arXiv subjects

Mauricio Flores

Publications and source records attributed to Mauricio Flores.

4 recordsLinked to original sources

arXiv2025

The evolution of PUCHEROS from a basic to a competitive tool for stellar astrophysics

We present PUCHEROS +, a new spectrograph developed as an enhanced version of PUCHEROS (Pontificia Universidad Catolica High Echelle Resolution Optical Spectrograph), which was the first high-resolution spectrograph built at the Pontificia Universidad Catolica de Chile (UC). With respect to its predecessor, PUCHEROS + includes a substantial number of improvements, mainly: a new scientific detector, improved objective optics, calibration system, guiding, active thermal control, and remote observing mode. These upgrades convert our early prototype into a much more powerful instrument for science. With a spectral resolution of R = 18000, a spectral range between 400 and 730 nm and an instrument efficiency of about 30 per cent, PUCHEROS + was tested at the ESO (European Southern Observatory) 1.52-m telescope where it has reached a limiting magnitude of about 12 in V band and radial velocity precision of about 30 m/s. The instrument was conceived as a pathfinder for the high-resolution echelle spectrograph PLATOSpec and at the same time, it demonstrates that a compact, relatively low-cost spectrograph can be efficiently employed for long-term monitoring campaigns and as support facility for space missions, in particular if operated remotely at relatively small- or medium-sized telescopes.

astro-ph.IM↗

arXiv2025

Transaction Categorization with Relational Deep Learning in QuickBooks

Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in this domain stem from the unique formatting of transaction descriptions, the wide variety of transaction categories, and the vast scale of the data involved. Furthermore, organizing transaction data in a relational database creates difficulties in developing a unified model that covers the entire database. In this work, we develop a novel graph-based model, named Rel-Cat, which is built directly over the relational database. We introduce a new formulation of transaction categorization as a link prediction task within this graph structure. By integrating techniques from natural language processing and graph machine learning, our model not only outperforms the existing production model in QuickBooks but also scales effectively to a growing customer base with a simpler, more effective architecture without compromising on accuracy. This design also helps tackle a key challenge of the cold start problem by adapting to minimal data.

cs.CE↗

arXiv2022

Analysis and algorithms for $\ell_p$-based semi-supervised learning on graphs

This paper addresses theory and applications of $\ell_p$-based Laplacian regularization in semi-supervised learning. The graph $p$-Laplacian for $p>2$ has been proposed recently as a replacement for the standard ($p=2$) graph Laplacian in semi-supervised learning problems with very few labels, where Laplacian learning is degenerate. In the first part of the paper we prove new discrete to continuum convergence results for $p$-Laplace problems on $k$-nearest neighbor ($k$-NN) graphs, which are more commonly used in practice than random geometric graphs. Our analysis shows that, on $k$-NN graphs, the $p$-Laplacian retains information about the data distribution as $p\to \infty$ and Lipschitz learning ($p=\infty$) is sensitive to the data distribution. This situation can be contrasted with random geometric graphs, where the $p$-Laplacian forgets the data distribution as $p\to \infty$. We also present a general framework for proving discrete to continuum convergence results in graph-based learning that only requires pointwise consistency and monotonicity. In the second part of the paper, we develop fast algorithms for solving the variational and game-theoretic $p$-Laplace equations on weighted graphs for $p>2$. We present several efficient and scalable algorithms for both formulations, and present numerical results on synthetic data indicating their convergence properties. Finally, we conduct extensive numerical experiments on the MNIST, FashionMNIST and EMNIST datasets that illustrate the effectiveness of the $p$-Laplacian formulation for semi-supervised learning with few labels. In particular, we find that Lipschitz learning ($p=\infty$) performs well with very few labels on $k$-NN graphs, which experimentally validates our theoretical findings that Lipschitz learning retains information about the data distribution (the unlabeled data) on $k$-NN graphs.

math.NA↗

arXiv2020

Learning Furniture Compatibility with Graph Neural Networks

We propose a graph neural network (GNN) approach to the problem of predicting the stylistic compatibility of a set of furniture items from images. While most existing results are based on siamese networks which evaluate pairwise compatibility between items, the proposed GNN architecture exploits relational information among groups of items. We present two GNN models, both of which comprise a deep CNN that extracts a feature representation for each image, a gated recurrent unit (GRU) network that models interactions between the furniture items in a set, and an aggregation function that calculates the compatibility score. In the first model, a generalized contrastive loss function that promotes the generation of clustered embeddings for items belonging to the same furniture set is introduced. Also, in the first model, the edge function between nodes in the GRU and the aggregation function are fixed in order to limit model complexity and allow training on smaller datasets; in the second model, the edge function and aggregation function are learned directly from the data. We demonstrate state-of-the art accuracy for compatibility prediction and "fill in the blank" tasks on the Bonn and Singapore furniture datasets. We further introduce a new dataset, called the Target Furniture Collections dataset, which contains over 6000 furniture items that have been hand-curated by stylists to make up 1632 compatible sets. We also demonstrate superior prediction accuracy on this dataset.

cs.CV↗