Contextual Information Triggered by Deep Click

Users often open new browser tabs or applications to look up something, e.g., additional information on a word found in the present tab. Such lookup of contextual information requires the user to frequently jump out of and return to the present tab or application, which is a tedious and distracting process. This disclosure describes techniques that provide user-interface cards that include contextual information based on the cursor position at the instant of a deep click of a haptic trackpad or touchscreen

Collaborative Bi-Aggregation for Directed Graph Embedding

Directed graphs model asymmetric relationships between nodes and research on directed graph embedding is of great significance in downstream graph analysis and inference. Learning source and target embedding of nodes separately to preserve edge asymmetry has become the dominant approach, but also poses challenge for learning representations of low or even zero in/out degree nodes that are ubiquitous in sparse graphs. In this paper, a collaborative bi-directional aggregation method (COBA) for directed graphs embedding is proposed by introducing spatial-based graph convolution. Firstly, the source and target embeddings of the central node are learned by aggregating from the counterparts of the source and target neighbors, respectively; Secondly, the source/target embeddings of the zero in/out degree central nodes are enhanced by aggregating the counterparts of opposite-directional neighbors (i.e. target/source neighbors); Finally, source and target embeddings of the same node are correlated to achieve collaborative aggregation. Extensive experiments on real-world datasets demonstrate that the COBA comprehensively outperforms state-of-the-art methods on multiple tasks and meanwhile validates the effectiveness of proposed aggregation strategies

Structural Imbalance Aware Graph Augmentation Learning

Graph machine learning (GML) has made great progress in node classification, link prediction, graph classification and so on. However, graphs in reality are often structurally imbalanced, that is, only a few hub nodes have a denser local structure and higher influence. The imbalance may compromise the robustness of existing GML models, especially in learning tail nodes. This paper proposes a selective graph augmentation method (SAug) to solve this problem. Firstly, a Pagerank-based sampling strategy is designed to identify hub nodes and tail nodes in the graph. Secondly, a selective augmentation strategy is proposed, which drops the noisy neighbors of hub nodes on one side, and discovers the latent neighbors and generates pseudo neighbors for tail nodes on the other side. It can also alleviate the structural imbalance between two types of nodes. Finally, a GNN model will be retrained on the augmented graph. Extensive experiments demonstrate that SAug can significantly improve the backbone GNNs and achieve superior performance to its competitors of graph augmentation methods and hub/tail aware methods.Comment: 13 pages, 11 figures, 7 table

Improving the resilience of post-disaster water distribution systems using a dynamic optimization framework

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step towards sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events and in an organized manner, to prioritize the use of available resources to restore service rapidly whilst minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve resilience of a post-disaster WDS through identifying optimal sequencing of recovery actions. To address this gap, a new dynamic optimization framework is proposed here where the resilience of a post-disaster WDS is evaluated using six different metrics. A tailored Genetic Algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include: (i) the near-optimal sequencing of recovery strategy heavily depends on the damage properties of the WDS, (ii) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time, and (iii) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS

Transport of soft materials in crowded environment

Fluorescent microscopy was used to study the transport of soft material objects in crowded environment. Delaunay triangulation and wavelet transform were adapted to extract more information from images of macromolecules with irregular shapes and heterogeneous transportation dynamics. Systems studied are actively transported endosomes in living cells, and diffusing semi-flexible polymer chains in rigid networks. By going beyond traditional particle tracking and trajectory analysis, it was discovered that for protein transportation rather than directing the protein-containing endosomes steadily towards intended destination with regulatory mechanisms as commonly believed, efficient random search is an alternative mechanism that can offer both high energy efficiency and delivery accuracy. The imaging study of double-stranded DNA molecules in actin and agarose gel showed that the popular mental image of a snake sliding back and forth may not be how polymers actually reptate. The application of advanced analytical tools to high resolution microscopy images of dynamic systems is expected to lead to the discoveries of many more new mechanisms and concepts

The theory of the line profile based on the absorption of X-ray diffraction and its experimental demonstration

We have studied the theory of the X-ray diffraction (XRD) absorption peak profile (Liu, K. et al., Adv X-ray Anal, 2010, 54, 17-23) in detail by further theoretical derivation and by verification of the experimental line profile of a standard sample. It was obtained that the deviation between theory and experiment is less than 9% for the standard samples, by ignoring the line profiles in the range of diffraction angle less than 60°, for which the instrumental broadening could not be ignored. And the theoretical formula between FWHM and the Bragg angle 2θ was derived which can be called as the ARF. The results show that the Caglioti's relations should be replaced by the formula derived in this work