Zobrazeno 1 - 10
of 1 273
pro vyhledávání: '"Silva, Mariana P."'
We investigate the utility of diffusion generative models to efficiently synthesise datasets that effectively train deep learning models for image analysis. Specifically, we propose novel $\Gamma$-distribution Latent Denoising Diffusion Models (LDMs)
Externí odkaz:
http://arxiv.org/abs/2409.19371
In mathematical proof education, there remains a need for interventions that help students learn to write mathematical proofs. Research has shown that timely feedback can be very helpful to students learning new skills. While for many years natural l
Externí odkaz:
http://arxiv.org/abs/2406.10268
Autor:
Munroe, Lindsay, da Silva, Mariana, Heidari, Faezeh, Grigorescu, Irina, Dahan, Simon, Robinson, Emma C., Deprez, Maria, So, Po-Wah
Clinical adoption of deep learning models has been hindered, in part, because the black-box nature of neural networks leads to concerns regarding their trustworthiness and reliability. These concerns are particularly relevant in the field of neuroima
Externí odkaz:
http://arxiv.org/abs/2406.17792
Autor:
Crespo, Maria Clara Ramos Morales, Rocha, Maria Lina de Souza Jeannine, Sturzeneker, Mariana Lourenço, Serras, Felipe Ribas, de Mello, Guilherme Lamartine, Costa, Aline Silva, Palma, Mayara Feliciano, Mesquita, Renata Morais, Guets, Raquel de Paula, da Silva, Mariana Marques, Finger, Marcelo, de Sousa, Maria Clara Paixão, Namiuti, Cristiane, Monte, Vanessa Martins do
This paper presents the first publicly available version of the Carolina Corpus and discusses its future directions. Carolina is a large open corpus of Brazilian Portuguese texts under construction using web-as-corpus methodology enhanced with proven
Externí odkaz:
http://arxiv.org/abs/2303.16098
Autor:
Dierle, Julia, Brown, Adam, Fischer, Horst, Glade-Beucke, Robin, Grigat, Jaron, Kuger, Fabian, Lindemann, Sebastian, Silva, Mariana Rajado, Schumann, Marc
The continuous emanation of $^{222}$Rn from detector surfaces causes the dominant background in current liquid xenon time projection chambers (TPCs) searching for dark matter. A significant reduction is required for the next generation of detectors w
Externí odkaz:
http://arxiv.org/abs/2209.00362
Autor:
Baur, Daniel, Bismark, Alexander, Brown, Adam, Dierle, Julia, Fischer, Horst, Glade-Beucke, Robin, Grigat, Jaron, Kaminsky, Basho, Kuger, Fabian, Lindemann, Sebastian, Masson, Darryl, Meinhardt, Patrick, Silva, Mariana Rajado, Schumann, Marc, Tönnies, Florian, Toschi, Francesco
Publikováno v:
JINST 18 T02004 (2023)
XeBRA is a flexible cryogenic platform designed to perform research and development for liquid xenon detectors searching for rare events. Its extra-large outer cryostat makes it possible to install a wide variety of detector designs. We present the s
Externí odkaz:
http://arxiv.org/abs/2208.14815
Autor:
Da Silva, Mariana, Sudre, Carole H., Garcia, Kara, Bass, Cher, Cardoso, M. Jorge, Robinson, Emma C.
Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and
Externí odkaz:
http://arxiv.org/abs/2108.08214
Autor:
Silva, Mariana P., Whitehead, Caragh, Ordonio, Reynante L., Fernando, Trinidad C., Castillo, Mark Philip B., Ordonio, Jeremias L., Larson, Tony, Upton, Daniel J., Hartley, Susan E., Gomez, Leonardo D.
Publikováno v:
In Biomass and Bioenergy March 2024 182
Autor:
Bass, Cher, da Silva, Mariana, Sudre, Carole, Williams, Logan Z. J., Tudosiu, Petru-Daniel, Alfaro-Almagro, Fidel, Fitzgibbon, Sean P., Glasser, Matthew F., Smith, Stephen M., Robinson, Emma C.
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based o
Externí odkaz:
http://arxiv.org/abs/2103.02561
Autor:
da Silva, Mariana, Garcia, Kara, Sudre, Carole H., Bass, Cher, Cardoso, M. Jorge, Robinson, Emma
We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations. Using prescribed maps of local atrophy and growth as input, the network learns to deform images according to a Neo-Hookean mo
Externí odkaz:
http://arxiv.org/abs/2012.07596