从单眼图像中学习的自我监督深度学习通常依赖于暂时相邻图像帧之间的2D像素光度关系。但是，他们既没有完全利用3D点的几何对应关系，也没有有效地应对闭塞或照明不一致引起的光度扭曲中的歧义。为了解决这些问题，这项工作提出了密度量构建网络（DEVNET），这是一种新型的自我监管的单眼深度学习框架，可以考虑3D空间信息，并利用相邻的相机flustums中的更强的几何约束。我们的DEVNET不是直接从单个图像中回归像素值，而是将摄像头划分为多个平行的平面，并预测每个平面上的点闭塞概率密度。最终的深度图是通过沿相应射线集成密度来生成的。在训练过程中，引入了新颖的正则化策略和损失功能，以减轻光度歧义和过度拟合。如果没有明显放大的模型参数的大小或运行时间，DEVNET在Kitti-2015室外数据集和NYU-V2室内数据集上均优于几个代表性基准。特别是，在深度估计的任务中，在Kitti-2015和NYU-V2上，DEVNET均减少了4％的根平方。代码可在https://github.com/gitkaichenzhou/devnet上找到。

尽管基于体素的方法已经获得了来自多摄像头的多人3D姿势估计的有希望的结果，但它们具有沉重的计算负担，尤其是对于大型场景。我们提出了更快的素素，以通过将特征体积重新投影到三个二维坐标平面并分别估算x，y，z坐标来解决挑战。为此，我们首先通过分别基于投影到XY平面和Z轴的体积功能来估算2D框及其高度，首先通过一个3D边界框来定位每个人。然后，对于每个人，我们分别估算三个坐标平面的部分关节坐标，然后将其融合以获得最终的3D姿势。该方法不含昂贵的3D-CNN，并将其素的速度提高了十倍，同时作为最先进的方法的竞争精度，证明了其在实时应用中的潜力。

在本文中，我们提出了一种新颖的指导性扩散纯化方法，以防御对抗攻击。我们的模型在CIFAR-10数据集上的PGD-L_INF攻击（EPS = 8/255）下实现了89.62％的鲁棒精度。我们首先探讨了未引导的扩散模型与随机平滑之间的基本相关性，从而使我们能够将模型应用于认证的鲁棒性。经验结果表明，当认证的L2半径R大于0.5时，我们的模型优于随机平滑的5％。

自主驾驶的当代深度学习对象检测方法通常会假定前缀类别的共同交通参与者，例如行人和汽车。大多数现有的探测器无法检测到罕见的物体和拐角案例（例如，越过街道的狗），这可能会导致某些情况下发生严重的事故，从而使真实世界应用可靠的自动驾驶不确定。阻碍了真正可靠的自动驾驶系统发展的主要原因是缺乏评估对象探测器在角案例上的性能的公共数据集。因此，我们介绍了一个名为CODA的具有挑战性的数据集，该数据集揭示了基于视力的检测器的关键问题。该数据集由1500个精心选择的现实世界驾驶场景组成，每个场景平均包含四个对象级角案例（平均），涵盖30多个对象类别。在CODA上，在大型自动驾驶数据集中训练的标准对象探测器的性能显着下降到3月的12.8％。此外，我们试验了最新的开放世界对象检测器，发现它也无法可靠地识别尾声中的新对象，这表明对自主驾驶的强大感知系统可能远离触及。我们希望我们的CODA数据集有助于对现实世界自动驾驶的可靠检测进行进一步的研究。我们的数据集将在https://coda-dataset.github.io上发布。

旨在促进现实世界，不断发展和可扩展的自主驾驶系统，我们展示了一个大规模数据集，用于通过从原始数据学习来标准化不同自我监督和半监督方法的评估，这是第一和最大的数据集到期。现有的自主驱动系统严重依赖于“完善”视觉感知模型（即，检测）使用广泛的注释数据培训，以确保安全性。然而，在部署强大的自动驾驶系统时，精致地标记所有情景和环境的实例（即夜，极端天气，城市）是不现实的。最近的自我监督和半监督学习的推进激励，希望通过协作利用大规模未标记的数据和少数标记数据来学习强大的检测模型。现有数据集只提供少量数据或涵盖具有完整注释的有限域，妨碍大规模预训练模型的探索。在这里，我们发布了一个大型2D自主/半监控的对象检测数据集，用于自动驾驶，名为SODA10M，其中包含1000万个未标记的图像和标有6个代表对象类别的20K图像。为了提高多样性，在不同天气条件下的27833个驾驶时间内收集图像，32个不同城市的时期和位置场景。我们提供广泛的实验和对现有的流行自主/半监督方法深度分析，并在自动驾驶范围内给出一些有趣的调查结果。实验表明，SODA10M可以作为不同的自我监督学习方法作为有前途的预训练数据集，这在微调驾驶域中的不同下游任务（即检测，语义/实例分段）进行微调时提供了卓越的性能。更多信息可以参考https://soda-2d.github.io。

As a crucial robotic perception capability, visual tracking has been intensively studied recently. In the real-world scenarios, the onboard processing time of the image streams inevitably leads to a discrepancy between the tracking results and the real-world states. However, existing visual tracking benchmarks commonly run the trackers offline and ignore such latency in the evaluation. In this work, we aim to deal with a more realistic problem of latency-aware tracking. The state-of-the-art trackers are evaluated in the aerial scenarios with new metrics jointly assessing the tracking accuracy and efficiency. Moreover, a new predictive visual tracking baseline is developed to compensate for the latency stemming from the onboard computation. Our latency-aware benchmark can provide a more realistic evaluation of the trackers for the robotic applications. Besides, exhaustive experiments have proven the effectiveness of the proposed predictive visual tracking baseline approach.

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is of high possibility to be degraded due to noises and distortions. In this paper, we propose two novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (i.e., signal and object)-domain curvature regularization model. Fast numerical optimization algorithms are developed relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, which are further accelerated by GPU implementation. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.