道德框架和情感会影响各种在线和离线行为，包括捐赠，亲环境行动，政治参与，甚至参与暴力抗议活动。自然语言处理中的各种计算方法（NLP）已被用来从文本数据中检测道德情绪，但是为了在此类主观任务中取得更好的性能，需要大量的手工注销训练数据。事实证明，以前对道德情绪注释的语料库已被证明是有价值的，并且在NLP和整个社会科学中都产生了新的见解，但仅限于Twitter。为了促进我们对道德修辞的作用的理解，我们介绍了道德基础Reddit语料库，收集了16,123个reddit评论，这些评论已从12个不同的子雷迪维特策划，由至少三个训练有素的注释者手工注释，用于8种道德情绪（即护理，相称性，平等，纯洁，权威，忠诚，瘦道，隐含/明确的道德）基于更新的道德基础理论（MFT）框架。我们使用一系列方法来为这种新的语料库（例如跨域分类和知识转移）提供基线道德句子分类结果。

基于变压器的模型用于实现各种深度学习任务的最新性能。由于基于变压器的模型具有大量参数，因此在下游任务上进行微调是计算密集型和饥饿的能量。此类型号的自动混合精液FP32/FP16微调以前已用于降低计算资源需求。但是，随着低位整数背面传播的最新进展，有可能进一步减少计算和记忆脚印。在这项工作中，我们探索了一种新颖的整数训练方法，该方法使用整数算术来进行正向传播和梯度计算，对基于变压器的模型中的线性，卷积，层和层和嵌入层的梯度计算。此外，我们研究了各种整数位宽度的效果，以找到基于变压器模型的整数微调所需的最小位宽度。我们使用整数层对流行的下游任务进行了微调和VIT模型。我们表明，16位整数模型与浮点基线性能匹配。将位宽度降低到10，我们观察到0.5平均得分下降。最后，将位宽度的进一步降低到8的平均得分下降为1.7分。

本文在完全和时间戳监督的设置中介绍了通过序列（SEQ2SEQ）翻译序列（SEQ2SEQ）翻译的统一框架。与当前的最新帧级预测方法相反，我们将动作分割视为SEQ2SEQ翻译任务，即将视频帧映射到一系列动作段。我们提出的方法涉及在标准变压器SEQ2SEQ转换模型上进行一系列修改和辅助损失函数，以应对与短输出序列相对的长输入序列，相对较少的视频。我们通过框架损失为编码器合并了一个辅助监督信号，并在隐式持续时间预测中提出了单独的对齐解码器。最后，我们通过提出的约束K-Medoids算法将框架扩展到时间戳监督设置，以生成伪分段。我们提出的框架在完全和时间戳监督的设置上始终如一地表现，在几个数据集上表现优于或竞争的最先进。

深度学习模型的计算复杂性不断增加，使他们在各种云和边缘平台上的培训和部署变得困难。用低位整数算术代替浮点算术是一种有希望的方法，可节省能量，记忆足迹和深度学习模型的延迟。因此，量化引起了近年来研究人员的注意。但是，没有详细研究使用整数数字形成功能齐全的整数训练管道，包括前进，后传播和随机梯度下降。我们的经验和数学结果表明，整数算术足以训练深度学习模型。与最近的建议不同，我们直接切换计算的数字表示。我们的新型训练方法形成了完全整数训练管道，与浮点相比，它不会改变损失和准确性的轨迹，也不需要任何特殊的超参数调整，分配调整或梯度剪辑。我们的实验结果表明，我们提出的方法在各种任务（包括视觉变压器），对象检测和语义分割等多种任务中有效。

视觉变压器（VIV）架构最近在各种计算机视觉任务中实现了竞争性能。与卷积神经网络（CNNS）相比，VITS背后的动机之一是较弱的感应偏差。然而，这也使VIT更难以训练。它们需要非常大的培训数据集，重型正常化和强大的数据增强。尽管两种架构之间存在显着差异，但用于培训VITS的数据增强策略主要是从CNN培训继承的。在这项工作中，我们经验性评估了如何在CNN（例如，Reset）对图像分类的VIT架构上进行的不同数据增强策略。我们介绍了一种风格的转移数据增强，称为STYLEAUM，这适合培训VITS，而RANDAURMMENT和AUGMIX通常最适合培训CNNS。我们还发现，除了分类损失之外，在培训VITS时，使用同一图像的多个增强之间的一致性损耗尤为有用。

无参考图像质量评估（NR-IQA）的目标是根据主观评估来估计感知图像质量，由于不存在原始参考图像，它是复杂和未解决的问题。在本文中，我们提出了一种新颖的模型来解决NR-IQA任务，利用卷积神经网络（CNNS）和变压器中的自我关注机制来解决来自输入图像的本地和非局部特征的混合方法来解决NR-IQA任务。我们通过CNN捕获图像的局部结构信息，然后避免提取的CNNS特征之间的局部偏压并获得图像的非本地表示，我们利用所提取的特征上的变压器，其中我们将它们塑造为顺序输入变压器模型。此外，为了改善主观和目标分数之间的单调性相关性，我们利用每个批处理内图像之间的相对距离信息，并强制执行它们之间的相对排名。最后但并非最不重要的是，我们观察到NR-IQA模型的性能在我们应用于输入到输入时申请等级变换（例如水平翻转）。因此，我们提出了一种利用自我保持性作为自我监督来源的方法，以改善NRIQA模型的鲁棒性。具体而言，我们为每个图像的质量评估模型的输出和其转换（水平翻转）强制实施自我一致性，以利用丰富的自我监控信息，并降低模型的不确定性。为了展示我们工作的有效性，我们在七个标准IQA数据集（合成和真实）上评估它，并显示我们的模型在各种数据集上实现最先进的结果。

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.

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.

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.