Tootfinder

from my link log —
Faster remainder by multiplication, with applications to compilers and software libraries.
https://arxiv.org/abs/1902.01961
saved 2021-05-23

Faster Remainder by Direct Computation: Applications to Compilers and Software Libraries
On common processors, integer multiplication is many times faster than integer division. Dividing a numerator n by a divisor d is mathematically equivalent to multiplication by the inverse of the divisor (n / d = n x 1/d). If the divisor is known in advance---or if repeated integer divisions will be performed with the same divisor---it can be beneficial to substitute a less costly multiplication for an expensive division. Currently, the remainder of the division by a constant is computed from…

This https://arxiv.org/abs/2505.09430 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Mini Diffuser: Fast Multi-task Diffusion Policy Training Using Two-level Mini-batches
We present a method that reduces, by an order of magnitude, the time and memory needed to train multi-task vision-language robotic diffusion policies. This improvement arises from a previously underexplored distinction between action diffusion and the image diffusion techniques that inspired it: In image generation, the target is high-dimensional. By contrast, in action generation, the dimensionality of the target is comparatively small, and only the image condition is high-dimensional. Our app…

Sources: WBD initiates a round of layoffs in its cable TV divisions, affecting well under 100 employees and largely impacting the Discovery side of the company (Peter White/Deadline)
https://deadline.com/2025/06/warner-bros-discovery-layoffs-tv-d…

Warner Bros. Discovery Initiates More Layoffs In Cable TV Divisions
Deadline understands that the company is set to layoff a number of people, particularly on the cable television side of its business.

SSIMBaD: Sigma Scaling with SSIM-Guided Balanced Diffusion for AnimeFace Colorization
Junpyo Seo (Department of Computer Science, Seoul National University), Hanbin Koo (Department of Computer Science, Seoul National University), Jieun Yook (Department of Computer Science, Seoul National University), Byung-Ro Moon (Department of Computer Science, Seoul National University)
…

SSIMBaD: Sigma Scaling with SSIM-Guided Balanced Diffusion for AnimeFace Colorization
We propose a novel diffusion-based framework for automatic colorization of Anime-style facial sketches. Our method preserves the structural fidelity of the input sketch while effectively transferring stylistic attributes from a reference image. Unlike traditional approaches that rely on predefined noise schedules - which often compromise perceptual consistency -- our framework builds on continuous-time diffusion models and introduces SSIMBaD (Sigma Scaling with SSIM-Guided Balanced Diffusion). …

Worst-to-first rankings: Which teams could skyrocket to the top of their divisions? https://www.espn.com/nfl/story/_/id/45391110/2025-nfl-season-worst-first-division-odds-predictions

This https://arxiv.org/abs/2505.20863 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
Quantum computing holds immense potential, yet its practical success depends on multiple factors, including advances in quantum circuit design. In this paper, we introduce a generative approach based on denoising diffusion models (DMs) to synthesize parameterized quantum circuits (PQCs). Extending the recent diffusion model pipeline of Fürrutter et al. [1], our model effectively conditions the synthesis process, enabling the simultaneous generation of circuit architectures and their continuous…

ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration
Annamaria Defilippo, Ugo Lomoio, Barbara Puccio, Pierangelo Veltri, Pietro Hiram Guzzi
https://arxiv.org/abs/2506.04271

ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration
Understanding and controlling diffusion processes in complex networks is critical across domains ranging from epidemiology to information science. Here, we present ExDiff, an interactive and modular computational framework that integrates network simulation, graph neural networks (GNNs), and explainable artificial intelligence (XAI) to model and interpret diffusion dynamics. ExDiff combines classical compartmental models with deep learning techniques to capture both the structural and temporal …

From One AFC West Club To Another: The Division Of RBs Stays Strong https://raiderramble.com/2025/06/04/from-one-afc-west-club-to-another-division-of-rbs/

This https://arxiv.org/abs/2405.10723 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning
Modern diffusion MRI sequences commonly acquire a large number of volumes with diffusion sensitization gradients of differing strengths or directions. Such sequences rely on echo-planar imaging (EPI) to achieve reasonable scan duration. However, EPI is vulnerable to off-resonance effects, leading to tissue susceptibility and eddy-current induced distortions. The latter is particularly problematic because it causes misalignment between volumes, disrupting downstream modelling and analysis. The e…

This https://arxiv.org/abs/2502.05221 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Blackout DIFUSCO
This study explores the integration of Blackout Diffusion into the DIFUSCO framework for combinatorial optimization, specifically targeting the Traveling Salesman Problem (TSP). Inspired by the success of discrete-time diffusion models (D3PM) in maintaining structural integrity, we extend the paradigm to a continuous-time framework, leveraging the unique properties of Blackout Diffusion. Continuous-time modeling introduces smoother transitions and refined control, hypothesizing enhanced solutio…