Multivariate time series forecasting constitutes important functionality in cyber-physical systems, whose prediction accuracy can be improved significantly by capturing temporal and multivariate correlations among multiple time series. State-of-the-art deep learning methods fail to construct models for full time series because model complexity grows exponentially with time series length. Rather, these methods construct local temporal and multivariate correlations within subsequences, but fail to capture correlations among subsequences, which significantly affect their forecasting accuracy. To capture the temporal and multivariate correlations among subsequences, we design a pattern discovery model, that constructs correlations via diverse pattern functions. While the traditional pattern discovery method uses shared and fixed pattern functions that ignore the diversity across time series. We propose a novel pattern discovery method that can automatically capture diverse and complex time series patterns. We also propose a learnable correlation matrix, that enables the model to capture distinct correlations among multiple time series. Extensive experiments show that our model achieves state-of-the-art prediction accuracy.

Image-text retrieval (ITR) is a challenging task in the field of multimodal information processing due to the semantic gap between different modalities. In recent years, researchers have made great progress in exploring the accurate alignment between image and text. However, existing works mainly focus on the fine-grained alignment between image regions and sentence fragments, which ignores the guiding significance of context background information. Actually, integrating the local fine-grained information and global context background information can provide more semantic clues for retrieval. In this paper, we propose a novel Hierarchical Graph Alignment Network (HGAN) for image-text retrieval. First, to capture the comprehensive multimodal features, we construct the feature graphs for the image and text modality respectively. Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities. Finally, the ultimate image and text features are further refined through three-level similarity functions to achieve the hierarchical alignment. To justify the proposed model, we perform extensive experiments on MS-COCO and Flickr30K datasets. Experimental results show that the proposed HGAN outperforms the state-of-the-art methods on both datasets, which demonstrates the effectiveness and superiority of our model.

Physics-Informed Neural Networks (PINNs) have recently been proposed to solve scientific and engineering problems, where physical laws are introduced into neural networks as prior knowledge. With the embedded physical laws, PINNs enable the estimation of critical parameters, which are unobservable via physical tools, through observable variables. For example, Power Electronic Converters (PECs) are essential building blocks for the green energy transition. PINNs have been applied to estimate the capacitance, which is unobservable during PEC operations, using current and voltage, which can be observed easily during operations. The estimated capacitance facilitates self-diagnostics of PECs. Existing PINNs are often manually designed, which is time-consuming and may lead to suboptimal performance due to a large number of design choices for neural network architectures and hyperparameters. In addition, PINNs are often deployed on different physical devices, e.g., PECs, with limited and varying resources. Therefore, it requires designing different PINN models under different resource constraints, making it an even more challenging task for manual design. To contend with the challenges, we propose Automated Physics-Informed Neural Networks (AutoPINN), a framework that enables the automated design of PINNs by combining AutoML and PINNs. Specifically, we first tailor a search space that allows finding high-accuracy PINNs for PEC internal parameter estimation. We then propose a resource-aware search strategy to explore the search space to find the best PINN model under different resource constraints. We experimentally demonstrate that AutoPINN is able to find more accurate PINN models than human-designed, state-of-the-art PINN models using fewer resources.

The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device processing of video streams to deliver real-time, high-quality services for privacy and robustness concerns. However, the performance of these applications is constrained by the raw video streams, which tend to be taken with small-aperture cameras of ubiquitous mobile platforms in dim light. Despite extensive low-light video enhancement solutions, they are unfit for deployment to mobile devices due to their complex models and and ignorance of system dynamics like energy budgets. In this paper, we propose AdaEnlight, an energy-aware low-light video stream enhancement system on mobile devices. It achieves real-time video enhancement with competitive visual quality while allowing runtime behavior adaptation to the platform-imposed dynamic energy budgets. We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.

Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approaches. However, automated CTS solutions remain in their infancy and are only able to find optimal architectures for predefined hyperparameters and scale poorly to large-scale CTS. To overcome these limitations, we propose SEARCH, a joint, scalable framework, to automatically devise effective CTS forecasting models. Specifically, we encode each candidate architecture and accompanying hyperparameters into a joint graph representation. We introduce an efficient Architecture-Hyperparameter Comparator (AHC) to rank all architecture-hyperparameter pairs, and we then further evaluate the top-ranked pairs to select a final result. Extensive experiments on six benchmark datasets demonstrate that SEARCH not only eliminates manual efforts but also is capable of better performance than manually designed and existing automatically designed CTS models. In addition, it shows excellent scalability to large CTS.

Graph neural networks (GNNs) have been successfully applied to early mild cognitive impairment (EMCI) detection, with the usage of elaborately designed features constructed from blood oxygen level-dependent (BOLD) time series. However, few works explored the feasibility of using BOLD signals directly as features. Meanwhile, existing GNN-based methods primarily rely on hand-crafted explicit brain topology as the adjacency matrix, which is not optimal and ignores the implicit topological organization of the brain. In this paper, we propose a spatial temporal graph convolutional network with a novel graph structure self-learning mechanism for EMCI detection. The proposed spatial temporal graph convolution block directly exploits BOLD time series as input features, which provides an interesting view for rsfMRI-based preclinical AD diagnosis. Moreover, our model can adaptively learn the optimal topological structure and refine edge weights with the graph structure self-learning mechanism. Results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that our method outperforms state-of-the-art approaches. Biomarkers consistent with previous studies can be extracted from the model, proving the reliable interpretability of our method.

