我们基于技能评分，对确定性太阳预测进行了首次全面的荟萃分析，筛选了Google Scholar的1,447篇论文，并审查了320篇论文的全文以进行数据提取。用多元自适应回归样条模型，部分依赖图和线性回归构建和分析了4,758点的数据库。值得注意的是，分析说明了数据中最重要的非线性关系和交互项。我们量化了对重要变量的预测准确性的影响，例如预测范围，分辨率，气候条件，区域的年度太阳辐照度水平，电力系统大小和容量，预测模型，火车和测试集以及使用不同的技术和投入。通过控制预测之间的关键差异，包括位置变量，可以在全球应用分析的发现。还提供了该领域科学进步的概述。

本文报道的研究通过应用计算机视觉技术将普通的垃圾桶转化为更聪明的垃圾箱。在传感器和执行器设备的支持下，垃圾桶可以自动对垃圾进行分类。特别是，垃圾箱上的摄像头拍摄垃圾的照片，然后进行中央处理单元分析，并决定将垃圾桶放入哪个垃圾箱中。我们的垃圾箱系统的准确性达到90％。此外，我们的模型已连接到Internet，以更新垃圾箱状态以进行进一步管理。开发了用于管理垃圾箱的移动应用程序。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. PINNs integrate the physical constraints by minimizing the partial differential equations (PDEs) residuals on a set of collocation points. The distribution of these collocation points appears to have a huge impact on the performance of PINNs and the assessment of the sampling methods for these points is still an active topic. In this paper, we propose a Fixed-Budget Online Adaptive Mesh Learning (FBOAML) method, which decomposes the domain into sub-domains, for training collocation points based on local maxima and local minima of the PDEs residuals. The stopping criterion is based on a data set of reference, which leads to an adaptive number of iterations for each specific problem. The effectiveness of FBOAML is demonstrated in the context of non-parameterized and parameterized problems. The impact of the hyper-parameters in FBOAML is investigated in this work. The comparison with other adaptive sampling methods is also illustrated. The numerical results demonstrate important gains in terms of accuracy of PINNs with FBOAML over the classical PINNs with non-adaptive collocation points. We also apply FBOAML in a complex industrial application involving coupling between mechanical and thermal fields. We show that FBOAML is able to identify the high-gradient location and even give better prediction for some physical fields than the classical PINNs with collocation points taken on a pre-adapted finite element mesh.

Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is balanced and there are sufficient annotated training examples. But since NER is inherently an imbalanced tagging problem, the model performance under the low-resource settings could suffer using these standard objective functions. Based on recent advances in area under the ROC curve (AUC) maximization, we propose to optimize the NER model by maximizing the AUC score. We give evidence that by simply combining two binary-classifiers that maximize the AUC score, significant performance improvement over traditional loss functions is achieved under low-resource NER settings. We also conduct extensive experiments to demonstrate the advantages of our method under the low-resource and highly-imbalanced data distribution settings. To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting. Furthermore, we show that our method is agnostic to different types of NER embeddings, models and domains. The code to replicate this work will be provided upon request.

Domain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often suffer from the challenging linguistic characteristics that clinical narratives possess, which leads to unsatisfactory performance. In this paper, we present a simple yet effective hardness-guided domain adaptation (HGDA) framework for bioNER tasks that can effectively leverage the domain hardness information to improve the adaptability of the learnt model in low-resource scenarios. Experimental results on biomedical datasets show that our model can achieve significant performance improvement over the recently published state-of-the-art (SOTA) MetaNER model

3D hand pose estimation from RGB images suffers from the difficulty of obtaining the depth information. Therefore, a great deal of attention has been spent on estimating 3D hand pose from 2D hand joints. In this paper, we leverage the advantage of spatial-temporal Graph Convolutional Neural Networks and propose LG-Hand, a powerful method for 3D hand pose estimation. Our method incorporates both spatial and temporal dependencies into a single process. We argue that kinematic information plays an important role, contributing to the performance of 3D hand pose estimation. We thereby introduce two new objective functions, Angle and Direction loss, to take the hand structure into account. While Angle loss covers locally kinematic information, Direction loss handles globally kinematic one. Our LG-Hand achieves promising results on the First-Person Hand Action Benchmark (FPHAB) dataset. We also perform an ablation study to show the efficacy of the two proposed objective functions.

本文考虑了在线配置器通常使用的一组替代方案中学习用户偏好的任务。在许多设置中，学习者在过去的互动过程中只有一组选定的替代方案。Fargier等。[2018]提出了一种在这种环境中学习用户偏好模型的方法，该模型对先前选择的替代方案进行了排名尽可能高；以及在这种情况下学习的算法，是一种特定的偏好模型：词典偏好树（LP-Trees）。在本文中，我们研究了与这种方法相关的复杂性理论问题。我们对学习LP-Tree的样本复杂性给出了上限，这在属性数量上是对数。我们还证明，计算最小化经验风险的LP树当仅限于线性LP-Trees的类别时，可以在多项式时间内完成。

这项研究介绍了我们对越南语言和语音处理任务（VLSP）挑战2021的文本处理任务的医疗保健领域的自动越南图像字幕的方法作为编码器的体系结构和长期的短期内存（LSTM）作为解码器生成句子。这些模型在不同的数据集中表现出色。我们提出的模型还具有编码器和一个解码器，但是我们在编码器中使用了SWIN变压器，LSTM与解码器中的注意模块结合在一起。该研究介绍了我们在比赛期间使用的培训实验和技术。我们的模型在vietcap4h数据集上达到了0.293的BLEU4分数，并且该分数在私人排行榜上排名3 $^{rd} $。我们的代码可以在\ url {https://git.io/jddjm}上找到。

预测学生的学习成绩是教育数据挖掘（EDM）的关键任务之一。传统上，这种模型的高预测质量被认为至关重要。最近，公平和歧视W.R.T.受保护的属性（例如性别或种族）引起了人们的关注。尽管EDM中有几种公平感知的学习方法，但对这些措施的比较评估仍然缺失。在本文中，我们评估了各种教育数据集和公平感知学习模型上学生绩效预测问题的不同群体公平措施。我们的研究表明，公平度量的选择很重要，对于选择等级阈值的选择同样。