新催化剂的发现是计算化学的重要主题之一，因为它有可能加速采用可再生能源。最近开发的深度学习方法，例如图形神经网络（GNNS）开放的新机会，以显着扩大新型高性能催化剂的范围。然而，由于模棱两可的连接方案和节点和边缘的众多嵌入，特定晶体结构的图表并不是一项简单的任务。在这里，我们提出了GNN的嵌入改进，该改进已通过Voronoi Tesselation修改，并能够预测开放催化剂项目数据集中催化系统的能量。通过Voronoi镶嵌计算图的富集，并将相应的触点固体角度和类型（直接或间接）视为边缘的特征，而Voronoi体积用作节点特征。辅助方法是通过内在的原子特性（电负性，周期和组位置）富集节点表示。提出的修改使我们能够改善原始模型的平均绝对误差，最终误差等于“开放催化剂项目数据集”上每个原子的651 MeV，并且在金属中数据集上的每个原子6 MeV。同样，通过考虑其他数据集，我们表明，明智的数据选择可以将误差降低到高于每个原子阈值20 MEV的值的值。

制药公司在严格监管且高度危险的环境中运营，单张单击可以导致严重的财务影响。因此，临床试验结果的公告倾向于确定事件的未来过程，因此受到公众的密切监视。在这项工作中，我们为结果颁布对公共药品市场价值的影响提供了统计证据。尽管大多数工作都集中在回顾性影响分析上，但本研究旨在预测公告诱发的股票价格变化的价值。为此，我们开发了一条管道，其中包括一个基于BERT的模型，用于提取公告的情感极性，一种用于预测预期回报的时间融合变压器，用于捕获事件关系的图形卷积网络以及预测价格变化的梯度提升。问题的挑战在于对正面和负面公告的反应固有不同的模式，反映在对负面新闻的更强烈，更明显的反应中。此外，在积极公告后，股票下降的现象肯定了价格行为的违反直觉。重要的是，我们发现了在预测框架内工作时应考虑的两个关键因素。第一个因素是该公司的药物组合规模，表明在小型药物多样化的情况下，公告的敏感性更大。第二个是与同一公司或诺斯科有关的事件的网络效应。所有发现和见解都是根据最大的FDA（食品药品监督管理局）公告数据集获得的，该数据集由过去五年中681家公司的5436个临床试验公告组成。

太阳能电池制造中的有效缺陷检测对于稳定的绿色能源技术制造至关重要。本文介绍了一种基于深度学习的自动检测模型SEMACNN，用于分类和语义分割电致发光图像，用于太阳能电池质量评估和异常检测。该模型的核心是基于马哈拉氏症距离的一种异常检测算法，该算法可以以半监督的方式对具有少量具有相关缺陷的数字电致发光图像的不平衡数据进行训练。这对于迅速将模型集成到工业格局中特别有价值。该模型已通过植物收集的数据集进行了训练，该数据集由68 748个带有母线网格的异质结太阳能电池的电致发光图像。我们的模型在验证子集中的精度达到92.5％，F1得分为95.8％，召回94.8％，精度为96.9％，由1049个手动注释的图像组成。该模型还在Open ELPV数据集上进行了测试，并证明了稳定的性能，准确性为94.6％，F1得分为91.1％。 SEMACNN模型展示了其性能和计算成本之间的良好平衡，这使其适用于集成到太阳能电池制造的质量控制系统中。

深度神经网络的规模和复杂性继续成倍增长，大大增加了这些模型训练和推断的能源消耗。我们介绍了一个开源软件包ECO2AI，以帮助数据科学家和研究人员以直接的方式跟踪其模型的能源消耗和同等的二氧化碳排放。在Eco2ai中，我们强调能源消耗跟踪和正确的区域二氧化碳排放会计的准确性。我们鼓励研究社区搜索具有较低计算成本的新最佳人工智能（AI）架构。动机还来自基于AI的温室气体与可持续AI和绿色AI途径隔离周期的概念。

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.