Fine-grained classification and counting of bone marrow erythroid cells are vital for evaluating the health status and formulating therapeutic schedules for leukemia or hematopathy. Due to the subtle visual differences between different types of erythroid cells, it is challenging to apply existing image-based deep learning models for fine-grained erythroid cell classification. Moreover, there is no large open-source datasets on erythroid cells to support the model training. In this paper, we introduce BMEC (Bone Morrow Erythroid Cells), the first large fine-grained image dataset of erythroid cells, to facilitate more deep learning research on erythroid cells. BMEC contains 5,666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells. To distinguish the erythroid cells, one key indicator is the cell shape which is closely related to the cell growth and maturation. Therefore, we design a novel shape-aware image classification network for fine-grained erythroid cell classification. The shape feature is extracted from the shape mask image and aggregated to the raw image feature with a shape attention module. With the shape-attended image feature, our network achieved superior classification performance (81.12\% top-1 accuracy) on the BMEC dataset comparing to the baseline methods. Ablation studies also demonstrate the effectiveness of incorporating the shape information for the fine-grained cell classification. To further verify the generalizability of our method, we tested our network on two additional public white blood cells (WBC) datasets and the results show our shape-aware method can generally outperform recent state-of-the-art works on classifying the WBC. The code and BMEC dataset can be found on https://github.com/wangye8899/BMEC.

隐式辐射功能作为重建和渲染3D场景的照片真实观点的强大场景表示形式出现。但是，这些表示的编辑性差。另一方面，诸如多边形网格之类的显式表示允许易于编辑，但不适合重建动态的人头中的准确细节，例如精细的面部特征，头发，牙齿，牙齿和眼睛。在这项工作中，我们提出了神经参数化（NEP），这是一种混合表示，提供了隐式和显式方法的优势。 NEP能够进行照片真实的渲染，同时允许对场景的几何形状和外观进行细粒度编辑。我们首先通过将3D几何形状参数化为2D纹理空间来解开几何形状和外观。我们通过引入显式线性变形层来启用几何编辑性。变形由一组稀疏的密钥点控制，可以明确和直观地移位以编辑几何形状。对于外观，我们开发了一个混合2D纹理，该纹理由明确的纹理图组成，以易于编辑和隐式视图以及时间相关的残差，以建模时间和视图变化。我们将我们的方法与几个重建和编辑基线进行比较。结果表明，NEP在保持高编辑性的同时达到了几乎相同的渲染精度。

对新生儿的运动和姿势评估使经验丰富的儿科医生可以预测神经发育障碍，从而可以早期干预相关疾病。但是，大多数用于人类姿势估计方法的最新AI方法都集中在成年人上，缺乏公开基准的婴儿姿势估计。在本文中，我们通过提出婴儿姿势数据集和深度聚合视觉变压器来填补这一空白，以进行人姿势估计，该姿势估计引入了一个快速训练的完整变压器框架，而无需使用卷积操作在早期阶段提取功能。它将变压器 + MLP概括为特征图内的高分辨率深层聚集，从而在不同视力级别之间实现信息融合。我们在可可姿势数据集上预先训练，并将其应用于新发布的大规模婴儿姿势估计数据集。结果表明，凝集可以有效地学习不同分辨率之间的多尺度特征，并显着提高婴儿姿势估计的性能。我们表明，在婴儿姿势估计数据集中，凝集优于混合模型hrformer和tokenpose。此外，在可可瓣姿势估计上，我们的凝集表现优于0.8 AP。我们的代码可在github.com/szar-lab/aggpose上获得。

主成分分析（PCA）是一种用于矢量数据的流行尺寸减少技术。因子PCA（FPCA）是PCA的PCA用于矩阵数据的概率扩展，这可以大大降低PCA中的参数数，同时产生令人满意的性能。然而，FPCA基于高斯假设，从而易于异常值。虽然将多元$ T $分布作为矢量数据的强大建模工具具有很长的历史，但其对矩阵数据的应用非常有限。主要原因是矢量化矩阵数据的维度通常非常高，尺寸越高，测量稳健性的击穿点越低。为了解决FPCA遭受的稳健性问题，并使其适用于矩阵数据，本文提出了一种强大的FPCA（RFPCA）的扩展，这是一个被称为矩阵 - 变化$ T $分布的$ T $ -Type分布。与多元$ T $分布一样，Matrix-Variate $ T $分布可以自适应地降价异常值并屈服于强大的估计。我们开发了一种用于参数估计的快速EM型算法。综合性和现实世界数据集的实验表明，RFPCA比较有利地与若干相关方法，RFPCA是一个简单但有力的矩阵值异常检测工具。

由于其显着的合成质量，最近，神经辐射场（NERF）最近对3D场景重建和新颖的视图合成进行了相当大的关注。然而，由散焦或运动引起的图像模糊，这通常发生在野外的场景中，显着降低了其重建质量。为了解决这个问题，我们提出了DeBlur-nerf，这是一种可以从模糊输入恢复尖锐的nerf的第一种方法。我们采用逐合成方法来通过模拟模糊过程来重建模糊的视图，从而使NERF对模糊输入的鲁棒。该仿真的核心是一种新型可变形稀疏内核（DSK）模块，其通过在每个空间位置变形规范稀疏内核来模拟空间变形模糊内核。每个内核点的射线起源是共同优化的，受到物理模糊过程的启发。该模块作为MLP参数化，具有能够概括为各种模糊类型。联合优化NERF和DSK模块允许我们恢复尖锐的NERF。我们证明我们的方法可用于相机运动模糊和散焦模糊：真实场景中的两个最常见的模糊。合成和现实世界数据的评估结果表明，我们的方法优于几个基线。合成和真实数据集以及源代码将公开可用于促进未来的研究。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. For promoting the development of this emerging research direction, in this survey, we comprehensively review and summarize the existing graph data augmentation (GDAug) techniques. Specifically, we first summarize a variety of feasible taxonomies, and then classify existing GDAug studies based on fine-grained graph elements. Furthermore, for each type of GDAug technique, we formalize the general definition, discuss the technical details, and give schematic illustration. In addition, we also summarize common performance metrics and specific design metrics for constructing a GDAug evaluation system. Finally, we summarize the applications of GDAug from both data and model levels, as well as future directions.

Speech-centric machine learning systems have revolutionized many leading domains ranging from transportation and healthcare to education and defense, profoundly changing how people live, work, and interact with each other. However, recent studies have demonstrated that many speech-centric ML systems may need to be considered more trustworthy for broader deployment. Specifically, concerns over privacy breaches, discriminating performance, and vulnerability to adversarial attacks have all been discovered in ML research fields. In order to address the above challenges and risks, a significant number of efforts have been made to ensure these ML systems are trustworthy, especially private, safe, and fair. In this paper, we conduct the first comprehensive survey on speech-centric trustworthy ML topics related to privacy, safety, and fairness. In addition to serving as a summary report for the research community, we point out several promising future research directions to inspire the researchers who wish to explore further in this area.

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets.

Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the graph streams, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (METR-LA and PEMS-BAY) and a large-scale spatio-temporal dataset that contains a variaty of non-stationary phenomena. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle locations and time slots with different patterns and be robustly adaptive to different anomalous situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.