大脑磁共振成像（MRI）扫描的自动分割和体积对于诊断帕金森氏病（PD）和帕金森氏症综合症（P-Plus）至关重要。为了提高诊断性能，我们在大脑分割中采用了深度学习（DL）模型，并将其性能与金标准的非DL方法进行了比较。我们收集了健康对照组（n = 105）和PD患者（n = 105），多个全身性萎缩（n = 132）和渐进性超核麻痹（n = 69）的大脑MRI扫描。 2020.使用金标准的非DL模型FreeSurfer（FS），我们对六个脑结构进行了分割：中脑，PON，CAUDATE，CAUDATE，PUTATATE，pALLIDUM和THIRD CNTRICLE，并将其视为DL模型的注释数据，代表性V -net和unet。计算了分化正常，PD和P-Plus病例的曲线下的骰子分数和面积。每位患者六个大脑结构的V-NET和UNETR的分割时间分别为3.48 +-0.17和48.14 +-0.97 s，比FS（15,735 +-1.07 s）快至少300倍。两种DL模型的骰子得分都足够高（> 0.85），它们的疾病分类AUC优于FS。为了分类正常与P-Plus和PD与多个全身性萎缩（小脑型）的分类，DL模型和FS显示出高于0.8的AUC。 DL显着减少了分析时间，而不会损害大脑分割和差异诊断的性能。我们的发现可能有助于在临床环境中采用DL脑MRI分割并提高大脑研究。

This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground radar using low-resolution multiband satellite images. We suggest a solution that performs data preprocessing appropriate to the challenge and then predicts rainfall movies using a novel RainUNet. RainUNet is a hierarchical U-shaped network with temporal-wise separable block (TS block) using a decoupled large kernel 3D convolution to improve the prediction performance. Various evaluation metrics show that our solution is effective compared to the baseline method. The source codes are available at https://github.com/jinyxp/Weather4cast-2022

Question Answering (QA) is a task that entails reasoning over natural language contexts, and many relevant works augment language models (LMs) with graph neural networks (GNNs) to encode the Knowledge Graph (KG) information. However, most existing GNN-based modules for QA do not take advantage of rich relational information of KGs and depend on limited information interaction between the LM and the KG. To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner. Specifically, QAT constructs Meta-Path tokens, which learn relation-centric embeddings based on diverse structural and semantic relations. Then, our Relation-Aware Self-Attention module comprehensively integrates different modalities via the Cross-Modal Relative Position Bias, which guides information exchange between relevant entities of different modalities. We validate the effectiveness of QAT on commonsense question answering datasets like CommonsenseQA and OpenBookQA, and on a medical question answering dataset, MedQA-USMLE. On all the datasets, our method achieves state-of-the-art performance. Our code is available at http://github.com/mlvlab/QAT.

Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects and their neighboring objects, they are dominant representation learning modules for SGG. However, existing MPNN-based frameworks assume the scene graph as a homogeneous graph, which restricts the context-awareness of visual relations between objects. That is, they overlook the fact that the relations tend to be highly dependent on the objects with which the relations are associated. In this paper, we propose an unbiased heterogeneous scene graph generation (HetSGG) framework that captures relation-aware context using message passing neural networks. We devise a novel message passing layer, called relation-aware message passing neural network (RMP), that aggregates the contextual information of an image considering the predicate type between objects. Our extensive evaluations demonstrate that HetSGG outperforms state-of-the-art methods, especially outperforming on tail predicate classes.

The ability of knowledge graphs to represent complex relationships at scale has led to their adoption for various needs including knowledge representation, question-answering, fraud detection, and recommendation systems. Knowledge graphs are often incomplete in the information they represent, necessitating the need for knowledge graph completion tasks, such as link and relation prediction. Pre-trained and fine-tuned language models have shown promise in these tasks although these models ignore the intrinsic information encoded in the knowledge graph, namely the entity and relation types. In this work, we propose the Knowledge Graph Language Model (KGLM) architecture, where we introduce a new entity/relation embedding layer that learns to differentiate distinctive entity and relation types, therefore allowing the model to learn the structure of the knowledge graph. In this work, we show that further pre-training the language models with this additional embedding layer using the triples extracted from the knowledge graph, followed by the standard fine-tuning phase sets a new state-of-the-art performance for the link prediction task on the benchmark datasets.

