三维（3D）图像（例如CT，MRI和PET）在医学成像应用中很常见，在临床诊断中很重要。语义歧义是许多医学图像标签的典型特征。这可能是由许多因素引起的，例如成像特性，病理解剖学以及二进制面具的弱表示，这给精确的3D分割带来了挑战。在2D医学图像中，使用软面膜代替图像垫形式产生的二进制掩码来表征病变可以提供丰富的语义信息，更全面地描述病变的结构特征，从而使后续诊断和分析受益。在这项工作中，我们将图像垫子介绍到3D场景中，以描述3D医学图像中的病变。 3D模态中图像垫的研究有限，并且没有与3D矩阵相关的高质量注释数据集，因此减慢了基于数据驱动的深度学习方法的发展。为了解决这个问题，我们构建了第一个3D医疗垫数据集，并通过质量控制和下游实验中的肺结节分类中令人信服地验证了数据集的有效性。然后，我们将四个选定的最新2D图像矩阵算法调整为3D场景，并进一步自定义CT图像的方法。此外，我们提出了第一个端到端的深3D垫网络，并实施了可靠的3D医疗图像垫测试基准，该基准将被发布以鼓励进一步的研究。

自我对准过程可以提供准确的罪恶初始态度。常规的两种方法通常包括粗糙和细微的对齐过程。粗对齐通常基于OBA（基于优化的对准）方法，批次估计自我对准开始时恒定的初始态度。 OBA迅速收敛，但是准确性很低，因为该方法不考虑IMU的偏差错误。细胞对齐应用递归的贝叶斯滤波器，这使得对IMU的系统误差估计更加准确，但与此同时，态度误差以较大的标题未对准角缓慢收敛。研究人员提出了统一的自我对准以在一个过程中实现自我对准，但是当未对准角度很大时，基于递归贝叶斯过滤器的现有方法仍然很慢。在本文中，提出了基于批处理估计器FGO（因子图优化）的统一方法。据作者所知，这是第一种批处理方法，能够同时估算IMU的所有系统误差和恒定的初始态度，并具有快速的收敛性和高精度。通过对旋转罪的模拟和物理实验来验证该方法的有效性。

难以通过二进制面具手动准确标记含糊不清的和复杂形状的目标。在医学图像分割中突出显示二元掩模下面的弱点，其中模糊是普遍的。在多个注释的情况下，通过二元面具对临床医生达成共识更具挑战性。此外，这些不确定的区域与病变结构有关，可能含有有利于诊断的解剖信息。然而，目前关于不确定性的研究主要关注模型培训和数据标签的不确定性。他们都没有调查病变本身的模糊性质的影响。通过图像消光，透过图像消光，将Alpha Matte作为软片介绍，代表医学场景中不确定的区域，并因此提出了一种新的不确定性量化方法来填补填补差距病变结构的不确定性研究。在这项工作中，我们在多任务框架中引入了一种新的架构，以在多任务框架中生成二进制掩模和alpha掩饰，这优于所有最先进的消光算法。建议的不确定性地图能够突出模糊地区和我们提出的新型多任务损失加权策略可以进一步提高性能并证明其具体的益处。为了充分评估我们提出的方法的有效性，我们首先用alpha哑布标记了三个医疗数据集，以解决医学场景中可用消光数据集的短缺，并证明alpha遮罩是一种比定性的二进制掩模更有效的标签方法和量化方面。

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.

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.

Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency. We believe the insufficient utilization of training signals should be responsible. To alleviate this issue, we introduce a conceptually simple yet learning-efficient MIM training scheme, termed Disjoint Masking with Joint Distillation (DMJD). For disjoint masking (DM), we sequentially sample multiple masked views per image in a mini-batch with the disjoint regulation to raise the usage of tokens for reconstruction in each image while keeping the masking rate of each view. For joint distillation (JD), we adopt a dual branch architecture to respectively predict invisible (masked) and visible (unmasked) tokens with superior learning targets. Rooting in orthogonal perspectives for training efficiency improvement, DM and JD cooperatively accelerate the training convergence yet not sacrificing the model generalization ability. Concretely, DM can train ViT with half of the effective training epochs (3.7 times less time-consuming) to report competitive performance. With JD, our DMJD clearly improves the linear probing classification accuracy over ConvMAE by 5.8%. On fine-grained downstream tasks like semantic segmentation, object detection, etc., our DMJD also presents superior generalization compared with state-of-the-art SSL methods. The code and model will be made public at https://github.com/mx-mark/DMJD.

Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitive to noise points. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change points in data streams with the tolerance of noise points. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.

In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation methods focus on independently and identically distributed observations and cannot handle the non-stationarity and temporal dependency in the common panel data setting. The treatment evaluators developed for panel data, on the other hand, typically ignore the individualized information. To fill the gap, in this paper, we initialize the study of HTE estimation in panel data. Under different assumptions for HTE identifiability, we propose the corresponding heterogeneous one-side and two-side synthetic learner, namely H1SL and H2SL, by leveraging the state-of-the-art HTE estimator for non-panel data and generalizing the synthetic control method that allows flexible data generating process. We establish the convergence rates of the proposed estimators. The superior performance of the proposed methods over existing ones is demonstrated by extensive numerical studies.