We propose reconstruction probing, a new analysis method for contextualized representations based on reconstruction probabilities in masked language models (MLMs). This method relies on comparing the reconstruction probabilities of tokens in a given sequence when conditioned on the representation of a single token that has been fully contextualized and when conditioned on only the decontextualized lexical prior of the model. This comparison can be understood as quantifying the contribution of contextualization towards reconstruction -- the difference in the reconstruction probabilities can only be attributed to the representational change of the single token induced by contextualization. We apply this analysis to three MLMs and find that contextualization boosts reconstructability of tokens that are close to the token being reconstructed in terms of linear and syntactic distance. Furthermore, we extend our analysis to finer-grained decomposition of contextualized representations, and we find that these boosts are largely attributable to static and positional embeddings at the input layer.

Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work.

Indian e-commerce industry has evolved over the last decade and is expected to grow over the next few years. The focus has now shifted to turnaround time (TAT) due to the emergence of many third-party logistics providers and higher customer expectations. The key consideration for delivery providers is to balance their overall operating costs while meeting the promised TAT to their customers. E-commerce delivery partners operate through a network of facilities whose strategic locations help to run the operations efficiently. In this work, we identify the locations of hubs throughout the country and their corresponding mapping with the distribution centers. The objective is to minimize the total network costs with TAT adherence. We use Genetic Algorithm and leverage business constraints to reduce the solution search space and hence the solution time. The results indicate an improvement of 9.73% in TAT compliance compared with the current scenario.

已知现代深度神经网络模型将错误地将分布式（OOD）测试数据分类为具有很高信心的分数（ID）培训课程之一。这可能会对关键安全应用产生灾难性的后果。一种流行的缓解策略是训练单独的分类器，该分类器可以在测试时间检测此类OOD样本。在大多数实际设置中，在火车时间尚不清楚OOD的示例，因此，一个关键问题是：如何使用合成OOD样品来增加ID数据以训练这样的OOD检测器？在本文中，我们为称为CNC的OOD数据增强提出了一种新颖的复合腐败技术。 CNC的主要优点之一是，除了培训集外，它不需要任何固定数据。此外，与当前的最新技术（SOTA）技术不同，CNC不需要在测试时间进行反向传播或结合，从而使我们的方法在推断时更快。我们与过去4年中主要会议的20种方法进行了广泛的比较，表明，在OOD检测准确性和推理时间方面，使用基于CNC的数据增强训练的模型都胜过SOTA。我们包括详细的事后分析，以研究我们方法成功的原因，并确定CNC样本的较高相对熵和多样性是可能的原因。我们还通过对二维数据集进行零件分解分析提供理论见解，以揭示（视觉和定量），我们的方法导致ID类别周围的边界更紧密，从而更好地检测了OOD样品。源代码链接：https：//github.com/cnc-ood

我们在多变量时间序列预测（MTSF）的域中制定了一个新的推理任务，称为变量子集预报（VSF），其中仅在推理过程中可用一小部分变量子集。由于长期数据丢失（例如，传感器故障）或列车 /测试之间的高 - >低资源域移动，因此在推理过程中没有变量。据我们所知，在文献中尚未研究MTSF模型在存在此类故障的情况下的稳健性。通过广泛的评估，我们首先表明，在VSF设置中，最新方法的性能显着降低。我们提出了一种非参数包装技术，该技术可以应用于任何现有的预测模型。通过在4个数据集和5个预测模型的系统实验中，我们表明我们的技术能够恢复模型的接近95 \％性能，即使仅存在15 \％的原始变量。

在本文中，我们介绍了一种快速运动脱棕色条件的生成对抗网络（FMD-CGAN），其有助于单个图像的盲运动去纹理。 FMD-CGAN在去修改图像后提供令人印象深刻的结构相似性和视觉外观。与其他深度神经网络架构一样，GAN也遭受大型模型大小（参数）和计算。在诸如移动设备和机器人等资源约束设备上部署模型并不容易。借助MobileNet基于MobileNet的架构，包括深度可分离卷积，我们降低了模型大小和推理时间，而不会丢失图像的质量。更具体地说，我们将模型大小与最近的竞争对手相比将3-60倍。由此产生的压缩去掩盖CGAN比其最接近的竞争对手更快，甚至定性和定量结果优于各种最近提出的最先进的盲运动去误紧模型。我们还可以使用我们的模型进行实时映像解擦干任务。标准数据集的当前实验显示了该方法的有效性。

度量启发是最新的框架，用于启发分类性能指标，可以根据任务和上下文最好地反映隐性用户偏好。但是，可用的启发策略仅限于预测率的线性（或准线性）函数，这实际上对包括公平性在内的许多应用可能是限制的。本文制定了一种策略，以引发由二次功能定义的更灵活的多类指标，旨在更好地反映人类的偏好。我们展示了它在启发基于二次违规的集体 - fair量指标中的应用。我们的策略仅需要相对的偏好反馈，对噪声是强大的，并且达到了近乎最佳的查询复杂性。我们将此策略进一步扩展到启发多项式指标，从而扩大了用例以进行度量启发。