U-shaped networks are widely used in various medical image tasks, such as segmentation, restoration and reconstruction, but most of them usually rely on centralized learning and thus ignore privacy issues. To address the privacy concerns, federated learning (FL) and split learning (SL) have attracted increasing attention. However, it is hard for both FL and SL to balance the local computational cost, model privacy and parallel training simultaneously. To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL. Previous works cannot preserve the data privacy, including the input, model parameters, label and output simultaneously. To effectively deal with all of them, we design a novel splitting method for U-shaped medical image networks, which splits the network into three parts hosted by different parties. Besides, the distributed learning methods usually suffer from a drift between local and global models caused by data heterogeneity. Based on this consideration, we propose a dynamic weight correction strategy (\textbf{DWCS}) to stabilize the training process and avoid model drift. Specifically, a weight correction loss is designed to quantify the drift between the models from two adjacent communication rounds. By minimizing this loss, a correction model is obtained. Then we treat the weighted sum of correction model and final round models as the result. The effectiveness of the proposed RoS-FL is supported by extensive experimental results on different tasks. Related codes will be released at https://github.com/Zi-YuanYang/RoS-FL.

计算机断层扫描（CT）在临床实践中非常重要，因为它强大的能力在没有任何侵入性检查的情况下提供患者的解剖信息，但其潜在的辐射风险引起了人们的关注。基于深度学习的方法在CT重建中被认为是有希望的，但是这些网络模型通常是通过从特定扫描协议获得的测量数据进行训练的，并且需要集中收集大量数据，这将导致严重的数据域移动，并引起隐私问题。 。为了缓解这些问题，在本文中，我们提出了一种基于超网络的联合学习方法，用于个性化CT成像，称为超fed。超fed的基本假设是，每个机构的优化问题可以分为两个部分：本地数据适应问题和全局CT成像问题，这些问题分别由机构特定的超网络和全球共享成像网络实现。全球共享成像网络的目的是从不同机构学习稳定而有效的共同特征。特定于机构的超网络经过精心设计，以获取超参数，以调节用于个性化本地CT重建的全球共享成像网络。实验表明，与其他几种最先进的方法相比，超档在CT重建中实现了竞争性能。它被认为是提高CT成像质量并达到没有隐私数据共享的不同机构或扫描仪的个性化需求的有希望的方向。这些代码将在https://github.com/zi-yuanyang/hyperfed上发布。

检测分配（OOD）输入对于安全部署现实世界的深度学习模型至关重要。在评估良性分布和OOD样品时，检测OOD示例的现有方法很好。然而，在本文中，我们表明，当在分发的分布和OOD输入时，现有的检测机制可以极其脆弱，其具有最小的对抗扰动，这不会改变其语义。正式地，我们广泛地研究了对共同的检测方法的强大分布检测问题，并表明最先进的OOD探测器可以通过对分布和ood投入增加小扰动来容易地欺骗。为了抵消这些威胁，我们提出了一种称为芦荟的有效算法，它通过将模型暴露于对抗性inlier和异常值示例来执行鲁棒训练。我们的方法可以灵活地结合使用，并使现有方法稳健。在共同的基准数据集上，我们表明芦荟大大提高了最新的ood检测的稳健性，对CiFar-10和46.59％的CiFar-100改善了58.4％的Auroc改善。

图形神经网络（GNN）已被证明具有强大的表示能力，可以利用该图形在图形结构数据（例如分子和社交网络）上的下游预测任务。他们通常通过从单个顶点的$ K $ - 霍普社区或图表中的枚举步行中汇总信息来学习表示形式。先前的研究表明，将加权方案纳入GNN的有效性。但是，到目前为止，这主要仅限于$ k $ hop的社区GNNS。在本文中，我们旨在设计一种将加权方案纳入步行式GNN并分析其效果的算法。我们提出了一种称为Aware的新型GNN模型，该模型使用注意方案汇总了有关图中的步行的信息。这导致了在标准设置中用于图形预测任务的端到端监督学习方法，其中输入是图形的邻接和顶点信息，并且输出是图形的预测标签。然后，我们对Aware进行理论，经验和解释性分析。我们在简化设置中的理论分析确定了可证明的保证的成功条件，证明了图表信息如何在表示中编码，以及意识中的加权方案如何影响表示和学习绩效。我们的实验表明，在分子财产预测和社交网络领域的标准设置中，在图形预测任务中意识到的强劲表现。最后，我们的解释研究表明，意识到可以成功捕获输入图的重要子结构。该代码可在$ \ href {https://github.com/mehmetfdemirel/aware} {github} $上获得。

The fundamental learning theory behind neural networks remains largely open. What classes of functions can neural networks actually learn? Why doesn't the trained network overfit when it is overparameterized?In this work, we prove that overparameterized neural networks can learn some notable concept classes, including two and three-layer networks with fewer parameters and smooth activations. Moreover, the learning can be simply done by SGD (stochastic gradient descent) or its variants in polynomial time using polynomially many samples. The sample complexity can also be almost independent of the number of parameters in the network.On the technique side, our analysis goes beyond the so-called NTK (neural tangent kernel) linearization of neural networks in prior works. We establish a new notion of quadratic approximation of the neural network (that can be viewed as a second-order variant of NTK), and connect it to the SGD theory of escaping saddle points.

Neural networks have many successful applications, while much less theoretical understanding has been gained. Towards bridging this gap, we study the problem of learning a two-layer overparameterized ReLU neural network for multi-class classification via stochastic gradient descent (SGD) from random initialization. In the overparameterized setting, when the data comes from mixtures of wellseparated distributions, we prove that SGD learns a network with a small generalization error, albeit the network has enough capacity to fit arbitrary labels. Furthermore, the analysis provides interesting insights into several aspects of learning neural networks and can be verified based on empirical studies on synthetic data and on the MNIST dataset.

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.