强度衍射断层扫描（IDT）是指用于从一组仅2D强度测量的样品成像样品的3D折射率（RI）分布的一类光学显微镜技术。由于相位信息的丢失和缺失的锥体问题，无伪影RI地图的重建是IDT的一个基本挑战。神经领域（NF）最近成为一种新的深度学习方法（DL），用于学习物理领域的连续表示。 NF使用基于坐标的神经网络来表示该场，通过将空间坐标映射到相应的物理量，在我们的情况下，复杂价值的折射率值。我们将DEPAF作为第一种基于NF的IDT方法，可以从仅强度和有限角度的测量值中学习RI体积的高质量连续表示。 DECAF中的表示形式是通过使用IDT向前模型直接从测试样品的测量值中学到的，而无需任何地面真相图。我们对模拟和实验生物学样品进行定性和定量评估DECAF。我们的结果表明，DECAF可以生成高对比度和无伪影RI图，并导致MSE超过现有方法的2.1倍。

电子健康记录（EHR）可获得的丰富纵向个体水平数据可用于检查治疗效果异质性。但是，使用EHR数据估算治疗效果提出了几个挑战，包括时变的混杂，重复和时间不一致的协变量测量，治疗分配和结果以及由于辍学导致的损失。在这里，我们开发了纵向数据（SDLD）算法的亚组发现，该算法是一种基于树的算法，用于使用纵向相互作用树算法结合使用纵向相互作用的一般数据驱动的方法，与纵向驱动的方法与纵向驱动的方法结合使用纵向相互作用，以发现具有异质治疗效果的亚组，并进行纵向研究。目标最大似然估计。我们将算法应用于EHR数据，以发现患有人免疫缺陷病毒（HIV）的人群的亚组，他们在接受非Dolutegravir抗逆转录病毒疗法（ART）接受非Dolutegravir抗逆转录病毒疗法（艺术）时的体重增加风险较高。

动态面部表达识别（FER）数据库为情感计算和应用提供了重要的数据支持。但是，大多数FER数据库都用几个基本的相互排斥性类别注释，并且仅包含一种模式，例如视频。单调的标签和模式无法准确模仿人类的情绪并实现现实世界中的应用。在本文中，我们提出了MAFW，这是一个大型多模式复合情感数据库，野外有10,045个视频Audio剪辑。每个剪辑都有一个复合的情感类别和几个句子，这些句子描述了剪辑中受试者的情感行为。对于复合情绪注释，每个剪辑都被归类为11种广泛使用的情绪中的一个或多个，即愤怒，厌恶，恐惧，幸福，中立，悲伤，惊喜，蔑视，焦虑，焦虑，无助和失望。为了确保标签的高质量，我们通过预期最大化（EM）算法来滤除不可靠的注释，然后获得11个单标签情绪类别和32个多标签情绪类别。据我们所知，MAFW是第一个带有复合情感注释和与情感相关的字幕的野外多模式数据库。此外，我们还提出了一种新型的基于变压器的表达片段特征学习方法，以识别利用不同情绪和方式之间表达变化关系的复合情绪。在MAFW数据库上进行的广泛实验显示了所提出方法的优势，而不是其他最先进的方法对单型和多模式FER的优势。我们的MAFW数据库可从https://mafw-database.github.io/mafw公开获得。

射频角动量（OAM）以射频在相同频率通道上多路复用一组正交模式的新方法，以实现高光谱效率。然而，基于经典的相位梯度的OAM模式检测方法需要完美的发射和接收天线对准，这极大地挑战了OAM通信的实际应用。在本文中，我们首先显示对OAM相位结构的非平行错位的影响，然后提出基于均匀圆形阵列（UCA）样本的OAM模式检测方法学习更通用的对准或非平行错位情况。具体而言，我们应用了三个分类器：k最近邻（knn），支持向量机（SVM）和背部传播神经网络（BPNN）到单模和多模式OAM检测。仿真结果验证了所提出的基于学习的OAM模式检测方法对未对准错误是强大的，尤其是BPNN分类器具有最佳的泛化性能。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

To generate high quality rendering images for real time applications, it is often to trace only a few samples-per-pixel (spp) at a lower resolution and then supersample to the high resolution. Based on the observation that the rendered pixels at a low resolution are typically highly aliased, we present a novel method for neural supersampling based on ray tracing 1/4-spp samples at the high resolution. Our key insight is that the ray-traced samples at the target resolution are accurate and reliable, which makes the supersampling an interpolation problem. We present a mask-reinforced neural network to reconstruct and interpolate high-quality image sequences. First, a novel temporal accumulation network is introduced to compute the correlation between current and previous features to significantly improve their temporal stability. Then a reconstruct network based on a multi-scale U-Net with skip connections is adopted for reconstruction and generation of the desired high-resolution image. Experimental results and comparisons have shown that our proposed method can generate higher quality results of supersampling, without increasing the total number of ray-tracing samples, over current state-of-the-art methods.

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.

Representing and synthesizing novel views in real-world dynamic scenes from casual monocular videos is a long-standing problem. Existing solutions typically approach dynamic scenes by applying geometry techniques or utilizing temporal information between several adjacent frames without considering the underlying background distribution in the entire scene or the transmittance over the ray dimension, limiting their performance on static and occlusion areas. Our approach $\textbf{D}$istribution-$\textbf{D}$riven neural radiance fields offers high-quality view synthesis and a 3D solution to $\textbf{D}$etach the background from the entire $\textbf{D}$ynamic scene, which is called $\text{D}^4$NeRF. Specifically, it employs a neural representation to capture the scene distribution in the static background and a 6D-input NeRF to represent dynamic objects, respectively. Each ray sample is given an additional occlusion weight to indicate the transmittance lying in the static and dynamic components. We evaluate $\text{D}^4$NeRF on public dynamic scenes and our urban driving scenes acquired from an autonomous-driving dataset. Extensive experiments demonstrate that our approach outperforms previous methods in rendering texture details and motion areas while also producing a clean static background. Our code will be released at https://github.com/Luciferbobo/D4NeRF.

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.

Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.