策略培训是一种多学科的康复方法，它教导技能减少中风后认知障碍者的残疾。与传统的康复方法相比，在随机，对照临床试验中已显示策略培训是促进独立性的更可行和有效的干预措施。标准化的保真度评估用于通过检查康复视频记录中的指导和定向口头提示来衡量治疗原则的依从性。尽管用于检测指导和定向的口头提示的忠诚度评估对于单一站点研究是有效的，但在大型多站点务实的务实试验中，它可能会变成劳动力密集，耗时且昂贵。为了应对广泛的战略培训实施的这一挑战，我们利用自然语言处理（NLP）技术来自动化策略培训保真度评估，即自动从康复会议的视频记录中自动识别有指导和指导的口头提示。我们开发了一种基于规则的NLP算法，一个长期术语存储器（LSTM）模型以及该任务的变压器（BERT）模型的双向编码器表示。 BERT模型以0.8075的F1得分实现了最佳性能。这项研究的发现在心理学和康复干预研究和实践方面具有广泛的希望。

健康素养是2030年健康人民的主要重点，这是美国国家目标和目标的第五次迭代。健康素养较低的人通常会遵循访问后的说明以及使用处方，这会导致健康结果和严重的健康差异。在这项研究中，我们建议通过自动在给定句子中翻译文盲语言来利用自然语言处理技术来提高患者教育材料的健康素养。我们从四个在线健康信息网站上刮擦了患者教育材料：medlineplus.gov，drugs.com，mayoclinic.org和reddit.com。我们分别在银标准培训数据集和黄金标准测试数据集上培训并测试了最先进的神经机译（NMT）模型。实验结果表明，双向长期记忆（BILSTM）NMT模型的表现超过了来自变压器（BERT）基于NMT模型的双向编码器表示。我们还验证了NMT模型通过比较句子中的健康文盲语言比率来翻译健康文盲语言的有效性。提出的NMT模型能够识别正确的复杂单词并简化为外行语言，同时该模型遭受句子完整性，流利性，可读性的影响，并且难以翻译某些医学术语。

临床自然语言处理（NLP）已成为医疗保健领域的一项新兴技术，该技术利用了电子健康记录（EHR）中大量的自由文本数据，以改善患者护理，支持临床决策并促进临床和转化科学研究。深度学习在许多临床NLP任务中都取得了最先进的表现。但是，培训深度学习模型通常需要大量注释的数据集，这些数据集通常是不公开的，并且可能耗时以在临床领域建立。在临床NLP中，使用较小的注释数据集是典型的，因此，确保深度学习模型的性能良好对于在现实世界应用中使用的模型至关重要。一种广泛采用的方法是微调现有的预训练语言模型（PLM），但是当培训数据集仅包含少数注释的样本时，这些尝试不足。最近已经研究了很少的学习（FSL）来解决这个问题。暹罗神经网络（SNN）已被广泛用作计算机视觉中的FSL方法，但在NLP中尚未得到很好的研究。此外，关于其在临床领域的应用的文献很少。在本文中，我们提出了两种基于SNN的FSL方法，用于临床NLP，包括带有二阶嵌入（SOE-SNN）的预训练的SNN（PT-SNN）和SNN。我们评估了有关两项临床任务的拟议方法，即临床文本分类和临床命名实体识别。我们测试了三个几次设置，包括4局，8局和16杆学习。使用三个PLM（包括Bert，Biobert和Bioclinicalbert）对两个临床NLP任务进行了测试。实验结果验证了拟议的基于SNN的FSL方法在两个临床NLP任务中的有效性。

临床领域中的语义文本相似性（STS）有助于提高诊断效率，并为下游数据挖掘任务产生简洁的文本。但是，鉴于诊所文本中涉及的高度领域知识，通用语言模型正确地推断出临床句子和输出相似性后的隐性医学关系仍然具有挑战性。在本文中，我们提出了一个图形增强的循环学习框架，以实现临床领域的相似性估计。该框架可以方便地在最先进的骨干语言模型上实现，并通过与基于辅助图的卷积网络（GCN）网络共同培训来利用域知识来提高其性能。我们报告了通过将生物临床BERT基线提高16.3％和27.9％，在GCN和共同训练框架中引入领域知识的成功。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.