中期图像的染色体分析和鉴定是基于细胞遗传学的医学诊断的关键部分。它主要用于识别遗传疾病和疾病的宪法，产前和获得异常。从中期鉴定染色体的过程是一个乏味的过程，需要训练有素的人员和几个小时才能进行。挑战尤其是在中期图像中处理触摸，重叠和聚集的染色体方面存在挑战，如果未正确进行分割，则会导致错误的分类。我们提出了一种自动化从给定的中期图像的检测和分割过程的方法，并在使用深层CNN体系结构中对其进行分类以了解染色体类型。我们已经使用了两种方法来处理中期中发现的重叠染色体的分离 - 一种涉及流域算法的方法，其次是自动编码器，另一种纯粹基于流域算法的方法。这些方法涉及自动化和非常最小的手动努力来执行分割，从而产生输出。手动努力确保了人类的直觉得到考虑，尤其是在处理触摸，重叠和聚类染色体时。分割后，使用深CNN模型将单个染色体图像分类为95.75 \％精度。此外，我们将分布策略从给定输出（通常可以在正常情况下的46个单个图像组成）中分类为单个类别，准确性为98 \％。我们的研究有助于得出结论，通过图像处理技术可以将参与染色体分割的纯手动努力自动化至非常好的水平，从而产生可靠且令人满意的结果。

点云是代表和存储3D几何数据的广泛使用的技术之一。在过去，已经提出了几种用于处理点云的方法。诸如PointNet和FoldingNet之类的方法已显示出3D形状分类和分割等任务的有希望的结果。这项工作提出了一个树结构化的自动编码器框架，以使用图形卷积利用层次信息来生成点云的强大嵌入。我们执行多个实验，以评估提出的编码器体系结构生成的嵌入质量，并可视化T-SNE映射，以突出显示其区分不同对象类的能力。我们进一步证明了所提出的框架在以下应用程序中的适用性：3D点云完成和基于单图的3D重建。

手写文档映像二值化由于书面内容和复杂的背景属性，如页面样式，纸张质量，污渍，阴影梯度和非均匀照明等复杂背景属性而挑战。虽然传统的阈值方法没有有效地推广在这种具有挑战性的真实情景中，但是在提供足够的训练数据时，基于深度的基于学习的方法表现得相对较好。但是，现有数据集的大小和多样性有限。这项工作提出了LS-HDIB - 一个大规模的手写文件映像二值化数据集，其中包含跨越众多真实情景的百万个文档图像。此外，我们介绍了一种新颖的技术，它使用自适应阈值和无缝克隆方法的组合来创建数据集，以准确的基础事实。通过广泛的定量和定性评估超过八种不同的基于深度学习的模型，我们在LS-HDIB数据集上培训并在看不见的图像上进行测试时，我们展示了这些模型的性能的增强。

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

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.

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.