伴随的药物给药会引起药物 - 药物相互作用（DDIS）。某些药物组合是有益的，但其他药物组合可能会引起以前未记录的负面影响。以前关于DDI预测的工作通常依赖于手工设计的领域知识，这是努力获得的。在这项工作中，我们提出了一个新型模型，即分子亚结构网络（MSAN），以有效预测药物对分子结构的潜在DDI。我们采用类似变压器的子结构提取模块，以获取与药物分子的各种子结构模式相关的固定代表媒介。然后，两种药物的子结构之间的相互作用强度将由基于相似性的相互作用模块捕获。在图形编码之前，我们还执行一个子结构删除增强，以减轻过度拟合。实际数据集的实验结果表明，我们提出的模型实现了最新的性能。我们还表明，通过案例研究，我们的模型的预测是高度解释的。

可靠的导航系统在机器人技术和自动驾驶中具有广泛的应用。当前方法采用开环过程，将传感器输入直接转换为动作。但是，这些开环方案由于概括不佳而在处理复杂而动态的现实情况方面具有挑战性。在模仿人类导航的情况下，我们添加了一个推理过程，将动作转换回内部潜在状态，形成了两阶段的感知，决策和推理的封闭环路。首先，VAE增强的演示学习赋予了模型对基本导航规则的理解。然后，在RL增强交互学习中的两个双重过程彼此产生奖励反馈，并共同增强了避免障碍能力。推理模型可以实质上促进概括和鲁棒性，并促进算法将算法的部署到现实世界的机器人，而无需精心转移。实验表明，与最先进的方法相比，我们的方法更适合新型方案。

自动检测异常轨迹是智能运输系统中大量应用的重要问题。许多现有的研究集中在区分异常轨迹和正常轨迹上，忽略了异常轨迹之间的巨大差异。最近的一项研究在鉴定异常轨迹模式方面取得了长足进步，并提出了一种两阶段算法，用于异常轨迹检测和分类（ATDC）。该算法具有出色的性能，但受到了一些局限性，例如高时间的复杂性和不良的解释。在这里，我们对ATDC算法进行了仔细的理论和经验分析，表明可以简化两个阶段的异常得分的计算，并且该算法的第二阶段比第一阶段重要得多。因此，我们开发了一种FastATDC算法，该算法在两个阶段都引入了随机抽样策略。实验结果表明，FastATDC在实际数据集上的速度比ATDC快10到20倍。此外，FastAtDC优于基线算法，与ATDC算法相当。

嵌套命名实体识别（Nested Ner）是自然语言处理中的基本任务。已经提出了各种基于跨度的方法来检测具有跨度表示的嵌套实体。但是，基于跨度的方法不考虑跨度与其他实体或短语之间的关系，这对NER任务很有帮助。此外，由于跨度枚举长度有限，基于跨度的方法在预测长实体方面难以预测。为了减轻这些问题，我们介绍了提出的和refine网络（PNRNET），这是一个嵌套NER的两阶段集预测网络。在建议阶段，我们使用基于跨度的预测指标来生成一些粗糙的实体预测作为实体建议。在精炼阶段，建议相互互动，并将更丰富的上下文信息纳入建议表示。精致的建议表示形式用于重新预测实体边界和类。这样，可以消除粗略建议中的错误，并且边界预测不再受到跨度枚举长度限制的约束。此外，我们构建了多尺度句子表示，它可以更好地对句子的层次结构进行建模，并提供比令牌级表示更丰富的上下文信息。实验表明，PNRNET在四个嵌套的NER数据集和一个Flat NER数据集上实现了最先进的性能。

Multiconer共享的任务旨在检测在多种语言的简短和低文本设置中，在语义上模棱两可且复杂的命名实体。缺乏上下文使人们对歧义的命名实体的认识充满挑战。为了减轻此问题，我们的团队Damo-NLP提出了一个基于知识的系统，我们在其中建立了基于Wikipedia的多语言知识基础，以向指定的实体识别（NER）模型提供相关的上下文信息。给定输入句子，我们的系统有效地从知识库中检索了相关上下文。然后，将原始输入句子加强此类上下文信息，从而可以捕获明显更好的上下文化令牌表示。我们的系统在Multiconer共享任务中赢得了13个曲目中的10个。

瞄准以像素 - 明智的语义类别描述陆地覆盖，遥感图像中的语义分割需要在广大地理位置上描绘不同的分布，这很难通过现有深层模型的架构中的均匀像素的前导路径难以实现。虽然已经设计了几种算法来选择用于自然图像分析的像素 - 方面的自适应前向路径，但它仍然缺乏关于如何获得最佳选择的理论支持。在本文中，我们在参数优化方面提供数学分析，指导我们设计一种称为隐藏路径选择网络（HPS-Net）的方法。借助从额外的迷你分支派生的隐藏变量，HPS-Net能够通过调整现有算法中的特征映射和像素 - 明智的路径选择之间的直接关系来解决无法访问的全球最佳的固有问题。路径选择。为了更好的培训和评估，我们进一步优化并将5级高芬图像数据集（GID-5）扩展为具有15个土地覆盖类别，即GID-15的新型。 GID-5和GID-15上的实验结果表明，所提出的模块可以稳定地提高不同深结构的性能，验证所提出的数学分析。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

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.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.