本研究提出了一种新颖的趋势检测和可视化方法 - 更具体地说，随着时间的推移，主题的变化建模。如果当前用于识别和可视化趋势的模型仅传达基于用法随机计数的单一单词的普及，那么本研究中的方法说明了一个主题正在发展的普及和方向。在这种情况下，方向是选定语料库中的独特亚主题。通过使用K-均值聚类和余弦相似性对主题的移动进行建模来对这种趋势进行建模，以将簇之间的距离分组。在收敛的场景中，可以推断出整个主题是在网络上的（主题之间的令牌，可以互换）。相反，一个不同的场景暗示每个主题的各自的令牌在相同的上下文中都不会找到（彼此之间越来越不同）。该方法对20个新闻组数据集中存在的各种媒体房屋的一组文章进行了测试。

Objective: Social Determinants of Health (SDOH) influence personal health outcomes and health systems interactions. Health systems capture SDOH information through structured data and unstructured clinical notes; however, clinical notes often contain a more comprehensive representation of several key SDOH. The objective of this work is to assess the SDOH information gain achievable by extracting structured semantic representations of SDOH from the clinical narrative and combining these extracted representations with available structured data. Materials and Methods: We developed a natural language processing (NLP) information extraction model for SDOH that utilizes a deep learning entity and relation extraction architecture. In an electronic health record (EHR) case study, we applied the SDOH extractor to a large existing clinical data set with over 200,000 patients and 400,000 notes and compared the extracted information with available structured data. Results: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found 19\% of current tobacco users, 10\% of drug users, and 32\% of homeless patients only include documentation of these risk factors in the clinical narrative. Conclusions: Patients who are at-risk for negative health outcomes due to SDOH may be better served if health systems are able to identify SDOH risk factors and associated social needs. Structured semantic representations of text-encoded SDOH information can augment existing structured, and this more comprehensive SDOH representation can assist health systems in identifying and addressing social needs.

Multi-object state estimation is a fundamental problem for robotic applications where a robot must interact with other moving objects. Typically, other objects' relevant state features are not directly observable, and must instead be inferred from observations. Particle filtering can perform such inference given approximate transition and observation models. However, these models are often unknown a priori, yielding a difficult parameter estimation problem since observations jointly carry transition and observation noise. In this work, we consider learning maximum-likelihood parameters using particle methods. Recent methods addressing this problem typically differentiate through time in a particle filter, which requires workarounds to the non-differentiable resampling step, that yield biased or high variance gradient estimates. By contrast, we exploit Fisher's identity to obtain a particle-based approximation of the score function (the gradient of the log likelihood) that yields a low variance estimate while only requiring stepwise differentiation through the transition and observation models. We apply our method to real data collected from autonomous vehicles (AVs) and show that it learns better models than existing techniques and is more stable in training, yielding an effective smoother for tracking the trajectories of vehicles around an AV.

越来越多的工作已经认识到利用机器学习（ML）进步的重要性，以满足提取访问控制属性，策略挖掘，策略验证，访问决策等有效自动化的需求。在这项工作中，我们调查和总结了各种ML解决不同访问控制问题的方法。我们提出了ML模型在访问控制域中应用的新分类学。我们重点介绍当前的局限性和公开挑战，例如缺乏公共现实世界数据集，基于ML的访问控制系统的管理，了解黑盒ML模型的决策等，并列举未来的研究方向。

脑肿瘤分割是医学图像处理中的重要任务。早期诊断脑肿瘤在改善治疗可能性方面发挥着至关重要的作用，并提高了患者的存活率。手动分割癌症诊断的脑肿瘤，从大量MRI图像中，都是难以耗时的任务。需要自动脑肿瘤图像分割。该项目的目的是提供MRI图像的自动脑肿瘤分割方法，以帮助准确且快速地定位肿瘤。

分析学习算法的挑战之一是客观值和随机噪声之间的循环纠缠。这也被称为“鸡肉和鸡蛋”现象，传统上，没有原则解决这个问题的方法。人们通过利用动态的特殊结构来解决问题，因此很难概括分析。在这项工作中，我们提出了一个简化的三步食谱，以解决“鸡肉和鸡蛋”问题，并为分析学习算法的随机动力学提供了一般框架。我们的框架构成了概率理论的标准技术，例如停止时间和Martingale浓度。我们通过对三个截然不同的学习问题进行统一分析，并具有强大的统一高概率收敛保证，从而证明了我们框架的力量和灵活性。这些问题是强烈凸功能，流主成分分析和带有随机梯度下降更新的线性匪徒的随机梯度下降。我们要么在所有三个动态上都改进或匹配最新界限。