常用于基准测试的两个游戏的情况。在12小时的运行期间，我们在每个模型上找到超过80次碰撞触发状态序列。我们展示了鼓舞的发现，碰撞触发状态虽然正常，但与正常状态相比，诱导不同的神经元激活模式。我们进一步开发了异常行为检测器，以硬化所有评估的模型，并使用MDPFuzzer的调查结果修复它们，以显着提高其鲁棒性而不会牺牲精度。

我们开发DeepTraversal，一个反馈驱动的框架来测试DNN。DeepTraversal首先启动离线阶段，以将各种形式的媒体数据映射到歧管。然后，在其在线测试阶段，DeameTraversal遍历准备的歧管空间以最大化DNN覆盖标准和触发预测误差。在我们的评估中，使用DNN执行各种任务（例如，分类，自动驾驶，机器翻译）和不同类型（图像，音频，文本）的媒体数据。DeepTraversal表现出比现有的方法相对于流行DNN覆盖标准的方法更好，并且它可以发现更大的数量和更高质量的错误触发输入。经过测试的DNN模型，经过深度干扰的调查结果，实现更好的准确性

本文总结了DNN测试标准的八种设计要求，考虑到分配性能和实际问题。然后，我们提出了一种新的标准NLC，满足所有这些设计要求。NLC将单个DNN层视为基本计算单元（而不是单个神经元），并捕获神经元输出分布的四个关键特征。因此，NLC表示为神经覆盖，这更准确地描述神经网络如何通过近似分布而不是神经元来理解输入。我们证明NLC与跨多个任务（分类和发电）和数据格式（图像和文本）的测试套件的多样性相关。它发现DNN预测误差的能力是有前途的。由NLC引导的测试输入突变导致暴露错误行为的更高质量和多样性。

预测短期交互会话的下一个交互是基于会话的推荐中的一个具有挑战性的任务。几乎所有现有的作品都依赖于项目转换模式，并在建模用户偏好时忽略用户历史会话的影响，这通常会导致非个性化推荐。此外，基于现有的个性化会话的推荐人仅基于当前用户的会话捕获用户首选项，而是忽略来自其他用户的历史会话的有用物品转换模式。为了解决这些问题，我们提出了一种新颖的异构全球图形神经网络（HG-GNN）以以微妙的方式利用所有会话的物品过渡，以便更好地推断用户偏好与当前和历史会话。为了有效利用所有用户的所有会话转换，我们提出了一种新的异构全局图，该图包含会话，用户项交互和全局共同发生项目的项目转换。此外，为了综合地从会话中捕获用户偏好，我们建议通过两个图形增强偏好编码器学习来自全局图的两个用户表示。具体地，我们在异构全球图上设计一种新的异构图形神经网络（HGNN），以了解具有丰富语义的长期用户偏好和项目表示。基于HGNN，我们提出了当前偏好编码器和历史偏好编码器，分别捕获来自当前和历史会话的不同级别的用户偏好。为实现个性化建议，我们将用户当前偏好和历史利益的表示集成到生成最终用户首选项表示。三个真实数据集的广泛实验结果表明，我们的模型优于其他最先进的方法。

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short) can provide a basis for the establishment of maritime-disaster warning systems, but they contain some systematic biases.The fifth-generation EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by about 5 days. To overcome this issue, a spatiotemporal deep-learning method could be used for nonlinear mapping between EC and ERA5 data, which would improve the quality of EC wind forecast data in real time. In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved double-encoder forecaster architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model. The study area was the western North Pacific (WNP), and real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021, divided into four seasons. Compared with the original EC forecasts, after correction using the MT-DETrajGRU model the wind speed and wind direction biases in the four seasons were reduced by 8-11% and 9-14%, respectively. In addition, the proposed method modelled the data uniformly under different weather conditions. The correction performance under normal and typhoon conditions was comparable, indicating that the data-driven mode constructed here is robust and generalizable.