Headline generation is a task of generating an appropriate headline for a given article, which can be further used for machine-aided writing or enhancing the click-through ratio. Current works only use the article itself in the generation, but have not taken the writing style of headlines into consideration. In this paper, we propose a novel Seq2Seq model called CLH3G (Contrastive Learning enhanced Historical Headlines based Headline Generation) which can use the historical headlines of the articles that the author wrote in the past to improve the headline generation of current articles. By taking historical headlines into account, we can integrate the stylistic features of the author into our model, and generate a headline not only appropriate for the article, but also consistent with the author's style. In order to efficiently learn the stylistic features of the author, we further introduce a contrastive learning based auxiliary task for the encoder of our model. Besides, we propose two methods to use the learned stylistic features to guide both the pointer and the decoder during the generation. Experimental results show that historical headlines of the same user can improve the headline generation significantly, and both the contrastive learning module and the two style features fusion methods can further boost the performance.

在这项研究中，我们提出了一种基于词素的方案，用于韩国依赖解析，并采用拟议方案来普遍依赖。我们介绍了语言原理，该基本原理说明了采用基于词素的格式的动机和必要性，并开发了脚本，这些脚本会在通用依赖项使用的原始格式和所提出的基于词素的格式自动之间转换。然后，统计和神经模型（包括udpipe和stanza）证明了提出的格式对韩国依赖解析的有效性，并以我们精心构造的基于词素的单词嵌入韩语。Morphud的表现优于所有韩国UD Treebanks的解析结果，我们还提供了详细的错误分析。

多模式性荧光脱氧葡萄糖（FDG）正电子发射断层扫描 /计算机断层扫描（PET / CT）已常规用于评估常见癌症，例如肺癌，淋巴瘤和黑色素瘤。这主要归因于以下事实：PET/CT结合了对PET肿瘤检测的高灵敏度和CT的解剖学信息。在PET/CT图像评估中，自动肿瘤分割是重要的一步，近年来，基于深度学习的方法已成为最新方法。不幸的是，现有的方法倾向于过度细分肿瘤区域，并包括正常摄取器官，炎症和其他感染等区域。在这项研究中，我们引入了一个假阳性还原网络以克服这一限制。我们首先引入了一个自制的预训练的全球分割模块，以使用自我监督的预训练的编码器粗糙地描绘候选肿瘤区域。然后，通过局部细化模块去除假阳性来完善候选肿瘤区域。我们对MICCAI 2022自动病变分割的实验在全身FDG-PET/CT（AUTOPET）挑战数据集中表明，我们的方法在初步测试数据中获得了0.9324的骰子得分，并在排行榜上排名第一。我们的方法在最终测试数据的前7位方法中也排名，最终排名将在2022 MICCAI AUTOPET研讨会期间宣布。我们的代码可在以下网址提供：https：//github.com/yigepeng/autopet_false_posisity_reduction。

有效处理3D数据一直是一个挑战。大规模点云上的空间操作以稀疏数据存储，需要额外的成本。由于变形金刚的成功吸引，研究人员正在使用多头关注视力任务。但是，变压器中的注意力计算在输入数量和点云等集合的空间直觉中具有二次复杂性。我们重新设计了这项工作中的“变压器”，并将它们纳入形状分类以及部分和场景细分的层次结构框架中。我们建议我们的当地注意力单元，该单元捕获了空间社区的特征。我们还通过利用每次迭代的采样和分组来计算有效且动态的全局交叉注意。最后，为了减轻点云的非异质性，我们提出了一个有效的多尺度令牌化（MST），该标记（MST）提取了尺度不变的令牌以供注意操作。所提出的分层模型以平均准确性实现最新的形状分类，并以先前的分割方法的相同，同时需要更少的计算。我们提出的体系结构预测分割标签的标签约为以前最有效方法的延迟和参数计数的一半，具有可比的性能。该代码可从https://github.com/yigewang-whu/cloudattention获得。

后门攻击已成为深度神经网络（DNN）的主要安全威胁。虽然现有的防御方法在检测或擦除后以后展示了有希望的结果，但仍然尚不清楚是否可以设计强大的培训方法，以防止后门触发器首先注入训练的模型。在本文中，我们介绍了\ emph {反后门学习}的概念，旨在培训\ emph {Clean}模型给出了后门中毒数据。我们将整体学习过程框架作为学习\ emph {clean}和\ emph {backdoor}部分的双重任务。从这种观点来看，我们确定了两个后门攻击的固有特征，因为他们的弱点2）后门任务与特定类（后门目标类）相关联。根据这两个弱点，我们提出了一般学习计划，反后门学习（ABL），在培训期间自动防止后门攻击。 ABL引入了标准培训的两级\ EMPH {梯度上升}机制，帮助分离早期训练阶段的后台示例，2）在后续训练阶段中断后门示例和目标类之间的相关性。通过对多个基准数据集的广泛实验，针对10个最先进的攻击，我们经验证明，后卫中毒数据上的ABL培训模型实现了与纯净清洁数据训练的相同性能。代码可用于\ url {https:/github.com/boylyg/abl}。

Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets. 1

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

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.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.