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M. Reyes

Publications and source records attributed to M. Reyes.

16 recordsLinked to original sources
arXiv2025

The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information

Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added value of conventional radiomics (CR) and deep learning (DL) MRI radiomics for glioblastoma prognosis (<= 6 vs > 6 months survival) on a large multi-center dataset. Methods: After patient selection, our curated dataset gathers 1152 glioblastoma (WHO 2016) patients from five Swiss centers and one public source. It included clinical (age, gender), molecular (MGMT, IDH), and baseline MRI data (T1, T1 contrast, FLAIR, T2) with tumor regions. CR and DL models were developed using standard methods and evaluated on internal and external cohorts. Sub-analyses assessed models with different feature sets (imaging-only, clinical/molecular-only, combined-features) and patient subsets (S-1: all patients, S-2: with molecular data, S-3: IDH wildtype). Results: The best performance was observed in the full cohort (S-1). In external validation, the combined-feature CR model achieved an AUC of 0.75, slightly, but significantly outperforming clinical-only (0.74) and imaging-only (0.68) models. DL models showed similar trends, though without statistical significance. In S-2 and S-3, combined models did not outperform clinical-only models. Exploratory analysis of CR models for overall survival prediction suggested greater relevance of imaging data: across all subsets, combined-feature models significantly outperformed clinical-only models, though with a modest advantage of 2-4 C-index points. Conclusions: While confirming the predictive value of anatomical MRI sequences for glioblastoma prognosis, this multi-center study found standard CR and DL radiomics approaches offer minimal added value over demographic predictors such as age and gender.

cs.LG↗
arXiv2024

Non-formally integrable centers admitting an algebraic inverse integrating factor

Westudy the existence of a class of inverse integrating factor for a family of non formally integrable systems, in general, whose lowest-degree quasi-homogeneous term is a Hamiltonian vector field. Once the existence of an inverse integrat ing factor is established, we characterize the systems having a center. Among others, we characterize the centers of the systems whose lowest-degree quasiho mogeneous term is (-y3,x3)T with an algebraic inverse integrating factor. Keywords: Nonlinear differential systems, Inverse integrating factor, Integrability problem, Degenerate center problem

math.DS↗
arXiv2022

Ensembles of Vision Transformers as a New Paradigm for Automated Classification in Ecology

Monitoring biodiversity is paramount to manage and protect natural resources. Collecting images of organisms over large temporal or spatial scales is a promising practice to monitor the biodiversity of natural ecosystems, providing large amounts of data with minimal interference with the environment. Deep learning models are currently used to automate classification of organisms into taxonomic units. However, imprecision in these classifiers introduces a measurement noise that is difficult to control and can significantly hinder the analysis and interpretation of data. {We overcome this limitation through ensembles of Data-efficient image Transformers (DeiTs), which not only are easy to train and implement, but also significantly outperform} the previous state of the art (SOTA). We validate our results on ten ecological imaging datasets of diverse origin, ranging from plankton to birds. On all the datasets, we achieve a new SOTA, with a reduction of the error with respect to the previous SOTA ranging from 29.35% to 100.00%, and often achieving performances very close to perfect classification. Ensembles of DeiTs perform better not because of superior single-model performances but rather due to smaller overlaps in the predictions by independent models and lower top-1 probabilities. This increases the benefit of ensembling, especially when using geometric averages to combine individual learners. While we only test our approach on biodiversity image datasets, our approach is generic and can be applied to any kind of images.

cs.CV↗
arXiv2021

Deep Learning Classification of Lake Zooplankton

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances,which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

cs.CV↗
arXiv2021

Underwater dual-magnification imaging for automated lake plankton monitoring

We present an approach for automated in-situ monitoring of phytoplankton and zooplankton communities based on a dual magnification dark-field imaging microscope/camera. We describe the Dual Scripps Plankton Camera (DSPC) system and associated image processing, and assess its capabilities in detecting and characterizing plankton species of different size and taxonomic categories, and in measuring their abundances in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scale with the same measurements derived by traditional microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland), delivered images of plankton individuals, colonies, and heterospecific aggregates without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior at hourly timescales. The DSPC was able to track the dynamics of taxa in the size range between ~10 $μ$m to ~ 1 cm, covering virtually all the components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances, despite imaging limitations in detecting small phytoplankton species and rare and large zooplankton taxa (e.g. carnivorous zooplankton). The most significant disagreements between traditional methods and the DSPC resided in the measurements of community properties of zooplankton, organisms that are heterogeneously distributed spatially and temporally, and whose demography appeared to be better captured by automated imaging. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and circadian fluctuations with a temporal frequency and morphological [continues...]

q-bio.PE↗
arXiv2017

Incorporating the Environmental Dimension into Multidimensional Poverty Measurement: An Initial Proposition

