本研究提出了一种具有动态障碍物和不均匀地形的部分可观察环境中的BipeDal运动的安全任务和运动计划（夯实）的分层综合框架。高级任务规划师采用线性时间逻辑（LTL），用于机器人及其环境之间的反应游戏合成，并为导航安全和任务完成提供正式保证。为了解决环境部分可观察性，在高级导航计划者采用信仰抽象，以估计动态障碍的位置。因此，合成的动作规划器向中级运动规划器发送一组运动动作，同时基于运动过程的阶数模型（ROM）结合从安全定理提取的安全机置规范。运动计划程序采用ROM设计安全标准和采样算法，以生成准确跟踪高级动作的非周期性运动计划。为了解决外部扰动，本研究还调查了关键帧运动状态的安全顺序组成，通过可达性分析实现了对外部扰动的强大转变。最终插值一组基于ROM的超参数，以设计由轨迹优化生成的全身运动机器，并验证基于ROM的可行部署，以敏捷机器人设计的20多个自由的Cassie机器人。

Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. Different tools are used for patient triage and one of the most common ones is the emergency severity index (ESI), which has a scale of five levels, where level 1 is the most urgent and level 5 is the least urgent. This paper proposes a framework for utilizing machine learning to develop an e-triage tool that can be used at EDs. A large retrospective dataset of ED patient visits is obtained from the electronic health record of a healthcare provider in the Midwest of the US for three years. However, the main challenge of using machine learning algorithms is that most of them have many parameters and without optimizing these parameters, developing a high-performance model is not possible. This paper proposes an approach to optimize the hyperparameters of machine learning. The metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The newly proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, ASA-CaB. Grid search (GS), which is a traditional approach used for machine learning fine-tunning is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The six algorithms are trained and tested using eight data groups obtained from the feature selection phase. The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, 83.2%, respectively.

The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause patients to leave before treatment is complete is vital to mitigate and potentially eliminate these adverse effects. This paper proposes a framework for studying the factors that affect LBTC outcomes in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization--one of the main challenges of machine learning model development. Three metaheuristic optimization algorithms are employed for optimizing the parameters of extreme gradient boosting (XGB), which are simulated annealing (SA), adaptive simulated annealing (ASA), and adaptive tabu simulated annealing (ATSA). The optimized XGB models are used to predict the LBTC outcomes for the patients under treatment in ED. The designed algorithms are trained and tested using four data groups resulting from the feature selection phase. The model with the best predictive performance is interpreted using SHaply Additive exPlanations (SHAP) method. The findings show that ATSA-XGB outperformed other mode configurations with an accuracy, area under the curve (AUC), sensitivity, specificity, and F1-score of 86.61%, 87.50%, 85.71%, 87.51%, and 86.60%, respectively. The degree and the direction of effects of each feature were determined and explained using the SHAP method.

With the growth of residential rooftop PV adoption in recent decades, the problem of 1 effective layout design has become increasingly important in recent years. Although a number 2 of automated methods have been introduced, these tend to rely on simplifying assumptions and 3 heuristics to improve computational tractability. We demonstrate a fully automated layout design 4 pipeline that attempts to solve a more general formulation with greater geometric flexibility that 5 accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses 6 MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis 7 rather than imposing any predefined layouts. Our results demonstrate that although several common 8 heuristics are often effective, they may not be universally suitable due to complications resulting 9 from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the 10 literature and propose a potential new rule of thumb that may help improve rooftop solar energy 11 potential when shading effects are considered.

在过去的十年中，基于深度学习的算法在遥感图像分析的不同领域中广泛流行。最近，最初在自然语言处理中引入的基于变形金刚的体系结构遍布计算机视觉领域，在该字段中，自我发挥的机制已被用作替代流行的卷积操作员来捕获长期依赖性。受到计算机视觉的最新进展的启发，遥感社区还见证了对各种任务的视觉变压器的探索。尽管许多调查都集中在计算机视觉中的变压器上，但据我们所知，我们是第一个对基于遥感中变压器的最新进展进行系统评价的人。我们的调查涵盖了60多种基于变形金刚的60多种方法，用于遥感子方面的不同遥感问题：非常高分辨率（VHR），高光谱（HSI）和合成孔径雷达（SAR）图像。我们通过讨论遥感中变压器的不同挑战和开放问题来结束调查。此外，我们打算在遥感论文中频繁更新和维护最新的变压器，及其各自的代码：https：//github.com/virobo-15/transformer-in-in-remote-sensing

静态机器学习模型的理想化，经过训练并永远部署，这是不切实际的。随着输入分布的变化，该模型不仅会失去准确性，因此减少对受保护类别的偏见的任何约束都可能无法按预期工作。因此，研究人员已经开始探索随着时间的推移保持算法公平性的方法。一项工作重点是动态学习：每批次后重新训练，而另一个工作则介绍了强大的学习，该学习试图使算法与未来所有可能的变化进行鲁棒性。动态学习试图在发生后不久减少偏见，而健壮的学习通常会产生（过于）保守的模型。我们提出了一种预期的动态学习方法，用于纠正算法在发生偏见之前减轻算法。具体而言，我们利用有关下一个周期中人口亚组（例如，男性和女性申请人的相对比率）的相对分布的预期，以确定正确的参数，以实现重要性权衡方法。对多个现实世界数据集的实验的结果表明，这种方法有望预期偏差校正。