我们提出了一种新型的二次编程公式，用于估计群体同步中的损坏水平，并使用这些估计来解决此问题。我们的目标函数利用了组的循环一致性，因此我们将我们的方法称为结构一致性（DESC）的检测和估计。该一般框架可以扩展到其他代数和几何结构。我们的表述具有以下优势：它可以忍受与信息理论界限一样高的腐败，它不需要对小组元素的估计值进行良好的初始化，它具有简单的解释，在某些温和的条件下，我们的全球最小值目标函数准确恢复了腐败水平。我们证明了方法在旋转平均的合成和真实数据实验上的竞争精度。

背景和客观：冷冻EM图像的对比度因冰层的厚度不均匀而变化。这种对比变化会影响2-D类平均，3-D AB-Initio建模和3-D异质性分析的质量。当前在3次迭代精炼期间进行对比度估计。结果，在类平均和AB-Initio建模的早期计算阶段无法获得估计值。本文旨在直接从AB-Initio阶段的采摘粒子图像解决对比度估计问题，而无需估计3-D体积，图像旋转或类平均值。方法：我们的分析基础的关键观察是，原始图像的2-D协方差矩阵与基础干净图像的协方差有关，噪声方差以及图像之间的对比度可变性。我们表明，对比度可变性可以从2-D协方差矩阵得出，并应用现有的协方差Wiener滤波（CWF）框架来估计它。我们还展示了CWF的修改，以估计单个图像的对比度。结果：与先前的CWF方法相比，我们的方法将对比度估计提高了很大。它的估计准确性通常与知道干净图像的地面真相协方差的甲骨文相提并论。更准确的对比度估计还提高了合成数据集和实验数据集所示的图像恢复质量。结论：本文提出了一种有效的方法，即不使用任何3-D体积信息，直接从嘈杂图像中进行对比度估计。它可以在单个粒子分析的早期阶段进行对比校正，并可以提高下游处理的准确性。

我们开发了从运动管道的结构中恢复损坏的keypoint匹配的新统计信息。统计信息基于Keypoint匹配图的群集结构中出现的一致性约束。统计数据旨在为损坏的匹配和未损坏的匹配提供较小的值。这些新统计数据与迭代重新重量方案相结合以过滤关键点，然后可以将其从运动管道馈送到任何标准结构中。可以有效地实现该滤波方法并将其缩放到大规模的数据集，因为它仅需要稀疏矩阵乘法。我们展示了这种方法对来自运动数据集的合成和实际结构的功效，并表明它在这些任务中实现了最先进的准确性和速度。

持续学习依次解决学习不同任务的设置。尽管以前的许多解决方案，但大多数仍然遭受重大忘记或昂贵的记忆成本。在这项工作中，针对这些问题，我们首先通过信息理论的镜头来研究持续学习过程，并观察到在学习时从前一个任务中的参数丢失的遗忘。新任务。从这个角度来看，我们提出了一种名为位级信息保留（BLIP）的新的连续学习方法，其通过更新位电平的参数来保留模型参数的信息增益，这可以用参数量化方便地实现。更具体地，BLIP首先列举具有对新输入任务的权重量化的神经网络，然后估计由任务数据提供的每个参数上的信息增益，以确定要冻结的比特以防止遗忘。我们进行广泛的实验，从分类任务到加强学习任务，结果表明，我们的方法更好地生成了与以前最先进的结果相比的结果。实际上，昙花一现接近零忘记，同时只需要在连续学习中需要恒定的记忆开销。

探讨了语言建模流行的变形金刚，用于近期解决视觉任务，例如，用于图像分类的视觉变压器（VIT）。 VIT模型将每个图像分成具有固定长度的令牌序列，然后应用多个变压器层以模拟它们的全局关系以进行分类。然而，当从像想象中的中型数据集上从头开始训练时，VIT对CNNS达到较差的性能。我们发现它是因为：1）输入图像的简单标记未能模拟相邻像素之间的重要局部结构，例如边缘和线路，导致训练采样效率低。 2）冗余注意骨干骨干设计对固定计算预算和有限的训练样本有限的具有限制性。为了克服这些限制，我们提出了一种新的令牌到令牌视觉变压器（T2T-VIT），它包含1）层 - 明智的代币（T2T）转换，通过递归聚合相邻来逐步地结构于令牌到令牌。代币进入一个令牌（令牌到令牌），这样可以建模由周围令牌所代表的本地结构，并且可以减少令牌长度; 2）一种高效的骨干，具有深度狭窄的结构，用于在实证研究后CNN建筑设计的激励变压器结构。值得注意的是，T2T-VIT将Vanilla Vit的参数计数和Mac减少了一半，同时从想象中从头开始训练时，改善了超过3.0 \％。它还优于Endnets并通过直接培训Imagenet训练来实现与MobileNets相当的性能。例如，T2T-VTO与Reset50（21.5M参数）的可比大小（21.5M参数）可以在图像分辨率384 $ \ Times 384上实现83.3 \％TOP1精度。 （代码：https://github.com/yitu-opensource/t2t-vit）

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.