网络科学将自己确立为建模时间序列和复杂系统的重要工具。这个建模过程包括将集合或单个时间序列转换为网络。节点可以代表完整的时间序列，段或单个值，而链接定义了所代表部分之间的关​​联或相似性。 R是数据科学，统计和机器学习中使用的主要编程语言之一，并提供许多软件包。但是，没有单个软件包提供将时间序列转换为网络的必要方法。本文介绍了TS2NET，这是一个用于将一个或多个时间序列建模为网络的R软件包。该软件包提供了时间序列距离函数，可以在超级计算机和超级计算机中轻松计算，以处理较大的数据集和方法，以将距离矩阵转换为网络。 TS2NET还提供了将单个时间序列转换为网络的方法，例如复发网络，可见性图和过渡网络。与其他软件包一起，TS2NET允许使用网络科学和图形挖掘工具从时间序列中提取信息。

本文提出了一种新的方法，可以通过蒙特卡洛树搜索来控制象征性音乐的情感。我们使用蒙特卡洛树搜索作为一种解码机制来指导语言模型学到的概率分布朝着给定的情感。在解码过程的每个步骤中，我们都会使用树木（Puct）的预测指标上的置信度来搜索分别由情绪分类器和歧视器给出的情感和质量平均值的序列。我们将语言模型用作管道的政策，并将情感分类器和歧视器的组合作为其价值功能。为了解码一段音乐中的下一个令牌，我们从搜索过程中创建的节点访问的分布中进行采样。我们使用直接从生成的样品计算的一组客观指标来评估生成样品相对于人类组成的碎片的质量。我们还进行了一项用户研究，以评估人类受试者如何看待生成的样品的质量和情感。我们将派斗与随机双目标梁搜索（SBB）和条件采样（CS）进行了比较。结果表明，在音乐质量和情感的几乎所有指标中，Puct的表现都优于SBB和CS。

监督运营商学习是一种新兴机器学习范例，用于建模时空动态系统的演变和近似功能数据之间的一般黑盒关系的应用。我们提出了一种新颖的操作员学习方法，LOCA（学习操作员耦合注意力），激励了最近的注意机制的成功。在我们的体系结构中，输入函数被映射到有限的一组特征，然后按照依赖于输出查询位置的注意重量平均。通过将这些注意重量与积分变换一起耦合，LOCA能够明确地学习目标输出功能中的相关性，使我们能够近似非线性运算符，即使训练集测量中的输出功能的数量非常小。我们的配方伴随着拟议模型的普遍表现力的严格近似理论保证。经验上，我们在涉及普通和部分微分方程的系统管理的若干操作员学习场景中，评估LOCA的表现，以及黑盒气候预测问题。通过这些场景，我们展示了最先进的准确性，对噪声输入数据的鲁棒性以及在测试数据集上始终如一的错误传播，即使对于分发超出预测任务。

知道如何在搜索引擎（SES）（例如Google或Wikipedia）中构建基于文本的搜索查询（SQS）已成为一项基本技能。尽管可以通过此类SE提供大量数据，但大多数结构化数据集都生活在其范围之外。可视化工具有助于这一限制，但是没有这样的工具接近通过通用SES获得的大量信息。为了填补这一空白，本文介绍了Q4EDA，这是一个新颖的框架，可转换用户在时间序列的视觉表示上执行的视觉选择查询，提供有效且稳定的SQS，可用于通用SES和相关信息的建议。用户通过将Gapminder的线条复制品与填充有Wikipedia文档的SE联系起来的应用程序来介绍和验证Q4EDA的实用性，并显示了Q4EDA如何支持和增强联合国世界指标的探索性分析。尽管有一些局限性，Q4EDA在其建议中仍然是独一无二的，它代表了提供基于用户与视觉表示的用户交互来查询文本信息的解决方案的真正进步。

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.

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.

We propose an ensemble approach to predict the labels in linear programming word problems. The entity identification and the meaning representation are two types of tasks to be solved in the NL4Opt competition. We propose the ensembleCRF method to identify the named entities for the first task. We found that single models didn't improve for the given task in our analysis. A set of prediction models predict the entities. The generated results are combined to form a consensus result in the ensembleCRF method. We present an ensemble text generator to produce the representation sentences for the second task. We thought of dividing the problem into multiple small tasks due to the overflow in the output. A single model generates different representations based on the prompt. All the generated text is combined to form an ensemble and produce a mathematical meaning of a linear programming problem.

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.

This paper deals with the problem of statistical and system heterogeneity in a cross-silo Federated Learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel Graph Signal Processing (GSP)-inspired aggregation rule based on graph filtering dubbed ``G-Fedfilt''. The proposed aggregator enables a structured flow of information based on the graph's topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous trade-off between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional Federated Averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time-scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models' gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to $3.99\% $ better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local datasets, while it is capable of yielding up to $2.41\%$ higher accuracy than FedAvg in the case of testing the generalization of the models.

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.