[目的]更好地了解在线评论，并帮助潜在的消费者，商人和产品制造商有效地获得用户对产品方面的评估，本文从在线评论的时间角度来探讨了用户关注和对产品方面的情感分布规律性。 [设计/方法/方法]在线评论的时间特征（购买时间和审核时间之间的购买时间，审核时间和时间间隔），类似的属性聚类以及属性级别的情感计算技术是基于340k智能手机评论来使用的在JD.com（中国著名的在线购物平台）的三种产品中，探讨了本文中用户对产品方面的关注和情感的分布规律。 [调查结果]经验结果表明，幂律分布可以符合用户对产品方面的关注，并且在短时间间隔发布的评论包含更多产品方面。此外，结果表明，在短时间间隔内，产品方面的用户情感值显着更高/较低，这有助于判断产品的优势和弱点。 [研究局限性]本文无法获得更多具有时间特征的产品的在线评论，以验证发现，因为对购物平台的评论的限制限制了。 [原创性/价值]这项工作揭示了用户对产品方面的关注和情感的分布规律，这在协助决策，优化审查演示和改善购物体验方面具有重要意义。

大数据学习为人工智能（AI）带来了成功，但是注释和培训成本很昂贵。将来，对小数据的学习是AI的最终目的之一，它要求机器识别依靠小数据作为人类的目标和场景。一系列的机器学习模型正在进行这种方式，例如积极学习，几乎没有学习，深度聚类。但是，其概括性能几乎没有理论保证。此外，它们的大多数设置都是被动的，也就是说，标签分布由一个指定的采样方案明确控制。这项调查遵循PAC（可能是近似正确）框架下的不可知论活动采样，以分析使用有监督和无监督的时尚对小数据学习的概括误差和标签复杂性。通过这些理论分析，我们从两个几何学角度对小数据学习模型进行了分类：欧几里得和非欧几里得（双曲线）平均表示，在此还提供了优化解决方案和讨论。稍后，然后总结了一些可能从小型数据学习中受益的潜在学习方案，还分析了它们的潜在学习方案。最后，还调查了一些具有挑战性的应用程序，例如计算机视觉，自然语言处理可能会受益于小型数据学习。

权力是一个不可避免的尚未识别的协作元素。权力动力学影响科学合作的各个方面。团队电力动力学可以通过团队功率级和团队电力层次结构来衡量。团队功率水平概念化为拥有资源，专业知识或团队决策权的平均水平。团队权力层次结构代表了团队中资源财产的垂直差异。在科学科学中，很少有研究从团队权力动力学的角度看过科学合作。本研究探讨了团队权力动力学如何影响团队的影响，以填补研究差距。在这项研究中，一个出版物的所有共同作者被视为一个团队。一支队伍的团队电力水平和团队电力层次由本团队共同作者的职业年龄的平均值和基尼指数来衡量。团队影响由这支球队撰写的文件的引用量化。通过分析来自科学（例如计算机科学，物理学），社会科学（例如，社会学，图书馆和信息科学）和艺术和人文学科（例如，艺术）的770万队，我们发现平坦的团队结构与更高相关团队影响。当团队功率水平增加时，带有低团队电力层次的团队比高队电力层次结构的队伍更多。这些调查结果已经在所有五个学科中重复，除了艺术之外的所有五个学科，以及来自计算机科学的各种类型的团队，包括来自工业或学术界的团队，不同的性别团队的团队，具有地理对比的团队，以及具有独特统一的团队。

We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.

Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit's output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater.

