Individualized Multilayer Tensor Learning with An Application in Imaging Analysis

This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles (TMVs) are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction models assuming a homogeneous feature structure. We develop an innovative multilayer tensor learning method to incorporate heterogeneity to a higher-order tensor decomposition and predict disease status effectively through utilizing subject-wise imaging features and multimodality information. Specifically, we construct a multilayer decomposition which leverages an individualized imaging layer in addition to a modality-specific tensor structure. One major advantage of our approach is that we are able to efficiently capture the heterogeneous spatial features of signals that are not characterized by a population structure as well as integrating multimodality information simultaneously. To achieve scalable computing, we develop a new bi-level block improvement algorithm. In theory, we investigate both the algorithm convergence property, tensor signal recovery error bound and asymptotic consistency for prediction model estimation. We also apply the proposed method for simulated and human breast cancer imaging data. Numerical results demonstrate that the proposed method outperforms other existing competing methods

Seismic analysis of 70 Ophiuchi A: A new quantity proposed

The basic intent of this paper is to model 70 Ophiuchi A using the latest asteroseismic observations as complementary constraints and to determine the fundamental parameters of the star. Additionally, we propose a new quantity to lift the degeneracy between the initial chemical composition and stellar age. Using the Yale stellar evolution code (YREC7), we construct a series of stellar evolutionary tracks for the mass range $M$ = 0.85 -- 0.93 $M_{\odot}$ with different composition $Y_{i}$ (0.26 -- 0.30) and $Z_{i}$ (0.017 -- 0.023). Along these tracks, we select a grid of stellar model candidates that fall within the error box in the HR diagram to calculate the theoretical frequencies, the large- and small- frequency separations using the Guenther's stellar pulsation code. Following the asymptotic formula of stellar $p$-modes, we define a quantity $r_{01}$ which is correlated with stellar age. Also, we test it by theoretical adiabatic frequencies of many models. Many detailed models of 70 Ophiuchi A have been listed in Table 3. By combining all non-asteroseismic observations available for 70 Ophiuchi A with these seismological data, we think that Model 60, Model 125 and Model 126, listed in Table 3, are the optimum models presently. Meanwhile, we predict that the radius of this star is about 0.860 -- 0.865 $R_{\odot}$ and the age is about 6.8 -- 7.0 Gyr with mass 0.89 -- 0.90 $M_{\odot}$. Additionally, we prove that the new quantity $r_{01}$ can be a useful indicator of stellar age.Comment: 23 pages, 5 figures, accepted by New Astronom

Asteroseismic study of solar-like stars: A method of estimating stellar age

Asteroseismology, as a tool to use the indirect information contained in stellar oscillations to probe the stellar interiors, is an active field of research presently. Stellar age, as a fundamental property of star apart from its mass, is most difficult to estimate. In addition, the estimating of stellar age can provide the chance to study the time evolution of astronomical phenomena. In our poster, we summarize our previous work and further present a method to determine age of low-mass main-sequence star.Comment: 2 pages, 1 figures, submitted to IAUS25