Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.

Multivariate time series forecasting constitutes important functionality in cyber-physical systems, whose prediction accuracy can be improved significantly by capturing temporal and multivariate correlations among multiple time series. State-of-the-art deep learning methods fail to construct models for full time series because model complexity grows exponentially with time series length. Rather, these methods construct local temporal and multivariate correlations within subsequences, but fail to capture correlations among subsequences, which significantly affect their forecasting accuracy. To capture the temporal and multivariate correlations among subsequences, we design a pattern discovery model, that constructs correlations via diverse pattern functions. While the traditional pattern discovery method uses shared and fixed pattern functions that ignore the diversity across time series. We propose a novel pattern discovery method that can automatically capture diverse and complex time series patterns. We also propose a learnable correlation matrix, that enables the model to capture distinct correlations among multiple time series. Extensive experiments show that our model achieves state-of-the-art prediction accuracy.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

High-quality traffic flow generation is the core module in building simulators for autonomous driving. However, the majority of available simulators are incapable of replicating traffic patterns that accurately reflect the various features of real-world data while also simulating human-like reactive responses to the tested autopilot driving strategies. Taking one step forward to addressing such a problem, we propose Realistic Interactive TrAffic flow (RITA) as an integrated component of existing driving simulators to provide high-quality traffic flow for the evaluation and optimization of the tested driving strategies. RITA is developed with fidelity, diversity, and controllability in consideration, and consists of two core modules called RITABackend and RITAKit. RITABackend is built to support vehicle-wise control and provide traffic generation models from real-world datasets, while RITAKit is developed with easy-to-use interfaces for controllable traffic generation via RITABackend. We demonstrate RITA's capacity to create diversified and high-fidelity traffic simulations in several highly interactive highway scenarios. The experimental findings demonstrate that our produced RITA traffic flows meet all three design goals, hence enhancing the completeness of driving strategy evaluation. Moreover, we showcase the possibility for further improvement of baseline strategies through online fine-tuning with RITA traffic flows.

市场需求紧迫，以最大程度地减少迅速伽马中子激活分析（PGNAA）光谱测量机的测试时间，以便它可以充当即时材料分析仪，例如立即对废物样品进行分类，并根据测试样品的检测成分确定最佳的回收方法。本文介绍了深度学习分类的新开发，并旨在减少PGNAA机器的测试时间。我们提出随机采样方法和类激活图（CAM）以生成“缩小”样品并连续训练CNN模型。随机采样方法（RSM）旨在减少样品中的测量时间，而类激活图（CAM）用于滤除缩小样品的不太重要的能量范围。我们将总PGNAA测量时间缩短到2.5秒，同时确保我们的数据集的精度约为96.88％，该数据集使用12种不同的物质。与分类不同的材料分类相比，具有相同元素以归档良好精度的物质需要更多的测试时间（样品计数率）。例如，铜合金的分类需要将近24秒的测试时间才能达到98％的精度。

最近，图神经网络显示了建模基于网络的推荐系统中复杂拓扑结构的优势。由于节点之间的各种相互作用以及来自各种类型的节点和边缘的大量语义，因此在多重异质网络中学习表达性节点表示的研究兴趣爆发。推荐系统中最重要的任务之一是预测特定边缘类型下两个节点之间的潜在连接（即关系）。尽管现有的研究利用明确的元数据来汇总邻居，但实际上，它们仅考虑了关系内部的元数据，因此无法通过相互关联信息来利用潜在的提升。此外，在各种关系下，尤其是在越来越多的节点和边缘类型的情况下，全面利用相互关系的元数据并不总是直接的。此外，两个节点之间不同关系的贡献很难衡量。为了应对挑战，我们提出了Hybridgnn，这是一种具有混合聚集流和分层的端到端GNN模型，以在多路复用方案中充分利用异质性。具体而言，Hybridgnn应用了一个随机的关系探索模块来利用不同关系之间的多重性属性。然后，我们的模型利用在关系内的元数据和随机探索下的混合聚集流以学习丰富的语义。为了探索不同聚合流的重要性并利用多重性属性，我们提出了一个新型的分层注意模块，该模块既利用了Metapath级别的注意力和关系级的关注。广泛的实验结果表明，与几个最先进的基线相比，Hybridgnn取得了最佳性能。

