Tootfinder

Fantasy football tips: Raiders RB on move in high-stakes drafts https://www.reviewjournal.com/sports/betting/fantasy-football-tips-raiders-rb-on-move-in-high-stakes-drafts-3422174/

Mehr junge Ukrainer suchen Schutz in Deutschland
Die Zahl der in Deutschland Schutzsuchenden aus der Ukraine ist in den vergangenen Wochen einem Medienbericht zufolge deutlich gestiegen. Die Aufhebung des Ausreiseverbots für wehrfähige ukrainische Männer zwischen 18 und 22 Jahren habe zu einer Zunahme von Schutzgesuchen dieser Gruppe "von etwa 100 pro Woche vor Inkrafttreten der Regelung auf derzeit circa 1.000 pro Woche gefü…
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Ukraine-Liveblog: ++ Stromabschaltungen in vielen Teilen der Ukraine ++
Nach erneuten russischen Angriffe auf Energieanlagen hat die Ukraine in vielen Landesteilen die Stromversorgung massiv eingeschränkt. Die Zahl junge Ukrainer, die als Schutzsuchende nach Deutschland kommen, ist im Sommer gestiegen.

GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning
Tai Vu, Robert Yang
https://arxiv.org/abs/2508.09207 https://arxiv.o…

GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning
The process of generating fully colorized drawings from sketches is a large, usually costly bottleneck in the manga and anime industry. In this study, we examine multiple models for image-to-image translation between anime characters and their sketches, including Neural Style Transfer, C-GAN, and CycleGAN. By assessing them qualitatively and quantitatively, we find that C-GAN is the most effective model that is able to produce high-quality and high-resolution images close to those created by hu…

Lectures on stochastic sewing with applications
Oleg Butkovsky
https://arxiv.org/abs/2510.12165 https://arxiv.org/pdf/2510.12165

Lectures on stochastic sewing with applications
These are lecture notes for a mini-course on stochastic sewing, taught at the University of Edinburgh and Beijing Institute of Technology in Spring/Summer 2025. The aim is to introduce the reader to stochastic sewing techniques and to show how they can be successfully applied to study various problems in stochastic analysis, including: regularization by noise for stochastic differential equations driven by Brownian motion or fractional Brownian motion, well-posedness of stochastic PDEs with irr…

Easy-to-Implement One-Step Schemes for Stochastic Integration
J. Woodfield, A. Lobbe
https://arxiv.org/abs/2510.12583 https://arxiv.org/pdf/2510.12583

Easy-to-Implement One-Step Schemes for Stochastic Integration
Convenient, easy to implement stochastic integration methods are developed on the basis of abstract one-step deterministic order $p$ integration techniques. The abstraction as an arbitrary one step map allows the inspection of easy to implement stochastic exponential time differencing Runge-Kutta (SETDRK), stochastic integrating factor Runge-Kutta (SIFRK) and stochastic RK (SRK) schemes. Such schemes require minimal modifications to existing deterministic schemes and converging to the Stratonov…

Accelerating Stochastic Energy System Optimization Models: Temporally Split Benders Decomposition
Shima Sasanpour, Manuel Wetzel, Karl-Ki\^en Cao, Hans Christian Gils, Andr\'es Ramos
https://arxiv.org/abs/2508.10789

Accelerating Stochastic Energy System Optimization Models: Temporally Split Benders Decomposition
Stochastic programming can be applied to consider uncertainties in energy system optimization models for capacity expansion planning. However, these models become increasingly large and time-consuming to solve, even without considering uncertainties. For two-stage stochastic capacity expansion planning problems, Benders decomposition is often applied to ensure that the problem remains solvable. Since stochastic scenarios can be optimized independently within subproblems, their optimization can …

Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization
Zihao Zhao, Christopher Yeh, Lingkai Kong, Kai Wang
https://arxiv.org/abs/2510.11590 https://…

Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization
Decision-focused learning (DFL) integrates predictive modeling and optimization by training predictors to optimize the downstream decision target rather than merely minimizing prediction error. To date, existing DFL methods typically rely on deterministic point predictions, which are often insufficient to capture the intrinsic stochasticity of real-world environments. To address this challenge, we propose the first diffusion-based DFL approach, which trains a diffusion model to represent the di…

Brock Bowers, Alex Cappa, Eric Stokes and Darnay Holmes Scrums | Week 2 vs. Chargers https://www.raiders.com/video/brock-bowers-alex-cappa-eric-stokes-darnay-holmes-scrums-week-2-chargers

Mehr junge Ukrainer suchen Schutz in Deutschland
Die Zahl der in Deutschland Schutzsuchenden aus der Ukraine ist in den vergangenen Wochen einem Medienbericht zufolge deutlich gestiegen. Die Aufhebung des Ausreiseverbots für wehrfähige ukrainische Männer zwischen 18 und 22 Jahren habe zu einer Zunahme von Schutzgesuchen dieser Gruppe "von etwa 100 pro Woche vor Inkrafttreten der Regelung auf derzeit circa 1.000 pro Woche gefü…
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Ukraine-Liveblog: ++ NATO und EU arbeiten an "Drohnenschutzwall" ++
Laut NATO-Generalsekretär Rutte arbeitet das Verteidigungsbündnis gemeinsam mit der EU an einem Abwehrwall gegen Drohnen. Die Zahl junge Ukrainer, die als Schutzsuchende nach Deutschland kommen, ist im Sommer gestiegen.

Adversarial robustness through Lipschitz-Guided Stochastic Depth in Neural Networks
Laith Nayal, Mahmoud Mousatat, Bader Rasheed
https://arxiv.org/abs/2509.10298 https://…

Adversarial robustness through Lipschitz-Guided Stochastic Depth in Neural Networks
Deep neural networks and Vision Transformers achieve state-of-the-art performance in computer vision but are highly vulnerable to adversarial perturbations. Standard defenses often incur high computational cost or lack formal guarantees. We propose a Lipschitz-guided stochastic depth (DropPath) method, where drop probabilities increase with depth to control the effective Lipschitz constant of the network. This approach regularizes deeper layers, improving robustness while preserving clean accur…