Along with the springing up of semantics-empowered communication (SemCom) researches, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present corresponding ecosystems, including theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content \& channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., reliable and goal-oriented communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate the future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.

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.

Generative adversarial networks (GANs) have made great success in image inpainting yet still have difficulties tackling large missing regions. In contrast, iterative algorithms, such as autoregressive and denoising diffusion models, have to be deployed with massive computing resources for decent effect. To overcome the respective limitations, we present a novel spatial diffusion model (SDM) that uses a few iterations to gradually deliver informative pixels to the entire image, largely enhancing the inference efficiency. Also, thanks to the proposed decoupled probabilistic modeling and spatial diffusion scheme, our method achieves high-quality large-hole completion. On multiple benchmarks, we achieve new state-of-the-art performance. Code is released at https://github.com/fenglinglwb/SDM.

Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we propose a local search algorithm for these problems, called BandHS, which applies two multi-armed bandits to guide the search directions when escaping local optima. One bandit is combined with all the soft clauses to help the algorithm select to satisfy appropriate soft clauses, and the other bandit with all the literals in hard clauses to help the algorithm select appropriate literals to satisfy the hard clauses. These two bandits can improve the algorithm's search ability in both feasible and infeasible solution spaces. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate the excellent performance and generalization capability of our proposed methods, that greatly boost the state-of-the-art local search algorithm, SATLike3.0, and the state-of-the-art SAT-based incomplete solver, NuWLS-c.

The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue.

We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant DINO~\cite{zhang2022dino}, and an efficient DETR training method Group DETR~\cite{chen2022group}. The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO. Group DETR v2 achieves $\textbf{64.5}$ mAP on COCO test-dev, and establishes a new SoTA on the COCO leaderboard https://paperswithcode.com/sota/object-detection-on-coco

由于其稀疏和细长的性质，估算3D空间中准确的车道线仍然具有挑战性。在这项工作中，我们提出了M^2-3dlanenet，这是一个有效3D车道检测的多模式框架。旨在集成来自多传感器的互补信息，M^2-3dlanenet首先将多模式特征提取具有模态特异性骨架，然后将它们融合在统一的鸟眼视图（BEV）空间中。具体而言，我们的方法由两个核心组成部分组成。 1）要获得准确的2D-3D映射，我们提出了自上而下的BEV生成。其中，使用线条限制的变形（LRDA）模块可用于以自上而下的方式有效地增强图像特征，从而充分捕获车道的细长特征。之后，它使用深度感知的举重将2D锥体特征投入到3D空间中，并通过枕形生成BEV特征。 2）我们进一步提出了自下而上的BEV融合，该融合通过多尺度的级联注意力汇总了多模式特征，从而集成了来自摄像头和激光雷达传感器的互补信息。足够的实验证明了M^2-3dlanenet的有效性，该实验的有效性超过了先前的最先进方法，即在OpenLane数据集上提高了12.1％的F1-SCORE改善。

表结构识别是文档图像分析域的关键部分。它的困难在于需要同时解析每个单元的物理坐标和逻辑指标。但是，现有的方法很难实现这两个目标，尤其是当表分裂线被模糊或倾斜时。在本文中，我们提出了一种基于端到端变压器的表面结构识别方法，称为信任。变压器由于其全局计算，完美的内存和并行计算而适合表结构识别。通过引入基于新型变压器基于查询的新型分裂模块和基于顶点的合并模块，表结构识别问题被脱钩到两个关节优化子任务中：多面向的表行/列分拆分和表格格里合并。基于查询的拆分模块通过变压器网络从长期依赖项中学习了强烈的上下文信息，准确预测了多个面向的表行/列分离器，并相应地获得了表的基本网格。基于顶点的合并模块能够在相邻的基本网格之间汇总局部上下文信息，从而能够合并准确属于同一跨越单元的基本束。我们对包括PubTabnet和Connthtable在内的几个流行基准进行实验，我们的方法实现了新的最新结果。特别是，信任在PubTabnet上以10 fps的速度运行，超过了先前的方法。

在存在未衡量的混杂因素的情况下，我们解决了数据融合的治疗效应估计问题，即在不同的治疗分配机制下收集的多个数据集。例如，营销人员可以在不同时间/地点为相同产品分配不同的广告策略。为了处理由未衡量的混杂因素和数据融合引起的偏见，我们建议将观察数据分为多组（每个组具有独立治疗分配机制），然后将组指标显式地模拟为潜在的组仪器变量（LATGIV），将其模拟为实施基于IV的回归。在本文中，我们概念化了这种思想，并开发了一个统一的框架，以（1）估计跨群体观察到的变量的分布差异； （2）对不同治疗分配机制的LATGIV模型； （3）插入latgivs以估计治疗响应函数。经验结果证明了与最新方法相比，LATGIV的优势。

最大常见的诱导子图（MC）是广泛的现实应用程序的重要NP硬化问题。分支结合（BNB）是MCS的一类有效算法的基础，当发现该解决方案比到目前为止发现的最佳解决方案更好时，包括连续选择以匹配和修剪的顶点以匹配和修剪。选择要匹配的顶点的方法对于BNB的性能至关重要。在本文中，我们提出了一种新的值函数和一种用于加强学习定义新的顶点选择方法的混合选择策略，并为MCS提出了一种称为MCSPLITDAL的新的BNB算法。广泛的实验表明，MCSPLITDAL显着改善了当前最佳BNB算法，MCSPLIT+LL和MCSPLIT+RL。还进行了经验分析，以说明为什么新的价值函数和混合选择策略有效。