Tootfinder

Tidy Modeling with R: #rstats #machinelearning

Tidy Modeling with R
The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This book provides a thorough introduction to how to use tidymodels, and an outline of good methodology and statistical practice for phases of the modeling process.

🇺🇦 #NowPlaying on KEXP's #Early
Gorillaz:
🎵 Tomorrow Comes Today
#Gorillaz
https://psychosilence.bandcamp.com/album/gorillaz-tomorrow-comes-today-psycho-silence-bootleg
https://open.spotify.com/track/6BL4PpswVjH9BAajbIbpmZ

$(P,\phi)$-Tamari lattices
Adrien Segovia
https://arxiv.org/abs/2510.06088 https://arxiv.org/pdf/2510.06088

$(P,ϕ)$-Tamari lattices
Given any poset $P$ and chain $ϕ$ in $P$, we define the $(P,ϕ)$-Tamari lattice. We study in depth these lattices and prove in particular that they are join-semidistributive, join-congruence uniform and left modular. We prove that the lattices of higher torsion classes of the higher Auslander and Nakayama algebras of type $\mathbb{A}$ are examples of $(P,ϕ)$-Tamari lattices and thus they inherit their properties. We also give general results related to left modular, extremal and congruence no…

Not finished today, but surely surely finished tomorrow!?
Mirrors stuck onto the doors. Quite a lot of mirror. Makes the room feel bigger.
All the trim and handles are on now. Just one hole for cables missing. Catches need to be put on the blind-doors. Adjusting the doors so they open and close smoothly. Couple of lights to be attached. Painting and decorating touch-up.
Actually do believe they'll finish tomorrow.

Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN
Mohsen Asghari Ilani, Yaser M. Banad
https://arxiv.org/abs/2509.05821

Brain Tumor Detection Through Diverse CNN Architectures in IoT Healthcare Industries: Fast R-CNN, U-Net, Transfer Learning-Based CNN, and Fully Connected CNN
Artificial intelligence (AI)-powered deep learning has advanced brain tumor diagnosis in Internet of Things (IoT)-healthcare systems, achieving high accuracy with large datasets. Brain health is critical to human life, and accurate diagnosis is essential for effective treatment. Magnetic Resonance Imaging (MRI) provides key data for brain tumor detection, serving as a major source of big data for AI-driven image classification. In this study, we classified glioma, meningioma, and pituitary tumo…

🇺🇦 Auf radioeins läuft...
Oddisee:
🎵 Tomorrow Cant Be Borrowed
#NowPlaying #Oddisee
https://oddiseemmg.bandcamp.com/track/tomorrow-cant-be-borrowed
https://open.spotify.com/track/5M6SxlexdN7HrnjNrNrwAK

Announcing Prairie Voice: Where Yesterday’s Wisdom Meets Tomorrow’s Questions
Prairie Voice launches today, not because the world needs another website, but because the present has become incomprehensible without the past. We live in an age of unprecedented change, facing questions that feel entirely new: How do we maintain human connection through screens? What does work mean when we produce nothing tangible? How do we raise children when childhood itself has been…

Regulating Big Tech is like regulating a malignant tumour. You don’t regulate a malignant tumour. You cut it out, with enough of a surgical margin to ensure you get every last putrid bit to avoid regrowth.
(This is moot, of course, as we’re not even regulating Big Tech, we’re feeding it with subsidies, government contracts, and access to national healthcare and other sensitive data while the folks who should be regulating them eye their next lucrative gig beyond the revolving doors of …

SER-Diff: Synthetic Error Replay Diffusion for Incremental Brain Tumor Segmentation
Sashank Makanaboyina
https://arxiv.org/abs/2510.06283 https://arxiv.org…

SER-Diff: Synthetic Error Replay Diffusion for Incremental Brain Tumor Segmentation
Incremental brain tumor segmentation is critical for models that must adapt to evolving clinical datasets without retraining on all prior data. However, catastrophic forgetting, where models lose previously acquired knowledge, remains a major obstacle. Recent incremental learning frameworks with knowledge distillation partially mitigate forgetting but rely heavily on generative replay or auxiliary storage. Meanwhile, diffusion models have proven effective for refining tumor segmentations, but h…

A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
Saptarshi Banerjee, Himadri Nath Saha, Utsho Banerjee, Rajarshi Karmakar, Jon Turdiev
https://arxiv.org/abs/2510.05123

A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI
Neuro-oncological prognostics are now vital in modern clinical neuroscience because brain tumors pose significant challenges in detection and management. To tackle this issue, we propose a cognitive digital twin framework that combines real-time EEG signals from a wearable skullcap with structural MRI data for dynamic and personalized tumor monitoring. At the heart of this framework is an Enhanced Vision Transformer (ViT++) that includes innovative components like Patch-Level Attention Regulari…