Multidimensional poverty measurement has captured the attention of policy-makers and researchers during recent years. Mexico is one of the most advanced countries in the measurement of poverty beyond income indicators. However, both in Mexico and other countries which have attempted at measuring poverty from a multidimensional perspective, the environmental dimension, has been well under-represented. Based on international evidence and, using the welfare-rights based methodological framework used in Mexico to measure multi-dimensional poverty officially, the paper proposes an indicator and six sub-indicators for measuring the lack of a minimum welfare to fulfil the right to a healthy environment through 6 sub-indicators measuring effective access, quality and continuity with regards to water, energy, biodiversity, air, spatial health, waste management and the vulnerability to poverty from climate change impacts.

physics.soc-ph↗
arXiv2017

On-sky demonstration of matched filters for wavefront measurements using ELT-scale elongated laser guide stars

The performance of adaptive optics systems is partially dependant on the algorithms used within the real-time control system to compute wavefront slope measurements. We demonstrate use of a matched filter algorithm for the processing of elongated laser guide star (LGS) Shack-Hartmann images, using the CANARY adaptive optics instrument on the 4.2m William Herschel Telescope and the European Southern Observatory Wendelstein LGS Unit placed 40m away. This algorithm has been selected for use with the forthcoming Thirty Meter Telescope, but until now had not been demonstrated on-sky. From the results of a first observing run, we show that the use of matched filtering improves our adaptive optics system performance, with increases in on-sky H-band Strehl measured up to about a factor of 1.1 with respect to a conventional centre of gravity approach. We describe the algorithm used, and the methods that we implemented to enable on-sky demonstration.

astro-ph.IM↗
arXiv2003

On Non-Abelian Holonomies

We provide a method and the results for the calculation of the holonomy of a Yang-Mills connection in an arbitrary triangular path, in an expansion (developed here to fifth order) in powers of the corresponding segments. The results might have applications in generalizing to Yang-Mills fields previous calculations of the corrections to particle dynamics induced by loop quantum gravity, as well as in the field of random lattices.

hep-th↗
arXiv2002

Second-order linear differential equations associated to a Riccati equation of constant coefficients

We present several second-order linear differential equations that are associated to a particular Riccati equation with only one constant parameter in its coefficients through the technique of supersymmetric factorizations and through a Dirac-like procedure. The latter approach is a minimal extension of the results obtained with the first technique in the sense that it includes up to two more constant parameters

math-ph↗
arXiv2002

Linear second-order differential equations for barotropic FRW cosmologies

Simple linear second-order differential equations have been written down for FRW cosmologies with barotropic fluids by Faraoni. His results have been extended by Rosu, who employed techniques belonging to nonrelativistic supersymmetry to obtain time-dependent adiabatic indices. Further extensions are presented here using the known connection between the linear second-order differential equations and Dirac-like equations in the same supersymmetric context. These extensions are equivalent to adding an imaginary part to the adiabatic index which is proportional to the mass parameter of the Dirac spinor. The natural physical interpretation of the imaginary part is related to the particular dissipation and instabilities of the barotropic FRW hydrodynamics that are introduced by means of this supersymmetric scheme

gr-qc↗
arXiv2000

Ermakov approach for Q=0 empty FRW minisuperspace oscillators

The Wheeler-DeWitt equation for empty FRW minisuperspace universes of Hartle-Hawking factor ordering parameter Q=0 is mapped onto the dynamics of a unit mass classical oscillator. The latter is studied by the classical Ermakov invariant method. Angle quantities are presented in the same context

gr-qc↗
arXiv1998

Supersymmetry of Demkov-Ostrovsky effective potentials in the R_0=0 sector

We present a supersymmetric analysis of the wave problem with a Demkov-Ostrovsky spherically symmetric class of focusing potentials at zero energy. Following a suggestion of Lévai, we work in the so-called R_0=0 sector in order to obtain the superpartner (fermionic) potentials within Witten's supersymmetric procedure. General solutions of the superpotential for the known physical cases are given explicitly.

quant-ph↗
arXiv1997

Bistability for asymmetric discrete random walks

We show that asymmetric time-continuous discrete random walks can display bistability for equal values of Jauslin's shifting parameters. The bistability becomes more pronounced at increased asymmetry parameter

cond-mat↗
arXiv1996

Electromagnetic modes of Maxwell fisheye lens

We provide an analysis of the radial structure of TE and TM modes of the Maxwell fisheye lens, by means of Maxwell equations as applied to the fisheye case. Choosing a lens of size R = 1 cm, we plot some of the modes in the infrared range.

atom-ph↗
arXiv1996

Supersymmetric features of the Maxwell fish-eye lens

We provide a supersymmetric analysis of the Maxwell fisheye (MF) wave problem at zero energy. Working in the so-called $R_{0}=0$ sector, we obtain the corresponding superpartner (fermionic) MF effective potential within Witten's one-dimensional (radial) supersymmetric procedure.

quant-ph↗