Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.

A central challenge of building more powerful Graph Neural Networks (GNNs) is the oversmoothing phenomenon, where increasing the network depth leads to homogeneous node representations and thus worse classification performance. While previous works have only demonstrated that oversmoothing is inevitable when the number of graph convolutions tends to infinity, in this paper, we precisely characterize the mechanism behind the phenomenon via a non-asymptotic analysis. Specifically, we distinguish between two different effects when applying graph convolutions -- an undesirable mixing effect that homogenizes node representations in different classes, and a desirable denoising effect that homogenizes node representations in the same class. By quantifying these two effects on random graphs sampled from the Contextual Stochastic Block Model (CSBM), we show that oversmoothing happens once the mixing effect starts to dominate the denoising effect, and the number of layers required for this transition is $O(\log N/\log (\log N))$ for sufficiently dense graphs with $N$ nodes. We also extend our analysis to study the effects of Personalized PageRank (PPR) on oversmoothing. Our results suggest that while PPR mitigates oversmoothing at deeper layers, PPR-based architectures still achieve their best performance at a shallow depth and are outperformed by the graph convolution approach on certain graphs. Finally, we support our theoretical results with numerical experiments, which further suggest that the oversmoothing phenomenon observed in practice may be exacerbated by the difficulty of optimizing deep GNN models.

Recently, there has been significant progress in teaching language models to perform step-by-step reasoning to solve complex numerical reasoning tasks. Chain-of-thoughts prompting (CoT) is by far the state-of-art method for these tasks. CoT uses language models to perform both reasoning and computation in the multi-step `thought' process. To disentangle computation from reasoning, we propose `Program of Thoughts' (PoT), which uses language models (mainly Codex) to express the reasoning process as a program. The computation is relegated to an external computer, which executes the generated programs to derive the answer. We evaluate PoT on five math word problem datasets (GSM, AQuA, SVAMP, TabMWP, MultiArith) and three financial-QA datasets (FinQA, ConvFinQA, TATQA) for both few-shot and zero-shot setups. Under both few-shot and zero-shot settings, PoT can show an average performance gain over CoT by around 12\% across all the evaluated datasets. By combining PoT with self-consistency decoding, we can achieve SoTA performance on all math problem datasets and near-SoTA performance on financial datasets. All of our data and code are released in Github\footnote{\url{https://github.com/wenhuchen/Program-of-Thoughts}}.

In recent years, aerial swarm technology has developed rapidly. In order to accomplish a fully autonomous aerial swarm, a key technology is decentralized and distributed collaborative SLAM (CSLAM) for aerial swarms, which estimates the relative pose and the consistent global trajectories. In this paper, we propose $D^2$SLAM: a decentralized and distributed ($D^2$) collaborative SLAM algorithm. This algorithm has high local accuracy and global consistency, and the distributed architecture allows it to scale up. $D^2$SLAM covers swarm state estimation in two scenarios: near-field state estimation for high real-time accuracy at close range and far-field state estimation for globally consistent trajectories estimation at the long-range between UAVs. Distributed optimization algorithms are adopted as the backend to achieve the $D^2$ goal. $D^2$SLAM is robust to transient loss of communication, network delays, and other factors. Thanks to the flexible architecture, $D^2$SLAM has the potential of applying in various scenarios.

近年来，移动机器人变得雄心勃勃，并在大规模场景中部署。作为对环境的高级理解，稀疏的骨骼图对更有效的全球计划有益。当前，现有的骨骼图生成解决方案受到了几个主要局限性，包括对不同地图表示的适应性不佳，对机器人检查轨迹的依赖和高计算开销。在本文中，我们提出了一种有效且柔性的算法，该算法生成轨迹独立的3D稀疏拓扑骨架图，捕获了自由空间的空间结构。在我们的方法中，采用了有效的射线采样和验证机制来找到独特的自由空间区域，这有助于骨架图顶点，并且在相邻的顶点作为边缘之间具有遍历性。周期形成方案还用于维持骨架图紧凑度。基准测试与最先进的作品的比较表明，我们的方法在较短的时间内生成稀疏的图形，从而提供了高质量的全球计划路径。在现实世界中进行的实验进一步验证了我们在现实情况下我们方法的能力。我们的方法将成为开源以使社区受益的开源。

