Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200$\times$27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. Code is available at https://github.com/Ranchosky/OAN.

Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches have been proposed, how well these approaches balance different tasks on each parameter still remains unclear. In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter. Specifically, we compute the total updates by the exponentially decaying Average of the squared Updates (AU) on a parameter from the corresponding task.Based on this novel metric, we observe that many parameters in existing MTL methods, especially those in the higher shared layers, are still dominated by one or several tasks. The dominance of AU is mainly due to the dominance of accumulative gradients from one or several tasks. Motivated by this, we propose a Task-wise Adaptive learning rate approach, AdaTask in short, to separate the \emph{accumulative gradients} and hence the learning rate of each task for each parameter in adaptive learning rate approaches (e.g., AdaGrad, RMSProp, and Adam). Comprehensive experiments on computer vision and recommender system MTL datasets demonstrate that AdaTask significantly improves the performance of dominated tasks, resulting SOTA average task-wise performance. Analysis on both synthetic and real-world datasets shows AdaTask balance parameters in every shared layer well.

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data.

Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all history information, the dialogue state in the last turn is typically adopted as the input for predicting the current state by DST models. However, these models tend to keep the predicted slot values unchanged, which is defined as state momentum in this paper. Specifically, the models struggle to update slot values that need to be changed and correct wrongly predicted slot values in the last turn. To this end, we propose MoNET to tackle state momentum via noise-enhanced training. First, the previous state of each turn in the training data is noised via replacing some of its slot values. Then, the noised previous state is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values. Furthermore, a contrastive context matching framework is designed to narrow the representation distance between a state and its corresponding noised variant, which reduces the impact of noised state and makes the model better understand the dialogue history. Experimental results on MultiWOZ datasets show that MoNET outperforms previous DST methods. Ablations and analysis verify the effectiveness of MoNET in alleviating state momentum and improving anti-noise ability.

COVID-19的传播表明，在不同的城市和社区之间，传播风险模式不是同质的，各种异质特征会影响传播轨迹。因此，对于预测性大流行监测，至关重要的是，在城市和社区中探索潜在的异质特征，以区分其特定的大流行扩散轨迹。为此，这项研究创建了一个网络嵌入模型，捕获跨县的访问网络以及异质特征，以根据其大流行传播轨迹来发现美国县的集群。我们从3月3日至2020年6月29日（初始波浪）收集了2,787个县的位置智能特征。其次，我们构建了一个人类访问网络，该网络将县特征作为节点属性和县之间的访问作为网络边缘。我们的归因网络嵌入方法整合了跨县访问网络的类型学特征以及异质性特征。我们对属性网络嵌入进行了聚类分析，以揭示与四个县群相对应的差异风险轨迹的四种原型。随后，我们确定了四个功能是原型之间独特的传输风险模式的重要特征。归因的网络嵌入方法和发现识别并解释了整个县的非殖民性大流行风险轨迹进行预测性大流行监测。这项研究还为大流行分析的基于数据驱动和深度学习的方法有助于补充大流行病政策分析的标准流行病学模型。

真实世界的文本应用程序通常涉及组成广泛的文本控制操作，例如编辑文本W.R.T.属性，操纵关键字和结构，并生成所需属性的新文本。事先的工作通常会学习/芬太尼语言模型（LM）以执行操作的个人或特定子集。最近的研究以插件方式研究了合并操作，通常在复杂序列空间中以昂贵的搜索或优化进行了研究。本文提出了一种新的有效方法，用于在紧凑的文本潜在空间中进行可复合的文本操作。文本潜在矢量的低维度和不同性使我们能够基于给定的任意插入运算符（例如属性分类器）基于普通微分方程（ODE）开发有效的采样器。通过通过有效的适应性将预告片的LMS（例如GPT2）连接到潜在空间，然后我们将采样向量解码为所需的文本序列。灵活的方法允许使用来自不同域中的任何相关数据获取的各种控制操作员（情感，时态，形式，关键字等）。实验表明，在我们的方法中构成这些操作员可以生成或编辑高质量文本，从而在发电质量和效率方面显着改善了以前的方法。

实现通用语言情报是自然语言处理的长期目标，标准评估基准发挥基本和指导作用。我们认为，对于通用语言智能评估，基准本身需要全面和系统。为此，我们提出了Cuge，一种中文语言理解和生成评估基准，具有以下特征：（1）分层基准框架，其中数据集主要选择和组织语言能力 - 任务数据集层次结构。 （2）多级评分策略，其中基于分层框架提供了不同级别的模型性能。为了促进CUGE，我们提供了一个公共排行榜，可以自定义，以支持灵活的模型判断标准。代表性预先训练的语言模型的评估结果表明了对通用语言智能的完善的充足空间。 Cuge在Cuge.baai.ac.cn上公开提供。

最近关于其他方式的核化图像T1辅助MRI重建的研究表明，进一步加速MRI收购其他方式的潜力。大多数最先进的方法通过开发用于固定的欠采样模式的网络架构来实现改进，而不完全利用方式之间的互补信息。尽管可以简单地修改现有的下采样模式学习算法以允许完全采样的T1加权MR图像来辅助模式学习，但是可以实现重建任务的显着改进。为此，我们提出了一个迭代框架，优化了MRI获取的另一种方式的采样下采样模式，可以在不同的下抽样因子中补充完全采样的T1加权MR图像，同时共同优化T1辅助MRI重建模型。具体地，我们所提出的方法利用两种模式之间的潜在信息的差异来确定可以最大化T1加权MR图像的辅助功率在改善MRI重建时最大化的采样模式。与常用的下采样模式和最先进的方法相比，我们在公共数据集中展示了我们在公共数据集上的学习的下采样模式的卓越表现，可以联合优化重建网络和欠采样模式以8倍的取样因子。

由于点云数据的稀缺性质，在大规模环境中使用激光雷达识别使用激光雷达的地方是具有挑战性的。在本文中，我们提出了BVMATCH，基于LIDAR的帧到帧位置识别框架，其能够估计2D相对姿势。基于地面区域可以近似作为平面的假设，我们将地面区域统一地分散到网格和项目3D LIDAR扫描到鸟瞰图（BV）图像。我们进一步使用了一组Log-Gabor过滤器来构建一个最大索引图（MIM），用于编码图像中结构的方向信息。我们从理论上分析MIM的方向特征，并引入了一种名为鸟瞰图特征变换（BVFT）的新颖描述符。所提出的BVFT对BV图像的旋转和强度变化不敏感。利用BVFT描述符，统一LIDAR将识别和将估算任务统一到BVMATCT框架中。在三个大规模数据集上进行的实验表明，BVMATCH在召回的位置识别和姿势估计精度的召回速率方面优于最先进的方法。

Graph Neural Networks(GNNs) are a family of neural models tailored for graph-structure data and have shown superior performance in learning representations for graph-structured data. However, training GNNs on large graphs remains challenging and a promising direction is distributed GNN training, which is to partition the input graph and distribute the workload across multiple machines. The key bottleneck of the existing distributed GNNs training framework is the across-machine communication induced by the dependency on the graph data and aggregation operator of GNNs. In this paper, we study the communication complexity during distributed GNNs training and propose a simple lossless communication reduction method, termed the Aggregation before Communication (ABC) method. ABC method exploits the permutation-invariant property of the GNNs layer and leads to a paradigm where vertex-cut is proved to admit a superior communication performance than the currently popular paradigm (edge-cut). In addition, we show that the new partition paradigm is particularly ideal in the case of dynamic graphs where it is infeasible to control the edge placement due to the unknown stochastic of the graph-changing process.