主题进化建模近几十年来收到了重大关注。虽然已经提出了各种主题演进模型，但大多数研究都关注单一文件语料库。但是，在实践中，我们可以轻松访问来自多个来源的数据，并且还可以观察它们之间的关系。然后，识别多个文本语料库之间的关系并进一步利用这种关系来提高主题建模。在这项工作中，我们专注于两个文本语料库之间的特殊关系，我们将其定义为“滞后关系”。这种关系表征了一个文本语料库会影响未来在另一个文本语料库中讨论的主题的现象。要发现引导滞后关系，我们提出了一个共同动态的主题模型，并开发了嵌入扩展，以解决大规模文本语料库的建模问题。通过认可的引导关系，可以改善两个文本语料库的相似性，可以改善在两种语料中学习的主题质量。我们使用合成数据进行数值调查联合动态主题建模方法的性能。最后，我们在两个文本语料库上应用拟议的模型，包括统计文件和毕业论文。结果表明，拟议的模型可以很好地认识到两种语料库之间的引导滞后关系，也发现了两种语料库的具体和共享主题模式。

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.

电子商务网站属性价值提取（AVE）的主要挑战是如何处理多种产品的大量属性。尽管该挑战是通过一个问题回答（QA）方法来解决的，该方法在给定查询（属性）的产品数据中找到值，但对于稀有和模棱两可的查询，它不能有效地工作。因此，我们根据基于质量质量质量检查的AVE的查询（属性）的可能答案（属性）提出了简单的知识驱动查询扩展。我们从培训数据中检索查询（属性）的值以扩展查询。我们用两个技巧来训练一个模型，即知识辍学和知识令牌混合，这模仿了测试中价值知识的不完善。我们清洁版的Aliexpress数据集的实验结果表明，我们的方法改善了AVE的性能（+6.08宏F1），尤其是对于稀有和模棱两可的属性（分别为+7.82和+6.86宏F1）。

高斯平滑的最佳运输（GOT）框架，在Goldfeld等人开创。 （2020）并随后被一系列后续文件，在统计，机器学习，信息理论和相关领域的研究人员中迅速引起了注意。在其中做出的一个关键观察是，通过适应Get框架而不是其未平滑的对应物，可以提升用于使用经验测量来近似于近似真实数据生成分布的维度的诅咒。目前的论文表明，相关观察适用于离散指数家庭模型中非参数混合分布的估计，在Get成本下，非参数MLE的估计精度可以加速到多项式速率。这与基于无缝度量的经典子多项式速率鲜明对比，这不能从信息理论的角度来改进。我们分析中的一个关键步骤是建立高斯复杂的LipsChitz函数的新杰克逊型近似。这种洞察力弥补了分析非参数MLES和新的框架的现有技术。

Bradley-terry-luce（BTL）模型是一个基准模型，用于个人之间的成对比较。尽管最近在几种流行程序的一阶渐近学上进行了最新进展，但对BTL模型中不确定性定量的理解基本上仍然不完整，尤其是当基础比较图很少时。在本文中，我们通过重点关注两个估计量的估计器来填补这一空白：最大似然估计器（MLE）和频谱估计器。使用统一的证明策略，我们在基础比较图的最稀少的可能的制度（最多达到某些多同源因​​素）中，为两个估计量提供了尖锐而均匀的非反应膨胀。这些扩展使我们能够获得：（i）两个估计器的有限维中心限制定理； （ii）构建个人等级的置信区间； （iii）$ \ ell_2 $估计的最佳常数，这是由MLE实现的，但不是由光谱估计器实现的。我们的证明是基于二阶剩余矢量的自洽方程和新的两次分析分析。

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.