In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve prediction accuracy. However, incorporating ML is generally considered to sacrifice interpretability inevitably. In addition, existing hybrid algorithms usually rely on theoretical models with statistical assumptions and focus only on the accuracy of aggregate predictions, and thus suffer from accuracy problems, especially in component estimates. In response to the above issues, this research explores the possibility of improving accuracy without losing interpretability in time series forecasting. We first quantitatively define interpretability for data-driven forecasts and systematically review the existing forecasting algorithms from the perspective of interpretability. Accordingly, we propose the W-R algorithm, a hybrid algorithm that combines decomposition and ML from a novel perspective. Specifically, the W-R algorithm replaces the standard additive combination function with a weighted variant and uses ML to modify the estimates of all components simultaneously. We mathematically analyze the theoretical basis of the algorithm and validate its performance through extensive numerical experiments. In general, the W-R algorithm outperforms all decomposition-based and ML benchmarks. Based on P50_QL, the algorithm relatively improves by 8.76% in accuracy on the practical sales forecasts of JD.com and 77.99% on a public dataset of electricity loads. This research offers an innovative perspective to combine the statistical and ML algorithms, and JD.com has implemented the W-R algorithm to make accurate sales predictions and guide its marketing activities.

In this paper, we present a pure-Python open-source library, called PyPop7, for black-box optimization (BBO). It provides a unified and modular interface for more than 60 versions and variants of different black-box optimization algorithms, particularly population-based optimizers, which can be classified into 12 popular families: Evolution Strategies (ES), Natural Evolution Strategies (NES), Estimation of Distribution Algorithms (EDA), Cross-Entropy Method (CEM), Differential Evolution (DE), Particle Swarm Optimizer (PSO), Cooperative Coevolution (CC), Simulated Annealing (SA), Genetic Algorithms (GA), Evolutionary Programming (EP), Pattern Search (PS), and Random Search (RS). It also provides many examples, interesting tutorials, and full-fledged API documentations. Through this new library, we expect to provide a well-designed platform for benchmarking of optimizers and promote their real-world applications, especially for large-scale BBO. Its source code and documentations are available at https://github.com/Evolutionary-Intelligence/pypop and https://pypop.readthedocs.io/en/latest, respectively.

Downsampling and feature extraction are essential procedures for 3D point cloud understanding. Existing methods are limited by the inconsistent point densities of different parts in the point cloud. In this work, we analyze the limitation of the downsampling stage and propose the pre-abstraction group-wise window-normalization module. In particular, the window-normalization method is leveraged to unify the point densities in different parts. Furthermore, the group-wise strategy is proposed to obtain multi-type features, including texture and spatial information. We also propose the pre-abstraction module to balance local and global features. Extensive experiments show that our module performs better on several tasks. In segmentation tasks on S3DIS (Area 5), the proposed module performs better on small object recognition, and the results have more precise boundaries than others. The recognition of the sofa and the column is improved from 69.2% to 84.4% and from 42.7% to 48.7%, respectively. The benchmarks are improved from 71.7%/77.6%/91.9% (mIoU/mAcc/OA) to 72.2%/78.2%/91.4%. The accuracies of 6-fold cross-validation on S3DIS are 77.6%/85.8%/91.7%. It outperforms the best model PointNeXt-XL (74.9%/83.0%/90.3%) by 2.7% on mIoU and achieves state-of-the-art performance. The code and models are available at https://github.com/DBDXSS/Window-Normalization.git.

Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients.

To enable the pre-trained models to be fine-tuned with local data on edge devices without sharing data with the cloud, we design an efficient split fine-tuning (SFT) framework for edge and cloud collaborative learning. We propose three novel techniques in this framework. First, we propose a matrix decomposition-based method to compress the intermediate output of a neural network to reduce the communication volume between the edge device and the cloud server. Second, we eliminate particular links in the model without affecting the convergence performance in fine-tuning. Third, we implement our system atop PyTorch to allow users to easily extend their existing training scripts to enjoy the efficient edge and cloud collaborative learning. Experiments results on 9 NLP datasets show that our framework can reduce the communication traffic by 96 times with little impact on the model accuracy.

这里介绍了人工智能研究所（IARAI）组织的2022年Landslide4sense（L4S）竞赛的科学结果。竞争的目的是根据全球收集的卫星图像的大规模多个来源自动检测滑坡。 2022 L4S旨在促进有关使用卫星图像的语义分割任务的深度学习模型（DL）模型最新发展的跨学科研究。在过去的几年中，由于卷积神经网络（CNN）的发展，基于DL的模型已经达到了对图像解释的期望。本文的主要目的是介绍本次比赛中介绍的细节和表现最佳的算法。获胜的解决方案详细介绍了Swin Transformer，Segformer和U-NET等最先进的模型。还考虑了先进的机器学习技术和诸如硬采矿，自我培训和混合数据增强之类的策略。此外，我们描述了L4S基准数据集，以促进进一步的比较，并在线报告准确性评估的结果。可以在\ textIt {未来开发排行榜上访问数据，以供将来评估，\ url {https://www.iarai.ac.ac.at/landslide4sense/challenge/}，并邀请研究人员提交更多预测结果，评估准确性在他们的方法中，将它们与其他用户的方法进行比较，理想情况下，改善了本文报告的滑坡检测结果。

