开发用于印度语言的命名实体识别（NER）系统一直是一个长期存在的挑战，主要是由于需要大量注释的清洁培训实例。本文通过利用英语和印度语言的并行语言和英语网数据集，为低资源设置中为印度语言提供了端到端框架。所提出的框架包括注释投影方法，其将单词对准分数和Ner标签预测置信度分数组合在源语言（英语）数据上，以在目标印度语言中生成弱标记的数据。我们使用教师学生模型的变体，并在教师模型的伪标签上共同优化它，并对生成的弱标记数据进行预测。我们还为三种印度语言提出了手动注释的测试集：Hindi，Bengali和Gujarati。我们评估了三种印度语言的测试组拟议框架的表现。与所有语言的零射击转移学习模型相比，实证结果显示最低10％的性能改进。这表明使用目标印度语言中所提出的注释投影方法生成的弱标记数据可以补充注释的源语言数据来提高性能。我们的代码在HTTPS://github.com/aksh555/cl-ner中公开提供

We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e.g. a robotic manipulator in a simulated kitchen. Instead of imitating low-level actions like joint velocities, our approach imitates the sequence of demonstrated semantic skills like "opening the microwave" or "turning on the stove". This allows us to transfer demonstrations across environments (e.g. real-world to simulated kitchen) and agent embodiments (e.g. bimanual human demonstration to robotic arm). We evaluate on three challenging cross-domain learning problems and match the performance of demonstration-accelerated RL approaches that require in-domain demonstrations. In a simulated kitchen environment, our approach learns long-horizon robot manipulation tasks, using less than 3 minutes of human video demonstrations from a real-world kitchen. This enables scaling robot learning via the reuse of demonstrations, e.g. collected as human videos, for learning in any number of target domains.

We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface -- the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant -- the user specification does not depend on the number of agents; it is persistent -- the specification remains active until the user specifies a new command; and it is real-time -- the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.

Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g., a pre-trained VGG, to obtain a latent representation. However, due to domain shift, pre-trained models from the natural image domain might not apply to other image domains, such as medical imaging. Notably, in medical imaging, evaluating the perceptual similarity is exclusively performed by specialists trained extensively in diverse medical fields. Thus, medical imaging remains devoid of task-specific, objective perceptual measures. This work answers the question: Is it necessary to rely on supervised learning to obtain an effective representation that could measure perceptual similarity, or is self-supervision sufficient? To understand whether recent contrastive self-supervised representation (CSR) may come to the rescue, we start with natural images and systematically evaluate CSR as a metric across numerous contemporary architectures and tasks and compare them with existing methods. We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings. We also demonstrate that CSR can significantly improve image quality in two image synthesis tasks. Finally, our extensive results suggest that perceptuality is an emergent property of CSR, which can be adapted to many image domains without requiring annotations.

Many studies have examined the shortcomings of word error rate (WER) as an evaluation metric for automatic speech recognition (ASR) systems, particularly when used for spoken language understanding tasks such as intent recognition and dialogue systems. In this paper, we propose Hybrid-SD (H_SD), a new hybrid evaluation metric for ASR systems that takes into account both semantic correctness and error rate. To generate sentence dissimilarity scores (SD), we built a fast and lightweight SNanoBERT model using distillation techniques. Our experiments show that the SNanoBERT model is 25.9x smaller and 38.8x faster than SRoBERTa while achieving comparable results on well-known benchmarks. Hence, making it suitable for deploying with ASR models on edge devices. We also show that H_SD correlates more strongly with downstream tasks such as intent recognition and named-entity recognition (NER).

