Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in implicit space and propose a novel approach to orient point cloud normals by incorporating isovalue constraints to the Poisson equation. Our key observation is that when using a point cloud with consistently oriented normals as the input for implicit surface reconstruction, the indicator function values of the sample points should be close to the isovalue of the surface. Based on this observation and the Poisson equation, we propose an optimization formulation that combines isovalue constraints with local consistency requirements for normals. We optimize normals and implicit functions simultaneously and solve for a globally consistent orientation. Thanks to the sparsity of the linear system, our method can work on an average laptop with reasonable computational time. Experiments show that our method can achieve high performance in non-uniform and noisy data and manage varying sampling densities, artifacts, multiple connected components, and nested surfaces.

大多数现有的半监督基于图的聚类方法通过完善亲和力矩阵或直接限制数据点的低维表示来利用监督信息。亲和力矩阵代表图形结构，对于半监督基于图的聚类的性能至关重要。但是，现有方法采用静态亲和力矩阵来学习数据点的低维表示，并且在学习过程中不会优化亲和力矩阵。在本文中，我们提出了一种新型的动态图结构学习方法，用于半监督聚类。在这种方法中，我们通过利用给定的成对约束来同时优化数据点的亲和力矩阵和低维表示。此外，我们提出了一种交替的最小化方法，并通过可靠的收敛来解决提出的非凸模型。在迭代过程中，我们的方法周期性地更新数据点的低维表示并完善了亲和力矩阵，从而导致动态亲和力矩阵（图结构）。具体而言，为了更新亲和力矩阵，我们强制使用具有明显不同的低维表示的数据点具有相关值为0。点。在不同设置下的八个基准数据集上的实验结果显示了所提出方法的优势。

表面重建是3D图形的基本问题。在本文中，我们提出了一种基于学习的基于云层云层的隐式表面重建的方法，没有正常。我们的方法是在潜在的能源理论中受到高斯引理的启发，这为指标功能提供了明确的整体公式。我们设计一个新颖的深神经网络，以执行表面积分，并从未定向和嘈杂的点云学习修改的指示灯。我们连接具有不同尺度的特征，以便准确地对整数的贡献。此外，我们提出了一种新颖的表面元件特征提取器来学习局部形状特性。实验表明，我们的方法从具有不同噪声尺度的点云的点云产生具有高正常一致性的平滑表面，并与当前的数据驱动和非数据驱动的方法相比，实现了最先进的重建性能。

扩散是分子从较高浓度的区域的运动到较低浓度的区域。它可用于描述数据点之间的交互。在许多机器学习问题包括转导半监督学习和少量学习的问题，标记和未标记的数据点之间的关系是高分类精度的关键组件。在本文中，由对流扩散颂歌的启发，我们提出了一种新颖的扩散剩余网络（Diff-Reset），将扩散机制引入内部的神经网络中。在结构化数据假设下，证明扩散机构可以提高距离直径比，从而提高了阶级间点间的可分离性，并减少了局部分类点之间的距离。该特性可以通过用于构建可分离超平面的剩余网络来轻松采用。各种数据集中的半监控图节点分类和几次拍摄图像分类的广泛实验验证了所提出的扩散机制的有效性。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.

Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy. In some cases, there may be unmeasured variables that can confound the action-reward or action-next-state relationships, rendering many existing OPE approaches ineffective. This paper develops an instrumental variable (IV)-based method for consistent OPE in confounded Markov decision processes (MDPs). Similar to single-stage decision making, we show that IV enables us to correctly identify the target policy's value in infinite horizon settings as well. Furthermore, we propose an efficient and robust value estimator and illustrate its effectiveness through extensive simulations and analysis of real data from a world-leading short-video platform.

Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy. It is critical in a number of sequential decision making problems ranging from healthcare to technology industries. Most of the work in existing literature is focused on evaluating the mean outcome of a given policy, and ignores the variability of the outcome. However, in a variety of applications, criteria other than the mean may be more sensible. For example, when the reward distribution is skewed and asymmetric, quantile-based metrics are often preferred for their robustness. In this paper, we propose a doubly-robust inference procedure for quantile OPE in sequential decision making and study its asymptotic properties. In particular, we propose utilizing state-of-the-art deep conditional generative learning methods to handle parameter-dependent nuisance function estimation. We demonstrate the advantages of this proposed estimator through both simulations and a real-world dataset from a short-video platform. In particular, we find that our proposed estimator outperforms classical OPE estimators for the mean in settings with heavy-tailed reward distributions.