Tootfinder

Optimization of Probabilistic Constellation Shaping for Optical OFDM Systems with Clipping Distortion
Thanh V. Pham, Susumu Ishihara
https://arxiv.org/abs/2507.07507

Optimization of Probabilistic Constellation Shaping for Optical OFDM Systems with Clipping Distortion
Optical orthogonal frequency-division multiplexing (OFDM) and probabilistic constellation shaping (PCS) have emerged as powerful techniques to enhance the performance of optical wireless communications (OWC) systems. While PCS improves spectral efficiency and adaptability, we show that its integration with optical OFDM can inadvertently increase the peak-to-average power ratio (PAPR) of the signal, exacerbating clipping distortion due to signal clipping. This letter investigates the impact of P…

AnatomyCarve: A VR occlusion management technique for medical images based on segment-aware clipping
Andrey Titov, Tina N. H. Nantenaina, Marta Kersten-Oertel, Simon Drouin
https://arxiv.org/abs/2507.05572

AnatomyCarve: A VR occlusion management technique for medical images based on segment-aware clipping
Visualizing 3D medical images is challenging due to self-occlusion, where anatomical structures of interest can be obscured by surrounding tissues. Existing methods, such as slicing and interactive clipping, are limited in their ability to fully represent internal anatomy in context. In contrast, hand-drawn medical illustrations in anatomy books manage occlusion effectively by selectively removing portions based on tissue type, revealing 3D structures while preserving context. This paper introd…

wild combination of words, cold war edition. if you read it literally, the headline is basically a david lynch script direction.

Generalized Gradient Norm Clipping & Non-Euclidean $(L_0,L_1)$-Smoothness
Thomas Pethick, Wanyun Xie, Mete Erdogan, Kimon Antonakopoulos, Tony Silveti-Falls, Volkan Cevher
https://arxiv.org/abs/2506.01913

Generalized Gradient Norm Clipping & Non-Euclidean $(L_0,L_1)$-Smoothness
This work introduces a hybrid non-Euclidean optimization method which generalizes gradient norm clipping by combining steepest descent and conditional gradient approaches. The method achieves the best of both worlds by establishing a descent property under a generalized notion of ($L_0$,$L_1$)-smoothness. Weight decay is incorporated in a principled manner by identifying a connection to the Frank-Wolfe short step. In the stochastic case, we show an order optimal $O(n^{-1/4})$ convergence rate b…

This https://arxiv.org/abs/2502.16492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Convergence of Clipped SGD on Convex $(L_0,L_1)$-Smooth Functions
We study stochastic gradient descent (SGD) with gradient clipping on convex functions under a generalized smoothness assumption called $(L_0,L_1)$-smoothness. Using gradient clipping, we establish a high probability convergence rate that matches the SGD rate in the $L$ smooth case up to polylogarithmic factors and additive terms. We also propose a variation of adaptive SGD with gradient clipping, which achieves the same guarantee. We perform empirical experiments to examine our theory and algor…

haha, i like to poison my personal data - among others - by using a random combination of unicode homoglyphs. this is a new result on a package i got delivered.

SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering
Jan Melechovsky, Ambuj Mehrish, Dorien Herremans
https://arxiv.org/abs/2508.03448 https://

SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering
Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of a…

I'm baffled that this guy is a respected analyst. Yeah, an infinite number of AI agents will spend their money on content from human creators, making those humans infinitely rich. I see no flaws in this model.

RaRa Clipper: A Clipper for Gaussian Splatting Based on Ray Tracer and Rasterizer
Da Li, Donggang Jia, Yousef Rajeh, Dominik Engel, Ivan Viola
https://arxiv.org/abs/2506.20202

RaRa Clipper: A Clipper for Gaussian Splatting Based on Ray Tracer and Rasterizer
With the advancement of Gaussian Splatting techniques, a growing number of datasets based on this representation have been developed. However, performing accurate and efficient clipping for Gaussian Splatting remains a challenging and unresolved problem, primarily due to the volumetric nature of Gaussian primitives, which makes hard clipping incapable of precisely localizing their pixel-level contributions. In this paper, we propose a hybrid rendering framework that combines rasterization and r…

Clipping coupons from the newspaper for 8 tokens-for-$1 before we biked 3 miles to the local arcade.
#videogames #arcade #genx

A high-order, conservative and positivity-preserving intersection-based remapping method between meshes with isoparametric curvilinear cells
Nuo Lei, Juan Cheng, Chi-Wang Shu
https://arxiv.org/abs/2506.18389

A high-order, conservative and positivity-preserving intersection-based remapping method between meshes with isoparametric curvilinear cells
This paper presents a novel intersection-based remapping method for isoparametric curvilinear meshes within the indirect arbitrary Lagrangian-Eulerian (ALE) framework, addressing the challenges of transferring physical quantities between high-order curved-edge meshes. Our method leverages the Weiler-Atherton clipping algorithm to compute intersections between curved-edge quadrangles, enabling robust handling of arbitrary order isoparametric curves. By integrating multi-resolution weighted essen…

This https://arxiv.org/abs/2412.19529 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
Recently, the study of heavy-tailed noises in first-order nonconvex stochastic optimization has gotten a lot of attention since it was recognized as a more realistic condition as suggested by many empirical observations. Specifically, the stochastic noise (the difference between the stochastic and true gradient) is considered to have only a finite $\mathfrak{p}$-th moment where $\mathfrak{p}\in\left(1,2\right]$ instead of assuming it always satisfies the classical finite variance assumption. To…

40 years ago this afternoon, the #GratefulDead on the beach in ventura, closing out the 1st half of their summer shows, a glorious day on the ocean. early summer ’85, show #14. BASTILLE YOUR FACE.
soundboard (some more clipping bass):

New modeling of the stray light noise in the main arms of the Einstein Telescope
M. Andr\'es-Carcasona, J. Grandes Umbert, D. Gonz\'alez-Lociga, M. Mart\'inez, Ll. M. Mir, H. Yamamoto
https://arxiv.org/abs/2506.18083

New modeling of the stray light noise in the main arms of the Einstein Telescope
Stray light represents a significant noise source for gravitational wave detectors, requiring an accurate modeling and mitigation to preserve the experiment's sensitivity. In this article, we present an updated and improved analysis of the stray-light induced noise in the Einstein Telescope main arm. The results presented here supersede previous studies taking into account a number of improvements, including baffle clipping effects, new numerical calculations for computing diffraction contribut…

From Continuous to Discrete: Cross-Domain Collaborative General Speech Enhancement via Hierarchical Language Models
Zhaoxi Mu, Rilin Chen, Andong Li, Meng Yu, Xinyu Yang, Dong Yu
https://arxiv.org/abs/2507.19062

This https://arxiv.org/abs/2412.19529 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
Recently, the study of heavy-tailed noises in first-order nonconvex stochastic optimization has gotten a lot of attention since it was recognized as a more realistic condition as suggested by many empirical observations. Specifically, the stochastic noise (the difference between the stochastic and true gradient) is considered to have only a finite $\mathfrak{p}$-th moment where $\mathfrak{p}\in\left(1,2\right]$ instead of assuming it always satisfies the classical finite variance assumption. To…