近年来，在各种特定于任务的情况下，盲目图像质量评估（BIQA）取得了巨大的成功，这些方案呈现出不变的失真类型和评估标准。但是，由于刚性结构和学习框架，它们不能应用于交叉任务BIQA方案，在这种情况下，失真类型和评估标准在实际应用中不断变化。本文提出了一个可扩展的增量学习框架（SILF），该框架可以在多个评估任务中依次执行BIQA，具有有限的记忆能力。更具体地说，我们开发了动态参数隔离策略，以依次更新特定于任务的参数子集，这些参数子集彼此之间并非重叠。每个参数子集都会暂时解决，以记住对其相应任务的一个评估偏好，并且可以在以下BIQA中自适应地重复使用先前的参数子集，以根据任务相关性实现更好的性能。为了抑制顺序任务学习中记忆容量的不受限制扩展，我们通过从先前解决的参数子集中逐渐和选择性地修剪不重要的神经元来开发可扩展的内存单元，这使我们能够忘记以前的经验的一部分，并释放有限的内存能力，以适应适应新的新任务。对11个IQA数据集进行的广泛实验表明，我们提出的方法在交叉任务BIQA中的其他最新方法显着优于其他最新方法。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

建模大规模复杂物理系统的最新进展已将研究的重点转移到数据驱动的技术上。但是，通过模拟复杂系统来生成数据集可能需要大量的计算资源。同样，获取实验数据集也可能很难。对于这些系统，通常在计算上便宜，但通常不准确，可用的模型可用。在本文中，我们为复杂的物理系统提出了一种双性模型建模方法，在这种情况下，我们使用深层操作员网络从True System的响应中建立了True System的响应与低保真响应之间的差异（DeepOnet），一种适用于近似非线性操作员的神经网络体系结构。我们将方法应用于具有参数不确定性并且部分未知的模型系统。三个数值示例用于显示所提出的方法对不确定且部分未知的复杂物理系统进行建模的功效。

设计具有高产和强可靠性的制造工艺依赖于罕见事件估计的有效方法。族记重要性分裂通过迭代选择和复制朝向罕见事件的实现来降低罕见事件概率估计的变化。当应用于需要修改后代实现的初始条件的确定性系统时，复制步骤很难。通常，将随机扰动应用于后代，以将它们的轨迹与父阶层分化。然而，这种随机扰动策略可能对某些系统有效，同时失败，防止概率估计的差异降低。该工作旨在使用诸如生成的对冲网络（GaN）的生成模型来解决这种限制，以产生与动态系统的吸引子一致的扰动。提出的GaN辅助重要性分裂方法（Ganisp）改善了所针对性的系统的方差减少。该方法的实现是在伴侣存储库中（https://github.com/nrel/ganisp）中的。

许多工程问题需要预测实现实现变异性或建模量的精致描述。在这种情况下，有必要采用未知高维空间的元素，其中可能具有数百万自由度。虽然存在能够具有具有已知形状的概率密度函数（PDF）的方法的方法，但是当分布未知时需要进行若干近似。在本文中，基础分布的采样方法以及底层分布的推动都是用一种称为生成对抗网络（GaN）的数据驱动方法，该方法列举了两个竞争的神经网络来生产可以有效地产生样本的网络从训练集分发。在实践中，通常需要从条件分布中绘制样品。当条件变量是连续的时，可以仅可用对应于调节变量的特定值的一个（如果有）数据点，这不足以估计条件分布。使用PDF的条件时刻的先验估计，处理此问题。这里比较两种方法，随机估计和外部神经网络，用于计算这些时刻;但是，可以使用任何优选的方法。在过滤的湍流流场的解构的情况下，证明了算法。结果表明，与最先进的方法相比，所提出的算法的所有版本有效地对目标条件分布进行了最小的影响，对样品的质量的影响最小。另外，该过程可以用作由连续变量的条件GaN（CGAN）产生的样本的分集的度量。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

RTE is a significant problem and is a reasonably active research community. The proposed research works on the approach to this problem are pretty diverse with many different directions. For Vietnamese, the RTE problem is moderately new, but this problem plays a vital role in natural language understanding systems. Currently, methods to solve this problem based on contextual word representation learning models have given outstanding results. However, Vietnamese is a semantically rich language. Therefore, in this paper, we want to present an experiment combining semantic word representation through the SRL task with context representation of BERT relative models for the RTE problem. The experimental results give conclusions about the influence and role of semantic representation on Vietnamese in understanding natural language. The experimental results show that the semantic-aware contextual representation model has about 1% higher performance than the model that does not incorporate semantic representation. In addition, the effects on the data domain in Vietnamese are also higher than those in English. This result also shows the positive influence of SRL on RTE problem in Vietnamese.

To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4- based language models for Vietnamese, including one model without word segmentation, and four models using RDRsegmenter, uitnlp, pyvi, or underthesea toolkits in the pre-processing data phase. According to comprehensive experimental results on two corpora, including the VLSP2016-SA corpus of technical article reviews from the news and social media and the UIT-VSFC corpus of the educational survey, we have two suggestions. Firstly, using traditional classifiers like Naive Bayes or Support Vector Machines, word segmentation maybe not be necessary for the Vietnamese sentiment classification corpus, which comes from the social domain. Secondly, word segmentation is necessary for Vietnamese sentiment classification when word segmentation is used before using the BPE method and feeding into the deep learning model. In this way, the RDRsegmenter is the stable toolkit for word segmentation among the uitnlp, pyvi, and underthesea toolkits.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.