Counting Process Based Dimension Reduction Methods for Censored Outcomes

We propose a class of dimension reduction methods for right censored survival data using a counting process representation of the failure process. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. The proposed method addresses two fundamental limitations of existing approaches. First, using the counting process formulation, it does not require any estimation of the censoring distribution to compensate the bias in estimating the dimension reduction subspace. Second, the nonparametric part in the estimating equations is adaptive to the structural dimension, hence the approach circumvents the curse of dimensionality. Asymptotic normality is established for the obtained estimators. We further propose a computationally efficient approach that simplifies the estimation equation formulations and requires only a singular value decomposition to estimate the dimension reduction subspace. Numerical studies suggest that our new approaches exhibit significantly improved performance for estimating the true dimension reduction subspace. We further conduct a real data analysis on a skin cutaneous melanoma dataset from The Cancer Genome Atlas. The proposed method is implemented in the R package "orthoDr".Comment: First versio

Surface relief grating near-eye display waveguide design

A near-eye display device (NED) is a visual optical system that places a miniature display in front of the human eye to provide an immersive viewing experience. NEDs have been playing an irreplaceable role in both early military flight applications and today's civil and entertainment applications. In this paper, we propose an easy-to-machine design of a near-eye display based on surface relief grating waveguides, taking into account the experience of previous designs of near-eye displays, the superior performance of the design, and the accuracy level of existing grating processing. The design is designed to meet the requirements of large field of view and large outgoing pupil extension as much as possible. The design is insensitive to the incident angle and achieves a full-field field-of-view angle of 40{\deg}, an angular uniformity error of 20% for diffraction efficiency, and an average diffraction efficiency of 80% for the full field of view. Based on the design, the overall simulation of the optical path of the NED device is completed, and the illumination uniformity of the outgoing pupil expansion of the device is analyzed through simulation.Comment: 12 pages; 10 figures; 2 table

Learning on Graphs with Out-of-Distribution Nodes

Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs. While existing GNNs have shown great performance on various tasks related to graphs, little attention has been paid to the scenario where out-of-distribution (OOD) nodes exist in the graph during training and inference. Borrowing the concept from CV and NLP, we define OOD nodes as nodes with labels unseen from the training set. Since a lot of networks are automatically constructed by programs, real-world graphs are often noisy and may contain nodes from unknown distributions. In this work, we define the problem of graph learning with out-of-distribution nodes. Specifically, we aim to accomplish two tasks: 1) detect nodes which do not belong to the known distribution and 2) classify the remaining nodes to be one of the known classes. We demonstrate that the connection patterns in graphs are informative for outlier detection, and propose Out-of-Distribution Graph Attention Network (OODGAT), a novel GNN model which explicitly models the interaction between different kinds of nodes and separate inliers from outliers during feature propagation. Extensive experiments show that OODGAT outperforms existing outlier detection methods by a large margin, while being better or comparable in terms of in-distribution classification.Comment: Accepted by KDD'2

Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation

Parallel randomized trial (RT) and real-world (RW) data are becoming increasingly available for treatment evaluation. Given the complementary features of the RT and RW data, we propose a test-based elastic integrative analysis of the RT and RW data for accurate and robust estimation of the heterogeneity of treatment effect (HTE), which lies at the heart of precision medicine. When the RW data are not subject to bias, e.g., due to unmeasured confounding, our approach combines the RT and RW data for optimal estimation by exploiting semiparametric efficiency theory. Utilizing the design advantage of RTs, we construct a built-in test procedure to gauge the reliability of the RW data and decide whether or not to use RW data in an integrative analysis. We characterize the asymptotic distribution of the test-based elastic integrative estimator under local alternatives, which provides a better approximation of the finite-sample behaviors of the test and estimator when the idealistic assumption required for the RW data is weakly violated. We provide a data-adaptive procedure to select the threshold of the test statistic that promises the smallest mean square error of the proposed estimator of the HTE. Lastly, we construct an elastic confidence interval that has a good finite-sample coverage property. We apply the proposed method to characterize who can benefit from adjuvant chemotherapy in patients with stage IB non-small cell lung cancer