Tuberculosis (TB), an infectious bacterial disease, is a significant cause of death, especially in low-income countries, with an estimated ten million new cases reported globally in $2020$. While TB is treatable, non-adherence to the medication regimen is a significant cause of morbidity and mortality. Thus, proactively identifying patients at risk of dropping off their medication regimen enables corrective measures to mitigate adverse outcomes. Using a proxy measure of extreme non-adherence and a dataset of nearly $700,000$ patients from four states in India, we formulate and solve the machine learning (ML) problem of early prediction of non-adherence based on a custom rank-based metric. We train ML models and evaluate against baselines, achieving a $\sim 100\%$ lift over rule-based baselines and $\sim 214\%$ over a random classifier, taking into account country-wide large-scale future deployment. We deal with various issues in the process, including data quality, high-cardinality categorical data, low target prevalence, distribution shift, variation across cohorts, algorithmic fairness, and the need for robustness and explainability. Our findings indicate that risk stratification of non-adherent patients is a viable, deployable-at-scale ML solution.

无监督的在线流媒体模型被认为是样本在线时尚到达超过$ $时​​隙到达的地方。有M $分类器，其混乱矩阵未知先验。在每个插槽中，最多一个样本可以由任何分类器标记。样品的精度是从各种分类器获得的该组标签的函数。样本的效用是其精度的标量倍数减去响应时间（出发时隙和到达槽的差），其中出发插槽也由算法决定。由于每个分类器可以在每个时隙最多一个样本中标记，因此在获得更大一组特定样本的标签之间存在权衡，以提高其准确性及其响应时间。考虑最大化所有样本的实用程序总和的问题，其中学习混淆矩阵，样本分类器匹配分配和样本出发插槽决策彼此相互依赖。所提出的算法首先了解了混淆矩阵，然后使用贪婪算法进行采样分类器匹配。一旦其增量效用变为非正数，样本会出发。我们表明，所提出的算法的竞争比率是$ \ FRAC {1} {2} - {\ Mathcal o} \ left（\ frac {\ log t} {t} \ oled）$。

由于在过去几年中兴趣的增长，基于梯度的政策控制方法也得到了控制问题的普及。并且正确地，由于梯度策略方法具有以端到端的方式优化利息度量的优点，并且在没有完全了解底层系统的情况下相对容易实现。在本文中，我们研究了基于梯度的策略优化方法的全局融合，用于离散时间和无模型的Markovian跳转线性系统（MJLS）的二次控制。我们超越了由于多个州而产生的宗教挑战，并通过缺乏系统动态缺乏了解，并使用梯度下降和自然政策梯度方法显示全球策略融合。我们还提供模拟研究来证实我们的索赔。

We introduce an end-to-end computational framework that enables hyperparameter optimization with the DeepHyper library, accelerated training, and interpretable AI inference with a suite of state-of-the-art AI models, including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-Net. We use these AI models and the benchmark QM9, hMOF, and MD17 datasets to showcase the prediction of user-specified materials properties in modern computing environments, and to demonstrate translational applications for the modeling of small molecules, crystals and metal organic frameworks with a unified, stand-alone framework. We deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership class computing environments.

Detecting actions in untrimmed videos should not be limited to a small, closed set of classes. We present a simple, yet effective strategy for open-vocabulary temporal action detection utilizing pretrained image-text co-embeddings. Despite being trained on static images rather than videos, we show that image-text co-embeddings enable openvocabulary performance competitive with fully-supervised models. We show that the performance can be further improved by ensembling the image-text features with features encoding local motion, like optical flow based features, or other modalities, like audio. In addition, we propose a more reasonable open-vocabulary evaluation setting for the ActivityNet data set, where the category splits are based on similarity rather than random assignment.

