传播相位对比度同步同步rotron MicrotoMography（PPC-SR $ {\ mu} $ CT）是对考古遗骸内部结构的非侵入性和非破坏性访问的黄金标准。在该分析中，需要分割虚拟标本以分开不同的部件或材料，通常需要相当多的人力努力的过程。在MicrotoMograph成像（ASEMI）项目的自动分割中，我们开发了一种自动分割这些容量图像的工具，使用手动分段样本来调谐和培训机器学习模型。对于一套四个古埃及动物木乃伊标本，与手动细分切片相比，达到了94-98％的整体准确性，使用深度学习（97-99％）接近现货商业软件的结果较低的复杂性。对分段输出的定性分析表明，我们的结果在对来自深度学习的人的可用性方面接近，证明了这些技术的使用。

Graph neural networks (GNNs) have pushed the state-of-the-art (SOTA) for performance in learning and predicting on large-scale data present in social networks, biology, etc. Since integrated circuits (ICs) can naturally be represented as graphs, there has been a tremendous surge in employing GNNs for machine learning (ML)-based methods for various aspects of IC design. Given this trajectory, there is a timely need to review and discuss some powerful and versatile GNN approaches for advancing IC design. In this paper, we propose a generic pipeline for tailoring GNN models toward solving challenging problems for IC design. We outline promising options for each pipeline element, and we discuss selected and promising works, like leveraging GNNs to break SOTA logic obfuscation. Our comprehensive overview of GNNs frameworks covers (i) electronic design automation (EDA) and IC design in general, (ii) design of reliable ICs, and (iii) design as well as analysis of secure ICs. We provide our overview and related resources also in the GNN4IC hub at https://github.com/DfX-NYUAD/GNN4IC. Finally, we discuss interesting open problems for future research.

移动机器人应用需要有关平台上各个传感器的几何位置的精确信息。此信息由外部校准参数给出，该参数定义了传感器如何相对于固定参考坐标系的旋转和翻译。错误的校准参数对典型的机器人估计任务有负面影响，例如大满贯。在这项工作中，我们提出了一种新方法，用于在机器人操作过程中连续估计校准参数。参数估计基于点云的匹配，这些点云是由传感器从多个静态观点获取的。因此，我们的方法不需要任何特殊的校准目标，并且适用于可以将测量值转换为点云的任何传感器。我们通过校准由2个LIDAR传感器，3个相机和一个成像雷达传感器组成的多传感器系统来证明我们方法的适用性。

无监督的域适应性通过将模型从标记的源域转移到未标记的目标域来证明减轻域移动的巨大潜力。尽管无监督的域适应已应用于各种复杂的视力任务，但只有很少的作品专注于自动驾驶的车道检测。这可以归因于缺乏公开可用的数据集。为了促进这些方向的研究，我们提出了Carlane，Carlane是用于2D车道检测的3条SIM到真实域的适应基准。 Carlane包括单目标数据集Molane和Tulane以及多目标数据集Mulane。这些数据集由三个不同的域构建，这些域涵盖了不同的场景，并包含163K唯一图像，其中118K被注释。此外，我们评估和报告系统的基线，包括我们自己的方法，这些方法基于典型的跨域自学学习。我们发现，与完全监督的基线相比，评估域适应方法的假阳性和假阴性率很高。这肯定了对卡莱恩等基准的必要性，以进一步加强无监督的领域适应道的研究。 Carlane，所有评估的模型和相应的实现都可以在https://carlanebench.github.io上公开获得。

本文提出了一种基于图形的正则化回归估计器 - 分层特征回归（HFR） - 从机器学习和图论域名的洞察力调动洞察力，以估算线性回归的鲁棒参数。估计器构造一个监督的特征图，该监督特征图沿其边缘分解参数，首先调整常见变化并连续地将特殊性模式结合到拟合过程中。图形结构具有对组靶标的参数收缩的影响，其中收缩程度由肝异常的控制，并且基团组合物以及收缩靶数是内源性的。该方法提供了丰富的资源，以便在数据中的潜在效果结构的视觉探索，并与一系列经验和模拟回归任务的常用正则化技术面板相比，展示了良好的预测精度和多功能性。

在自治机器人的背景下，最重要的任务之一是在导航期间防止对机器人的潜在损坏。为此目的，通常认为必须处理已知的概率障碍，然后计算与每个障碍物碰撞的概率。然而，在复杂的场景或非结构化环境中，可能难以检测这种障碍。在这些情况下，使用公制地图，其中每个位置存储占用的信息。最常见的公制地图类型是贝叶斯占用地图。然而，由于其离散性，这种类型的地图对计算持续路径的风险评估不太适合。因此，我们介绍了一种名为Lambda领域的新型地图，专门用于风险评估。我们首先提出了一种方法来计算这样的地图和对路径的通用风险的期望。然后，我们用一个用例展示了我们的通用配方的益处，该用例将风险定义为路径上的预期碰撞力。使用这种风险定义和Lambda领域，我们表明我们的框架能够在具有物理度量的同时进行经典路径规划。此外，Lambda领域为处理非结构化环境（如高草）提供了一种自然的方式。如果标准环境表示始终会产生围绕此类障碍的轨迹，我们的框架允许机器人在意识到所采取的风险时穿过草地。

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.