为了在盲图超级分辨率（SR）上取得有希望的结果，一些尝试利用低分辨率（LR）图像来预测内核并改善SR性能。但是，由于不可用的现实世界模糊内核，这些监督的内核预测（SKP）方法是不切实际的。尽管提出了一些无监督的降解预测（UDP）方法来绕过此问题，但\ textIt {contercestency}之间的降解嵌入和SR功能之间仍然具有挑战性。通过探索降解嵌入与SR功能之间的相关性，我们观察到共同学习内容和降解感知功能是最佳的。基于此观察结果，提出了一个名为CDSR的内容和退化的SR网络。具体而言，CDSR包含三个新建立的模块：（1）将基于重量的编码器（LPE）应用于共同提取内容和降解功能； （2）采用基于域查询的基于注意力的模块（DQA）来适应不一致； （3）基于密码的空格压缩模块（CSC），可以抑制冗余信息。对几个基准测试的广泛实验表明，即使与最先进的SKP方法相比，提议的CDSR的表现都优于现有的UDP模型，并在PSNR和SSIM上实现竞争性能。

人蛋白质组包含一个庞大的相互作用激酶和底物网络。即使某些激酶被证明是治疗靶标的非常有用的，但大多数仍在研究中。在这项工作中，我们提出了一种新颖的知识图表示方法，以预测研究研究的新型相互作用伙伴。我们的方法使用通过整合IPTMNET，蛋白质本体论，基因本体论和BIOKG的数据构建的磷蛋白知识图。通过在三元组上进行定向的随机步行，与修改后的Skipgram或CBOW模型一起进行定向的随机步行，从而学习了该知识图中激酶和底物的表示。然后，这些表示形式被用作监督分类模型的输入，以预测研究不细的激酶的新型相互作用。我们还提供了对预测相互作用的后预测分析和对磷酸蛋白质学知识图的消融研究，以了解对研究的激酶的生物学的见解。

近年来，美国经历了一个缺乏预定的药物过量死亡的阿片类药物。研究发现这种过量的死亡与邻域级特征有关，从而提供了识别有效干预的机会。通常，诸如普通的最小二乘（OLS）或最大似然估计（MLE）的技术用于记录邻域级因素，在解释这种不利结果时。然而，这些技术较低的是在混淆因素之间确定非线性关系。因此，在这项研究中，我们应用基于机器学习的技术，以识别特拉华州社区的阿片式风险，并探讨这些因素使用福芙添加剂解释（Shaf）的相关性。我们发现与社区环境有关的因素，随后受教育，然后犯罪，与较高的阿片类药物风险高度相关。多年来我们还探讨了这些相关性的变化，了解流行病的变化动态。此外，我们发现，随着近年来，由于疫情从法律（即，海洛因和芬太尼）药物从法律（即，海洛因和芬太尼）转移，与阿片类药风险的环境，犯罪和健康相关变量的相关性显着增加虽然经济和社会人口统计变量的相关性降低了。近年来，教育相关因素的相关性与近年来略有增加，表明需要提高对阿片类药物流行病的认识。

Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which is the ever-changing targets at every iteration when multiple agents update their policies at the same time. Starting from first principle, in this paper, we manage to solve the non-stationarity problem by proposing bidirectional action-dependent Q-learning (ACE). Central to the development of ACE is the sequential decision-making process wherein only one agent is allowed to take action at one time. Within this process, each agent maximizes its value function given the actions taken by the preceding agents at the inference stage. In the learning phase, each agent minimizes the TD error that is dependent on how the subsequent agents have reacted to their chosen action. Given the design of bidirectional dependency, ACE effectively turns a multiagent MDP into a single-agent MDP. We implement the ACE framework by identifying the proper network representation to formulate the action dependency, so that the sequential decision process is computed implicitly in one forward pass. To validate ACE, we compare it with strong baselines on two MARL benchmarks. Empirical experiments demonstrate that ACE outperforms the state-of-the-art algorithms on Google Research Football and StarCraft Multi-Agent Challenge by a large margin. In particular, on SMAC tasks, ACE achieves 100% success rate on almost all the hard and super-hard maps. We further study extensive research problems regarding ACE, including extension, generalization, and practicability. Code is made available to facilitate further research.

深度学习的最新进展使视频（称为深击）的现实数字变化。这项技术引起了关于虚假和真实性的重要社会关注，使许多深层检测算法的发展充满了动力。同时，培训数据和野外视频数据之间存在显着差异，这可能会破坏其实际功效。我们模拟了数据损坏技术，并检查了FaceForensics ++数据集损坏变体的最先进的深膜检测算法的性能。尽管DeepFake检测模型与与培训时间增加一致的视频损坏相符，但我们发现它们仍然容易受到视频腐败的影响，这些腐败模拟视频质量的降低。的确，在加蓬总统邦戈（Bongo）的新年地址的视频中，自信地验证了原始视频的算法，该视频的高度损坏的变体是伪造的。我们的工作在全球背景下对实用的深层检测进行了探索的技术和道德途径。

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.