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9.9 MB |
Deep Learning Based Data Fusion Approaches for the Assessment of Cognitive States on EEG Signals
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Yauri Vidalón, José Elías ;
Hernàndez i Sabaté, Aura, dir. ;
Gil Resina, Debora, dir.
Durant mil·lennis, l'estudi de la parella cervell-ment ha fascinat la humanitat per entendre la naturalesa complexa dels estats cognitius. Un estat cognitiu és l'estat de la ment en un moment concret i implica activitats cognitives per adquirir i processar informació per prendre una decisió, resoldre un problema o assolir un objectiu. [...]
Por milenios, el estudio del par cerebro-mente ha fascinado a la humanidad con el fin de comprender la compleja naturaleza de los estados cognitivos. Un estado cognitivo es el estado de la mente en un momento específico e involucra todas las actividades cognitivas para adquirir y procesar información con el fin de tomar una decisión, resolver un problema o lograr un objetivo. [...]
For millennia, the study of the couple brain-mind has fascinated the humanity in order to understand the complex nature of cognitive states. A cognitive state is the state of the mind at a specific time and involves cognition activities to acquire and process information for making a decision, solving a problem, or achieving a goal. [...]
Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtica.
2023
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12 p, 4.0 MB |
Domain generalization in deep learning for contrast-enhanced imaging
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Sendra-Balcells, C. (Universitat de Barcelona) ;
Campello, Victor M (Universitat de Barcelona) ;
Martín-Isla, C. (Universitat de Barcelona) ;
Viladés Medel, David (Institut d'Investigació Biomèdica Sant Pau) ;
Descalzo, Martin (Institut d'Investigació Biomèdica Sant Pau) ;
Guala, Andrea (Hospital Universitari Vall d'Hebron) ;
Rodriguez-Palomares, Jose F (Hospital Universitari Vall d'Hebron) ;
Lekadir, K. (Universitat de Barcelona) ;
Universitat Autònoma de Barcelona
The domain generalization problem has been widely investigated in deep learning for non-contrast imaging over the last years, but it received limited attention for contrast-enhanced imaging. However, there are marked differences in contrast imaging protocols across clinical centers, in particular in the time between contrast injection and image acquisition, while access to multi-center contrast-enhanced image data is limited compared to available datasets for non-contrast imaging. [...]
2022 -  10.1016/j.compbiomed.2022.106052
Computers in Biology and Medicine, Vol. 149 (october 2022) , p. 106052
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4.
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13 p, 2.2 MB |
Reinforcement learning in videogames
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Villar Casino, Raúl ;
Casas Roma, Jordi, dir. (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació) ;
Universitat Autònoma de Barcelona.
Escola d'Enginyeria
This work aims to delve into the main Reinforcement Learning (RL) models and explore their potential in environments of varying complexity. As a starting point, an exhaustive review of the state-of-the-art was conducted, covering both tabular methods (Q-Learning, Value Iteration, Monte Carlo) and Deep RL (Deep Q-Learning, PPO). [...]
Aquest treball pretén aprofundir en els principals models d'aprenentatge per reforç (RL) i explorar el seu potencial en entorns de diversa complexitat. Com a punt de partida, es va realitzar una revisió exhaustiva de l'estat de l'art, abastant tant mètodes tabulars (Q-Learning, Value Iteration, MonteCarlo) com Deep RL (Deep Q-Learning, PPO). [...]
Este trabajo tiene como objetivo profundizar en los principales modelos de Aprendizaje por Refuerzo (RL) y explorar su potencial en entornos de diversa complejidad. Como punto de partida, se realizó una revisión exhaustiva del estado del arte, abarcando tanto métodos tabulares (Q-Learning, Iteración de Valores, Monte Carlo) como Aprendizaje por Refuerzo Profundo (Deep Q-Learning, PPO). [...]
2024
Enginyeria Informàtica [958]
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23 p, 7.8 MB |
Deep learning applications in single-cell genomics and transcriptomics data analysis
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Erfanian, Nafiseh (Birjand University of Medical Sciences, Birjand, Iran) ;
Heydari, A.Ali (University of California, USA) ;
Feriz, Adib Miraki (Birjand University of Medical Sciences, Birjand, Iran) ;
Iañez, Pablo (Institut Germans Trias i Pujol. Institut de Recerca contra la Leucèmia Josep Carreras) ;
Derakhshani, Afshin (University of Calgary, Canada) ;
Ghasemigol, Mohammad (University of North Dakota, USA) ;
Farahpour, Mohsen (University of Birjand, Iran) ;
Razavi, Seyyed Mohammad (University of Birjand, Iran) ;
Nasseri, Saeed (Birjand University of Medical Sciences, Iran) ;
Safarpour, Hossein (Birjand University of Medical Sciences, Iran) ;
Sahebkar, Amirhossein (Mashhad University of Medical Sciences, Mashhad, Iran)
Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. [...]