大多数现有的复合面部表达识别（FER）方法依赖于用于训练的大型化合物表达数据。但是，收集此类数据是劳动密集型且耗时的。在本文中，我们解决了跨域少数学习（FSL）设置中的复合FER任务，该设置仅需要几个在目标域中的复合表达式样本。具体而言，我们提出了一个新型的级联分解网络（CDNET），该网络将基于顺序分解机制的几个学习到分解模块层叠，以获得可转移的特征空间。为了减轻我们任务中基本班级有限的过度拟合问题，部分正则化策略旨在有效利用情节培训和批处理培训的最佳功能。通过在多个基本表达数据集上进行类似任务的培训，CDNET了解了可以轻松适应以识别看不见的化合物表达式的学习能力。对利润和野外复合表达数据集进行的广泛实验证明了我们提出的CDNET与几种最先进的FSL方法的优越性。代码可在以下网址获得：https：//github.com/zouxinyi0625/cdnet。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

机械通气是ICU中最广泛使用的疗法中最广泛的疗法之一。然而，尽管在麻醉与科迪德相关的终身支持中具有广泛的应用，但仍有许多有害挑战。我们将这些视为控制问题：呼吸机必须根据规定的气道压力轨迹进出患者的肺部。基于PID方法的行业标准控制器既不是最佳的也不是强大的。我们的数据驱动方法学习通过在从呼吸机收集的数据上培训的模拟器本身进行培训来控制侵入式呼吸机。该方法优于流行的加固学习算法，甚至比PID更精确且强大地控制物理呼吸机。这些结果强调了有效的数据驱动方法可以用于侵入性通风，并表明更通用的通风形式（例如，无侵入性，适应性）也可能是可享受的。

由于特定属性的定位不准确，监控场景中的行人属性识别仍然是一个具有挑战性的任务。在本文中，我们提出了一种基于注意力（VALA）的新型视图 - 属性定位方法，其利用查看信息来指导识别过程，专注于对特定属性对应区域的特定属性和注意机制。具体地，查看信息由视图预测分支利用，以生成四个视图权重，表示来自不同视图的属性的信心。然后将视图重量交付回撰写以撰写特定的视图属性，该属性将参与和监督深度特征提取。为了探索视图属性的空间位置，引入区域关注来聚合空间信息并编码视图特征的通道间依赖性。随后，特定于细小的特定属性特定区域是本地化的，并且通过区域关注获得了来自不同空间位置的视图属性的区域权重。通过将视图权重与区域权重组合来获得最终视图 - 属性识别结果。在三个宽数据集（RAP，RAPV2和PA-100K）上的实验证明了与最先进的方法相比我们的方法的有效性。

The Shapley value (SV) is adopted in various scenarios in machine learning (ML), including data valuation, agent valuation, and feature attribution, as it satisfies their fairness requirements. However, as exact SVs are infeasible to compute in practice, SV estimates are approximated instead. This approximation step raises an important question: do the SV estimates preserve the fairness guarantees of exact SVs? We observe that the fairness guarantees of exact SVs are too restrictive for SV estimates. Thus, we generalise Shapley fairness to probably approximate Shapley fairness and propose fidelity score, a metric to measure the variation of SV estimates, that determines how probable the fairness guarantees hold. Our last theoretical contribution is a novel greedy active estimation (GAE) algorithm that will maximise the lowest fidelity score and achieve a better fairness guarantee than the de facto Monte-Carlo estimation. We empirically verify GAE outperforms several existing methods in guaranteeing fairness while remaining competitive in estimation accuracy in various ML scenarios using real-world datasets.