自适应实验可以增加当前学生从教学干预的现场实验中获得更好结果的机会。在此类实验中，在收集更多数据时将学生分配到条件变化的可能性，因此可以将学生分配给可能表现更好的干预措施。数字教育环境降低了进行此类适应性实验的障碍，但很少在教育中应用。原因之一可能是研究人员可以访问很少的现实案例研究，这些案例研究说明了在特定情况下这些实验的优势和缺点。我们通过使用Thompson采样算法进行自适应实验来评估学生在学生中提醒的效果，并将其与传统的统一随机实验进行比较。我们将其作为有关如何进行此类实验的案例研究，并提出了有关自适应随机实验可能或多或少有用的条件的一系列开放问题。

在教育环境中进行随机实验提出了一个问题，即我们如何使用机器学习技术来改善教育干预措施。使用自适应实验中的汤普森采样（TS）（TS）等多臂匪徒（MAB）算法，即使在干预完成之前，也可以通过增加对最佳状态（ARM）的分配可能性来获得更好的结果的机会。这是比传统的A/B测试的优势，该测试可能会分配相等数量的学生为最佳和非最佳条件。问题是勘探探索权衡取舍。尽管自适应政策旨在收集足够的信息来分配更多的学生以可靠地提供更好的武器，但过去的工作表明，这可能还不够探索，无法就武器是否有所不同，得出可靠的结论。因此，在整个实验中提供额外的均匀随机（UR）探索是很有趣的。本文展示了一个真实的自适应实验，该实验是关于学生如何与教师每周的电子邮件提醒互动以建立时间管理习惯的。我们感兴趣的指标是打开电子邮件率，它跟踪由不同主题行的武器。这些是按照不同的分配算法传递的：ur，ts和我们确定为ts {\ dag} - 结合了TS和UR奖励以更新其先验者。我们强调了这些自适应算法的问题 - 在没有显着差异时可能会剥削手臂 - 并解决它们的原因和后果。未来的方向包括研究最佳臂的早期选择不是理想的情况以及自适应算法如何解决它们的情况。

基于决策树（DT）的分类和回归思想，最近提议在总体分类和回归任务中提供更高的性能。以更高的计算复杂性为代价，达到了其性能的改进。在这项工作中，我们研究了两种加速SLM的方法。首先，我们采用粒子群优化（PSO）算法来加快对当前尺寸的线性组合表示的判别尺寸的搜索。线性组合中最佳权重的搜索在计算上很重。它是通过原始SLM中的概率搜索来完成的。 PSO的SLM加速需要减少10-20倍的迭代。其次，我们利用SLM实施中的并行处理。实验结果表明，加速的SLM方法在训练时间中达到577的速度系数，同时保持原始SLM的可比分类/回归性能。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

像汤普森采样等多武装强盗算法可用于进行自适应实验，其中最大化奖励意味着数据用于逐步为更多参与者分配更有效的武器。这些转让策略增加了统计假设试验的风险，鉴定武器之间的差异，当没有一个时，并且在真正是一个是一个时，武器的差异存在差异。我们为2臂实验仿真，探讨了两种算法，这些算法结合了统计分析的均匀随机化的益处，具有通过Thompson采样（TS）实现的奖励最大化的益处。首先，前两种汤普森采样增加了固定量的均匀随机分配（UR）随时间均匀传播。二，一种新的启发式算法，称为TS Postdiff（差异后概率）。 Ts Postdiff采用贝叶斯方法来混合TS和UR：使用UR分配分配参与者的概率是后部概率，即两个臂之间的差异是“小”（低于某个阈值），允许在存在时探索更多的探索很少或没有奖励获得。我们发现TS PostDiff方法跨多种效果大小进行良好，因此不需要根据真实效果大小的猜测进行调整。

