❝I felt an anger that I hadn’t felt in his past films. Anger that felt directed towards the way humans work, the unfairness of this whole world.❞ https://www.theguardian.com/music/2024/apr/08/eiko-ishibashi-film-co…
Spring was fully here today & tomorrow will already be summer, literally! After an almost sleepless night, I took a break from working on a new exciting thi.ng package/library and took the family for a 8km hike before the weather will be already getting hot over the weekend. Also was a great time/light for getting reacquainted with the 50mm lens (haven't touched it in years)...
#NaturePhotography
Hi friends!
The last two #photos from my photowalk in January! I rarely do macros but that day I wasn't out for hiking but really for #photography. So I thought, I could also step down into the frozen stream, take some time and look for tiny structures.
Needless to say that I was…
Stages of blooming. Daisy, Jan 2024. Los Angeles County Arboretum, Arcadia, California, USA. #nature #naturephotography #daisyflower
Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Andr\'es Bell-Navas, Nourelhouda Groun, Mar\'ia Villalba-Orero, Enrique Lara-Pezzi, Jes\'us Garicano-Mena, Soledad Le Clainche
https://arxiv.org/abs/2404.19579 https://arxiv.org/pdf/2404.19579
arXiv:2404.19579v1 Announce Type: new
Abstract: Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. Also considering the increase of medical data, much pressure is put on the health industry to develop systems for early and accurate heart disease recognition. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed, which analyses in real-time echocardiography video sequences. The system works in two stages. The first one transforms the data included in a database of echocardiography sequences into a machine-learning-compatible collection of annotated images which can be used in the training stage of any kind of machine learning-based framework, and more specifically with deep learning. This includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first time to the authors' knowledge, for both data augmentation and feature extraction in the medical field. The second stage is focused on building and training a Vision Transformer (ViT), barely explored in the related literature. The ViT is adapted for an effective training from scratch, even with small datasets. The designed neural network analyses images from an echocardiography sequence to predict the heart state. The results obtained show the superiority of the proposed system and the efficacy of the HODMD algorithm, even outperforming pretrained Convolutional Neural Networks (CNNs), which are so far the method of choice in the literature.
Size, nanostructure, and composition dependence of bimetallic Au-Pd supported on ceria-zirconia mixed oxide catalysts for selective oxidation of benzyl alcohol
Carol Olmos, Lidia Esther Chinchilla, Alberto Villa, Juan Jos\'e Delgado, Ana Bel\'en Hungr\'ia, Ginesa Blanco, Laura Prati, Jos\'e Juan Calvino, Xiaowei Chen
https://