在现代世界中，数据科学和分析以优化或预测结果的应用无处不在。数据科学和分析已经优化了市场中存在的几乎所有领域。在我们的调查中，我们专注于如何在体育领域采用分析领域，以及它如何促进游戏的转型，从评估现场玩家及其选择到赢得团队的预测以及大型体育比赛的门票和商业方面的营销。我们将介绍体育分析领域采用的不同运动的分析工具，算法和方法论，并介绍我们对同一体育的看法，我们还将比较和对比这些现有方法。通过这样做，我们还将介绍任何希望尝试体育数据并分析游戏的各个方面的人考虑的最佳工具，算法和分析方法。

数字技术的发展和体育运动的日益普及激发了创新者，通过引入幻想体育平台FSP，将体育倾向的用户带到一个全新的不同层次上。数据科学和分析的应用在现代世界中无处不在。数据科学和分析打开门，以获得更深入的理解和帮助，以帮助决策过程。我们坚信，我们可以采用数据科学来预测FSP上的获胜幻想板球团队，Dream 11.我们建立了一个预测模型，可以预测潜在游戏中玩家的性能。我们结合了贪婪和背包算法的组合，开出了11名球员的组合，创建了一支幻想板球团队，这是最重要的统计赔率，即最大的团队成为最强的团队，从而使我们有更大的机会赢得梦想中的赌注。 11 FSP。我们使用Pycaret Python库来帮助我们理解并采用最佳回归算法来进行问题陈述，以做出精确的预测。此外，我们使用Plotly Python图书馆为我们提供了对团队的视觉见解，并且玩家通过计算前瞻性游戏的统计和主观因素来表演。交互作用图帮助我们提高了我们的预测模型的建议。您要么赢得大，赢得小巧，要么根据预期游戏中为您的幻想团队选出的球员的表现而失去赌注，而我们的模型增加了您赢得大的可能性。

Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn these disturbances online, in a risk-aware context. First, we introduce the concept of a Surface-at-Risk, a risk measure for stochastic processes that extends Value-at-Risk -- a commonly utilized risk measure in the risk-aware controls community. Second, we model the norm of the state discrepancy between the model and the true system evolution as a scalar-valued stochastic process and determine an upper bound to its Surface-at-Risk via Gaussian Process Regression. Third, we provide theoretical results on the accuracy of our fitted surface subject to mild assumptions that are verifiable with respect to the data sets collected during system operation. Finally, we experimentally verify our procedure by augmenting a drone's controller and highlight performance increases achieved via our risk-aware approach after collecting less than a minute of operating data.

Reinforcement Learning (RL) can solve complex tasks but does not intrinsically provide any guarantees on system behavior. For real-world systems that fulfill safety-critical tasks, such guarantees on safety specifications are necessary. To bridge this gap, we propose a verifiably safe RL procedure with probabilistic guarantees. First, our approach probabilistically verifies a candidate controller with respect to a temporal logic specification, while randomizing the controller's inputs within a bounded set. Then, we use RL to improve the performance of this probabilistically verified, i.e. safe, controller and explore in the same bounded set around the controller's input as was randomized over in the verification step. Finally, we calculate probabilistic safety guarantees with respect to temporal logic specifications for the learned agent. Our approach is efficient for continuous action and state spaces and separates safety verification and performance improvement into two independent steps. We evaluate our approach on a safe evasion task where a robot has to evade a dynamic obstacle in a specific manner while trying to reach a goal. The results show that our verifiably safe RL approach leads to efficient learning and performance improvements while maintaining safety specifications.

Nowadays, the PQ flexibility from the distributed energy resources (DERs) in the high voltage (HV) grids plays a more critical and significant role in grid congestion management in TSO grids. This work proposed a multi-stage deep reinforcement learning approach to estimate the PQ flexibility (PQ area) at the TSO-DSO interfaces and identifies the DER PQ setpoints for each operating point in a way, that DERs in the meshed HV grid can be coordinated to offer flexibility for the transmission grid. In the estimation process, we consider the steady-state grid limits and the robustness in the resulting voltage profile against uncertainties and the N-1 security criterion regarding thermal line loading, essential for real-life grid operational planning applications. Using deep reinforcement learning (DRL) for PQ flexibility estimation is the first of its kind. Furthermore, our approach of considering N-1 security criterion for meshed grids and robustness against uncertainty directly in the optimization tasks offers a new perspective besides the common relaxation schema in finding a solution with mathematical optimal power flow (OPF). Finally, significant improvements in the computational efficiency in estimation PQ area are the highlights of the proposed method.

在这封信中，作者提出了一种两步的方法来评估和验证真正的系统，以满足其运作目标的能力。具体而言，每当系统目标具有可量化的满意度量时，即信号时间逻辑规范，屏障函数等 - 作者通过详细说明的贝叶斯优化程序，制定了两个单独的优化问题。这种双向方法具有量化系统模拟器与其硬件对应物之间的SIM2重组的增加的益处。我们的贡献是双重的。首先，我们在解决这些优化问题时表现出了我们概述的优化过程的可重复性。其次，我们表明相同的过程可以通过识别在不同环境中运行的模拟器和其硬件对应物之间的SIM2重差来区分不同环境之间。