2023 - 10.1016/j.biopha.2023.115077
Biomedicine & pharmacotherapy, Vol. 165 (september 2023)
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15 p, 3.5 MB |
A Scalable Reduced-Complexity Compression of Hyperspectral Remote Sensing Images Using Deep Learning
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Mijares i Verdú, Sebastià (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ;
Ballé, Johannes (Google Research) ;
Laparra, Valero (Universitat de Valencia. Laboratori de Processament d'imatges) ;
Bartrina Rapesta, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ;
Hernández-Cabronero, Miguel (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ;
Serra Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especially when the number of bands is large. [...]
2023 - 10.3390/rs15184422
Remote sensing (Basel), Vol. 15, Issue 18 (September 2023) , art. 4422
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25 p, 6.4 MB |
Intelligent Control of Wastewater Treatment Plants Based on Model-Free Deep Reinforcement Learning
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Aponte-Rengifo, Oscar (Universidad de Salamanca. Departamento de Informática y Automática) ;
Francisco, Mario (Universidad de Salamanca. Departamento de Informática y Automática) ;
Vilanova i Arbós, Ramon (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes) ;
Vega, Pastora (Universidad de Salamanca. Departamento de Informática y Automática) ;
Revollar, Silvana (Universidad de Salamanca. Departamento de Informática y Automática)
In this work, deep reinforcement learning methodology takes advantage of transfer learning methodology to achieve a reasonable trade-off between environmental impact and operating costs in the activated sludge process of Wastewater treatment plants (WWTPs). [...]
2023 - 10.3390/pr11082269
Processes, Vol. 11, Issue 8 (August 2023) , art. 2269
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55 p, 707.1 KB |
Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology
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Pitarch, Carla (Universitat Politècnica de Catalunya. Departament d'Informàtica) ;
Ungan, Gulnur Semahat (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular) ;
Julià Sapé, Ma. Margarita (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí") ;
Vellido, Alfredo (Universitat Politècnica de Catalunya. Departament d'Informàtica)
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. [...]
2024 - 10.3390/cancers16020300
Cancers, Vol. 16, Issue 2 (January 2024) , art. 300
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23 p, 337.7 KB |
Who counts in poverty research?
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Edmiston, Daniel (University of Leeds)
Mainstream poverty analysis currently renders certain people and degrees of privation more socially legible than others across high-income countries. This article examines how these hierarchies carry through to and corrupt wider social scientific analysis, inscribing differential value to actors and phenomena in ways that undermine social understanding and explanation. [...]
2023 - 10.1177/00380261231213
The Sociological Review, 2023, p. 1-23
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51 p, 2.3 MB |
Rewarding deep brain stimulation at the medial forebrain bundle favours avoidance conditioned response in a remote memory test, hinders extinction and increases neurogenesis
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Huguet, Gemma (Universitat de Girona. Departament de Biologia) ;
Kádár, Elisabet (Universitat de Girona. Departament de Biologia) ;
Serrano, Noelia (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de Ciències de la Salut) ;
Tapias-Espinosa, Carles (Universitat Autònoma de Barcelona. Departament de Psiquiatria i de Medicina Legal) ;
García-Brito, Soleil (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de Ciències de la Salut) ;
Morgado-Bernal, Ignacio (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de Ciències de la Salut) ;
Aldavert Vera, Laura (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de Ciències de la Salut) ;
Segura Torres, Pilar (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de Ciències de la Salut) ;
Universitat Autònoma de Barcelona.
Institut de Neurociències
Intracranial Self-Stimulation (ICSS) at the medial forebrain bundle consistently facilitates learning and memory in rats when administered post-training or when administered non-concurrent to training, but its scope regarding remote memory has not yet been studied. [...]
2020 - 10.1016/j.bbr.2019.112308
Behavioural brain researchh, Vol. 378 (2020) , p. 112308
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