Tootfinder

anybody doing Advent of FPGA in Amaranth? https://blog.janestreet.com/advent-of-fpga-challenge-2025/
it seems fun, and, moreover, if you have feedback as a learner I will be more than happy to use it to improve the language

Sure, you might be tempted by December’s worldly allure of Advent of Code, but…
Do you know what else starts today?
#shortbread season.
https://seancoates.com/blogs/shortbread

Merry Christmas, and welcome to December! 🎄 ⛄ 🎅
#Christmas #December #Santa #Advent <…

"Changes to Advent of Code starting this December"
TL;DR: There's going to be fewer days and no global leader board.
https://www.reddit.com/r/adventofcode/comments/1ocwh04/changes_to_advent_of_code_starting_this_decembe…

An Order of Magnitude Time Complexity Reduction for Gaussian Graphical Model Posterior Sampling Using a Reverse Telescoping Block Decomposition
Zejin Gao, Ksheera Sagar, Anindya Bhadra
https://arxiv.org/abs/2509.26385

An Order of Magnitude Time Complexity Reduction for Gaussian Graphical Model Posterior Sampling Using a Reverse Telescoping Block Decomposition
We consider the problem of fully Bayesian posterior estimation and uncertainty quantification in undirected Gaussian graphical models via Markov chain Monte Carlo (MCMC) under recently-developed element-wise graphical priors, such as the graphical horseshoe. Unlike the conjugate Wishart family, these priors are non-conjugate; but have the advantage that they naturally allow one to encode a prior belief of sparsity in the off-diagonal elements of the precision matrix, without imposing a structur…

LayerD: Decomposing Raster Graphic Designs into Layers
Tomoyuki Suzuki, Kang-Jun Liu, Naoto Inoue, Kota Yamaguchi
https://arxiv.org/abs/2509.25134 https://…

LayerD: Decomposing Raster Graphic Designs into Layers
Designers craft and edit graphic designs in a layer representation, but layer-based editing becomes impossible once composited into a raster image. In this work, we propose LayerD, a method to decompose raster graphic designs into layers for re-editable creative workflow. LayerD addresses the decomposition task by iteratively extracting unoccluded foreground layers. We propose a simple yet effective refinement approach taking advantage of the assumption that layers often exhibit uniform appeara…

Ja - Ihr wartet schon viel zu lange auf unseren #Kulturtip zum 1. #Advent - hier ist er.
Viel Freude damit ;)
https://youtu.be/-PHvq5iOVTE

Performance and Numerical Aspects of Decompositional Factorizations with FP64 Floating-Point Emulation in INT8
Piotr Luszczek, Vijay Gadepally, LaToya Anderson, William Arcand, David Bestor, William Bergeron, Alex Bonn, Daniel J. Burrill, Chansup Byun, Michael Houle, Matthew Hubbell, Hayden Jananthan, Michael Jones, Peter Michaleas, Guillermo Morales, Julia Mullen, Andrew Prout, Albert Reuther, Antonio Rosa, Charles Yee, Jeremy Kepner

Performance and Numerical Aspects of Decompositional Factorizations with FP64 Floating-Point Emulation in INT8
Mixing precisions for performance has been an ongoing trend as the modern hardware accelerators started including new, and mostly lower-precision, data formats. The advantage of using them is a great potential of performance gain and energy savings. The disadvantage are the numerical issues not present in the standard-mandated floating-point formats. Split integer emulation of FP64 takes this to an extreme with the computation performed only by fixed-point tensor core units. We present the new …

Interpreting deep learning-based stellar mass estimation via causal analysis and mutual information decomposition
Wei Zhang, Qiufan Lin, Yuan-Sen Ting, Shupei Chen, Hengxin Ruan, Song Li, Yifan Wang
https://arxiv.org/abs/2509.23901

Interpreting deep learning-based stellar mass estimation via causal analysis and mutual information decomposition
End-to-end deep learning models fed with multi-band galaxy images are powerful data-driven tools used to estimate galaxy physical properties in the absence of spectroscopy. However, due to a lack of interpretability and the associational nature of such models, it is difficult to understand how the information additional to integrated photometry (e.g., morphology) contributes to the estimation task. Improving our understanding in this field would enable further advances into unraveling the physi…

Towards Reliable Generation of Executable Workflows by Foundation Models
Sogol Masoumzadeh, Keheliya Gallaba, Dayi Lin, Ahmed E. Hassan
https://arxiv.org/abs/2509.25117 https://…

Towards Reliable Generation of Executable Workflows by Foundation Models
Recent advancements in Foundation Models (FMs) have demonstrated significant progress in comprehending complex natural language to perform intricate tasks. Successfully executing these tasks often requires orchestrating calls to FMs alongside other software components. However, manually decomposing a task into a coherent sequence of smaller, logically aggregated steps, commonly referred to as workflows, demands considerable effort and specialized domain knowledge. While FMs can assist in genera…