Tootfinder

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…

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…

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…

"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…

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…

COCONUT: A time-evolving coronal model with an energy decomposition strategy
Haopeng Wang, Stefaan Poedts, Andrea Lani, Luis Linan, Tinatin Baratashvili, Hyun-Jin Jeong, Rayan Dhib, Yuhao Zhou, Yucong Li, Mahdi Najafi-Ziyazi, Juan Wang, Brigitte Schmieder, Wensi Wang
https://arxiv.org/abs/2508.20423

COCONUT: A time-evolving coronal model with an energy decomposition strategy
In this paper, we propose an energy decomposition method to improve the numerical stability of time-evolving magnetohydrodynamic (MHD) coronal models, enabling them to resolve the stronger magnetic field during solar maxima without significantly filtering out small-scale structures from the observed magnetograms.We advance the decomposed energy that excludes the magnetic energy, instead of the total energy, in time. It avoids the operation of subtracting a large magnetic energy from the total e…

En Lidl han traído este año calendarios de adviento de Ferrero Rocher y de gominolas Fini. Pequeño recordatorio de que si os hacéis con unos a partir de mediados de diciembre (si quedan), cuestan menos de la mitad y os podéis comer media caja con la excusa de "poneros al día". Síganme para mšs consejitos.

ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
Tianjian Liu, Fanqi Wan, Jiajian Guo, Xiaojun Quan
https://arxiv.org/abs/2508.20973 https://

ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
Proactive dialogue has emerged as a critical and challenging research problem in advancing large language models (LLMs). Existing works predominantly focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models' proactive conversation abilities. In this work, we propose ProactiveEval, a unified framework designed for evaluating proactive dialogue capabilities of LLMs. This framework decomposes proactive dialogue in…

Noise-Coded Illumination for Forensic and Photometric Video Analysis
Peter F. Michael, Zekun Hao, Serge Belongie, Abe Davis
https://arxiv.org/abs/2507.23002 https://

Noise-Coded Illumination for Forensic and Photometric Video Analysis
The proliferation of advanced tools for manipulating video has led to an arms race, pitting those who wish to sow disinformation against those who want to detect and expose it. Unfortunately, time favors the ill-intentioned in this race, with fake videos growing increasingly difficult to distinguish from real ones. At the root of this trend is a fundamental advantage held by those manipulating media: equal access to a distribution of what we consider authentic (i.e., "natural") video. In this p…