Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and solve it using a few MLS slices with sparse labels. Meanwhile, with the help of diffusion models, we are able to use a great number of unlabeled MLS data and 2793 non-MLS cases for representation learning and regularization. The extracted representation reflects how the image is different from a non-MLS image and regularization serves an important role in the sparse-to-dense refinement of the deformation field. Our experiment on a real clinical brain hemorrhage dataset has achieved state-of-the-art performance and can generate interpretable deformation fields.

Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model. Traditional knowledge distillation methods include response-based methods and feature-based methods. Response-based methods are used the most widely but suffer from lower upper limit of model performance, while feature-based methods have constraints on the vocabularies and tokenizers. In this paper, we propose a tokenizer-free method liberal feature-based distillation (LEAD). LEAD aligns the distribution between teacher model and student model, which is effective, extendable, portable and has no requirements on vocabularies, tokenizer, or model architecture. Extensive experiments show the effectiveness of LEAD on several widely-used benchmarks, including MS MARCO Passage, TREC Passage 19, TREC Passage 20, MS MARCO Document, TREC Document 19 and TREC Document 20.

Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' interest in recent years. The current multi-modal fusion methods mainly focus on camera data and LiDAR data, but pay little attention to the kinematic information provided by the bottom sensors of the vehicle, such as acceleration, vehicle speed, angle of rotation. These information are not affected by complex external scenes, so it is more robust and reliable. In this paper, we introduce the existing application fields of vehicle bottom information and the research progress of related methods, as well as the multi-modal fusion methods based on bottom information. We also introduced the relevant information of the vehicle bottom information data set in detail to facilitate the research as soon as possible. In addition, new future ideas of multi-modal fusion technology for autonomous driving tasks are proposed to promote the further utilization of vehicle bottom information.

Controller design for bipedal walking on dynamic rigid surfaces (DRSes), which are rigid surfaces moving in the inertial frame (e.g., ships and airplanes), remains largely uninvestigated. This paper introduces a hierarchical control approach that achieves stable underactuated bipedal robot walking on a horizontally oscillating DRS. The highest layer of our approach is a real-time motion planner that generates desired global behaviors (i.e., the center of mass trajectories and footstep locations) by stabilizing a reduced-order robot model. One key novelty of this layer is the derivation of the reduced-order model by analytically extending the angular momentum based linear inverted pendulum (ALIP) model from stationary to horizontally moving surfaces. The other novelty is the development of a discrete-time foot-placement controller that exponentially stabilizes the hybrid, linear, time-varying ALIP model. The middle layer of the proposed approach is a walking pattern generator that translates the desired global behaviors into the robot's full-body reference trajectories for all directly actuated degrees of freedom. The lowest layer is an input-output linearizing controller that exponentially tracks those full-body reference trajectories based on the full-order, hybrid, nonlinear robot dynamics. Simulations of planar underactuated bipedal walking on a swaying DRS confirm that the proposed framework ensures the walking stability under different DRS motions and gait types.

不利的天气条件（例如阴霾，雨水和雪）通常会损害被捕获的图像的质量，从而导致在正常图像上训练的检测网络在这些情况下概括了很差。在本文中，我们提出了一个有趣的问题 - 如果图像恢复和对象检测的结合可以提高不利天气条件下尖端探测器的性能。为了回答它，我们提出了一个有效但统一的检测范式，该范式通过动态增强学习将这两个子任务桥接在一起，以在不利的天气条件下辨别对象，称为Togethernet。与现有的努力不同，这些努力将图像除去/der绘制为预处理步骤，而是考虑了一个多任务联合学习问题。遵循联合学习方案，可以共享由恢复网络产生的清洁功能，以在检测网络中学习更好的对象检测，从而有助于TogEthERNET在不利天气条件下增强检测能力。除了联合学习体系结构外，我们还设计了一个新的动态变压器功能增强模块，以提高togethernet的功能提取和表示功能。对合成和现实世界数据集的广泛实验表明，我们的togethernet在定量和质量上都超过了最先进的检测方法。源代码可从https://github.com/yz-wang/togethernet获得。

图像平滑是一项基本的低级视觉任务，旨在保留图像的显着结构，同时删除微不足道的细节。图像平滑中已经探索了深度学习，以应对语义结构和琐碎细节的复杂纠缠。但是，当前的方法忽略了平滑方面的两个重要事实：1）受限数量的高质量平滑地面真相监督的幼稚像素级回归可能会导致域的转移，并导致对现实世界图像的概括问题； 2）纹理外观与对象语义密切相关，因此图像平滑需要意识到语义差异以应用自适应平滑强度。为了解决这些问题，我们提出了一个新颖的对比语义引导的图像平滑网络（CSGIS-NET），该网络在促进强大的图像平滑之前结合了对比的先验和语义。通过利用不希望的平滑效应作为负面教师，并结合分段任务以鼓励语义独特性来增强监督信号。为了实现所提出的网络，我们还使用纹理增强和平滑标签（即VOC-Smooth）丰富了原始的VOC数据集，它们首先桥接图像平滑和语义分割。广泛的实验表明，所提出的CSGI-NET大量优于最先进的算法。代码和数据集可在https://github.com/wangjie6866/csgis-net上找到。

