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Explores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.
Explores quantum state evolution, unitary operators, Heisenberg representation, spin precession, and observable averages.
Delves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.
Explores anoxygenic photosynthesis, redox potential, ATP generation, bacterial adaptation to low light, and the evolution of oxygenic photosynthesis.
Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