编织的复合材料是通过隔板和纬纱以图案或编织方式进行的。通过更改图案或材料，可以显着改变编织复合材料的机械性能。但是，尚不清楚编织复合体系结构（图案，材料）在机械性能上的作用。在本文中，我们通过我们提出的物理受限的神经网络（PCNN）探讨了编织复合体系结构（编织模式，编织材料序列）与相应模量之间的关系。此外，我们采用统计学习方法来优化编织复合体系结构以改善机械响应。我们的结果表明，PCNN可以有效地预测所需模量的编织体系结构，其精度比几种基线模型高得多。 PCNN可以与基于功能的优化相结合，以确定初始设计阶段的最佳编织复合体系结构。除了将编织复合体系结构与其机械响应联系起来外，我们的研究还提供了对建筑特征如何控制机械响应的深入了解。我们预计我们提出的框架将主要促进编织的综合分析和优化过程，并成为将物理知识引导的神经网络引入复杂结构分析的起点。

在本文中，我们提出了一种用于在离散时间马尔可夫链（DTMC）上指定的概率超普通统计模型检查（SMC）的贝叶斯方法。尽管使用顺序概率比测试（SPRT）的HyperPCTL*的SMC曾经探索过，但我们基于贝叶斯假说检验开发了一种替代SMC算法。与PCTL*相比，由于它们在DTMC的多个路径上同时解释，验证HyperPCTL*公式是复杂的。此外，由于SMC无法返回Subformulae的满意度问题，因此扩展非稳定设置的自下而上的模型检查算法并不直接，相反，它仅通过高级返回正确的答案。信心。我们根据修改后的贝叶斯测试，提出了一种HyperPCTL* SMC的递归算法，该测试因递归满意度结果的不确定性而导致。我们已经在Python工具箱Hybrover中实现了算法，并将我们的方法与基于SPRT的SMC进行了比较。我们的实验评估表明，我们的贝叶斯SMC算法在验证时间和推断给定HyperPCTL*公式的满意度所需的样品数量方面的性能更好。

从一个或多个未分类桩中挑选一个或多个物体对于机器人系统而言仍然是不平凡的。当桩由包含彼此纠缠的单个项目的颗粒材料（GM）组成时，尤其如此，导致挑选出更多的选择。这种容易发生的GM的关键特征之一是从桩中的主要物体延伸的突起存在。这项工作描述了后者在引起机械纠缠及其对选择一致性的影响方面所扮演的角色。 IT报告了实验，其中采摘具有不同突出长度（PLS）的GMS导致挑选质量差异增加了76％，这表明PL是采摘策略设计中的一项信息功能。此外，为了应对这种效果，它提出了一种新的传播（SNP）方法，可大大减少纠结，从而使选择更加一致。与试图从桩中的无缠结点进行选择的先前方法相比，提出的方法导致选择误差（PE）的降低高达51％，并显示出对先前看不见的GMS的良好概括。

如果我们想在将它们部署在现实中之前在模拟中训练机器人，那么假定减少SIM2REAL差距的人似乎很自然，并且几乎是不言而喻的，涉及创建富裕性的模拟器（因为现实就是事实）。我们挑战了这一假设并提出了相反的假设-SIM2REAL转移机器人可以通过较低（不是更高）的保真度模拟来改善。我们使用3种不同的机器人（A1，Aliengo，Spot）对这一假设进行了系统的大规模评估 - 在现实世界中以及2个不同的模拟器（栖息地和Igibson）。我们的结果表明，与期望相反，增加忠诚无助于学习。由于模拟速度缓慢（防止大规模学习）和对模拟物理学不准确的过度拟合，因此性能较差。取而代之的是，使用现实世界数据构建机器人运动的简单模型可以改善学习和概括。

移动通知系统在各种应用程序中起着重要作用，以通信，向用户发送警报和提醒，以告知他们有关新闻，事件或消息的信息。在本文中，我们将近实时的通知决策问题制定为马尔可夫决策过程，在该过程中，我们对奖励中的多个目标进行了优化。我们提出了一个端到端的离线增强学习框架，以优化顺序通知决策。我们使用基于保守的Q学习的双重Q网络方法来应对离线学习的挑战，从而减轻了分配转移问题和Q值高估。我们说明了完全部署的系统，并通过离线和在线实验证明了拟议方法的性能和好处。