Search Results for author:
Found 9 papers, 0 papers with code
Joint News, Attention Spillover,and Market Returns
We analyze over 2. 6 million news articles and propose a novel measure of aggregate joint news coverage of firms.
Dynamic Network Embeddings for Network Evolution Analysis
Network embeddings learn to represent nodes as low-dimensional vectors to preserve the proximity between nodes and communities of the network for network analysis.
Multivariate Spatial Data Visualization: A Survey
Multivariate spatial data plays an important role in computational science and engineering simulations.
A Co-analysis Framework for Exploring Multivariate Scientific Data
In this paper, we propose a co-analysis framework based on biclusters, which are two subsets of variables and voxels with close scalar-value relationships, to guide the process of visually exploring multivariate data.
Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations
We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs.
SPAN: Subgraph Prediction Attention Network for Dynamic Graphs
This paper proposes a novel model for predicting subgraphs in dynamic graphs, an extension of traditional link prediction.
A Time-Varying Network for Cryptocurrencies
To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities.
voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data
Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex.
MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation
Furthermore, the segmentation accuracy is improved by 2. 4%-5. 5% with the increasing volume of training data.
