The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.

本文提出了一个称为定点中心的网络中心家庭。这种中心性家族是通过与基础网络相关的置换置换量映射的固定点来定义的。这种中心性概念立即扩展为定义以图形为特征的无限图的定点中心。在轻度假设下，此类中心相对于基础图和图形的变化的变化界限。定点中心与网络上的各种不同模型连接，包括图形神经网络，网络上的静态和动态游戏以及马尔可夫决策过程。

这项工作将病毒在网络上传播的模型与其等效的神经网络表示。基于此连接，我们提出了一种新的神经网络体系结构，称为传输神经网络（Transnns），其中激活功能主要与链接相关，并允许具有不同的激活水平。此外，这种连接导致具有可调或可训练参数的三个新激活函数的发现和推导。此外，我们证明具有单个隐藏层和固定非零偏置项的Transns是通用函数近似器。最后，我们提出了基于Transnn的连续时间流行网络模型的新基本派生。

对抗性深度学习是为了训练强大的DNN，以防止对抗性攻击，这是深度学习的主要研究之一。游戏理论已被用来回答有关对抗性深度学习的一些基本问题，例如具有最佳鲁棒性的分类器的存在以及给定类别的分类器的最佳对抗样本。在以前的大多数工作中，对抗性深度学习是同时进行的，并且假定策略空间是某些概率分布，以使NASH平衡存在。但是，此假设不适用于实际情况。在本文中，我们通过将对抗性深度学习作为顺序游戏提出，为分类器是具有给定结构的DNN的实际情况提供了这些基本问题的答案。证明了这些游戏的Stackelberg Equilibria的存在。此外，当使用Carlini-Wagner的边缘损失时，平衡DNN具有相同结构的所有DNN中最大的对抗精度。从游戏理论方面也研究了对抗性深度学习的鲁棒性和准确性之间的权衡。

在预先建立的3D环境图中，高精度摄像头重新定位技术是许多任务的基础，例如增强现实，机器人技术和自动驾驶。近几十年来，基于点的视觉重新定位方法已经发达了，但在某些不足的情况下不足。在本文中，我们设计了一条完整的管道，用于使用点和线的相机姿势完善，其中包含创新设计的生产线提取CNN，名为VLSE，线匹配和姿势优化方法。我们采用新颖的线表示，并根据堆叠的沙漏网络自定义混合卷积块，以检测图像上的准确稳定的线路功能。然后，我们采用基于几何的策略，使用表极约束和再投影过滤获得精确的2D-3D线对应关系。构建了以下点线关节成本函数，以通过基于纯点的本地化的初始粗姿势优化相机姿势。在开放数据集（即线框上的线提取器）上进行了足够的实验，在INLOC DUC1和DUC2上的定位性能，以确认我们的点线关节姿势优化方法的有效性。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

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.

Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.