To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers $13$ common text generation tasks and their corresponding $83$ datasets and further incorporates $45$ PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs. We also implement $4$ efficient training strategies and provide $4$ generation objectives for pre-training new PLMs from scratch. To be unified, we design the interfaces to support the entire research pipeline (from data loading to training and evaluation), ensuring that each step can be fulfilled in a unified way. Despite the rich functionality, it is easy to use our library, either through the friendly Python API or command line. To validate the effectiveness of our library, we conduct extensive experiments and exemplify four types of research scenarios. The project is released at the link: https://github.com/RUCAIBox/TextBox.

Visual localization plays an important role for intelligent robots and autonomous driving, especially when the accuracy of GNSS is unreliable. Recently, camera localization in LiDAR maps has attracted more and more attention for its low cost and potential robustness to illumination and weather changes. However, the commonly used pinhole camera has a narrow Field-of-View, thus leading to limited information compared with the omni-directional LiDAR data. To overcome this limitation, we focus on correlating the information of 360 equirectangular images to point clouds, proposing an end-to-end learnable network to conduct cross-modal visual localization by establishing similarity in high-dimensional feature space. Inspired by the attention mechanism, we optimize the network to capture the salient feature for comparing images and point clouds. We construct several sequences containing 360 equirectangular images and corresponding point clouds based on the KITTI-360 dataset and conduct extensive experiments. The results demonstrate the effectiveness of our approach.

在许多现实世界中的机器学习应用中，亚种群的转移存在着极大地存在，指的是包含相同亚种群组的培训和测试分布，但在亚种群频率中有所不同。重要性重新加权是通过对训练数据集中每个样本施加恒定或自适应抽样权重来处理亚种群转移问题的正常方法。但是，最近的一些研究已经认识到，这些方法中的大多数无法改善性能，而不是经验风险最小化，尤其是当应用于过度参数化的神经网络时。在这项工作中，我们提出了一个简单而实用的框架，称为“不确定性感知混合”（UMIX），以根据样品不确定性重新加权“混合”样品来减轻过度参数化模型中的过度拟合问题。基于训练 - 注射器的不确定性估计为每个样品的拟议UMIX配备，以灵活地表征亚群分布。我们还提供有见地的理论分析，以验证UMIX是否在先前的工作中实现了更好的概括界限。此外，我们在广泛的任务上进行了广泛的经验研究，以验证我们方法的有效性，既有定性和定量。

在阻碍强化学习（RL）到现实世界中的问题的原因之一，两个因素至关重要：与培训相比，数据有限和测试环境的不匹配。在本文中，我们试图通过分配强大的离线RL的问题同时解决这些问题。特别是，我们学习了一个从源环境中获得的历史数据，并优化了RL代理，并在扰动的环境中表现良好。此外，我们考虑将算法应用于大规模问题的线性函数近似。我们证明我们的算法可以实现$ O（1/\ sqrt {k}）$的次级临时性，具体取决于线性函数尺寸$ d $，这似乎是在此设置中使用样品复杂性保证的第一个结果。进行了不同的实验以证明我们的理论发现，显示了我们算法与非持bust算法的优越性。

随着LIDAR传感器在自动驾驶中的流行率，3D对象跟踪受到了越来越多的关注。在点云序列中，3D对象跟踪旨在预测给定对象模板中连续帧中对象的位置和方向。在变压器成功的驱动下，我们提出了点跟踪变压器（PTTR），它有效地预测了高质量的3D跟踪，借助变压器操作，以粗到1的方式导致。 PTTR由三个新型设计组成。 1）我们设计的关系意识采样代替随机抽样，以在亚采样过程中保留与给定模板相关的点。 2）我们提出了一个点关系变压器，以进行有效的特征聚合和模板和搜索区域之间的特征匹配。 3）基于粗糙跟踪结果，我们采用了一个新颖的预测改进模块，通过局部特征池获得最终的完善预测。此外，以捕获对象运动的鸟眼视图（BEV）的有利特性（BEV）的良好属性，我们进一步设计了一个名为PTTR ++的更高级的框架，该框架既包含了点的视图和BEV表示）产生高质量跟踪结果的影响。 PTTR ++实质上提高了PTTR顶部的跟踪性能，并具有低计算开销。多个数据集的广泛实验表明，我们提出的方法达到了卓越的3D跟踪准确性和效率。

随机块模型（SBM）是用于网络数据最广泛使用的生成模型之一。鉴于块或社区成员身份，许多连续的动态网络模型都建立在与SBM相同的假设上：有条件地有条件地独立在真实网络中观察到。我们提出了多元社区霍克斯（Mulch）模型，这是一种非常灵活的基于社区的模型，用于连续时间网络，使用结构化的多元霍克斯工艺在节点对之间引入依赖性。我们使用基于光谱聚类和基于可能性的本地改进程序拟合模型。我们发现，我们所提出的覆盖模型比在预测和生成任务中都比现有模型更准确。

Zigzag flattening (ZF) is commonly utilized as a default option to get the image patches ordering in deep models, e.g. vision transformers (ViTs). Notably, when decomposing multi-scale images, ZF could not maintain the invariance of feature point positions.To this end, we investigate the Hilbert flattening (HF) as an alternative for sequence ordering in vision tasks. HF has proven to be superior to other flatten approaches in maintaining spatial locality, when performing multi-scale transformations of dimensional space. In applications, we design a position encoding method based on HF, beating absolute position encoding non-trivially in Transformer architecture. It also can be used to feature down-sampling and feature/image interpolation. Extensive experiments demonstrate that it can yield consistent performance boosts for several popular architectures and applications. The code will be released upon acceptance.

在点云序列中，3D对象跟踪目的是在给定模板点云的情况下预测当前搜索点云中的对象的位置和方向。通过变压器的成功，我们提出了点跟踪变压器（PTTR），其有效地在变压器操作的帮助下以粗良好的方式预测高质量的3D跟踪结果。 PTTR由三种新颖的设计组成。 1）除了随机抽样中，我们设计关系感知采样，以保护在子采样期间给定模板的相关点。 2）此外，我们提出了一种由自我关注和跨关注模块组成的点关系变压器（PRT）。全局自我关注操作捕获远程依赖性，以便分别增强搜索区域和模板的编码点特征。随后，我们通过横向关注匹配两组点特征来生成粗略跟踪结果。 3）基于粗略跟踪结果，我们采用了一种新颖的预测细化模块来获得最终精制预测。此外，我们根据Waymo Open DataSet创建一个大型点云单个对象跟踪基准。广泛的实验表明，PTTR以准确性和效率达到优越的点云跟踪。

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-consuming and inefficient. With the development of deep learning methods, some models with good performance have emerged, but the generalization ability should be further strengthened. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation performance with a high recall rate is achieved by the proposed depthwise separable convolution residual block and channel-spatial attention gate. Then, a classification network is cascaded to further improve the detection performance of mitosis nuclei. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained compared with the current state-of-the-art algorithms. In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper. The code will be available at: https://github.com/antifen/mitosis-nuclei-segmentation.