The technocrat epoch is overflowing with new technologies and such cutting-edge facilities accompany the risks and pitfalls. Robotic process automation is another innovation that empowers the computerization of high-volume, manual, repeatable, everyday practice, rule-based, and unmotivating human errands. The principal objective of Robotic Process Automation is to supplant monotonous human errands with a virtual labor force or a computerized specialist playing out a similar work as the human laborer used to perform. This permits human laborers to zero in on troublesome undertakings and critical thinking. Robotic Process Automation instruments are viewed as straightforward and strong for explicit business process computerization. Robotic Process Automation comprises intelligence to decide if a process should occur. It has the capability to analyze the data presented and provide a decision based on the logic parameters set in place by the developer. Moreover, it does not demand for system integration, like other forms of automation. Be that as it may since the innovation is yet arising, the Robotic Process Automation faces a few difficulties during the execution.

模型预测控制（MPC）方法被广泛用于机器人技术，因为它们允许在机器人移动时计算更新的轨迹。他们通常需要启发式参考，以进行跟踪术语和成本功能参数的正确调整，以便获得良好的性能。例如，当腿部机器人必须对环境的干扰（例如，推动后恢复）或以静态不稳定步态跟踪某个目标时，算法的有效性会降解。在这项工作中，我们提出了一个新型基于优化的参考生成器，名为州长，该发电机利用线性倒置的摆模型来计算质量中心的参考轨迹，同时考虑了步态的可能不足（例如，在小跑中）。获得的轨迹用作我们先前工作中提出的非线性MPC成本函数的参考[1]。我们还提出了一个公式，可以保证一定的响应时间达到目​​标，而无需调整成本条款的权重。此外，校正了立足点以将机器人朝目标推动。我们证明了在与Aliengo机器人不同情况下的模拟和实验中，我们的方法的有效性。

语言识别（LID）是自动语音识别（ASR）的建议的第一步，用于检测音频标本的口语。但是，在能够多语言语音处理的最先进的系统中，用户必须在使用它们之前明确设置一种或多种语言。因此，盖子在基于ASR的系统无法在多语言环境中解析导致语音识别失败的语言的情况下起着非常重要的作用。我们提出了一个基于注意力的卷积复发性神经网络（CRNN），该网络与音频标本的Mel频率Cepstral系数（MFCC）功能一起工作。此外，我们重现了一些最先进的方法，即卷积神经网络（CNN）和卷积复发性神经网络（CRNN），并将它们与我们提出的方法进行比较。我们对13种不同的印度语言进行了广泛的评估，我们的模型分类精度超过98％。我们的盖子模型对噪声非常强大，并在嘈杂的情况下提供了91.2％的精度。提出的模型很容易扩展到新语言。

草图是一种从个人的创造性角度传达视觉场景的媒介。添加颜色基本上增强了草图的总体表征。本文提出了通过利用轮廓绘制数据集来模仿人绘制着色草图的两种方法。我们的第一个方法通过应用k-means颜色聚类辅助的图像处理技术来呈现彩色的轮廓草图。第二种方法使用生成的对抗性网络来开发一个可以从先前未观察到的图像生成彩色草图的模型。我们评估通过定量和定性评估获得的结果。

在腿的运动中重新规划对于追踪所需的用户速度，在适应地形并拒绝外部干扰的同时至关重要。在这项工作中，我们提出并测试了实验中的实时非线性模型预测控制（NMPC），用于腿部机器人，以实现各种地形上的动态运动。我们引入了一种基于移动性的标准来定义NMPC成本，增强了二次机器人的运动，同时最大化腿部移动性并提高对地形特征的适应。我们的NMPC基于实时迭代方案，使我们能够以25美元的价格重新计划在线，\ Mathrm {Hz} $ 2 $ 2 $ 2美元的预测地平线。我们使用在质量框架中心中定义的单个刚体动态模型，以提高计算效率。在仿真中，测试NMPC以横穿一组不同尺寸的托盘，走进V形烟囱，并在崎岖的地形上招揽。在真实实验中，我们展示了我们的NMPC与移动功能的有效性，使IIT为87美元\，\ Mathrm {kg} $四分之一的机器人HIQ，以实现平坦地形上的全方位步行，横穿静态托盘，并适应在散步期间重新定位托盘。