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'This pistol is something a bit different,' according to a firearms expert. Despite its generic appearance, this 18th century firearm features a comparatively unique design. Breakthroughs, discoveries, and DIY tips sent every weekday. It's difficult to resist raising an eyebrow at an Oklahoma robbery suspect's alleged recent weapon-of-choice . According to several Oklahoma news outlets including WKTUL, a 24-year-old man was arrested on December 6 by Tulsa police after allegedly robbing a liquor store using what employees described as an "old-timey musket."

Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scalability. While various methods have been proposed to speed up their convergence, the model selection phase is often ignored. In fact, in theoretical works most of the time assumptions are made, for example, on the prior knowledge of the norm of the optimal solution, while in the practical world validation methods remain the only viable approach. In this paper, we propose a new kernel-based stochastic gradient descent algorithm that performs model selection while training, with no parameters to tune, nor any form of cross-validation. The algorithm builds on recent advancement in online learning theory for unconstrained settings, to estimate over time the right regularization in a data-dependent way. Optimal rates of convergence are proved under standard smoothness assumptions on the target function as well as preliminary empirical results.

Recently, machine unlearning, which seeks to erase specific data stored in the pre-trained or fine-tuned models, has emerged as a crucial protective measure for LLMs. However, unlearning approaches for LLMs that have been considered thus far have focused on the removal of independent data points and have not taken into account that the stored facts are logically connected to one another and form an implicit knowledge graph. To facilitate the development of structural unlearning methods, which are essential for the practical application of unlearning, we propose PISTOL, a pipeline for compiling multi-scenario datasets for benchmarking structural LLM unlearning. Additionally, leveraging sample datasets synthesized using PISTOL, we conducted benchmarks with four distinct unlearning methods on both Llama2-7B and Mistral-7B models. This analysis helps to illustrate the prevailing challenges in effectively and robustly removing highly inter-connected data, batched data, or data skewed towards a specific domain. It also highlights the choice of pre-trained model can impact unlearning performance. This work not only advances our understandings on the limitation of current LLMs unlearning methods and proposes future research directions, but also provides a replicable framework for ongoing exploration and validation in the field.

Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more and more importance, thanks to their scalability. While various methods have been proposed to speed up their convergence, the model selection phase is often ignored. In fact, in theoretical works most of the time assumptions are made, for example, on the prior knowledge of the norm of the optimal solution, while in the practical world validation methods remain the only viable approach. In this paper, we propose a new kernel-based stochastic gradient descent algorithm that performs model selection while training, with no parameters to tune, nor any form of cross-validation. The algorithm builds on recent advancement in online learning theory for unconstrained settings, to estimate over time the right regularization in a data-dependent way. Optimal rates of convergence are proved under standard smoothness assumptions on the target function as well as preliminary empirical results.

In this paper, we introduce Uni3D-LLM, a unified framework that leverages a Large Language Model (LLM) to integrate tasks of 3D perception, generation, and editing within point cloud scenes. This framework empowers users to effortlessly generate and modify objects at specified locations within a scene, guided by the versatility of natural language descriptions. Uni3D-LLM harnesses the expressive power of natural language to allow for precise command over the generation and editing of 3D objects, thereby significantly enhancing operational flexibility and controllability. By mapping point cloud into the unified representation space, Uni3D-LLM achieves cross-application functionality, enabling the seamless execution of a wide array of tasks, ranging from the accurate instantiation of 3D objects to the diverse requirements of interactive design. Through a comprehensive suite of rigorous experiments, the efficacy of Uni3D-LLM in the comprehension, generation, and editing of point cloud has been validated. Additionally, we have assessed the impact of integrating a point cloud perception module on the generation and editing processes, confirming the substantial potential of our approach for practical applications.

This paper describes a feature extraction and gaze estimation software, named \textit{Pistol} that can be used with Pupil Invisible projects and other eye trackers in the future. In offline mode, our software extracts multiple features from the eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris vector, eye movement types from pupil and iris velocities, marker detection, marker distance, 2D gaze estimation for the pupil center, iris center, pupil vector, and iris vector using Levenberg Marquart fitting and neural networks. The gaze signal is computed in 2D for each eye and each feature separately and for both eyes in 3D also for each feature separately. We hope this software helps other researchers to extract state-of-the-art features for their research out of their recordings. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FPISTOL&mode=list

Ten years after the debut of Devil May Cry 4, Nero is back in the driver's seat and he's never looked better. It's not just the haircut, either -- Devil May Cry 5 runs on the RE Engine built for Resident Evil 7: Biohazard, and Capcom's goal is to build a photorealistic game filled with gooey demons, witty one-liners and flashy combos. The title's first hands-on demo at Gamescom 2018 highlights these exact elements and wraps all of it up in an ichor-crusted, gorgeous package. Devil May Cry 4 was the first game in the franchise to star Nero, a reluctant ally to series protagonist Dante. Nero is a human with a smattering of supernatural abilities, including a demon-powered arm named Devil Bringer.

Gaming culture is rife with graphic representations of gun violence and has been since arcade goers first blew aliens out of Space Invader's skies. You'll be hard-pressed to find more than a handful of AAA titles designed for adults (sit down Rayman) that don't rely on firearms, or use gore in substitution, either as a primary tool for the gameplay or as a thematic element. While firearms have been a mainstay in video games since the mid '70s, few games have cemented their position in popular culture than 1993's pioneering first person shooter, Doom. Not only did Doom bring the FPS genre into mainstream gaming culture, they also helped to normalize a level of gore not often seen since 1988's Turbografx 16 hit, Splatterhouse, one of the first games to ever carry a parental warning label. Doom's influence is clear in nearly every game in the genre.