arXiv subjects

Yukang Jiang

Publications and source records attributed to Yukang Jiang.

8 recordsLinked to original sources

arXiv2024

Reconstructing and Forecasting Marine Dynamic Variable Fields across Space and Time Globally and Gaplessly

Spatiotemporal projections in marine science are essential for understanding ocean systems and their impact on Earth's climate. However, existing AI-based and statistics-based inversion methods face challenges in leveraging ocean data, generating continuous outputs, and incorporating physical constraints. We propose the Marine Dynamic Reconstruction and Forecast Neural Networks (MDRF-Net), which integrates marine physical mechanisms and observed data to reconstruct and forecast continuous ocean temperature-salinity and dynamic fields. MDRF-Net leverages statistical theories and techniques, incorporating parallel neural network sharing initial layer, two-step training strategy, and ensemble methodology, facilitating in exploring challenging marine areas like the Arctic zone. We have theoretically justified the efficacy of our ensemble method and the rationality of it by providing an upper bound on its generalization error.The effectiveness of MDRF-Net's is validated through a comprehensive simulation study, which highlights its capability to reliably estimate unknown parameters. Comparison with other inversion methods and reanalysis data are also conducted, and the global test error is 0.455°C for temperature and 0.0714psu for salinity. Overall, MDRF-Net effectively learns the ocean dynamics system using physical mechanisms and statistical insights, contributing to a deeper understanding of marine systems and their impact on the environment and human use of the ocean.

stat.AP↗

arXiv2023

The impact of spatio-temporal travel distance on epidemics using an interpretable attention-based sequence-to-sequence model

Amidst the COVID-19 pandemic, travel restrictions have emerged as crucial interventions for mitigating the spread of the virus. In this study, we enhance the predictive capabilities of our model, Sequence-to-Sequence Epidemic Attention Network (S2SEA-Net), by incorporating an attention module, allowing us to assess the impact of distinct classes of travel distances on epidemic dynamics. Furthermore, our model provides forecasts for new confirmed cases and deaths. To achieve this, we leverage daily data on population movement across various travel distance categories, coupled with county-level epidemic data in the United States. Our findings illuminate a compelling relationship between the volume of travelers at different distance ranges and the trajectories of COVID-19. Notably, a discernible spatial pattern emerges with respect to these travel distance categories on a national scale. We unveil the geographical variations in the influence of population movement at different travel distances on the dynamics of epidemic spread. This will contribute to the formulation of strategies for future epidemic prevention and public health policies.

physics.soc-ph↗

arXiv2022

Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model

The paper proposes a time-varying parameter global vector autoregressive (TVP-GVAR) framework for predicting and analysing developed region economic variables. We want to provide an easily accessible approach for the economy application settings, where a variety of machine learning models can be incorporated for out-of-sample prediction. The LASSO-type technique for numerically efficient model selection of mean squared errors (MSEs) is selected. We show the convincing in-sample performance of our proposed model in all economic variables and relatively high precision out-of-sample predictions with different-frequency economic inputs. Furthermore, the time-varying orthogonal impulse responses provide novel insights into the connectedness of economic variables at critical time points across developed regions. We also derive the corresponding asymptotic bands (the confidence intervals) for orthogonal impulse responses function under standard assumptions.

econ.EM↗

arXiv2020

A Fast and Effective Method of Macula Automatic Detection for Retina Images

Retina image processing is one of the crucial and popular topics of medical image processing. The macula fovea is responsible for sharp central vision, which is necessary for human behaviors where visual detail is of primary importance, such as reading, writing, driving, etc. This paper proposes a novel method to locate the macula through a series of morphological processing. On the premise of maintaining high accuracy, our approach is simpler and faster than others. Furthermore, for the hospital's real images, our method is also able to detect the macula robustly.

eess.IV↗

arXiv2020

Computational analysis of pathological image enables interpretable prediction for microsatellite instability

Microsatellite instability (MSI) is associated with several tumor types and its status has become increasingly vital in guiding patient treatment decisions. However, in clinical practice, distinguishing MSI from its counterpart is challenging since the diagnosis of MSI requires additional genetic or immunohistochemical tests. In this study, interpretable pathological image analysis strategies are established to help medical experts to automatically identify MSI. The strategies only require ubiquitous Haematoxylin and eosin-stained whole-slide images and can achieve decent performance in the three cohorts collected from The Cancer Genome Atlas. The strategies provide interpretability in two aspects. On the one hand, the image-level interpretability is achieved by generating localization heat maps of important regions based on the deep learning network; on the other hand, the feature-level interpretability is attained through feature importance and pathological feature interaction analysis. More interestingly, both from the image-level and feature-level interpretability, color features and texture characteristics are shown to contribute the most to the MSI predictions. Therefore, the classification models under the proposed strategies can not only serve as an efficient tool for predicting the MSI status of patients, but also provide more insights to pathologists with clinical understanding.

stat.ML↗

arXiv2020

Correlation Coefficient Analysis of the Age of Information in Multi-Source Systems

This paper studies the age of information (AoI) on an information updating system such that multiple sources share one server to process packets of updated information. In such systems, packets from different sources compete for the server, and thus they may suffer from being interrupted, being backlogged, and becoming stale. Therefore, in order to grasp structures of such systems, it is crucially important to study a metric indicating a correlation of different sources. In this paper, we aim to analyze the correlation of AoIs on a single-server queueing system with multiple sources. As our contribution, we provide the closed-form expression of the correlation coefficient of the AoIs. To this end, we first derive the Laplace-Stieltjes transform of the stationary distribution of each AoI for the multiple sources. Some nontrivial properties on the systems are revealed from our analysis results.

cs.PF↗

arXiv2020

Joint performance analysis of ages of information in a multi-source pushout server

Age of information (AoI) has been widely accepted as a measure quantifying freshness of status information in real-time status update systems. In many of such systems, multiple sources share a limited network resource and therefore the AoIs defined for the individual sources should be correlated with each other. However, there are not found any results studying the correlation of two or more AoIs in a status update system with multiple sources. In this work, we consider a multi-source system sharing a common service facility and provide a framework to investigate joint performance of the multiple AoIs. We then apply our framework to a simple pushout server with multiple sources and derive a closed-form formula of the joint Laplace transform of the AoIs in the case with independent M/G inputs. We further show some properties of the correlation coefficient of AoIs in the two-source system.

cs.PF↗

arXiv2020

The Effects of Stringent Interventions for Coronavirus Pandemic

The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and societal importance to evaluate the effects of interventions in the pandemic of COVID-19. In this paper, with help of synthetic control method, regression discontinuity and a Susceptible-Infected and infectious without isolation-Hospitalized in isolation-Removed (SIHR) model, we evaluate the horizontal and longitudinal effects of stringent interventions implemented in Wenzhou, a representative urban city of China, where stringent interventions were enforced to curb its own epidemic situation with rapidly increasing newly confirmed cases. We found that there were statistically significant treatment effects of those stringent interventions which reduced the cumulative confirmed cases of COVID-19. Those reduction effects would increase over time. Also, if the stringent interventions were delayed by 2 days or mild interventions were implemented instead, the expected number of cumulative confirmed cases would have been nearly 2 times or 5 times of the actual number. The effects of stringent interventions are significant in mitigating the epidemic situation of COVID-19. The slower the interventions were implemented, the more severe the epidemic would have been, and the stronger the interventions would have been required.

physics.soc-ph↗