心肌的准确分割和运动估计在临床领域一直很重要，这基本上有助于下游诊断。但是，现有方法不能始终保证心肌分割的形状完整性。此外，运动估计需要在不同帧上对心肌区域的点对应关系。在本文中，我们提出了一种新型的端到端深度统计形状模型，以关注具有形状完整性和边界对应关系的心肌分割。具体而言，心肌形状由固定数量的点表示，其变化是通过主成分分析（PCA）提取的。深神经网络用于预测转换参数（仿射和变形），然后将其用于将平均点云转转到图像域。此外，引入了一个可区分的渲染层，以将掩码的监督纳入框架中，以了解更准确的点云。通过这种方式，所提出的方法能够在不进行后处理的情况下始终如一地产生解剖上合理的分割掩码。此外，预测的点云还保证了顺序图像的边界对应关系，这有助于下游任务，例如心肌的运动估计。我们进行了几项实验，以证明在几个基准数据集上提出的方法的有效性。

磁共振光谱成像（MRSI）是量化体内代谢物的必不可少的工具，但是低空间分辨率限制了其临床应用。基于深度学习的超分辨率方法为改善MRSI的空间分辨率提供了有希望的结果，但是与实验获得的高分辨率图像相比，超级分辨图像通常是模糊的。已经使用生成对抗网络进行了尝试，以提高图像视觉质量。在这项工作中，我们考虑了另一种类型的生成模型，即基于流的模型，与对抗网络相比，训练更稳定和可解释。具体而言，我们提出了一个基于流动的增强器网络，以提高超分辨率MRSI的视觉质量。与以前的基于流的模型不同，我们的增强器网络包含了来自其他图像模式（MRI）的解剖信息，并使用可学习的基础分布。此外，我们施加指南丢失和数据一致性丢失，以鼓励网络在保持高忠诚度的同时以高视觉质量生成图像。从25名高级神经胶质瘤患者获得的1H-MRSI数据集上进行的实验表明，我们的增强子网络的表现优于对抗网络和基线基线方法。我们的方法还允许视觉质量调整和不确定性估计。

在血管成形术的临床程序中（即开放式堵塞冠状动脉），在X射线荧光镜检查的指导下，需要将气球和支架等装置（例如气球和支架）放置在动脉中。由于X射线剂量的局限性，所得图像通常是嘈杂的。为了检查这些设备的正确放置，平均进行了多个运动补偿帧以增强视图。因此，设备跟踪是为此目的的必要过程。即使设计为具有易于跟踪的放射性标记的血管成形术设备，但由于标记尺寸较小和血管成形术中的复杂场景，当前的方法难以提供令人满意的结果。在本文中，我们提出了一个用于单个支架跟踪的端到端深度学习框架，该框架由三个层次模块组成：基于U-NET的Landmark检测，基于重新连接的支架提案和功能提取，以及图形卷积神经网络（GCN）基于暂时聚集空间信息和外观特征的支架跟踪。实验表明，与基于点的跟踪模型相比，我们的方法在检测中的性能明显更好。此外，其快速推理速度满足临床要求。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR. Our code and pre-trained models will be released.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.