Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning. Although KGNN effectively models the structural information from knowledge graphs, these frameworks amplify the underlying data bias that leads to discrimination towards certain groups or individuals in resulting applications. Additionally, as existing debiasing approaches mainly focus on the entity-wise bias, eliminating the multi-hop relational bias that pervasively exists in knowledge graphs remains an open question. However, it is very challenging to eliminate relational bias due to the sparsity of the paths that generate the bias and the non-linear proximity structure of knowledge graphs. To tackle the challenges, we propose Fair-KGNN, a KGNN framework that simultaneously alleviates multi-hop bias and preserves the proximity information of entity-to-relation in knowledge graphs. The proposed framework is generalizable to mitigate the relational bias for all types of KGNN. We develop two instances of Fair-KGNN incorporating with two state-of-the-art KGNN models, RGCN and CompGCN, to mitigate gender-occupation and nationality-salary bias. The experiments carried out on three benchmark knowledge graph datasets demonstrate that the Fair-KGNN can effectively mitigate unfair situations during representation learning while preserving the predictive performance of KGNN models.

学习不平衡是数据挖掘的基本挑战，在每个课程中，培训样本的比例不成比例。过度采样是通过为少数族裔生成合成样本来解决不平衡学习的有效技术。尽管已经提出了许多过采样算法，但它们在很大程度上依赖启发式方法，这可能是最佳选择的，因为我们可能需要针对不同数据集和基本分类器的不同采样策略，并且无法直接优化性能指标。在此激励的情况下，我们研究了开发一种基于学习的过采样算法以优化分类性能，这是一项艰巨的任务，因为庞大和等级的决策空间。在高水平上，我们需要确定要生成多少合成样品。在低级别，我们需要确定合成样品的位置，这取决于高级决策，因为样品的最佳位置在不同数量的样品中可能有所不同。为了应对挑战，我们提出了一种自动采样算法，可以共同优化不同级别的决策。由Smote〜 \ cite {Chawla2002smote}的成功的动机及其扩展，我们将生成过程作为Markov决策过程（MDP），由三个级别的策略组成，以在Smote搜索空间内生成合成样本。然后，我们利用深层的层次加强学习来优化验证数据的性能指标。在六个现实世界数据集上进行的广泛实验表明，自动变量极大地超过了最新的重新采样算法。该代码在https://github.com/daochenzha/autosmote上

我们研究了时间序列分类（TSC），是时间序列数据挖掘的根本任务。事先从两个主要方向接近TSC：（1）基于相似性的方法，用于基于最近邻居的时间系列，（2）直接以数据驱动的方式学习分类表示的深度学习模型。在这两条研究线内的不同工作机制激励，我们的目的是以与共同模拟时间序列相似度的方式连接它们并学习表示。这是一个具有挑战性的任务，因为目前尚不清楚我们应该如何有效地利用相似性信息。为了解决挑战，我们提出了相似度感知的时序分类（SIMTSC），这是一种概念上简单且一般的框架，其模型与图形神经网络（GNN）的相似性信息。具体地，我们将TSC标记为图中的节点分类问题，其中节点对应于时间序列，并且链路对应于配对相似性。我们进一步设计了一种图形施工策略和具有负采样的批量培训算法，以提高培训效率。我们将SIMTSC与RESENT作为骨干网和动态时间翘曲（DTW）作为相似度测量。在完整的UCR数据集和几个多变量数据集上的广泛实验证明了在监督和半监督设置中将相似信息纳入深度学习模型的有效性。我们的代码可在https://github.com/daochenzha/simtsc提供

动作识别是通过广泛应用程序进行视频理解的重要任务。但是，开发有效的动作识别解决方案通常需要进行广泛的工程工作，以构建和测试模块及其超参数的不同组合。在此演示中，我们提出了Autovideo，这是一种用于自动视频动作识别的Python系统。Autovideo的特征是1）标准管道语言之后的高度模块化和可扩展的基础架构，2）管道构造的原始列表，3）数据驱动的调谐器来保存管道调整的努力，4）易于使用图形用户界面（GUI）。Autovideo在MIT许可证上发行，网址为https://github.com/datamllab/autovideo

With the fast development of big data, it has been easier than before to learn the optimal decision rule by updating the decision rule recursively and making online decisions. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes of the stochastic gradient and establish the asymptotic normality of the parameter estimator. Our proposed estimator significantly improves the asymptotic efficiency over the previous averaged SGD approach via inverse probability weights. We also conduct an optimality analysis on the weights in a linear regression setting. We provide a Bahadur representation of the proposed estimator and show that the remainder term in the Bahadur representation entails a slower convergence rate compared to classical SGD due to the adaptive data collection.

Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification. Due to the intrinsic hardness of model counting, approximate techniques have been developed to solve real-world instances of model counting. This paper designs a new anytime approach called PartialKC for approximate model counting. The idea is a form of partial knowledge compilation to provide an unbiased estimate of the model count which can converge to the exact count. Our empirical analysis demonstrates that PartialKC achieves significant scalability and accuracy over prior state-of-the-art approximate counters, including satss and STS. Interestingly, the empirical results show that PartialKC reaches convergence for many instances and therefore provides exact model counting performance comparable to state-of-the-art exact counters.

Robots are traditionally bounded by a fixed embodiment during their operational lifetime, which limits their ability to adapt to their surroundings. Co-optimizing control and morphology of a robot, however, is often inefficient due to the complex interplay between the controller and morphology. In this paper, we propose a learning-based control method that can inherently take morphology into consideration such that once the control policy is trained in the simulator, it can be easily deployed to robots with different embodiments in the real world. In particular, we present the Embodiment-aware Transformer (EAT), an architecture that casts this control problem as conditional sequence modeling. EAT outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired robot embodiment, past states, and actions, our EAT model can generate future actions that best fit the current robot embodiment. Experimental results show that EAT can outperform all other alternatives in embodiment-varying tasks, and succeed in an example of real-world evolution tasks: stepping down a stair through updating the morphology alone. We hope that EAT will inspire a new push toward real-world evolution across many domains, where algorithms like EAT can blaze a trail by bridging the field of evolutionary robotics and big data sequence modeling.

Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behaviors. In this paper, we introduce the first multimodal dataset for modeling persuasion behaviors. Our dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes. We provide extensive experiments to show how dialogue context and visual signals benefit persuasion strategy prediction. We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes. Our dataset, code, and models can be found at https://persuasion-deductiongame.socialai-data.org.

Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i.e., sim-to-real transfer). Despite considerable progress, the capacity and scalability of traditional neural networks are still limited, which may hinder their applications in more complex environments. In contrast, the Transformer architecture has shown its superiority in a wide range of large-scale sequence modeling tasks, including natural language processing and decision-making problems. In this paper, we propose Terrain Transformer (TERT), a high-capacity Transformer model for quadrupedal locomotion control on various terrains. Furthermore, to better leverage Transformer in sim-to-real scenarios, we present a novel two-stage training framework consisting of an offline pretraining stage and an online correction stage, which can naturally integrate Transformer with privileged training. Extensive experiments in simulation demonstrate that TERT outperforms state-of-the-art baselines on different terrains in terms of return, energy consumption and control smoothness. In further real-world validation, TERT successfully traverses nine challenging terrains, including sand pit and stair down, which can not be accomplished by strong baselines.

Graphene quantum dots provide a platform for manipulating electron behaviors in two-dimensional (2D) Dirac materials. Most previous works were of the "forward" type in that the objective was to solve various confinement, transport and scattering problems with given structures that can be generated by, e.g., applying an external electrical field. There are applications such as cloaking or superscattering where the challenging problem of inverse design needs to be solved: finding a quantum-dot structure according to certain desired functional characteristics. A brute-force search of the system configuration based directly on the solutions of the Dirac equation is computational infeasible. We articulate a machine-learning approach to addressing the inverse-design problem where artificial neural networks subject to physical constraints are exploited to replace the rigorous Dirac equation solver. In particular, we focus on the problem of designing a quantum dot structure to generate both cloaking and superscattering in terms of the scattering efficiency as a function of the energy. We construct a physical loss function that enables accurate prediction of the scattering characteristics. We demonstrate that, in the regime of Klein tunneling, the scattering efficiency can be designed to vary over two orders of magnitudes, allowing any scattering curve to be generated from a proper combination of the gate potentials. Our physics-based machine-learning approach can be a powerful design tool for 2D Dirac material-based electronics.