我们引入了基于高斯工艺回归和边缘化图内核（GPR-MGK）的探索性主动学习（AL）算法，以最低成本探索化学空间。使用高通量分子动力学模拟生成数据和图神经网络（GNN）以预测，我们为热力学性质预测构建了一个主动学习分子模拟框架。在特定的靶向251,728个烷烃分子中，由4至19个碳原子及其液体物理特性组成：密度，热能和汽化焓，我们使用AL算法选择最有用的分子来代表化学空间。计算和实验测试集的验证表明，只有313个（占总数的0.124 \％）分子足以训练用于计算测试集的$ \ rm r^2> 0.99 $的精确GNN模型和$ \ rm rm r^2>>实验测试集0.94 $。我们重点介绍了提出的AL算法的两个优点：与高通量数据生成和可靠的不确定性量化的兼容性。

高信心重叠的预测和准确的对应关系对于以部分到派对方式对齐成对点云至关重要。但是，重叠区域和非重叠区域之间存在固有的不确定性，这些区域一直被忽略并显着影响注册绩效。除了当前的智慧之外，我们提出了一种新颖的不确定性意识到的重叠预测网络，称为Utopic，以解决模棱两可的重叠预测问题。据我们所知，这是第一个明确引入重叠不确定性以指向云注册的人。此外，我们诱导特征提取器通过完成解码器隐式感知形状知识，并为变压器提供几何关系嵌入，以获得转换 - 不变性的几何形状感知特征表示。凭借更可靠的重叠得分和更精确的密度对应关系的优点，即使对于有限的重叠区域的输入，乌托邦也可以实现稳定而准确的注册结果。关于合成和实际基准的广泛定量和定性实验证明了我们的方法优于最先进的方法。代码可从https://github.com/zhileichen99/utopic获得。

图形神经网络（GNN）是具有无核数据的应用的有前途的方法。但是，具有数亿节点的大规模图上的培训GNN既是资源又是耗时的。与DNN不同，GNN通常具有更大的内存足迹，因此GPU内存能力和PCIE带宽是GNN培训中的主要资源瓶颈。为了解决此问题，我们提出分叉：一种图形量化方法，通过显着减少内存足迹和PCIE带宽要求来加速GNN训练，以便GNN可以充分利用GPU计算功能。我们的关键见解是，与DNN不同，GNN不太容易发生量化引起的输入特征的信息丢失。我们确定图形特征量化中的主要准确性影响因素，从理论上证明，分叉训练会收敛到网络，在该网络中，损失在未压缩网络的最佳损失的$ \ epsilon $之内。我们使用几种流行的GNN模型和数据集对分叉进行了广泛的评估，包括最大的公共图数据集MAG240M上的图形。结果表明，分叉达到30以上的压缩率，并在边际准确性损失的情况下提高了GNN训练速度200％-320％。特别是，分叉在一小时内仅使用四个GPU在MAG240M上的训练图来实现记录。

随着深度卷积神经网络的兴起，对象检测在过去几年中取得了突出的进步。但是，这种繁荣无法掩盖小物体检测（SOD）的不令人满意的情况，这是计算机视觉中臭名昭著的挑战性任务之一，这是由于视觉外观不佳和由小目标的内在结构引起的嘈杂表示。此外，用于基准小对象检测方法基准测试的大规模数据集仍然是瓶颈。在本文中，我们首先对小物体检测进行了详尽的审查。然后，为了催化SOD的发展，我们分别构建了两个大规模的小物体检测数据集（SODA），SODA-D和SODA-A，分别集中在驾驶和空中场景上。 SODA-D包括24704个高质量的交通图像和277596个9个类别的实例。对于苏打水，我们收集2510个高分辨率航空图像，并在9个类别上注释800203实例。众所周知，拟议的数据集是有史以来首次尝试使用针对多类SOD量身定制的大量注释实例进行大规模基准测试。最后，我们评估主流方法在苏打水上的性能。我们预计发布的基准可以促进SOD的发展，并产生该领域的更多突破。数据集和代码将很快在：\ url {https://shaunyuan22.github.io/soda}上。