机械模拟器是流行病学的必不可少的工具，可以在不同条件下探索复杂，动态感染的行为并导航不确定的环境。基于ODE的模型是能够快速模拟且可实现基于梯度的优化的主要范式，但可以简化有关人群同质性的假设。基于代理的模型（ABM）是一种越来越流行的替代范式，可以代表接触相互作用的异质性，并具有颗粒状细节和个人行为的代理。但是，常规的ABM框架没有可区分的，并且在可伸缩性方面提出了挑战。因此，将它们连接到辅助数据源是非平凡的。在本文中，我们介绍了GradABM，这是ABMS的新型可扩展，快速和可区分的设计。 GradABM在商品硬件上几秒钟内运行模拟，并启用快速前进和可区分的反向模拟。这使得可以与深度神经网络合并并无缝整合异质数据源以帮助校准，预测和政策评估。我们通过对实际Covid-19和流感数据集进行了广泛的实验来证明GradABM的功效。我们很乐观，这项工作将使ABM和AI社区更加紧密。

机器学习已成功构建许多顺序决策，作为监督预测，或通过加强学习的最佳决策政策识别。在数据约束的离线设置中，两种方法可能会失败，因为它们假设完全最佳行为或依赖于探索可能不存在的替代方案。我们介绍了一种固有的不同方法，该方法识别出状态空间的可能的“死角”。我们专注于重症监护病房中患者的状况，其中``“医疗死亡端”表明患者将过期，无论所有潜在的未来治疗序列如何。我们假设“治疗安全”为避免与其导致死亡事件的机会成比例的概率成比例的治疗，呈现正式证明，以及作为RL问题的帧发现。然后，我们将三个独立的深度神经模型进行自动化状态建设，死端发现和确认。我们的经验结果发现，死亡末端存在于脓毒症患者的真正临床数据中，并进一步揭示了安全处理与施用的差距。

We consider a long-term average profit maximizing admission control problem in an M/M/1 queuing system with a known arrival rate but an unknown service rate. With a fixed reward collected upon service completion and a cost per unit of time enforced on customers waiting in the queue, a dispatcher decides upon arrivals whether to admit the arriving customer or not based on the full history of observations of the queue-length of the system. \cite[Econometrica]{Naor} showed that if all the parameters of the model are known, then it is optimal to use a static threshold policy - admit if the queue-length is less than a predetermined threshold and otherwise not. We propose a learning-based dispatching algorithm and characterize its regret with respect to optimal dispatch policies for the full information model of \cite{Naor}. We show that the algorithm achieves an $O(1)$ regret when all optimal thresholds with full information are non-zero, and achieves an $O(\ln^{3+\epsilon}(N))$ regret in the case that an optimal threshold with full information is $0$ (i.e., an optimal policy is to reject all arrivals), where $N$ is the number of arrivals and $\epsilon>0$.

Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.

Gait has been used in clinical and healthcare applications to assess the physical and cognitive health of older adults. Acoustic based gait detection is a promising approach to collect gait data of older adults passively and non-intrusively. However, there has been limited work in developing acoustic based gait detectors that can operate in noisy polyphonic acoustic scenes of homes and care homes. We attribute this to the lack of good quality gait datasets from the real-world to train a gait detector on. In this paper, we put forward a novel machine learning based filter which can triage gait audio samples suitable for training machine learning models for gait detection. The filter achieves this by eliminating noisy samples at an f(1) score of 0.85 and prioritising gait samples with distinct spectral features and minimal noise. To demonstrate the effectiveness of the filter, we train and evaluate a deep learning model on gait datasets collected from older adults with and without applying the filter. The model registers an increase of 25 points in its f(1) score on unseen real-word gait data when trained with the filtered gait samples. The proposed filter will help automate the task of manual annotation of gait samples for training acoustic based gait detection models for older adults in indoor environments.

Bayesian causal structure learning aims to learn a posterior distribution over directed acyclic graphs (DAGs), and the mechanisms that define the relationship between parent and child variables. By taking a Bayesian approach, it is possible to reason about the uncertainty of the causal model. The notion of modelling the uncertainty over models is particularly crucial for causal structure learning since the model could be unidentifiable when given only a finite amount of observational data. In this paper, we introduce a novel method to jointly learn the structure and mechanisms of the causal model using Variational Bayes, which we call Variational Bayes-DAG-GFlowNet (VBG). We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model. Our results on simulated data suggest that VBG is competitive against several baselines in modelling the posterior over DAGs and mechanisms, while offering several advantages over existing methods, including the guarantee to sample acyclic graphs, and the flexibility to generalize to non-linear causal mechanisms.

动态运动原语（DMP）为编码，生成和调整复杂的最终效应轨迹提供了极大的多功能性。 DMP也非常适合从人类演示中学习操纵技巧。但是，DMP的反应性质限制了其用于工具使用和对象操纵任务的适用性，这些任务涉及非全面约束，例如切割手术刀切割或导管转向。在这项工作中，我们通过添加一个耦合项来扩展笛卡尔空间DMP公式，该耦合术语强制执行一组预定义的非独立约束。我们使用udwadia-kalaba方法获得约束强迫项的闭合形式表达式。这种方法提供了一种干净，实用的解决方案，以确保运行时的限制满意度。此外，约束强迫项的提议的分析形式可实现有效的轨迹优化，但受约束。我们通过展示如何从人类示范中学习机器人切割技能来证明这种方法的有用性。

机器学习（ML）是指根据大量数据预测有意义的输出或对复杂系统进行分类的计算机算法。 ML应用于各个领域，包括自然科学，工程，太空探索甚至游戏开发。本文的重点是在化学和生物海洋学领域使用机器学习。在预测全球固定氮水平，部分二氧化碳压力和其他化学特性时，ML的应用是一种有前途的工具。机器学习还用于生物海洋学领域，可从各种图像（即显微镜，流车和视频记录器），光谱仪和其他信号处理技术中检测浮游形式。此外，ML使用其声学成功地对哺乳动物进行了分类，在特定的环境中检测到濒临灭绝的哺乳动物和鱼类。最重要的是，使用环境数据，ML被证明是预测缺氧条件和有害藻华事件的有效方法，这是对环境监测的重要测量。此外，机器学习被用来为各种物种构建许多对其他研究人员有用的数据库，而创建新算法将帮助海洋研究界更好地理解海洋的化学和生物学。

机器人的感知目前处于在有效的潜在空间中运行的现代方法与数学建立的经典方法之间的跨道路，并提供了可解释的，可信赖的结果。在本文中，我们引入了卷积的贝叶斯内核推理（Convbki）层，该层在可分离的卷积层中明确执行贝叶斯推断，以同时提高效率，同时保持可靠性。我们将层应用于3D语义映射的任务，在该任务中，我们可以实时学习激光雷达传感器信息的语义几何概率分布。我们根据KITTI数据集的最新语义映射算法评估我们的网络，并通过类似的语义结果证明了延迟的提高。

在模拟中测试黑盒感知控制系统面临两个困难。首先，模拟中的感知输入缺乏现实世界传感器输入的保真度。其次，对于合理准确的感知系统，遇到罕见的故障轨迹可能需要进行许多模拟。本文结合了感知误差模型 - 基于传感器的检测系统的替代模型与状态依赖性自适应重要性抽样。这使我们能够有效地评估模拟中现实世界感知控制系统的罕见故障概率。我们使用配备RGB障碍物检测器的自动制动系统进行的实验表明，我们的方法可以使用廉价的模拟来计算准确的故障概率。此外，我们展示了安全指标的选择如何影响能够可靠地采样高概率失败的学习建议分布的过程。