Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

降解的图像通常存在于字符图像的一般来源中，从而导致特征识别结果不令人满意。现有的方法有专门的努力来恢复降级的角色图像。但是，这些方法获得的降解结果似乎并不能提高字符识别性能。这主要是因为当前方法仅着眼于像素级信息，而忽略了角色的关键特征，例如其字形，从而在脱索过程中导致字符标志性损害。在本文中，我们介绍了一个基于字形融合和注意力机制（即Churformer）的新型通用框架，以精确地恢复角色图像而不改变其固有的字形。与现有的框架不同，Charformer引入了一个并行目标任务，用于捕获其他信息并将其注入DICONISE骨架的图像，这将在字符图像DeNoising期间保持角色字形的一致性。此外，我们利用基于注意力的网络进行全局本地特征交互，这将有助于处理盲目的denoising和增强deNoSising绩效。我们将Charformer与多个数据集上的最新方法进行比较。实验结果表明了杂形和质量上的优势。

光学特征识别系统的重要初步步骤是检测文本行。为了在缺少标签的历史数据的背景下解决此任务，我们提出了一种能够提高行检测性能的自定进度学习算法。我们猜想，具有更多地面界限框的页面不太可能缺少注释。基于这个假设，我们就基面框的数量按降序排序训练示例，并将其组织成K批次。使用我们的自定进度学习方法，我们在K迭代中训练一排探测器，逐渐添加了较少的接地注释的批次。在每次迭代中，我们使用非最大最大抑制作用将地面真相边界的边界框与伪装框（由模型本身预测的边界框）组合在一起，并在下一次训练迭代中包括所得的注释。我们证明，我们的自进度学习策略在两个历史文档的两个数据集上带来了显着的绩效提高，从而提高了Yolov4的平均精度，一个数据集超过12％，另一个数据集则超过39％。