人脸识别是模式识别区域中非常重要的领域。它在军事和金融中有多种申请，名称为几个。在本文中，将提出与最近邻的方法的稀疏PCA的组合（以及与内核脊回归方法），并将应用于解决面部识别问题。实验结果表明，稀疏PCA方法的组合（使用近端梯度法和FISTA方法）和一个特定分类系统的准确性可能低于PCA方法和一个特定分类系统的组合的精度，但有时稀疏PCA方法的组合（使用近端梯度法或Fista方法）和一个特定的分类系统导致更好的准确性。此外，我们认识到，使用Fista方法计算稀疏PCA算法的过程总比使用近端梯度方法计算稀疏PCA算法的过程。

本文介绍了伯特嵌入法和图形卷积神经网络的新方法。采用这种组合来解决文本分类问题。最初，我们将BERT嵌入方法应用于文本（在BBC新闻数据集和IMDB电影评论数据集）中，以便将所有文本转换为数字向量。然后，图形卷积神经网络将应用于这些数字向量，以将这些文本分类为其AP的兴趣类/标签。实验表明，图形卷积神经网络模型的性能优于具有CLAS-SICE机器学习模型的BERT嵌入方法的组合的性能。

Graphs的许多模型属于边缘无关的点产品型号的框架。这些模型输出所有节点之间存在的边缘的概率，并且两个节点之间的链路的概率随与节点相关联的矢量的点乘积而增加。最近的工作表明，这些模型无法捕获实际图中的关键结构，特别是异种结构，其中在不同节点之间发生链接。我们提出了一种独立的图形生成模型，它足以捕捉到异源性，B）产生非负嵌入物，这允许在社区方面解释的链接预测，C）有效地在具有梯度的真实图中优化跨熵损失下降。我们的理论结果展示了我们模型的表现力，其能够使用最大程度的线性的多个簇进行准确地重建图表，以及其在数据中捕获异常和精梳性的能力。此外，我们的实验展示了我们模型对多种重要应用任务等多个重要应用程序任务的有效性，例如多标签聚类和链路预测。

在过去的二十年中，我们看到万维网的链接结构被建模为有向图。在本文中，我们将将万维网的链接结构建模为定向超图。此外，我们将为该定向超图开发Pagerank算法。由于缺乏万维网定向HyperGraph数据集，我们将将Pagerank算法应用于代谢网络，该网络是定向的HyperGraph本身。实验表明，我们的新型Pagerank算法成功地应用于该代谢网络。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. However, scholarly literature shows varying research areas and various interpretations of COC. This calls for a cohesive architecture that delineates the nature of COC. We argue that if Herbert Simon considered the design science is the science of artificial, cognitive systems are the products of cognitive science or 'the newest science of the artificial'. Therefore, building a conceptual basis for COC is an essential step into prospective cognitive computing-based systems. This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. Then, we compare the statistical analysis results with previous qualitative analysis results to confirm our findings. The study also comprehensively surveys the recent research on COC to identify the state of the art and connect the advances in varied research disciplines in COC. The study found that there are three underlaying computing paradigms, Von-Neuman, Neuromorphic Engineering and Quantum Computing, that comprehensively complement the structure of cognitive computation. The research discuss possible applications and open research directions under the COC umbrella.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.