由于非平稳性，现实世界多变量时间序列（MTS）的分布会随着时间而变化，称为分布漂移。大多数现有的MT预测模型都会极大地遭受分销漂移的影响，并随着时间的推移降低了预测性能。现有方法通过适应最新到达数据或根据未来数据得出的元知识进行自我纠正来解决分布漂移。尽管在MT的预测中取得了巨大的成功，但这些方法几乎无法捕获固有的分布变化，尤其是从分布的角度来看。因此，我们提出了一个新型的框架时间条件变化自动编码器（TCVAE），以对MTS中历史观察结果和未来数据之间的动态分布依赖性进行建模，并将依赖性作为时间条件分布推断为利用潜在变量。具体而言，新型的颞鹰注意机制代表了随后馈入馈送前网络的时间因素，以估计潜在变量的先前高斯分布。时间因素的表示进一步动态地调整了基于变压器的编码器和解码器的结构，以利用门控注意机制来变化。此外，我们引入条件连续归一化流量，以将先前的高斯转化为复杂且无形式的分布，以促进对时间条件分布的灵活推断。在六个现实世界MTS数据集上进行的广泛实验表明，与最先进的MTS预测基线相比，TCVAE的出色鲁棒性和有效性。我们进一步说明了TCVAE通过多方面的案例研究和现实情况下的可视化来说明TCVAE的适用性。

故事结束一代旨在为给定的故事背景产生合理的结局。该领域的大多数现有研究都集中在产生连贯或多元化的故事结尾，而他们忽略了不同的角色可能会导致给定故事的不同结局。在本文中，我们提出了一个面向角色的故事结束生成器（Coseg），以自定义故事中每个角色的结局。具体来说，我们首先提出一个角色建模模块，以从故事背景中提取的描述性经历中学习角色的个性。然后，受到化学反应中离子交换机制的启发，我们设计了一个新颖的矢量断裂/形成模块，以通过类似信息交换程序来学习每个字符和相应上下文之间的固有相互作用。最后，我们利用注意力机制学习有效的特定角色相互作用，并将每种相互作用馈送到解码器中，以生成角色 - 与角色的结尾。广泛的实验结果和案例研究表明，与最先进的方法相比，Coseg在生成的结局质量方面取得了重大改善，并且有效地自定义了不同字符的结局。

在这项工作中，我们重新审视了弱到较强的一致性框架，该框架由半监视分类的FixMatch推广，在该分类中，对弱扰动的图像的预测可作为其强烈扰动版本的监督。有趣的是，我们观察到，这种简单的管道已经转移到我们的细分方案时已经在最近的高级工作中取得了竞争成果。它的成功在很大程度上依赖于强大数据增强的手动设计，但是，这可能是有限的，并且不足以探索更广泛的扰动空间。在此激励的情况下，我们提出了一个辅助特征扰动流作为补充，从而导致了扩大的扰动空间。另一方面，为了充分探测原始的图像级增强，我们提出了一种双流扰动技术，从而使两个强大的观点能够同时受到共同的弱视图的指导。因此，我们整体统一的双流扰动方法（Unipatch）在Pascal，CityScapes和Coco基准的所有评估方案中都显着超过所有现有方法。我们还证明了我们方法在遥感解释和医学图像分析中的优越性。代码可从https://github.com/liheyoung/unimatch获得。

脑小血管疾病的成像标记提供了有关脑部健康的宝贵信息，但是它们的手动评估既耗时又受到实质性内部和间际变异性的阻碍。自动化评级可能受益于生物医学研究以及临床评估，但是现有算法的诊断可靠性尚不清楚。在这里，我们介绍了\ textIt {血管病变检测和分割}（\ textit {v textit {where valdo？}）挑战，该挑战是在国际医学图像计算和计算机辅助干预措施（MICCAI）的卫星事件中运行的挑战（MICCAI） 2021.这一挑战旨在促进大脑小血管疾病的小而稀疏成像标记的自动检测和分割方法的开发，即周围空间扩大（EPVS）（任务1），脑微粒（任务2）和预先塑造的鞋类血管起源（任务3），同时利用弱和嘈杂的标签。总体而言，有12个团队参与了针对一个或多个任务的解决方案的挑战（任务1 -EPVS 4，任务2 -Microbleeds的9个，任务3 -lacunes的6个）。多方数据都用于培训和评估。结果表明，整个团队和跨任务的性能都有很大的差异，对于任务1- EPV和任务2-微型微型且对任务3 -lacunes尚无实际的结果，其结果尤其有望。它还强调了可能阻止个人级别使用的情况的性能不一致，同时仍证明在人群层面上有用。