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Found 9 papers, 2 papers with code
Operator-Based Detecting, Learning, and Stabilizing Unstable Periodic Orbits of Chaotic Attractors
This paper examines the use of operator-theoretic approaches to the analysis of chaotic systems through the lens of their unstable periodic orbits (UPOs).
Characterizing the load profile in power grids by Koopman mode decomposition of interconnected dynamics
We emphasize that the load dynamics are constructed based on coherent spatiotemporal patterns that are intrinsic to the dynamics and are capable of encoding rich dynamical features at multiple time scales.
Leveraging Wastewater Monitoring for COVID-19 Forecasting in the US: a Deep Learning study
We supplement the daily confirmed cases with viral loads and other socio-economic factors as covariates to the models.
Using Machine Learning to Evaluate Real Estate Prices Using Location Big Data
Past researchers have been known to utilize static real estate data (e. g. number of beds, baths, square footage) or even a combination of real estate and demographic information to predict property prices.
GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type Identification
Various tools exist to facilitate this processing but need to be organized to standardize the workflow from data wrangling to visualization, cell type identification, and analysis of changes in cellular activity, both from the standpoint of malignant cells and immune stromal cells that eliminate them.
A purely data-driven framework for prediction, optimization, and control of networked processes: application to networked SIS epidemic model
Networks are landmarks of many complex phenomena where interweaving interactions between different agents transform simple local rule-sets into nonlinear emergent behaviors.
A new method for quantifying network cyclic structure to improve community detection
A distinguishing property of communities in networks is that cycles are more prevalent within communities than across communities.
Designing Optimal Multiplex Networks for Certain Laplacian Spectral Properties
However, for larger budgets, the optimal weights are generally non-uniform.
Networking and Internet Architecture Social and Information Networks Optimization and Control
New methods for incorporating network cyclic structures to improve community detection
We investigate the use of two methods for quantifying this richness---loop modulus (LM) and renewal non-backtracking random walks (RNBRW)---to improve the performance of existing community detection algorithms.
Social and Information Networks Physics and Society
