Tootfinder

Karstadt, und jetzt?
https://weddingweiser.de/alles-unklar-bei-galeria-karstadt/

NeuralODEs for VLEO simulations: Introducing thermoNET for Thermosphere Modeling
Dario Izzo, Giacomo Acciarini, Francesco Biscani
https://arxiv.org/abs/2405.19384

NeuralODEs for VLEO simulations: Introducing thermoNET for Thermosphere Modeling
We introduce a novel neural architecture termed thermoNET, designed to represent thermospheric density in satellite orbital propagation using a reduced amount of differentiable computations. Due to the appearance of a neural network on the right-hand side of the equations of motion, the resulting satellite dynamics is governed by a NeuralODE, a neural Ordinary Differential Equation, characterized by its fully differentiable nature, allowing the derivation of variational equations (hence of the …

Vient de trier les aides de jeux pour la partie I des masques de nyarlathotep. Putain ça en fait.
#jdr #adc

🔊 Auf radioeins läuft...
Chemtrails:
🎵 Bang Bang
#NowPlaying #Chemtrails
https://open.spotify.com/track/3nNNyuUrL9TiOwILop7ZHf
https://adoseofchemtrails.bandcamp.com/track/bang-bang

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.

Die Dönerpreise machen nachdenklich. Was wurde aus dem Imbissessen Nr.1? https://weddingweiser.de/ansichtssache-doener-war-frueher-schoener

Prequel des masques de Nyarlatothep. #jdr #adc

🔊 #NowPlaying on BBCRadio3's #InTune
Anton Bruckner, Bamberg Symphony Orchestra & Jakub Hrůša:
🎵 Symphony No 9 II. Scherzo. Bewegt, lebhaft - Trio. Schnell
#AntonBruckner #BambergSymphonyOrchestra #JakubHrůša
https://open.spotify.com/track/47pyOKcNERIL0T4XcvwCzp

6/6
Embrace the power of informed choices and consider how L-Glutamine may fit into your holistic approach to health. 🌟
#AminoAcids #HealthyLiving #CorporateWellness #LGlutamineBenefits #NeuroHealth #DigestiveWellness #ActiveLifestyle #NutrientScience