Publications de Fernando Perez Cruz

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Compound structural identification for non-targeted screening of organic molecules in complex mixtures is commonly carried out using liquid chromatography coupled to tandem mass spectrometry (UHPLC-HRMS/MS and related techniques). Instrumental developments ...

Type 2 diabetes mellitus (T2DM) is associated with the development of chronic comorbidities, which can lead to high drug utilization and adverse events. We aimed to identify common comorbidity clusters and explore the progression over time in newly treated ...

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dic ...

Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chai ...

This paper describes a pipeline for automatically generating level of detail (LOD) models (digital twins), specifically LOD2 and LOD3, from free-standing buildings. Our approach combines structure from motion (SfM) with deep-learning-based segmentation tec ...

Even today, the assessment of avalanche danger is by and large a subjective yet data-based decision-making process. Human experts analyse heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowada ...

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

A data-driven inverse design method based on neural networks is proposed for turbomachinery. In the devised methodology, design parameters are provided as input to the neural network, and performance attributes, e.g. efficiency, as output. Once trained, th ...

Deep Neural Networks (DNNs) trained on proprietary company data offer a competitive edge for the owning entity. However, these models can be attractive to competitors (or malicious entities), who can copy or clone these proprietary DNN models to use them t ...

Optoacoustic (OA) imaging is based on optical excitation of biological tissues with nanosecond-duration laser pulses and detection of ultrasound (US) waves generated by thermoelastic expansion following light absorption. The image quality and fidelity of O ...

Random access (RA) schemes are a topic of high interest in machine-type communication (MTC). In RA protocols, backoff techniques such as exponential backoff (EB) are used to stabilize the system to avoid low throughput and excessive delays. However, these ...

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data's inherent structure or context by solving a pretext task. With ...

Regulation of chemicals requires knowledge of their toxicological effects on a large number of species, which has traditionally been acquired through in vivo testing. The recent effort to find alternatives based on machine learning, however, has not focuse ...

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss ...

We present a design approach that uses machine learning to enhance architect's design experience. Nowadays, architects and engineers use software for parametric design to generate, simulate, and evaluate multiple design instances. In this paper, we propose ...

Fernando Perez Cruz

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Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry

Comorbidity clusters associated with newly treated type 2 diabetes mellitus: a Bayesian nonparametric analysis

PassGAN: A Deep Learning Approach for Password Guessing

Facilitated machine learning for image-based fruit quality assessment

Generating LOD3 building models from structure-from-motion and semantic segmentation

Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

DATA-DRIVEN INVERSE DESIGN METHOD FOR TURBOMACHINERY

TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding

Signal domain adaptation network for limited-view optoacoustic tomography

Deep Reinforcement Learning for Random Access in Machine-Type Communication

TOPO-Loss for continuity-preserving crack detection using deep learning

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification

Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Augmented intelligence for architectural design with conditional autoencoders: Semiramis case study