Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems. Specifically, it applies a hierarchical multi-output Gaussian process to capture the correlation between data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization exercises. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a strictly limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm against nine state-of-the-art peer algorithms.

缺乏标记的培训数据是许多应用程序中机器学习的瓶颈。为了解决瓶颈，一个有希望的方向是数据编程方法，该方法汇总了弱监督信号的不同来源，以轻松生成标记的数据。数据编程使用标签功能（LF）编码每个弱监督源，这是一个预测嘈杂标签的用户提供的程序。生成的标签的质量取决于标签聚合模型，该模型汇总了所有LFS的所有嘈杂标签以推断地面真相标签。现有的标签聚合方法通常依赖于各种假设，并且在整个数据集中都不强大，因为我们将在经验上显示。我们首次提供了一种分析标签聚合方法，该方法是最小化假设的，并且在最小化某种形式的平均预测误差方面是最佳的。由于分析形式的复杂性是指数级的，因此我们训练一个学会成为分析方法的模型。经过训练后，该模型可用于任何看不见的数据集，该模型可以在线性时间内单个正向通行证中每个数据集的地面真相标签。我们显示该模型可以使用合成生成的数据进行训练，并为模型设计有效的体系结构。在14个现实世界数据集上，我们的模型在准确性（平均为3.5点）和效率（平均降低六倍）方面大大优于现有方法。

医生经常基于患者的图像扫描，例如磁共振成像（MRI），以及患者的电子健康记录（EHR），如年龄，性别，血压等。尽管在计算机视觉或自然语言研究领域的图像或文本分析中提出了大量的自动方法，但已经为医学图像的融合和医疗问题的EHR数据进行了更少的研究。在现有的早期或中间融合方法中，两种方式的特征串联仍然是一个主流。为了更好地利用图像和EHR数据，我们提出了一种多模态注意力模块，该模块使用EHR数据来帮助选择传统CNN的图像特征提取过程期间的重要区域。此外，我们建议将多头Machnib纳入门控多媒体单元（GMU），使其能够在不同子空间中平行熔断图像和EHR特征。在两个模块的帮助下，可以使用两个模态增强现有的CNN架构。预测脑内出血患者的Glasgow结果规模（GOS）和分类Alzheimer病的实验表明，该方法可以自动关注任务相关领域，并通过更好地利用图像和EHR功能来实现更好的结果。

单眼深度估计和散焦估计是计算机视觉中的两个基本任务。大多数现有方法将深度估计和散焦估计视为两个独立的任务，忽略了它们之间的牢固联系。在这项工作中，我们提出了一个由编码器组成的多任务学习网络，该网络具有两个解码器，以估算单个集中图像的深度和散焦图。通过多任务网络，深度估计促进了散焦估计，从而在弱纹理区域中获得更好的结果，而散焦估计促进了通过两个地图之间强烈的物理连接的深度估计。我们设置了一个数据集（名为All-3D数据集），该数据集是第一个由100K的全焦点图像组成的全真实图像数据集，具有焦点深度，深度图和Defocus映射的集中图像。它使网络能够学习深度和真实散焦图像之间的功能和固体物理连接。实验表明，与合成的图像相比，网络从实际集中图像中学习更多的固体特征。从这种多任务结构中受益，不同的任务相互促进，我们的深度和散焦估计的性能明显优于其他最新算法。代码和数据集将在https://github.com/cubhe/mddnet上公开可用。

该点扩散函数（PSF）在许多计算成像应用中起着至关重要的作用，例如焦点/散焦，深度估计和荧光显微镜的形状。但是，散焦过程的数学模型尚不清楚。在这项工作中，我们开发了一种替代方法来估计点扩散函数的精确数学模型来描述散焦过程。我们首先得出PSF的数学算法，该算法用于生成不同的焦点深度的模拟聚焦图像。然后，我们计算模拟的聚焦图像与真实聚焦图像之间的相似性损耗函数，在该图像中，我们根据Docus直方图设计了一种新颖有效的度量，以评估聚焦图像之间的差异。在解决损耗函数的最小值后，这意味着我们找到了PSF的最佳参数。我们还构建了一个由聚焦系统和结构化的光系统组成的硬件系统，以获取全焦点图像，具有相应焦点深度的聚焦图像以及相同视图中的深度图。作为数据集的三种类型的图像用于获得精确的PSF。我们对标准平面和实际对象的实验表明，所提出的算法可以准确描述散焦过程。通过评估实际集中图像之间的差异，即我们的算法生成的焦点图像，即其他人生成的焦点图像，进一步证明了我们算法的准确性。结果表明，我们算法的损失平均比其他算法少40％。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

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.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.