Hidden Markov models reveal brain state dynamics from EEG data.
problem Analyzing brain network dynamics from EEG data.
method Hidden Markov model vs. classical microstate analysis.
result Both approaches identify similar state topographies but differ in state lifetimes and temporal properties.
New method shows stable phase synchronised patterns in EEG signals during face perception tasks.
problem Traditional phase synchronisation measures do not capture temporal evolution.
method Proposes a new method to identify synchrostates, small sets of unique phase synchronised patterns.
result Consistent existence of synchrostates in multi-channel EEG recordings across different subject groups.
We propose the use of the functional determinant of geometric operators in constructing an entropy functional associated to geometric flows. Our approach is based on the direct computation of the partition function, with a well-defined set of microstates and macrostates in the canonical ensemble. The approach is motiva…
Geometry of hypersurfaces defined by the relation which generalizes classical formula for free energy in terms of microstates is studied. Induced metric, Riemann curvature tensor, Gauss-Kronecker curvature and associated entropy are calculated. Special class of ideal statistical hypersurfaces is analyzed in details. No…
New method uses HDP-HMM and multitaper spectral estimation for automated sleep state classification.
problem Manual sleep scoring is subjective, time-consuming, and doesn't capture neural dynamics.
method Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) with multitaper spectral estimation.
result Automated algorithm recovers sleep dynamics and identifies subject-specific microstates.
General equilibrium equations in economics play the same role with many-body Newtonian equations in physics. Accordingly, each solution of the general equilibrium equations can be regarded as a possible microstate of the economic system. Since Arrow's Impossibility Theorem and Rawls' principle of social fairness will p…
Novel algorithm detects causal macrovariables from high-dimensional data.
problem Leveraging high-dimensional observational datasets for coarse-grained causal models.
method Inspired by information bottlenecks, novel algorithm detects macrovariables and investigates causal relationships through additive noise models.
result Algorithm robustly detects and infers causal relationships in both synthetic and real climate datasets.
A novel method clusters protein conformations from MD simulations.
problem Clustering long MD protein dynamics for identifying states and behavior.
method Adversarial Autoencoder (AAE) for conformation clustering.
result Identifies many salient features of the folding process.
Black holes offer insights into machine learning's loss landscapes.
problem Understanding the loss landscape in machine learning.
method Comparing machine learning loss landscapes to black hole entropy.
result Black holes provide an infinite family of potential landscapes with known minima.
This paper uses entropy to derive stock price dynamics and option valuation.
problem Deriving stock price dynamics and option valuation from information constraints.
method Develops an entropic inference framework to derive stochastic processes from information constraints, representing price changes through two channels: continuous and jump.
result The derived dynamics is the Merton jump diffusion, with Geometric Brownian Motion as the no jump limit.
New method calibrates ABMs using graph neural networks for microdata.
problem Calibrating ABMs to granular microdata with high-dimensional learning tasks.
method Temporal graph neural networks for learning parameter posteriors.
result Graph neural networks offer inductive biases for Bayesian inference with ABM microstates.