手机数据可以改善计划的目标吗？通过将来自阿富汗的“大推动”反贫困计划与计划受益人的详细手机日志结合在一起，我们研究了机器学习方法可以在多大程度上准确地区分有资格从不合格家庭中获得计划收益的超贫困家庭。我们表明，利用手机数据的机器学习方法可以识别超贫困家庭的准确性，几乎与基于调查的消费和财富量度一样准确。而将基于调查的措施与手机数据结合起来比基于单个数据源的措施更准确。

With the increasing use of Graph Neural Networks (GNNs) in critical real-world applications, several post hoc explanation methods have been proposed to understand their predictions. However, there has been no work in generating explanations on the fly during model training and utilizing them to improve the expressive power of the underlying GNN models. In this work, we introduce a novel explanation-directed neural message passing framework for GNNs, EXPASS (EXplainable message PASSing), which aggregates only embeddings from nodes and edges identified as important by a GNN explanation method. EXPASS can be used with any existing GNN architecture and subgraph-optimizing explainer to learn accurate graph embeddings. We theoretically show that EXPASS alleviates the oversmoothing problem in GNNs by slowing the layer wise loss of Dirichlet energy and that the embedding difference between the vanilla message passing and EXPASS framework can be upper bounded by the difference of their respective model weights. Our empirical results show that graph embeddings learned using EXPASS improve the predictive performance and alleviate the oversmoothing problems of GNNs, opening up new frontiers in graph machine learning to develop explanation-based training frameworks.

基准套件提供了对进化算法解决问题能力的有用度量，但是组成问题通常太复杂了，无法清洁算法的优势和劣势。在这里，我们介绍了基准套件档案（``进化运行中的选择方案的诊断概述''），以实证分析有关剥削和探索重要方面的选择方案。利用从根本上是攀岩，但我们考虑两种情况：纯剥削，可以独立优化表示形式中的每个位置，并且受到限制的利用，在该位置之间，由于位置之间的相互作用，向上进展更加有限。当优化路径不太清楚时，需要探索；我们认为能够遵循多个独立的爬山途径和跨健身山谷的能力。这些场景的每种组合都会产生独特的适应性景观，有助于表征与给定选择方案相关的进化动力学。我们分析了六个流行的选择方案。锦标赛的选择和截断选择都在剥削指标方面表现出色，但在需要探索时表现不佳；相反，新颖的搜索在探索方面表现出色，但未能利用梯度。在克服欺骗时，健身共享表现良好，但在所有其他诊断方面都很差。非主导的分类是维持由居住在多个Optima居住的个体组成的不同人群的最佳选择，但努力有效利用梯度。词汇酶选择平衡搜索空间探索而不牺牲剥削，通常在诊断方面表现良好。我们的工作证明了诊断对快速建立对选择方案特征的直观理解的价值，然后可以将其用于改进或开发新的选择方法。

Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a homophily assumption to learn embeddings with the guidance of supervisory signals, leaving the unsupervised heterophilous scenario relatively unexplored. This problem becomes especially relevant in fields where a scarcity of labels exists. Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily. Specifically, we design a dual-channel feature embedding pipeline to discriminate r-ego networks using node attributes and structural information separately. We employ heterophily adapted self-supervised learning objective functions to optimise the framework to learn intrinsic node embeddings. We show that SELENE's components improve the quality of node embeddings, facilitating the discrimination of connected heterophilous nodes. Comprehensive empirical evaluations on both synthetic and real-world datasets with varying homophily ratios validate the effectiveness of SELENE in homophilous and heterophilous settings showing an up to 12.52% clustering accuracy gain.

我们研究了具有神经网络控制器（NNC）的闭环动态系统的验证问题。此问题通常还原为计算可达状态集。在考虑动态系统和神经网络的隔离时，基于分别称为泰勒模型和Zonotopes的集合表示，该任务存在精确的方法。然而，这些方法对NNC的组合是非微不足道的，因为当在集合表示之间转换时，依赖性信息在每个控制周期中丢失，并且累积的近似误差快速使结果呈现。我们提出了一种基于泰勒模型和ZONotopes的链接近算法，得到了NNC的精确可达性算法。因为该算法仅在孤立方法的界面上起作用，所以适用于一般动态系统和神经网络，可以从这些领域的未来进展中受益。我们的实施提供了最先进的绩效，是第一个成功分析NNC年可达性竞争的所有基准问题。