Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

虽然微调预训练的网络已成为训练图像分割模型的流行方式，但这种用于图像分割的骨干网络经常使用图像分类源数据集（例如ImageNet）进行预训练。尽管图像分类数据集可以为骨干网络提供丰富的视觉特征和歧视能力，但它们无法以端到端的方式完全预训练目标模型（即骨干+分割模块）。由于分类数据集中缺乏分割标签，因此在微调过程中进行分割模块在微调过程中随机初始化。在我们的工作中，我们提出了一种利用伪语义分割标签（PSSL）的方法，以启用基于分类数据集的图像分割模型的端到端预训练。 PSSL的启发是受到观察的启发，即通过CAM，Smoothgrad和Lime等解释算法获得的分类模型的解释结果将接近视觉对象的像素簇。具体而言，通过解释分类结果并汇总了从多个分类器查询的解释集合来降低单个模型引起的偏差，从而为每个图像获得PSSL。使用PSSL，对于ImageNet的每个图像，提出的方法都利用加权分割学习程序来预先培训分割网络。实验结果表明，在Imagenet伴随PSSL作为源数据集的情况下，提出的端到端预训练策略成功地增强了各种分割模型的性能，即PSPNET-RESNET50，DEEPLABV3-RESNET50和OCRNET-HRNET-HRNETENET-HRNETENET-HRNETENET-HRNETENET-HRNETW18，和在许多细分任务上，例如CAMVID，VOC-A，VOC-C，ADE20K和CityScapes，并有重大改进。源代码可在https://github.com/paddlepaddle/paddleseg上使用。

基于池的主动学习（AL）通过依次从大型未标记数据池中选择信息的未标记样本并从Oracle/Ontoter中查询标签，从而取得了巨大成功。但是，现有的AL采样策略可能在分布外（OOD）数据方案中无法很好地工作，其中未标记的数据池包含一些不属于目标任务类别的数据示例。在OOD数据情景下实现良好的AL性能是一项具有挑战性的任务，因为Al采样策略与OOD样本检测之间的自然冲突。 Al选择很难由当前基本分类器进行分类的数据（例如，预测类概率具有较高熵的样品），而OOD样品往往具有比分布更均匀的预测类概率（即高熵）（即高熵）（ID ） 数据。在本文中，我们提出了一种采样方案，即用于主动学习的蒙特 - 卡洛帕累托优化（POAL），该方案从未标记的数据库中选择了具有固定批次大小的未标记样品的最佳子集。我们将AL采样任务施加为多目标优化问题，因此我们基于两个冲突的目标利用Pareto优化：（1）正常的AL数据采样方案（例如，最大熵）和（2）作为OOD样本。实验结果表明其对经典机器学习（ML）和深度学习（DL）任务的有效性。

时空视频接地（STVG）是一项具有挑战性的任务，旨在根据自然语言查询在语义上在语义上定位感兴趣的对象的时空管。大多数以前的作品不仅严重依赖于更快的R-CNN提取的锚盒，而且还简单地将视频视为一系列单独的帧，因此缺乏其时间建模。取而代之的是，在本文中，我们是第一个为STVG提出的无锚框架的人，称为Gaussian基于高斯内核的交叉模态网络（GKCMN）。具体而言，我们利用每个视频框架的基于高斯内核的热图来定位与查询相关的对象。混合的串行和并行连接网络进一步开发，以利用框架之间的空间和时间关系以更好地接地。VIDSTG数据集的实验结果证明了我们提出的GKCMN的有效性。

作为世界各地的Covid-19大流行横冲直撞，对视频会议激增的需求。为此，实时肖像分割成为一种流行的功能，以取代会议参与者的背景。虽然为从生命场景中提取身体姿势的分段提供了具有丰富的数据集，模型和算法，但纵向分割尚未在视频会议上下文中覆盖很好。为了促进该领域的进步，我们介绍了名为PP-Humanseg的开源解决方案。这项工作是第一个构建一个大型视频纵向数据集，其中包含291个会议场景中的291个视频，其中14K细微的帧和扩展到多摄像头电话。此外，我们提出了一种用于语义分割的新型语义连接感知学习（SCL），其引入了语义连接感知丢失，以提高来自连接的角度的分段结果。我们提出了一种超轻量级模型，具有SCL的实际肖像分割，实现IOO之间的最佳权衡和推理的速度。我们数据集的广泛评估展示了SCL和我们的模型的优越性。源代码可在https://github.com/paddlepaddle/paddleseg上获得。

最近，神经技术已用于自动生成源代码。这些方法在有望获得声明语言的同时，在命令式语言的数据集上的性能差得多。由于通常将声明性语言嵌入了现实世界软件开发中的命令式语言（即Turducken式编程）中，因此声明语言的有希望的结果几乎不会导致手动软件开发工作大幅减少。在本文中，我们定义了一项新的代码生成任务：鉴于自然语言评论，此任务旨在用嵌入式声明语言以基本命令性语言生成程序。据我们所知，这是第一个Turducken风格的代码生成任务。对于此任务，我们将Lyra：Python中的数据集提出了嵌入式SQL。该数据集包含来自现实世界项目的2,000个精心注释的数据库操作程序。每个程序都与中文评论和英文评论配对。在我们的实验中，我们采用了变压器，伯特风格和GPT风格的模型作为基础。在最佳环境中，GPT风格模型的生成性能比其他模型更好，在使用中文和英语评论时，AST精确匹配的精度分别为24％和25.5％。因此，我们认为Lyra为代码生成提供了新的挑战。但是，克服这一挑战可能会大大提高代码生成技术在现实世界软件开发中的适用性。

现有图形神经网络（GNNS）很大程度上依赖于节点嵌入品，其表示节点作为其标识，类型或内容的矢量。但是，具有未分配的节点的图表广泛存在于现实世界中的应用程序（例如，匿名社交网络）。以前的GNN可以将随机标签分配给节点（将伪影介绍给GNN）或分配给所有节点的一个嵌入（这不能明确区分一个节点）。此外，当这些GNN应用于未分配的节点分类问题时，它们具有不需要的标准性属性，其基本上无法以多种可能的输出来解决数据。在本文中，我们分析了节点分类问题现有方法的限制。灵感来自我们的分析，我们提出了一种推广的标准性质和优先标记技术，满足所需的属性渐近。实验结果表明，我们在几种未分配的节点分类任务中实现了高性能。

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are usually sub-optimal and unsatisfactory to end customers. Weak supervision is a promising paradigm for obtaining considerable labels in a low-cost way, which enables the customers to label data by writing heuristic rules rather than annotating each instance individually. However, in the time-series domain, it is hard for people to write reasonable labeling functions as the time-series data is numerically continuous and difficult to be understood. In this paper, we propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system, which enables a user to improve the results of unsupervised anomaly detection by performing only a small amount of interactions with the system. To achieve this goal, the system integrates weak supervision and active learning collaboratively while generating labeling functions automatically using only a few labeled data. All of these techniques are complementary and can promote each other in a reinforced manner. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions in both weak supervision and active learning areas. Also, the system has been tested in a real scenario in industry to show its practicality.

This paper investigates the use of artificial neural networks (ANNs) to solve differential equations (DEs) and the construction of the loss function which meets both differential equation and its initial/boundary condition of a certain DE. In section 2, the loss function is generalized to $n^\text{th}$ order ordinary differential equation(ODE). Other methods of construction are examined in Section 3 and applied to three different models to assess their effectiveness.