Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. While previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, the deployment of these systems remains a challenge. Previous work described the lack of involving stakeholders to design such functionalities as one of the major causes. In this paper, we present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises in an effective and acceptable way with four therapists and five post-stroke survivors. Through iterative questionnaires and interviews, we found that both post-stroke survivors and therapists appreciated the potential benefits of AI and robotic coaches to achieve more systematic management and improve their self-efficacy and motivation on rehabilitation therapy. In addition, our evaluation sheds light on several practical concerns (e.g. a possible difficulty with the interaction for people with cognitive impairment, system failures, etc.). We discuss the value of early involvement of stakeholders and interactive techniques that complement system failures, but also support a personalized therapy session for the better deployment of AI and robotic exercise coaches.

One of the main limitations of the commonly used Absolute Trajectory Error (ATE) is that it is highly sensitive to outliers. As a result, in the presence of just a few outliers, it often fails to reflect the varying accuracy as the inlier trajectory error or the number of outliers varies. In this work, we propose an alternative error metric for evaluating the accuracy of the reconstructed camera trajectory. Our metric, named Discernible Trajectory Error (DTE), is computed in four steps: (1) Shift the ground-truth and estimated trajectories such that both of their geometric medians are located at the origin. (2) Rotate the estimated trajectory such that it minimizes the sum of geodesic distances between the corresponding camera orientations. (3) Scale the estimated trajectory such that the median distance of the cameras to their geometric median is the same as that of the ground truth. (4) Compute the distances between the corresponding cameras, and obtain the DTE by taking the average of the mean and root-mean-square (RMS) distance. This metric is an attractive alternative to the ATE, in that it is capable of discerning the varying trajectory accuracy as the inlier trajectory error or the number of outliers varies. Using the similar idea, we also propose a novel rotation error metric, named Discernible Rotation Error (DRE), which has similar advantages to the DTE. Furthermore, we propose a simple yet effective method for calibrating the camera-to-marker rotation, which is needed for the computation of our metrics. Our methods are verified through extensive simulations.

我们提出了一种用于多旋转平均的新型等级方法，称为Hara。我们的方法基于Triplet支持的层次逐渐初始化旋转图。关键的想法是通过利用许多强三联网支持的边缘和逐渐添加具有较弱和更少支持的边缘来构建生成树。这降低了在生成树中添加异常值的风险。因此，我们获得了一个强大的初始解决方案，使我们能够在非线性优化之前过滤异常值。通过更新的修改，我们的方法还可以集成有效的2D-2D对应关系的数量。我们对合成和实际数据集进行广泛的评估，证明了最先进的结果。

在本文中，我们开发了一种高效的回顾性深度学习方法，称为堆叠U-网，具有自助前沿，解决MRI中刚性运动伪影的问题。拟议的工作利用损坏的图像本身使用额外的知识前瞻，而无需额外的对比度数据。所提出的网络通过共享来自相同失真对象的连续片的辅助信息来学习错过的结构细节。我们进一步设计了一种堆叠的U-网的细化，便于保持图像空间细节，从而提高了像素到像素依赖性。为了执行网络培训，MRI运动伪像的模拟是不可避免的。我们使用各种类型的图像前瞻呈现了一个密集的分析：来自同一主题的其他图像对比的提出的自助前锋和前锋。实验分析证明了自助前锋的有效性和可行性，因为它不需要任何进一步的数据扫描。

Steering language generation towards objectives or away from undesired content has been a long-standing goal in utilizing language models (LM). Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding. Specifically, we adopt the actor-critic framework to train an LM-steering critic from non-differentiable reward models. And similar to weighted decoding, our method freezes the language model and manipulates the output token distribution using called critic, improving training efficiency and stability. Evaluation of our method on three controlled generation tasks, namely topic control, sentiment control, and detoxification, shows that our approach generates more coherent and well-controlled texts than previous methods. In addition, CriticControl demonstrates superior generalization ability in zero-shot settings. Human evaluation studies also corroborate our findings.

Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning. However, the ICL performance does not scale well with the number of available training samples as it is limited by the inherent input length constraint of the underlying language model. Meanwhile, many studies have revealed that language models are also powerful feature extractors, allowing them to be utilized in a black-box manner and enabling the linear probing paradigm, where lightweight discriminators are trained on top of the pre-extracted input representations. This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. PALP inherits the scalability of linear probing and the capability of enforcing language models to derive more meaningful representations via tailoring input into a more conceivable form. Throughout in-depth investigations on various datasets, we verified that PALP significantly enhances the input representations closing the gap between ICL in the data-hungry scenario and fine-tuning in the data-abundant scenario with little training overhead, potentially making PALP a strong alternative in a black-box scenario.

Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce hallucinated responses not supported by the input source, which greatly hinders their application. The heterogeneity between external knowledge and dialogue context challenges representation learning and source integration, and further contributes to unfaithfulness. To handle this challenge and generate more faithful responses, this paper presents RHO ($\rho$) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG). We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism. In addition, we devise a response re-ranking technique based on walks over KG sub-graphs for better conversational reasoning. Experimental results on OpenDialKG show that our approach significantly outperforms state-of-the-art methods on both automatic and human evaluation by a large margin, especially in hallucination reduction (17.54% in FeQA).

We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides important contextual information for visual navigation, and exhibits unique spatial structure mainly composed of flat walls and rectangular obstacles. Our adaptation approach encourages the inherent regularities on the estimated maps to guide the agent to overcome the prevalent domain discrepancy in a novel environment. Specifically, we propose an efficient learning curriculum to handle the visual and dynamics corruptions in an online manner, self-supervised with pseudo clean maps generated by style transfer networks. Because the map-based representation provides spatial knowledge for the agent's policy, our formulation can deploy the pretrained policy networks from simulators in a new setting. We evaluate MoDA in various practical scenarios and show that our proposed method quickly enhances the agent's performance in downstream tasks including localization, mapping, exploration, and point-goal navigation.

在过去的十年中，我们看到了工业数据，计算能力的巨大改善以及机器学习的重大理论进步。这为在大规模非线性监控和控制问题上使用现代机器学习工具提供了机会。本文对过程行业的应用进行了对最新结果的调查。

在未来几十年中部署的高级反应堆将面临放松管制的能源市场，并可能采用灵活的运营来提高盈利能力。为了帮助从基本负载到柔性操作范式的过渡，寻求自动操作。这项工作着重于自主操作的控制方面。具体而言，层次控制系统旨在支持常规操作瞬变期间的约束执法。在系统中，集成了数据驱动的建模，基于物理的状态观察和经典控制算法，以提供适应性和健壮的解决方案。 320 MW氟化物冷却的高温卵石床反应器是证明控制系统的设计基础。分层控制系统由监督层和低级层组成。监督层收到更改系统操作条件的请求，并根据已分配的约束接受或拒绝它们。发出限制条件以使工厂保持最佳操作区域。低级层与系统的执行器接口，以实现要求的更改，同时保持跟踪和调节职责。为了接受监督层的请求，采用了参考调查算法。为了建模反应器的动力学，使用了系统识别算法，动态模式分解。为了估计无法直接测量的过程变量的演变，采用了无味的卡尔曼滤波器，并结合了核动力学的非线性模型。这些算法的组成导致了40％功率降低瞬变期间约束执法的数值证明。通过修改约束值并在瞬态期间执行这些系统来证明所提出系统的适应性。在嘈杂的环境下执行约束也证明了鲁棒性。