arXiv subjects

Jiawei Long

Publications and source records attributed to Jiawei Long.

3 recordsLinked to original sources

arXiv2022

Magnet-free nonreciprocal metasurface for on-demand bi-directional phase modulation

Unconstrained by Lorentz reciprocity, nonreciprocal metasurfaces are uniquely capable of encoding distinctive optical functions on forward- and backward-propagating waves. The nonreciprocal metasurfaces reported to date require external electric or magnetic field biasing or rely on nonlinear effects, both of which are challenging to practically implement. Here, we propose and experimentally realize a magnet-free, linear, and passive nonreciprocal metasurface based on self-biased magnetic meta-atoms. Record transmittance up to 77% and operation angle reaching 64 degree are experimentally demonstrated. Moreover, on-demand bidirectional phase modulation in a "LEGO-like" manner is theoretically proposed and experimentally demonstrated, enabling a cohort of nonreciprocal functionalities such as microwave isolation, nonreciprocal beam steering, nonreciprocal focusing, and nonreciprocal holography. The design can also be extended to MHz and optical frequencies, taking advantage of the wide variety of self-biased gyrotropic materials available. We foresee that the nonreciprocal metasurfaces demonstrated in this work will have a significant practical impact for applications ranging from nonreciprocal antennas and radomes to full-duplex wireless communication and radar systems.

physics.optics↗

arXiv2021

Analyzing the State of COVID-19: Real-time Visual Data Analysis, Short-Term Forecasting, and Risk Factor Identification

The COVID-19 outbreak was initially reported in Wuhan, China, and it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO. It has now spread to over 180 countries, and it has gradually evolved into a worldwide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. To accomplish this objective, I have built a website to analyze and deliver the latest state of the disease and relevant analytical insights. The website is designed to cater to the general audience, and it aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical methods, accurate data modeling, state-of-the-art natural language processing techniques, and reliable data sources. This paper discusses the major methodologies which are utilized to generate the insights displayed on the website, which include an automatic data ingestion pipeline, normalization techniques, moving average computation, ARIMA time-series forecasting, and logistic regression models. In addition, the paper highlights key discoveries that have been derived in regard to COVID-19 using the methodologies.

stat.AP↗

arXiv2019

Cluster Analysis of High-Dimensional scRNA Sequencing Data

With ongoing developments and innovations in single-cell RNA sequencing methods, advancements in sequencing performance could empower significant discoveries as well as new emerging possibilities to address biological and medical investigations. In the study, we will be using the dataset collected by the authors of Systematic comparative analysis of single cell RNA-sequencing methods. The dataset consists of single-cell and single nucleus profiling from three types of samples - cell lines, peripheral blood mononuclear cells, and brain tissue, which offers 36 libraries in six separate experiments in a single center. Our quantitative comparison aims to identify unique characteristics associated with different single-cell sequencing methods, especially among low-throughput sequencing methods and high-throughput sequencing methods. Our procedures also incorporate evaluations of every method's capacity for recovering known biological information in the samples through clustering analysis.

stat.AP↗