在本文中，我们建议利用对话的独特特征，共享参与者的常识性知识，以解决总结它们的困难。我们提出了病态的框架，该框架使用常识推论作为其他背景。与以前仅依赖于输入对话的工作相比，Sick使用外部知识模型来生成丰富的常识推断，并选择具有基于相似性选择方法的最可能的推理。基于生病的，病人++的理解为监督，在总结多任务学习环境中的对话时，添加了产生常识推断的任务。实验结果表明，通过注入常识性知识，我们的框架比现有方法产生更多信息和一致的摘要。

作为行业4.0时代的一项新兴技术，数字双胞胎因其承诺进一步优化流程设计，质量控制，健康监测，决策和政策制定等，通过全面对物理世界进行建模，以进一步优化流程设计，质量控制，健康监测，决策和政策，因此获得了前所未有的关注。互连的数字模型。在一系列两部分的论文中，我们研究了不同建模技术，孪生启用技术以及数字双胞胎常用的不确定性量化和优化方法的基本作用。第二篇论文介绍了数字双胞胎的关键启示技术的文献综述，重点是不确定性量化，优化方法，开源数据集和工具，主要发现，挑战和未来方向。讨论的重点是当前的不确定性量化和优化方法，以及如何在数字双胞胎的不同维度中应用它们。此外，本文介绍了一个案例研究，其中构建和测试了电池数字双胞胎，以说明在这两部分评论中回顾的一些建模和孪生方法。 GITHUB上可以找到用于生成案例研究中所有结果和数字的代码和预处理数据。

作为行业4.0时代的一项新兴技术，数字双胞胎因其承诺进一步优化流程设计，质量控制，健康监测，决策和政策制定等，通过全面对物理世界进行建模，以进一步优化流程设计，质量控制，健康监测，决策和政策，因此获得了前所未有的关注。互连的数字模型。在一系列两部分的论文中，我们研究了不同建模技术，孪生启用技术以及数字双胞胎常用的不确定性量化和优化方法的基本作用。第一篇论文介绍了目前从事这一研究领域的许多学科的数字双胞胎趋势的详尽文献综述。然后，通过将数据分类为两个主要类别：基于数据流的方向，将数字双胞胎建模和双胞胎启用技术分为两个主要类别：物理到虚拟和虚拟物理。最后，本文在未来十年中提供了有关数字双技术轨迹的观点，并介绍了一些新兴研究领域，这些领域可能在未来的数字双胞胎研究中很可能有很大的用途。在本综述的第二部分中，讨论了不确定性量化和优化的作用，展示了电池数字双胞胎，并共享了数字双胞胎未来的更多观点。

我们提出了一种新型算法，用于单眼深度估计，将度量深度图分解为归一化的深度图和尺度特征。所提出的网络由共享编码器和三个解码器组成，称为G-NET，N-NET和M-NET，它们分别估算了梯度图，归一化的深度图和度量深度图。M-NET学习使用G-NET和N-NET提取的相对深度特征更准确地估算度量深度。所提出的算法具有一个优点，即它可以使用无度量深度标签的数据集来提高度量深度估计的性能。各种数据集的实验结果表明，所提出的算法不仅为最先进的算法提供竞争性能，而且即使只有少量的度量深度数据可用于培训，也会产生可接受的结果。

由于在具有不同资源预算的各种平台上的模型部署方便，因此具有自适应位的深度神经网络量化已引起了人们的关注。在本文中，我们提出了一种元学习方法来实现这一目标。具体而言，我们提出了MEBQAT，这是一种简单而有效的自适应量化意识训练（QAT）的方法，在该方法中，通过重新定义元学习任务以合并位宽，将元学习与QAT有效合并。部署在平台上后，MEBQAT允许将（Meta-）训练的模型量化为任何候选位宽，然后有助于进行推理，而无需过多准确地量化。此外，通过一些学习方案，MEBQAT还可以通过添加常规优化或基于公制的元学习来使模型以及任何看不见的目标类调整模型。我们设计了MEBQAT的变体，以支持（1）（1）位置自适应量化方案和（2）新的几次学习方案，在该方案中，量化位低和目标类都是共同调整的。我们通过实验证明了它们在多个QAT方案中的有效性。通过将它们的性能与（Bitwidth-dedicatied）QAT，现有的Bitwidth自适应QAT和Vanilla Meta-Learning进行比较，我们发现将Bitwidths合并到元学习任务中可以达到更高的鲁棒性。