arXiv subjects

Raul Moreno

Publications and source records attributed to Raul Moreno.

4 recordsLinked to original sources

arXiv2026

Speeding up the annotation process in semantic segmentation industrial applications

Current machine learning models commonly require large and well-annotated datasets. However, the annotation process often becomes a bottleneck, with increased complexity leading to higher chances of human errors. Within this context, our goal in this paper is to leverage unsupervised algorithms to improve data annotation efficiency for complex semantic segmentation problems in industrial materials science. Previous research has quantified labeling time and others explored unsupervised methods. However, to the best of our knowledge, this is the first study to quantify how much unsupervised algorithms accelerate the labeling process. We aim to validate the extent to which this laborious process can be accelerated, focusing on semantic segmentation tasks that involve annotating each pixel of high-resolution images, such as the microstructure characterization challenge in materials science. Specifically, we demonstrate that by using unsupervised computer vision algorithms, the time required for the labeling process can be reduced from 170 hours to 37 hours, achieving an approximate reduction of 78\%. The dataset we work with includes large images of dimensions 1280x959 and 960x703, which further increases the complexity of the annotation task. Despite these challenges, we create and share the largest public steel microstructure segmentation dataset to date, available under MIT License with permanent DOI, contributing a fully annotated, high-resolution dataset to the field. Additionally, this is the first work to compare the labeling time from scratch (a common approach in previous studies) to the labeling time when using these unsupervised algorithms as a pre-annotation step. Furthermore, we provide a Deep Learning model trained on this dataset, validated by field experts, and deployed in an industrial setting, serving as an initial benchmark for this public dataset.

cs.CV↗

arXiv2026

Do Climate Models Need Microphysical and Convective Parameterizations to Generate Accurate Precipitation Fields?

Accurately representing surface precipitation is crucial for the operational use of weather and climate models. Presently, global numerical weather prediction (NWP) models struggle to accurately generate precipitation due to their parametrization of unresolved deep convective clouds and, in regions of grid-resolved ascent, inadequate parameterizations of cloud microphysics. Here we bypass these parameterizations with a machine learning model that diagnoses precipitation from 13 ERA5 fields that are easily observed and assimilated, as opposed for example, to fields like rain or cloud liquid water. We train a pair of models; ML_ERA5 using ERA5 precipitation as the target, and ML_IMERG using a satellite based precipitation product. ML_ERA5 closely reproduces the ERA5 precipitation at all intensities. When evaluated against the satellite dataset, ML_IMERG closely matches observations, notably reproducing the diurnal cycle of the satellite product. ML_IMERG generally captures extremes better than ERA5 while also reducing ERA5's overproduction of light precipitation. When evaluated against a third ground-and-radar-based dataset, ML_IMERG inherits the strengths of the satellite dataset which is superior to ERA5 in the summer months.

physics.ao-ph↗

arXiv2024

A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate

A key challenge for computationally intensive state-of-the-art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000-year periods with no smoothing or drift. DLESyM simulations equal or exceed key metrics of seasonal and interannual variability--such as tropical cyclogenesis over the range of observed intensities, the cycle of the Indian Summer monsoon, and the climatology of mid-latitude blocking events--when compared to historical simulations from four leading models from the 6th Climate Model Intercomparison Project. DLESyM, trained on both historical reanalysis data and satellite observations, is an accurate, highly efficient model of the coupled Earth system, empowering long-range sub-seasonal and seasonal forecasts while using a fraction of the energy and computational time required by traditional models.

physics.ao-ph↗

arXiv2020

Partially Observable Markov Decision Process Modelling for Assessing Hierarchies

Hierarchical clustering has been shown to be valuable in many scenarios. Despite its usefulness to many situations, there is no agreed methodology on how to properly evaluate the hierarchies produced from different techniques, particularly in the case where ground-truth labels are unavailable. This motivates us to propose a framework for assessing the quality of hierarchical clustering allocations which covers the case of no ground-truth information. This measurement is useful, e.g., to assess the hierarchical structures used by online retailer websites to display their product catalogues. Our framework is one of the few attempts for the hierarchy evaluation from a decision-theoretic perspective. We model the process as a bot searching stochastically for items in the hierarchy and establish a measure representing the degree to which the hierarchy supports this search. We employ Partially Observable Markov Decision Processes (POMDP) to model the uncertainty, the decision making, and the cognitive return for searchers in such a scenario.

stat.ML↗