降解的图像通常存在于字符图像的一般来源中，从而导致特征识别结果不令人满意。现有的方法有专门的努力来恢复降级的角色图像。但是，这些方法获得的降解结果似乎并不能提高字符识别性能。这主要是因为当前方法仅着眼于像素级信息，而忽略了角色的关键特征，例如其字形，从而在脱索过程中导致字符标志性损害。在本文中，我们介绍了一个基于字形融合和注意力机制（即Churformer）的新型通用框架，以精确地恢复角色图像而不改变其固有的字形。与现有的框架不同，Charformer引入了一个并行目标任务，用于捕获其他信息并将其注入DICONISE骨架的图像，这将在字符图像DeNoising期间保持角色字形的一致性。此外，我们利用基于注意力的网络进行全局本地特征交互，这将有助于处理盲目的denoising和增强deNoSising绩效。我们将Charformer与多个数据集上的最新方法进行比较。实验结果表明了杂形和质量上的优势。

构建高质量的角色图像数据集很具有挑战性，因为现实世界图像通常受图像退化的影响。将当前图像恢复方法应用于此类现实世界字符图像时存在局限性，因为（i）字符图像中的噪声类别与一般图像中的噪声类别不同； （ii）现实世界字符图像通常包含更复杂的图像降解，例如不同噪声水平的混合噪声。为了解决这些问题，我们提出了一个现实世界角色恢复网络（RCRN），以有效恢复降级的角色图像，其中使用字符骨架信息和比例安装特征提取来获得更好的恢复性能。所提出的方法由骨架提取器（SENET）和角色图像修复器（CIRNET）组成。 Senet旨在保持角色的结构一致性并使复杂的噪声正常化。然后，Cirnet从降级的角色图像及其骨骼中重建了清洁图像。由于缺乏现实世界字符图像恢复的基准，我们构建了一个包含1,606个字符图像的数据集，这些图像具有现实世界中的降级，以评估所提出方法的有效性。实验结果表明，RCRN在定量和质量上优于最先进的方法。

科学家在寻找最佳的输入资源来解决目标预测任务的最佳输入资源方面的困难是在知识图图图上训练算法的主要障碍之一。除此之外，一个关键的挑战是确定如何操纵（和嵌入）这些数据，这些数据通常以特定的三元组（即主题，谓词，对象）的形式来启用学习过程。在本文中，我们描述了Liveschema倡议，即一个门户，该网关提供了一个服务家庭，可以轻松访问，分析，转换和利用知识图模式，其主要目标是促进这些资源在机器学习用例中的重复使用。作为该计划的早期实施，我们还推进了一个在线目录，该目录依赖于800多个资源，并提供了第一组示例服务。

长尾效应是一个常见的问题，它限制了对现实世界数据集中深度学习模型的性能。由于字符使用频率差异，角色图像数据集的开发还受到这种不平衡数据分布的影响。因此，当当前的角色识别方法应用于现实世界数据集时，尤其是尾巴中缺少训练样本的字符类别，例如不常见的字符或历史文档中的字符。在本文中，我们通过自由基提取（即REZCR）提出一个零摄像的角色识别框架，以提高几个样本字符类别的识别性能，在其中我们通过分解和分解和分解和分解和分解和分解字符的图形单位来利用有关的信息重建拼字法之后的字符。 REZCR由基于注意力的激进信息提取器（RIE）和基于知识图的角色推理器（KGR）组成。 RIE的目的是认识到候选激进分子及其从角色图像中可能的结构关系。结果将被馈入KGR，以通过使用预设计的字符知识图来识别目标字符。我们在多个数据集上验证我们的方法，REZCR显示出有希望的实验结果，尤其是对于少数样本字符数据集。

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.