arXiv subjects

Hernan Aguirre

Publications and source records attributed to Hernan Aguirre.

5 recordsLinked to original sources

arXiv2026

Position-dependent tight-binding model for Li impurities in monolayer and bilayer graphene

Lithium adsorption and intercalation can significantly modify the low-energy electronic properties of graphene-based materials, making their characterization relevant for understanding Li-ion transport and storage in graphitic electrodes. In this work, we investigate the electronic structure of a Li ion adsorbed on monolayer graphene (MLG) and intercalated within AB-stacked bilayer graphene (BLG). To this end, we develop a semi-empirical tight-binding model that incorporates Li-position dependence. Its parameters are determined by fitting to density-functional-theory calculations for different configurations, heights, and supercell sizes. The obtained model accurately reproduces the electronic bands near the Fermi level for both MLG and BLG and provides a transparent interpretation of the impurity-induced modifications in terms of symmetry breakings, intervalley mixing, and band-gap openings. We find that the perturbation introduced by the Li ion is strongly localized and that its effect decreases with increasing supercell size. The fitted parameters further reveal systematic differences between MLG and BLG in the spatial profile of the impurity potential. The obtained results provide an efficient framework for studying dilute Li impurities and constitute a useful starting point for future investigations of Li diffusion and impurity-induced transport phenomena in graphene-based materials.

cond-mat.mes-hall↗

arXiv2018

Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network

Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of system parameters, such as hyper parameter tuning of machine learning based systems when being installed for a specific task. However, automation is often jeopardized by the need of strategy parameter tuning for BBDO algorithms. An expert with the domain knowledge must undergo time-consuming strategy parameter tuning. This paper proposes a parameterless BBDO algorithm based on information geometric optimization, a recent framework for black box optimization using stochastic natural gradient. Inspired by some theoretical implications, we develop an adaptation mechanism for strategy parameters of the stochastic natural gradient method for discrete search domains. The proposed algorithm is evaluated on commonly used test problems. It is further extended to two examples of simultaneous optimization of the hyper parameters and the connection weights of deep learning models, leading to a faster optimization than the existing approaches without any effort of parameter tuning.

cs.LG↗

arXiv2018

Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork

This paper proposes a series of new approaches to improve Generative Adversarial Network (GAN) for conditional image synthesis and we name the proposed model as ArtGAN. One of the key innovation of ArtGAN is that, the gradient of the loss function w.r.t. the label (randomly assigned to each generated image) is back-propagated from the categorical discriminator to the generator. With the feedback from the label information, the generator is able to learn more efficiently and generate image with better quality. Inspired by recent works, an autoencoder is incorporated into the categorical discriminator for additional complementary information. Last but not least, we introduce a novel strategy to improve the image quality. In the experiments, we evaluate ArtGAN on CIFAR-10 and STL-10 via ablation studies. The empirical results showed that our proposed model outperforms the state-of-the-art results on CIFAR-10 in terms of Inception score. Qualitatively, we demonstrate that ArtGAN is able to generate plausible-looking images on Oxford-102 and CUB-200, as well as able to draw realistic artworks based on style, artist, and genre. The source code and models are available at: https://github.com/cs-chan/ArtGAN

cs.CV↗

arXiv2017

ArtGAN: Artwork Synthesis with Conditional Categorical GANs

This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces. The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated images) to the generator from the discriminator. With the feedback from the label information, the generator is able to learn faster and achieve better generated image quality. Empirically, we show that the proposed ARTGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.

cs.CV↗

arXiv2014

An Analysis on Selection for High-Resolution Approximations in Many-Objective Optimization

This work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set. Several search-assessment indicators are defined to trace the dynamics of survival selection and measure the ability to simultaneously keep optimal solutions and discover new ones under different population sizes, set as a fraction of the size of the Pareto optimal set.

cs.NE↗