Tootfinder

Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics
Alex Colagrande, Paul Caillon, Eva Feillet, Alexandre Allauzen
https://arxiv.org/abs/2507.02748

Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics
Transformers have become the de facto standard for a wide range of tasks, from image classification to physics simulations. Despite their impressive performance, the quadratic complexity of standard Transformers in both memory and time with respect to the input length makes them impractical for processing high-resolution inputs. Therefore, several variants have been proposed, the most successful relying on patchification, downsampling, or coarsening techniques, often at the cost of losing the f…

Downstream: efficient cross-platform algorithms for fixed-capacity stream downsampling
Connor Yang, Joey Wagner, Emily Dolson, Luis Zaman, Matthew Andres Moreno
https://arxiv.org/abs/2506.12975

Downstream: efficient cross-platform algorithms for fixed-capacity stream downsampling
Due to ongoing accrual over long durations, a defining characteristic of real-world data streams is the requirement for rolling, often real-time, mechanisms to coarsen or summarize stream history. One common data structure for this purpose is the ring buffer, which maintains a running downsample comprising most recent stream data. In some downsampling scenarios, however, it can instead be necessary to maintain data items spanning the entirety of elapsed stream history. Fortunately, approaches g…

Classification based deep learning models for lung cancer and disease using medical images
Ahmad Chaddad, Jihao Peng, Yihang Wu
https://arxiv.org/abs/2507.01279

Classification based deep learning models for lung cancer and disease using medical images
The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the established ResNet framework. This model is specifically designed to improve the prediction of lung cancer and diseases using the images. To address the challenge of missing feature information that occurs during the downsampling process in CNNs, we integrate …

AWR6843 focus stacked dataset at 100x stitching now.
11663 tiles, downsampled from 1992 x 1992 to 498 x 498. This should give a final resolution of 188 nm/pix, so we're still very slightly oversampled from the ~300nm theoretical optical resolution of the objective.
Raw dataset is 46.27 Gpix, down to 2.892 Gpix after downsampling. Given the 30% tile overlap we're probably looking at about a 1.4 Gpix final stitch.