Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of \emph{preference drift}. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 11 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec.

在这项工作中，我们使用功能磁共振成像（fMRI）专注于具有挑战性的任务，神经疾病分类。在基于人群的疾病分析中，图卷积神经网络（GCN）取得了显着的成功。但是，这些成就与丰富的标记数据密不可分，对虚假信号敏感。为了改善在标签有效的设置下的fMRI表示学习和分类，我们建议在GCN上使用新颖的，理论驱动的自我监督学习（SSL）框架，即在FMRI分析门上用于时间自我监督学习的CCA。具体而言，要求设计合适有效的SSL策略来提取fMRI的形成和鲁棒特征。为此，我们研究了FMRI动态功能连接（FC）的几种新的图表增强策略，用于SSL培训。此外，我们利用规范相关分析（CCA）在不同的时间嵌入中，并呈现理论含义。因此，这产生了一个新颖的两步GCN学习程序，该过程包括在未标记的fMRI人群图上的（i）SSL组成，并且（ii）在小标记的fMRI数据集上进行了微调，以进行分类任务。我们的方法在两个独立的fMRI数据集上进行了测试，这表明自闭症和痴呆症诊断方面表现出色。

用户表示对于在工业中提供高质量的商业服务至关重要。最近普遍的用户表示已经获得了许多兴趣，我们可以摆脱训练每个下游应用程序的繁琐工作的繁琐工作。在本文中，我们试图改善来自两个观点的通用用户表示。首先，提出了一种对比的自我监督学习范式来指导代表模型培训。它提供了一个统一的框架，允许以数据驱动的方式进行长期或短期兴趣表示学习。此外，提出了一种新型多息提取模块。该模块介绍了兴趣字典以捕获给定用户的主要兴趣，然后通过行为聚合生成其兴趣的面向的表示。实验结果证明了学习用户陈述的有效性和适用性。

基于补丁的方法和深度网络已经采用了解决图像染色问题，具有自己的优势和劣势。基于补丁的方法能够通过从未遮盖区域搜索最近的邻居修补程序来恢复具有高质量纹理的缺失区域。但是，这些方法在恢复大缺失区域时会带来问题内容。另一方面，深度网络显示有希望的成果完成大区域。尽管如此，结果往往缺乏类似周围地区的忠诚和尖锐的细节。通过汇集两个范式中，我们提出了一种新的深度染色框架，其中纹理生成是由从未掩蔽区域提取的补丁样本的纹理记忆引导的。该框架具有一种新颖的设计，允许使用深度修复网络训练纹理存储器检索。此外，我们还介绍了贴片分配损失，以鼓励高质量的贴片合成。所提出的方法在三个具有挑战性的图像基准测试中，即地位，Celeba-HQ和巴黎街道视图数据集来说，该方法显示出质量和定量的卓越性能。

Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently, prior knowledge of these factors will be beneficial when modeling their future state, i.e., via image generation. However, most of the medical image generation tasks only rely on the input from a single image, thus ignoring the sequential dependency even when longitudinal data is available. Sequence-aware deep generative models, where model input is a sequence of ordered and timestamped images, are still underexplored in the medical imaging domain that is featured by several unique challenges: 1) Sequences with various lengths; 2) Missing data or frame, and 3) High dimensionality. To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images. Recently, diffusion models have shown promising results on high-fidelity image generation. Our method extends this new technique by introducing a sequence-aware transformer as the conditional module in a diffusion model. The novel design enables learning longitudinal dependency even with missing data during training and allows autoregressive generation of a sequence of images during inference. Our extensive experiments on 3D longitudinal medical images demonstrate the effectiveness of SADM compared with baselines and alternative methods.

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.

Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons. Because such biological networks can have rich dynamics, they are well-designed to cope with dynamical interactions of the types that occur in nature, while traditional machine learning networks may struggle to make sense of such data. Here we connected a simple model neuronal network (based on the 'linear summation neuron model' featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory and 10 inhibitory neurons, randomly connected) to a robot finger with multiple types of force sensors when interacting with materials of different levels of compliance. Scope: to explore the performance of the network on classification accuracy. Therefore, we compared the performance of the network output with principal component analysis of statistical features of the sensory data as well as its mechanical properties. Remarkably, even though the LSM was a very small and untrained network, and merely designed to provide rich internal network dynamics while the neuron model itself was highly simplified, we found that the LSM outperformed these other statistical approaches in terms of accuracy.

高性能深度学习方法通​​常依赖于大型注释培训数据集，由于医疗图像标签的高成本，在许多临床应用中很难获得。现有的数据评估方法通常需要事先了解标签，而这些标签是不可行的，以实现“知道要标记的数据”的目标。为此，我们制定并提出了一种新颖有效的数据评估策略，指数边缘奇异值（检查）得分，以根据通过自我求助的学习（SSL）网络提取的有用的潜在表示，对未标记的医学图像数据进行排名。 。由SSL嵌入空间的理论含义激励，我们利用蒙版的自动编码器进行特征提取。此外，在排除数据集中的数据点之后，我们根据最大奇异值的边际变化评估数据质量。我们对病理数据集进行了广泛的实验。我们的结果表明，我们提出的方法选择最有价值的数据的有效性和效率。

现有的可解释人工智能（XAI）算法的界限仅限于技术用户对解释性的需求所基于的问题。这项研究范式不成比例地忽略了XAI的非技术最终用户的较大群体，他们没有技术知识，但需要在其AI-ASS辅助批判性决定中进行解释。缺乏以解释性为重点的功能支持可能会阻碍在医疗保健，刑事司法，金融和自动驾驶系统等高风险领域中对AI的安全和负责任的使用。在这项工作中，我们探讨了如何设计为最终用户的关键任务量身定制的XAI如何激发新技术问题的框架。为了引起用户对XAI算法的解释和要求，我们首先将八个解释表格确定为AI研究人员和最终用户之间的通信工具，例如使用功能，示例或规则来解释。然后，我们在实现不同的解释目标（例如验证AI决策并改善用户的预测结果）的背景下，使用32名外行参与者进行用户研究。基于用户研究结果，我们确定并提出新颖的XAI技术问题，并根据用户的解释目标提出评估度量验证能力。我们的工作表明，在最终用户使用XAI中解决技术问题可以激发新的研究问题。这样的最终用户启发的研究问题有可能通过使人工智能民主化并确保在关键领域中对AI负责使用，从而促进社会利益。

联合学习（FL）是一种趋势培训范式，用于利用分散培训数据。 FL允许客户端在本地更新几个时期的模型参数，然后将它们共享到全局模型以进行聚合。在聚集之前，该训练范式具有多本地步骤更新，使对抗性攻击暴露了独特的漏洞。对手训练是一种流行而有效的方法，可以提高网络对抗者的鲁棒性。在这项工作中，我们制定了一种一般形式的联邦对抗学习（FAL），该形式是从集中式环境中的对抗性学习改编而成的。在FL培训的客户端，FAL具有一个内部循环，可以生成对抗性样本进行对抗训练和外循环以更新本地模型参数。在服务器端，FAL汇总了本地模型更新并广播聚合的模型。我们设计了全球强大的训练损失，并将FAL培训作为最小最大优化问题。与依赖梯度方向的经典集中式培训中的收敛分析不同，由于三个原因，很难在FAL中分析FAL的收敛性：1）Min-Max优化的复杂性，2）模型未在梯度方向上更新聚合之前的客户端和3）客户间异质性的多局部更新。我们通过使用适当的梯度近似和耦合技术来应对这些挑战，并在过度参数化的制度中介绍收敛分析。从理论上讲，我们的主要结果表明，我们的算法下的最小损失可以收敛到$ \ epsilon $ Small，并具有所选的学习率和交流回合。值得注意的是，我们的分析对于非IID客户是可行的。