R2是一种新颖的在线任何角度路径计划者，它使用基于启发式错误或射线铸造方法在具有非凸线，多边形障碍物的2D地图中找到最佳路径。R2与传统的自由空间计划者具有竞争力，如果查询具有直接视线，请迅速找到路径。在很少有障碍轮廓的大稀疏地图上，在实践中可能会发生，R2的表现要优于自由空间规划师，并且可能比最先进的自由空间扩展计划者Anya快得多。在带有许多轮廓的地图上，Anya的性能比R2快。R2建立在Rayscan上，引入了懒惰搜索和源 - 路边计数器，可在连续的轮廓上乐观地找到继任者。这种新颖的方法绕过了锯齿状轮廓上的大多数继任者，以减少昂贵的视线检查，因此不需要预处理才能成为在线竞争性的任何角度策划者。

Gradient-enhanced Kriging (GE-Kriging) is a well-established surrogate modelling technique for approximating expensive computational models. However, it tends to get impractical for high-dimensional problems due to the large inherent correlation matrix and the associated high-dimensional hyper-parameter tuning problem. To address these issues, we propose a new method in this paper, called sliced GE-Kriging (SGE-Kriging) for reducing both the size of the correlation matrix and the number of hyper-parameters. Firstly, we perform a derivative-based global sensitivity analysis to detect the relative importance of each input variable with respect to model response. Then, we propose to split the training sample set into multiple slices, and invoke Bayes' theorem to approximate the full likelihood function via a sliced likelihood function, in which multiple small correlation matrices are utilized to describe the correlation of the sample set. Additionally, we replace the original high-dimensional hyper-parameter tuning problem with a low-dimensional counterpart by learning the relationship between the hyper-parameters and the global sensitivity indices. Finally, we validate SGE-Kriging by means of numerical experiments with several benchmarks problems. The results show that the SGE-Kriging model features an accuracy and robustness that is comparable to the standard one but comes at much less training costs. The benefits are most evident in high-dimensional problems.

现有的单眼深度估计方法在不同的场景中实现了出色的鲁棒性，但它们只能检索仿射不变的深度，最多可达到未知的规模和变化。但是，在一些基于视频的场景中，例如视频中的视频深度估计和3D场景重建，驻留在人均预测中的未知量表和偏移可能会导致深度不一致。为了解决这个问题，我们提出了一种局部加权的线性回归方法，以恢复比例并以非常稀疏的锚点的转移，从而确保沿连续帧的比例一致性。广泛的实验表明，我们的方法可以在几个零击基准测试中最多将现有最新方法的性能提高50％。此外，我们合并了超过630万个RGBD图像，以训练强大而健壮的深度模型。我们产生的Resnet50-Backbone模型甚至胜过最先进的DPT VIT-LALGE模型。结合基于几何的重建方法，我们制定了一种新的密集3D场景重建管道，该管道受益于稀疏点的比例一致性和单眼方法的鲁棒性。通过对视频进行简单的人均预测，可以恢复准确的3D场景形状。

迭代线性二次调节器（ILQR）在解决非线性系统模型的轨迹优化问题方面已广泛普及。但是，作为一种基于模型的拍摄方法，它在很大程度上依赖于准确的系统模型来更新最佳控制动作和通过正向集成确定的轨迹，从而变得容易受到不可避免的模型的影响。最近，针对最佳控制问题的基于学习的方法进行的大量研究工作在解决未知系统模型方面已经取得了显着发展，尤其是当系统与环境具有复杂的相互作用时。然而，通常需要一个深层的神经网络来拟合大量的采样数据。在这项工作中，我们提出了神经-ILQR，这是一种在不受约束的控制空间上进行学习的拍摄方法，其中使用简单结构的神经网络代表局部系统模型。在此框架中，通过同时完善最佳策略和神经网络迭代，可以实现轨迹优化任务，而无需依靠系统模型的先验知识。通过对两项说明性控制任务的全面评估，在系统模型中存在不准确性的情况下，提出的方法显示出胜过常规ILQR。