神经网络（NNS）的能力在顺序地学习和记住多项任务是由于其灾难性遗忘（CF）问题而在实现一般人工智能方面面临艰难的挑战。幸运的是，最新的OWM正交权重修改）和其他几种连续学习（CL）方法表明了一些有希望的克服CF问题的方法。但是，现有的CL方法都没有探讨以下三个关键问题，以便有效地克服CF问题：即，它有助于在其顺序任务学习期间对NN的有效权重修改有所了解？当新学习任务的数据分布与先前学习的任务相对应的更改时，是否应该采用统一/特定的权重修改策略？对于给定的CL方法，可学习任务的上限是什么？ ect。为了实现这一点，在本文中，我们首先揭示了新的学习任务的权重梯度的事实是由新任务的输入空间和先前学习任务的重量空间顺序确定。在这种观察和递归最小二乘法的情况下，我们通过增强型OWM提出了一种新的高效和有效的连续学习方法EOWM。我们理论上和明确地赋予了我们的EOWM的学习任务的上限。在基准测试上进行的广泛实验表明，我们的EOWM是有效性，优于所有最先进的CL基线。

尽管深度神经网络（DNN）在各种应用中取得了突出的性能，但众所周知，DNN易于在清洁/原始样品中具有难以察觉的扰动的对抗性实施例/样品（AES）。克服对抗对抗攻击的现有防御方法的弱点，这破坏了原始样本的信息，导致目标分类器精度的减少，提高了增强的反对对抗攻击方法IDFR（通过输入去噪和功能恢复） 。所提出的IDFR是由增强型输入丹麦优化的增强型输入丹麦（ID）和隐藏的有损特征恢复器（FR）组成。在基准数据集上进行的广泛实验表明，所提出的IDFR优于各种最先进的防御方法，对保护目标模型免受各种对抗黑盒或白盒攻击的高度有效。 \脚注{souce代码释放：\ href {https://github.com/id-fr/idfr} {https：//github.com/id-fr/idfr}}

The past two decades have seen increasingly rapid advances in the field of multi-view representation learning due to it extracting useful information from diverse domains to facilitate the development of multi-view applications. However, the community faces two challenges: i) how to learn robust representations from a large amount of unlabeled data to against noise or incomplete views setting, and ii) how to balance view consistency and complementary for various downstream tasks. To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation. In addition, we employ a clustering task to guide the fusion network to prevent it from leading to trivial solutions. For balancing consistency and complementary, then, we design an asymmetrical contrastive strategy that aligns the view-common representation and each view-specific representation. These modules are incorporated into a unified method known as CLustering-guided cOntrastiVE fusioN (CLOVEN). We quantitatively and qualitatively evaluate the proposed method on five datasets, demonstrating that CLOVEN outperforms 11 competitive multi-view learning methods in clustering and classification. In the incomplete view scenario, our proposed method resists noise interference better than those of our competitors. Furthermore, the visualization analysis shows that CLOVEN can preserve the intrinsic structure of view-specific representation while also improving the compactness of view-commom representation. Our source code will be available soon at https://github.com/guanzhou-ke/cloven.

Open Information Extraction (OpenIE) aims to extract relational tuples from open-domain sentences. Traditional rule-based or statistical models have been developed based on syntactic structures of sentences, identified by syntactic parsers. However, previous neural OpenIE models under-explore the useful syntactic information. In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures. To better fuse heterogeneous information from both graphs, we adopt multi-view learning to capture multiple relationships from them. Finally, the finetuned constituency and dependency representations are aggregated with sentential semantic representations for tuple generation. Experiments show that both constituency and dependency information, and the multi-view learning are effective.

Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains towards a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on four real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. The implementation code of CoTMix is available at \href{https://github.com/emadeldeen24/CoTMix}{github.com/emadeldeen24/CoTMix}.

As an efficient way to integrate multiple distributed energy resources and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads, energy storage systems, price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor-critic with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training.

Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature-based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

High order structures (cavities and cliques) of the gene network of influenza A virus reveal tight associations among viruses during evolution and are key signals that indicate viral cross-species infection and cause pandemics. As indicators for sensing the dynamic changes of viral genes, these higher order structures have been the focus of attention in the field of virology. However, the size of the viral gene network is usually huge, and searching these structures in the networks introduces unacceptable delay. To mitigate this issue, in this paper, we propose a simple-yet-effective model named HyperSearch based on deep learning to search cavities in a computable complex network for influenza virus genetics. Extensive experiments conducted on a public influenza virus dataset demonstrate the effectiveness of HyperSearch over other advanced deep-learning methods without any elaborated model crafting. Moreover, HyperSearch can finish the search works in minutes while 0-1 programming takes days. Since the proposed method is simple and easy to be transferred to other complex networks, HyperSearch has the potential to facilitate the monitoring of dynamic changes in viral genes and help humans keep up with the pace of virus mutations.

电力电子转换器已被广泛用于航空航天系统，直流传输，分布式能源，智能电网等，电源电子转换器的可靠性一直是学术界和行业的热点。执行电力电子转换器开放电路故障和智能故障诊断以避免次要故障，减少操作和维护成本，并提高电力电子系统的可靠性，这一点很重要。首先，分析和总结电力电子转换器的故障特征。其次，对电源电子转换器中的一些基于AI的故障诊断方法和应用示例进行了审查，并提出了基于随机森林和瞬态故障特征的故障诊断方法，用于三相功率电子转换器。最后，指出了未来的研究挑战和基于AI的故障诊断方法的方向。

问答（QA）在回答定制域中的问题方面表现出了令人印象深刻的进展。然而，域的适应性仍然是质量检查系统最难以捉摸的挑战之一，尤其是当质量检查系统在源域中训练但部署在不同的目标域中时。在这项工作中，我们调查了问题分类对质量检查域适应的潜在好处。我们提出了一个新颖的框架：问题回答的问题分类（QC4QA）。具体而言，采用问题分类器将问题类分配给源数据和目标数据。然后，我们通过伪标记以自我监督的方式进行联合培训。为了优化，源和目标域之间的域间差异通过最大平均差异（MMD）距离降低。我们还最大程度地减少了同一问题类别的质量质量适应性表现的QA样本中的类内部差异。据我们所知，这是质量检查域适应中的第一部作品，以通过自我监督的适应来利用问题分类。我们证明了拟议的QC4QA的有效性，并在多个数据集上针对最先进的基线进行了一致的改进。