no code implementations • 23 May 2024 • Jiecheng Lu, Yan Sun, Shihao Yang
Recent Transformer-based large language models (LLMs) demonstrate in-context learning ability to perform various functions based solely on the provided context, without updating model parameters.
no code implementations • 4 Mar 2024 • Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang
For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones.
no code implementations • 29 Feb 2024 • Jun-En Ding, Shihao Yang, Anna Zilverstand, Feng Liu
The excessive consumption of marijuana can induce substantial psychological and social consequences.
no code implementations • 14 Oct 2023 • Jiecheng Lu, Xu Han, Shihao Yang
Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual relationships.
1 code implementation • 4 Jul 2023 • Simin Ma, Junghwan Lee, Nicoleta Serban, Shihao Yang
Tailoring treatment for individual patients is crucial yet challenging in order to achieve optimal healthcare outcomes.
1 code implementation • 22 Dec 2022 • Zhaohui Li, Shihao Yang, Jeff Wu
Many methods for PDE parameter inference involve a large number of evaluations for numerical solutions to PDE through algorithms such as the finite element method, which can be time-consuming, especially for nonlinear PDEs.
no code implementations • 20 Dec 2022 • Samuel W. K. Wong, Shihao Yang, S. C. Kou
Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.
no code implementations • 3 Feb 2022 • Tao Wang, Simin Ma, Soobin Baek, Shihao Yang
As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decision on medical resources allocations such as ICU beds, ventilators, and personnel to prepare for the surge of COVID-19 pandemics.
no code implementations • 20 Jul 2021 • Philip Protter, Qianfan Wu, Shihao Yang
To capture the stimulating effects between multiple types of order book events, we use the multivariate Hawkes process to model the self- and mutually-exciting event arrivals.
no code implementations • 31 May 2021 • Shixiang Zhu, Alexander Bukharin, Liyan Xie, Khurram Yamin, Shihao Yang, Pinar Keskinocak, Yao Xie
Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease.
no code implementations • 27 May 2021 • Chaofan Huang, Simin Ma, Shihao Yang
Ordinary differential equations (ODEs), commonly used to characterize the dynamic systems, are difficult to propose in closed-form for many complicated scientific applications, even with the help of domain expert.
no code implementations • 3 May 2021 • Siawpeng Er, Shihao Yang, Tuo Zhao
The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has cast a significant threat to mankind.
1 code implementation • 16 Sep 2020 • Shihao Yang, Samuel W. K. Wong, S. C. Kou
MAGI uses a Gaussian process model over time-series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system.
Methodology Dynamical Systems
no code implementations • 4 Jun 2020 • Shihao Yang, Shaoyang Ning, S. C. Kou
ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people's Internet search pattern.
Applications
no code implementations • 5 May 2015 • Shihao Yang, Mauricio Santillana, S. C. Kou
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives.