Real-time individual endpoint prediction has always been a challenging task but of great clinic utility for both patients and healthcare providers. With 6,879 chronic kidney disease stage 4 (CKD4) patients as a use case, we explored the feasibility and performance of gated recurrent units with decay that models Weibull probability density function (GRU-D-Weibull) as a semi-parametric longitudinal model for real-time individual endpoint prediction. GRU-D-Weibull has a maximum C-index of 0.77 at 4.3 years of follow-up, compared to 0.68 achieved by competing models. The L1-loss of GRU-D-Weibull is ~66% of XGB(AFT), ~60% of MTLR, and ~30% of AFT model at CKD4 index date. The average absolute L1-loss of GRU-D-Weibull is around one year, with a minimum of 40% Parkes serious error after index date. GRU-D-Weibull is not calibrated and significantly underestimates true survival probability. Feature importance tests indicate blood pressure becomes increasingly important during follow-up, while eGFR and blood albumin are less important. Most continuous features have non-linear/parabola impact on predicted survival time, and the results are generally consistent with existing knowledge. GRU-D-Weibull as a semi-parametric temporal model shows advantages in built-in parameterization of missing, native support for asynchronously arrived measurement, capability of output both probability and point estimates at arbitrary time point for arbitrary prediction horizon, improved discrimination and point estimate accuracy after incorporating newly arrived data. Further research on its performance with more comprehensive input features, in-process or post-process calibration are warranted to benefit CKD4 or alike terminally-ill patients.

目的是对临床文本去识别的自然语言处理（NLP）模型的评估取决于临床注释的可用性，临床注释通常由于隐私问题而受到限制。 NLP沙盒是一种通过采用联合模型到数据的方法来减轻NLP模型缺乏数据和评估框架的方法。这使得无偏见的联合模型评估无需共享多个机构的敏感数据。材料和方法我们利用Synapse协作框架，容器化软件和OpenAPI Generator来构建NLP沙盒（NLPSANDBOX.IO）。我们使用来自三个机构的数据评估了两个最先进的NLP去识别注释模型Philter和Neuroner。我们使用来自外部验证站点的数据进一步验证了模型性能。结果我们通过去识别临床模型评估证明了NLP沙箱的有用性。外部开发人员能够将其模型纳入NLP沙盒模板中，并提供用户体验反馈。讨论我们证明了使用NLP沙箱对临床文本去识别模型进行多站点评估的可行性，而无需共享数据。标准化模型和数据模式可以使模型传输和实现平稳。为了概括NLP沙箱，数据所有者和模型开发人员需要进行工作，以开发合适和标准化的模式，并调整其数据或模型以适合模式。结论NLP沙箱降低了利用临床数据进行NLP模型评估的障碍，并促进了联合会的NLP模型的联合，多站点，无偏见的评估。

目标为可以处理多答题问题的临床问答（QA）系统的开发和评估创建数据集。我们利用2018年国家NLP临床挑战（N2C2）语料库的注释关系来产生QA数据集。 1-0和1-o-n药物 - 理性关系形成了不可批售和多答案的条目，它代表了现有临床QA数据集缺乏的具有挑战性的情景。结果结果rxwhyqa dataSet包含91,440个QA条目，其中一半是未签发的，并且应答的21％（n = 19,269）需要多个答案。数据集符合社区审查的斯坦福问题应答DataSet（Squad）格式。讨论RXWhyQA对于比较需要处理零和多答案挑战的不同系统非常有用，要求对误报和假阴性答案的双重缓解。结论我们创建并共用了一个临床QA数据集，重点是多答题问题，以代表真实世界的情景。

虽然我们注意临床自然语言处理（NLP）的最新进展，但我们可以注意到临床和翻译研究界的一些抵抗，因为透明度，可解释性和可用性有限，采用NLP模型。在这项研究中，我们提出了一种开放的自然语言处理开发框架。我们通过实施NLP算法为国家Covid队列协作（N3C）进行了评估。基于Covid-19相关临床笔记的信息提取的利益，我们的工作包括1）使用Covid-19标志和症状作为用例的开放数据注释过程，2）一个社区驱动的规则集合平台，3）合成文本数据生成工作流程，用于生成信息提取任务的文本而不涉及人为受试者。 Corpora来自来自三个不同机构的文本（Mayo Clinic，肯塔基州大学，明尼苏达大学）。用单个机构（Mayo）规则集进行了金标准注释。这导致了0.876,0.706和0.694的F-Scors分别用于Mayo，Minnesota和肯塔基测试数据集。作为N3C NLP子群体的联盟努力的研究表明，创建联邦NLP算法开发和基准测试平台的可行性，以增强多机构临床NLP研究和采用。虽然我们在这项工作中使用Covid-19作为用例，但我们的框架足以适用于临床NLP的其他兴趣领域。

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the feasibility of FL in theory, in the industrial practice of Metaverse, the problems of non-independent and identically distributed (non-i.i.d.) data, learning forgetting caused by streaming industrial data, and scarce communication bandwidth remain key barriers to realize practical FL. Facing the above three challenges simultaneously, this paper presents a high-performance and efficient system named HFEDMS for incorporating practical FL into Industrial Metaverse. HFEDMS reduces data heterogeneity through dynamic grouping and training mode conversion (Dynamic Sequential-to-Parallel Training, STP). Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics and calibrates classifier parameters (Semantic Compression and Compensation, SCC). Finally, the network parameters of the feature extractor and classifier are synchronized in different frequencies (Layer-wiseAlternative Synchronization Protocol, LASP) to reduce communication costs. These techniques make FL more adaptable to the heterogeneous streaming data continuously generated by industrial equipment, and are also more efficient in communication than traditional methods (e.g., Federated Averaging). Extensive experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show that HFEDMS improves the classification accuracy by at least 6.4% compared with 8 benchmarks and saves both the overall runtime and transfer bytes by up to 98%, proving its superiority in precision and efficiency.

Closed-circuit video (CCTV) inspection has been the most popular technique for visually evaluating the interior status of pipelines in recent decades. Certified inspectors prepare the pipe repair document based on the CCTV inspection. The traditional manual method of assessing sewage structural conditions from pipe repair documents takes a long time and is prone to human mistakes. The automatic identification of necessary texts has received little attention. By building an automated framework employing Natural Language Processing (NLP), this study presents an effective technique to automate the identification of the pipe defect rating of the pipe repair documents. NLP technologies are employed to break down textual material into grammatical units in this research. Further analysis entails using words to discover pipe defect symptoms and their frequency and then combining that information into a single score. Our model achieves 95.0% accuracy,94.9% sensitivity, 94.4% specificity, 95.9% precision score, and 95.7% F1 score, showing the potential of the proposed model to be used in large-scale pipe repair documents for accurate and efficient pipeline failure detection to improve the quality of the pipeline. Keywords: Sewer pipe inspection, Defect detection, Natural language processing, Text recognition

联合学习（FL）是一种机器学习技术，它使参与者能够在不交换私人数据的情况下协作培训高质量的模型。利用跨索洛FL（CS-FL）设置的参与者是具有不同任务需求的独立组织，他们不仅关心数据隐私，而且由于知识产权的考虑而独立培训其独特的模型。大多数现有的FL方法无法满足上述方案。在本文中，我们提出了一种基于未标记数据的伪标记的FL方法，该方法是通过诸如辅助的过程。据我们所知，这是第一种与异质任务，异质模型和异质培训算法同时兼容的第一种FL方法。实验结果表明，所提出的方法比竞争能力更好。对于非独立和相同分布的（IID）设置和异质模型而言，尤其如此，其中提出的方法可实现35％的性能提高。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

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.