Chung-Pin Chou

A guided Monte Carlo approach to optimization problems

Conversational agent is a system that provides user with proper information and maintains the context of dialogue based on natural language. When experts design the network for conversational agent of a domain, the network is usually very complicated and is hard to be understood. So the simplification of network by separating variables in the domain is helpful to design the conversational agent more efficiently. Composing Bayesian network as two stages, we aim to design conversational agent easily and analyze user’s query in detail. Also, by using previous information of dialogue, it is possible to maintain the context of conversation. Actually implementing it for a guide of web pages, we can confirm the usefulness of the proposed architecture for conversational agent.

Guided simulated annealing method for optimization problems

Incorporating the concept of order parameter of the mean-field theory into the simulated annealing method, we present an optimization algorithm, the guided simulated annealing method. In this method mean-field order parameters are calculated to guide the configuration search for the global minimum. Allowing fluctuations and improvement of mean-field values iteratively, this method successfully identifies global minima for several difficult optimization problems. Application of this method to the HP lattice-protein model has found another lowest-energy state for an N=100 sequence that was not found by other methods before. Results for spin glass models are also presented which show improvement over the previous results.

Spectral weights, d -wave pairing amplitudes, and particle-hole tunneling asymmetry of a strongly correlated superconductor

The spectral weights (SWs) for adding and removing an electron of the Gutzwiller projected

Mechanism of formation of half-doped stripes in underdoped cuprates

d

Low-energy spectra of the t-J-type models studied by variational approach

-wave superconducting (SC) state of the

Variational Monte Carlo simulations using tensor-product projected states

t\text{\ensuremath{-}}J

Matrix-product-based Projected Wave Functions Ansatz for Quantum Many-body Ground States

-type models are studied numerically on finite lattices. We restrict the study to the uniform system but exactly treat the strong correlation between electrons, we show that the product of weights is equal to the pairing amplitude squared, the same as in the weakly coupled case. In addition, we derive a rigorous relation of SW with doping in the electron doped system and obtain particle-hole asymmetry of the conductance-proportional quantity within the SC gap energy and, also, the anticorrelation between gap sizes and peak heights observed in tunneling spectroscopy on high

Grand canonical variational approach for the t − J model

{T}_{c}

Variational study on the cluster glass wave function for the 2D extended t – J model

cuprates.

Impurity scattering effects in STM studies of high-Tc superconductors

Using a variational Monte-Carlo approach with a recently proposed stripe wave function, we showed that the strong correlation included in a t-J-type model has essentially all the necessary ingredients to form these stripes with modulations of charge density, spin magnetization, and pair field. If a perturbative effect of electron-phonon coupling to renormalize the effective mass or the hopping rate of holes is considered with the model, we find the half-doped stripes, which has on the average one half of a hole in one period of charge modulation, to be most stable, energetic wise in the underdoped region,