Shuo Chen

Shuo Chen
Are you Shuo Chen?

Claim your profile, edit publications, add additional information:

Contact Details

Name
Shuo Chen
Affiliation
Location

Pubs By Year

Pub Categories

 
Statistics - Applications (4)
 
Computer Science - Cryptography and Security (2)
 
Quantitative Biology - Neurons and Cognition (2)
 
Physics - Optics (1)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (1)
 
Statistics - Machine Learning (1)
 
Computer Science - Learning (1)
 
Quantitative Biology - Populations and Evolution (1)
 
Computer Science - Distributed; Parallel; and Cluster Computing (1)
 
Mathematics - Dynamical Systems (1)

Publications Authored By Shuo Chen

The singular value decomposition (SVD) is a widely used matrix factorization tool which underlies plenty of useful applications, e.g. recommendation system, abnormal detection and data compression. Read More

Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Read More

In this work, we establish the response of scalar systems with multiple discrete delays based on the Laplace transform. The time response function is expressed as the sum of infinite series of exponentials acting on eigenvalues inside countable branches of the Lambert W functions. Eigenvalues in each branch of Lambert W function are computed by a numerical iteration. Read More

Neuropsychiatric disorders impact functional connectivity of the brain at the network level. The identification and statistical testing of disorder-related networks remains challenging. We propose novel methods to streamline the detection and testing of the hidden, disorder-related connectivity patterns as network-objects. Read More

Emerging brain network studies suggest that interactions between various distributed neuronal populations may be characterized by an organized complex topological structure. Many brain diseases are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to identify network-level differentially expressed connections that have low false positive rates, sufficient statistical power, and high reproducibility. Read More

In this paper, we consider to estimate network/community induced large covariance matrices. The massive biomedical data (e.g. Read More

Motivated by analyzing a national data base of annual air pollution and cardiovascular disease mortality rate for 3100 counties in the U.S. (areal data), we develop a novel statistical framework to automatically detect spatially varying region-wise associations between air pollution exposures and health outcomes. Read More

The monolithic integration of electronics and photonics has attracted enormous attention due to its potential applications. However, the realization of such hybrid circuits has remained a challenge because it requires optical communication at nanometer scales. A major challenge to this integration is the identification of a suitable material. Read More

This study aims to present an adaptive audio watermarking method using ideas of wavelet-based entropy (WBE). The method converts low-frequency coefficients of discrete wavelet transform (DWT) into the WBE domain, followed by the calculations of mean values of each audio as well as derivation of some essential properties of WBE. A characteristic curve relating the WBE and DWT coefficients is also presented. Read More