在低收入环境中，电力公用事业最关键的信息是客户的预期消耗。在环境中难以做的电力消耗评估，其中大部分家庭尚未拥有电连接。在这种情况下，预期消费的绝对水平可以从5-100千瓦时/月的范围内，导致这些客户之间的可变性。如果在具有较高消耗的人的那些上，珍贵的资源是有利害的。这是第一次研究它在低收入环境中的善意，试图预测建筑物的消费，而不是总行政区域的消费。我们通过在肯尼亚20,000个地理参考电力客户的情况下培训卷积神经网络（CNN）上电气化的白天卫星图像，其中包括20,000个地理参考电力客户（肯尼亚住宅客户的0.01％）。这是可能的两阶段方法，它使用了一种新的建筑分割方法来利用更大的不成本卫星图像耗尽，以充分利用大多数稀缺和昂贵的客户数据。我们的方法表明，竞争精度可以在建筑水平上实现，解决消费变异性的挑战。这项工作表明，建筑的特征和周围的上下文在预测消费水平方面都很重要。我们还评估将较低分辨率的地理空间数据集添加到培训过程中，包括夜间灯和人口普查数据。结果已经有助于通知网站选择和分配级别规划，通过肯尼亚各个结构水平的粒度预测，没有理由不能扩展到其他国家。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

As various city agencies and mobility operators navigate toward innovative mobility solutions, there is a need for strategic flexibility in well-timed investment decisions in the design and timing of mobility service regions, i.e. cast as "real options" (RO). This problem becomes increasingly challenging with multiple interacting RO in such investments. We propose a scalable machine learning based RO framework for multi-period sequential service region design & timing problem for mobility-on-demand services, framed as a Markov decision process with non-stationary stochastic variables. A value function approximation policy from literature uses multi-option least squares Monte Carlo simulation to get a policy value for a set of interdependent investment decisions as deferral options (CR policy). The goal is to determine the optimal selection and timing of a set of zones to include in a service region. However, prior work required explicit enumeration of all possible sequences of investments. To address the combinatorial complexity of such enumeration, we propose a new variant "deep" RO policy using an efficient recurrent neural network (RNN) based ML method (CR-RNN policy) to sample sequences to forego the need for enumeration, making network design & timing policy tractable for large scale implementation. Experiments on multiple service region scenarios in New York City (NYC) shows the proposed policy substantially reduces the overall computational cost (time reduction for RO evaluation of > 90% of total investment sequences is achieved), with zero to near-zero gap compared to the benchmark. A case study of sequential service region design for expansion of MoD services in Brooklyn, NYC show that using the CR-RNN policy to determine optimal RO investment strategy yields a similar performance (0.5% within CR policy value) with significantly reduced computation time (about 5.4 times faster).

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

With growing sophistication and volume of cyber attacks combined with complex network structures, it is becoming extremely difficult for security analysts to corroborate evidences to identify multistage campaigns on their network. This work develops HeAT (Heated Alert Triage): given a critical indicator of compromise (IoC), e.g., a severe IDS alert, HeAT produces a HeATed Attack Campaign (HAC) depicting the multistage activities that led up to the critical event. We define the concept of "Alert Episode Heat" to represent the analysts opinion of how much an event contributes to the attack campaign of the critical IoC given their knowledge of the network and security expertise. Leveraging a network-agnostic feature set, HeAT learns the essence of analyst's assessment of "HeAT" for a small set of IoC's, and applies the learned model to extract insightful attack campaigns for IoC's not seen before, even across networks by transferring what have been learned. We demonstrate the capabilities of HeAT with data collected in Collegiate Penetration Testing Competition (CPTC) and through collaboration with a real-world SOC. We developed HeAT-Gain metrics to demonstrate how analysts may assess and benefit from the extracted attack campaigns in comparison to common practices where IP addresses are used to corroborate evidences. Our results demonstrates the practical uses of HeAT by finding campaigns that span across diverse attack stages, remove a significant volume of irrelevant alerts, and achieve coherency to the analyst's original assessments.