对比性语言图像预训练（剪辑）已被证明可以学习具有出色传递性的视觉表示，从而实现了零击分类的有希望的准确性。为了进一步提高其下游性能，现有作品在剪辑上提出了其他可学习的模块，并通过几次训练集对其进行微调。但是，由此产生的额外培训成本和数据要求严重阻碍了模型部署和知识转移的效率。在本文中，我们引入了一种自由午餐的增强方法CALIP，以通过无参数注意模块来提高Clip的零拍摄性能。具体而言，我们指导视觉和文本表示相互交互，并通过注意探索跨模式的信息特征。由于预训练大大降低了两种方式之间的嵌入距离，因此我们在注意力中丢弃所有可学习的参数，并在双向更新多模式特征，从而使整个过程无参数且无培训。通过这种方式，图像与文本感知信号混合在一起，文本表示形式被视觉引导以获得更好的自适应零射击对齐。我们在14个数据集的各种基准上评估CALIP，用于2D图像和3D Point Cloud几乎没有分类，显示出一致的零弹性性能改进了夹子。基于此，我们进一步在Calip的注意模块中插入了少量线性层，并在少量射击设置下验证我们的鲁棒性，与现有方法相比，这也可以实现领先的性能。这些广泛的实验证明了我们的方法在有效增强夹子方面的优势。

社会过程的持续数字化转化为时间序列数据的扩散，这些数据涵盖了诸如欺诈检测，入侵检测和能量管理等应用，在这种应用程序中，异常检测通常对于启用可靠性和安全性至关重要。许多最近的研究针对时间序列数据的异常检测。实际上，时间序列异常检测的特征是不同的数据，方法和评估策略，现有研究中的比较仅考虑了这种多样性的一部分，这使得很难为特定问题设置选择最佳方法。为了解决这一缺点，我们介绍了有关数据，方法和评估策略的分类法，并使用分类法提供了无监督时间序列检测的全面概述，并系统地评估和比较了最先进的传统以及深度学习技术。在使用九个公开可用数据集的实证研究中，我们将最常用的性能评估指标应用于公平实施标准下的典型方法。根据分类法提供的结构化，我们报告了经验研究，并以比较表的形式提供指南，以选择最适合特定应用程序设置的方法。最后，我们为这个动态领域提出了研究方向。

最近的顺序推荐模型越来越多地依赖连续的短期用户相互作用序列来建模用户兴趣。但是，这些方法引起了人们对短期和长期利益的关注。 （1）{\ IT短期}：交互序列可能不是由单一的兴趣引起的，而是来自几个相互交织的利益，即使在短时间内，也导致了它们无法模拟Skip行为的失败； （2）{\ it长期}：相互作用序列主要是在离散的间隔内稀疏观察，而不是长期连续的。这使得难以推断长期利益，因为只能考虑到跨序列的利益动态，因此只能得出离散的利息表示。在这项研究中，我们通过学习来解决这些问题（1）短期利益的多尺度表示； （2）长期利益的动态意识表示。为此，我们提出了一个\ textbf {i} nterest \ textbf {d} ynamics建模框架，使用生成\ textbf {n} eural \ textbf {p textbf {p} rocesses，coincined IDNP，以从功能角度来看，以模拟用户兴趣。 IDNP学习了一个全球兴趣函数家族，以定义每个用户的长期兴趣作为功能实例化，从而通过功能连续性表现出兴趣动态。具体而言，IDNP首先将每个用户的短期交互编码为多尺度表示，然后将其汇总为用户上下文。通过将潜在的全球兴趣与用户上下文相结合，IDNP然后重建长期用户兴趣功能，并在即将到来的查询时间段上预测交互。此外，即使相互作用序列受到限制和非连续性，IDNP也可以建模此类兴趣功能。在四个现实世界数据集上进行的广泛实验表明，我们的模型在各种评估指标上的最先进。

最近的研究表明，深度神经网络（DNNS）极易受到精心设计的对抗例子的影响。对那些对抗性例子的对抗性学习已被证明是防御这种攻击的最有效方法之一。目前，大多数现有的对抗示例生成方法基于一阶梯度，这几乎无法进一步改善模型的鲁棒性，尤其是在面对二阶对抗攻击时。与一阶梯度相比，二阶梯度提供了相对于自然示例的损失格局的更准确近似。受此启发的启发，我们的工作制作了二阶的对抗示例，并使用它们来训练DNNS。然而，二阶优化涉及Hessian Inverse的耗时计算。我们通过将问题转换为Krylov子空间中的优化，提出了一种近似方法，该方法显着降低了计算复杂性以加快训练过程。在矿工和CIFAR-10数据集上进行的广泛实验表明，我们使用二阶对抗示例的对抗性学习优于其他FISRT-阶方法，这可以改善针对广泛攻击的模型稳健性。