We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs). The system is challenging to be simulated with both the traditional numerical schemes and the vanilla physics-informed neural networks(PINNs) due to the multiscale characteristics. Under the framework of PINNs, we employ a micro-macro decomposition technique to construct a new asymptotic-preserving(AP) loss function, which includes the residual of the governing equations in the micro-macro coupled form, the initial and boundary conditions with additional diffusion limit information, the conservation laws, and a few labeled data. A convergence analysis is performed for the proposed method, and a number of numerical examples are presented to illustrate the efficiency of MD-APNNs, and particularly, the importance of the AP property in the neural networks for the diffusion dominating problems. The numerical results indicate that MD-APNNs lead to a better performance than APNNs or pure data-driven networks in the simulation of the nonlinear non-stationary GRTEs.

Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large dataset challenges astronomers to identify and extract strong lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next step, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys) and simulated data of upcoming large-scale sky surveys, such as the Euclid and the CSST.

在本文中，我们研究了Micro-Video平台中的对象效果建议的新主题，这对于许多实际应用（例如广告插入）来说是一项具有挑战性但重要的任务。为了避免引入由图像框架直接学习视频内容引起的背景偏见的问题，我们建议利用3D人类姿势中隐藏的有意义的肢体语言进行推荐。为此，在这项工作中，引入了一种新型的人类姿势驱动的对象效应建议网络称为poserec。 Poserec利用了3D人姿势检测的优势，并从多框架3D人姿势中学习信息进行视频项目注册，从而导致高质量的对象效应建议性能。此外，为了解决对象效应建议中存在的固有的歧义和稀疏性问题，我们进一步提出了一种新颖的物品感知的隐性原型学习模块，并提供了一种新颖的姿势感知的托管性托管性硬性阴性挖掘模块，以更好地学习姿势 - 项目。更重要的是，为了为新研究主题进行基准方法，我们构建了一个新数据集，用于对象效果建议，名为Pose-Obe。对姿势攻击的广泛实验表明，我们的方法比强基础可以取得更高的性能。

在现实世界中，时间序列的课程通常在最后一次标记，但是许多应用程序需要在每个时间点进行分类时间序列。例如关键患者的结果仅在最后确定，但应始终诊断出他以及时治疗。因此，我们提出了一个新概念：时间序列的连续分类（CCT）。它要求模型在不同的时间阶段学习数据。但是时间序列动态发展，导致不同的数据分布。当模型学习多分布时，它总是会忘记或过度贴身。我们建议，有意义的学习计划是由于一个有趣的观察而潜在的：通过信心来衡量，模型学习多个分布的过程类似于人类学习的过程多重知识。因此，我们提出了一种新型的CCT（C3T）的置信度引导方法。它可以模仿邓宁·克鲁格效应所描述的交替人类信心。我们定义了安排数据的客观信心，以及控制学习持续时间的自信。四个现实世界数据集的实验表明，C3T比CCT的所有基准更准确。

Recently, over-height vehicle strike frequently occurs, causing great economic cost and serious safety problems. Hence, an alert system which can accurately discover any possible height limiting devices in advance is necessary to be employed in modern large or medium sized cars, such as touring cars. Detecting and estimating the height limiting devices act as the key point of a successful height limit alert system. Though there are some works research height limit estimation, existing methods are either too computational expensive or not accurate enough. In this paper, we propose a novel stereo-based pipeline named SHLE for height limit estimation. Our SHLE pipeline consists of two stages. In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image. Then, in stage 2, the depth is temporally measured, extracted and filtered to calculate the height limit device. To benchmark the height limit estimation task, we build a large-scale dataset named "Disparity Height", where stereo images, pre-computed disparities and ground-truth height limit annotations are provided. We conducted extensive experiments on "Disparity Height" and the results show that SHLE achieves an average error below than 10cm though the car is 70m away from the devices. Our method also outperforms all compared baselines and achieves state-of-the-art performance. Code is available at https://github.com/Yang-Kaixing/SHLE.

Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting. This is intrinsically an NP-hard knapsack problem where the exploration space becomes explosively larger as the feature dimension increases. Without the help of domain knowledge, solving this problem via heuristic method, such as Branch-and-Bound, suffers from exponential complexity, yet can bring arbitrarily bad attack results. We address the challenge via the lens of multi-armed bandit based combinatorial search. Our proposed method, namely FEAT, treats modifying each categorical feature as pulling an arm in multi-armed bandit programming. Our objective is to achieve highly efficient and effective attack using an Orthogonal Matching Pursuit (OMP)-enhanced Upper Confidence Bound (UCB) exploration strategy. Our theoretical analysis bounding the regret gap of FEAT guarantees its practical attack performance. In empirical analysis, we compare FEAT with other state-of-the-art domain-agnostic attack methods over various real-world categorical data sets of different applications. Substantial experimental observations confirm the expected efficiency and attack effectiveness of FEAT applied in different application scenarios. Our work further hints the applicability of FEAT for assessing the adversarial vulnerability of classification systems with high-dimensional categorical inputs.

Full-body reconstruction is a fundamental but challenging task. Owing to the lack of annotated data, the performances of existing methods are largely limited. In this paper, we propose a novel method named Full-body Reconstruction from Part Experts~(FuRPE) to tackle this issue. In FuRPE, the network is trained using pseudo labels and features generated from part-experts. An simple yet effective pseudo ground-truth selection scheme is proposed to extract high-quality pseudo labels. In this way, a large-scale of existing human body reconstruction datasets can be leveraged and contribute to the model training. In addition, an exponential moving average training strategy is introduced to train the network in a self-supervised manner, further boosting the performance of the model. Extensive experiments on several widely used datasets demonstrate the effectiveness of our method over the baseline. Our method achieves the state-of-the-art performance. Code will be publicly available for further research.

大规模的地方认可是一项基本但具有挑战性的任务，在自主驾驶和机器人技术中起着越来越重要的作用。现有的方法已经达到了可接受的良好性能，但是，其中大多数都集中精力设计精美的全球描述符学习网络结构。长期以来忽略了特征概括和描述后的特征概括和描述符的重要性。在这项工作中，我们提出了一种名为GIDP的新方法，以学习良好的初始化并引起描述符，以供大规模识别。特别是，在GIDP中分别提出了无监督的动量对比度云预处理模块和基于重新的描述符后增强模块。前者旨在在训练位置识别模型之前对Point Cloud编码网络进行良好的初始化，而后来的目标是通过推理时间重新掌握预测的全局描述符。在室内和室外数据集上进行的广泛实验表明，我们的方法可以使用简单和一般的点云编码主干来实现最先进的性能。

中国人在马来群岛各国的中国社区中突出特征。在这些国家，中国人经历了对当地语言和文化的调整过程，这导致每个国家发生中国变体。在本文中，我们对从五个马来群岛国家收集的中国新闻文本进行了定量分析看法。统计结果表明，这五个国家中使用的中国变体与现代中国大陆同行不同。同时，我们设法提取并分类了每个国家使用的几个中文单词。所有这些差异反映了中国人如何在海外发展，并证明了ROM当地社会和文化对中国发展的深远影响。

这项工作提出了一种有丝分裂检测方法，只有一个香草卷积神经网络（CNN）。我们的方法由两个步骤组成：给定图像，我们首先使用滑动窗口技术应用CNN来提取具有有丝分裂的斑块。然后，我们计算每个提取的斑块的类激活图，以获得有丝分裂的精确位置。为了提高模型的推广性，我们使用一系列数据增强技术训练CNN，与噪声标记的图像相抵制的损失以及主动的学习策略。我们的方法在MIDOG 2022挑战的初步测试阶段中，通过有效网络B3模型获得了0.7323的F1得分。