arXiv subjects

Shuo Chen

Publications and source records attributed to Shuo Chen.

183 records · Page 11Linked to original sources

arXiv2016

Generation and Detection of Surface Plasmon Polaritons by Transition Metal Dichalcogenides for Chip-level Electronic-Photonic Integrated Circuits

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. After discussing the material aspect of the challenge, we identified atomically thin transition metal dichalcogenides (TMDs) as a perfect material platform to implement the circuit. The selection of TMDs is based on their very distinct property: monolayer TMDs are able to emit and absorb light at the same wavelength determined by direct exciton transitions. To prove the concept, we fabricated simple devices consisting of silver nanowires as plasmonic waveguides and monolayer TMDs as active optoelectronic media. Using photoexcitation, direct optical imaging and spectral analysis, we demonstrated generation and detection of surface plasmon polaritons by monolayer TMDs. Regarded as novel materials for electronics and photonics, transition metal dichalcogenides are expected to find new applications in next generation integrated circuits.

cond-mat.mes-hall↗

arXiv2016

Differentially Expressed Functional Connectivity Networks with K-partite Graph Topology

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. In this paper, we propose a novel statistical approach to fulfill this goal by leveraging the topological structure of differentially expressed functional connections or edges in a graphical representation. We propose a new algorithm to automatically detect the latent topology of a k-partite graph structure, and we also provide statistical inferential techniques to test the detected topology. We evaluate our new methods via extensive numerical studies. We also apply our new approach to resting state fMRI data (24 cases and 18 controls) for Parkinson's disease research. The detected connectivity network biomaker with the k-partite graph topological structure reveals underlying neural features distinguishing Parkinson's disease patients from healthy control subjects.

q-bio.NC↗

arXiv2015

Automatic Region-wise Spatially Varying Coefficient Regression Model: an Application to National Cardiovascular Disease Mortality and Air Pollution Association Study

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. The automatic region-wise spatially varying coefficient model consists three parts: we first compute the similarity matrix between the exposure-health outcome associations of all spatial units, then segment the whole map into a set of disjoint regions based on the adjacency matrix with constraints that all spatial units within a region are contiguous and have similar association, and lastly estimate the region specific associations between exposure and health outcome. We implement the framework by using regression and spectral graph techniques. We develop goodness of fit criteria for model assessment and model selection. The simulation study confirms the satisfactory performance of our model. We further employ our method to investigate the association between airborne particulate matter smaller than 2.5 microns (PM 2.5) and cardiovascular disease mortality. The results successfully identify regions with distinct associations between the mortality rate and PM 2.5 that may provide insightful guidance for environmental health research.

stat.AP↗