It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures. Existing methods mainly resolve this issue by retraining the entire model to impose the uncertainty quantification capability so that the learned model can achieve desired performance in accuracy and uncertainty prediction simultaneously. However, training the model from scratch is computationally expensive and may not be feasible in many situations. In this work, we consider a more practical post-hoc uncertainty learning setting, where a well-trained base model is given, and we focus on the uncertainty quantification task at the second stage of training. We propose a novel Bayesian meta-model to augment pre-trained models with better uncertainty quantification abilities, which is effective and computationally efficient. Our proposed method requires no additional training data and is flexible enough to quantify different uncertainties and easily adapt to different application settings, including out-of-domain data detection, misclassification detection, and trustworthy transfer learning. We demonstrate our proposed meta-model approach's flexibility and superior empirical performance on these applications over multiple representative image classification benchmarks.

Missing data are ubiquitous in real world applications and, if not adequately handled, may lead to the loss of information and biased findings in downstream analysis. Particularly, high-dimensional incomplete data with a moderate sample size, such as analysis of multi-omics data, present daunting challenges. Imputation is arguably the most popular method for handling missing data, though existing imputation methods have a number of limitations. Single imputation methods such as matrix completion methods do not adequately account for imputation uncertainty and hence would yield improper statistical inference. In contrast, multiple imputation (MI) methods allow for proper inference but existing methods do not perform well in high-dimensional settings. Our work aims to address these significant methodological gaps, leveraging recent advances in neural network Gaussian process (NNGP) from a Bayesian viewpoint. We propose two NNGP-based MI methods, namely MI-NNGP, that can apply multiple imputations for missing values from a joint (posterior predictive) distribution. The MI-NNGP methods are shown to significantly outperform existing state-of-the-art methods on synthetic and real datasets, in terms of imputation error, statistical inference, robustness to missing rates, and computation costs, under three missing data mechanisms, MCAR, MAR, and MNAR.

Adversarial perturbation plays a significant role in the field of adversarial robustness, which solves a maximization problem over the input data. We show that the backward propagation of such optimization can accelerate $2\times$ (and thus the overall optimization including the forward propagation can accelerate $1.5\times$), without any utility drop, if we only compute the output gradient but not the parameter gradient during the backward propagation.

3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leveraging 3D networks results in huge computational costs, which are far beyond that of 2D networks. In this paper, we propose a novel Hilbert curve-based cross-dimensionality distillation approach that facilitates the knowledge of 3D networks to improve the performance of 2D networks. The proposed Hilbert Distillation (HD) method preserves the structural information via the Hilbert curve, which maps high-dimensional (>=2) representations to one-dimensional continuous space-filling curves. Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations. We further propose a Variable-length Hilbert Distillation (VHD) method to dynamically shorten the walking stride of the Hilbert curve in activation feature areas and lengthen the stride in context feature areas, forcing the 2D networks to pay more attention to learning from activation features. The proposed algorithm outperforms the current state-of-the-art distillation techniques adapted to cross-dimensionality distillation on two classification tasks. Moreover, the distilled 2D networks by the proposed method achieve competitive performance with the original 3D networks, indicating the lightweight distilled 2D networks could potentially be the substitution of cumbersome 3D networks in the real-world scenario.

Clustering is a fundamental problem in network analysis that finds closely connected groups of nodes and separates them from other nodes in the graph, while link prediction is to predict whether two nodes in a network are likely to have a link. The definition of both naturally determines that clustering must play a positive role in obtaining accurate link prediction tasks. Yet researchers have long ignored or used inappropriate ways to undermine this positive relationship. In this article, We construct a simple but efficient clustering-driven link prediction framework(ClusterLP), with the goal of directly exploiting the cluster structures to obtain connections between nodes as accurately as possible in both undirected graphs and directed graphs. Specifically, we propose that it is easier to establish links between nodes with similar representation vectors and cluster tendencies in undirected graphs, while nodes in a directed graphs can more easily point to nodes similar to their representation vectors and have greater influence in their own cluster. We customized the implementation of ClusterLP for undirected and directed graphs, respectively, and the experimental results using multiple real-world networks on the link prediction task showed that our models is highly competitive with existing baseline models. The code implementation of ClusterLP and baselines we use are available at https://github.com/ZINUX1998/ClusterLP.