Tootfinder

Redundancy Channels in the Conformal Bootstrap
Stefanos R. Kousvos, Andreas Stergiou
https://arxiv.org/abs/2507.05338 https://arxiv.o…

Redundancy Channels in the Conformal Bootstrap
A method for obstructing symmetry enhancement in numerical conformal bootstrap calculations is proposed. Symmetry enhancement refers to situations where bootstrap studies initialised with a certain symmetry end up allowing theories with higher symmetry. In such cases, it is shown that redundant operators in the less symmetric theory can descend from primary scaling operators of the more symmetric one, motivating the imposition of spectral gaps that are justified in the former but not the latter…

Die EU-Kommission treibt die neue EU-weite #Vorratsdatenspeicherung voran: Teilt der EU-Kommission eure Meinung zu dem Thema bis zum 18. Juni 2025 mit. #VDS

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory
Yi-Chun Liao, Chieh-Lin Tsai, Yuan-Hao Chang, Cam\'elia Slimani, Jalil Boukhobza, Tei-Wei Kuo
https://arxiv.org/abs/2506.05994

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory
Although deep learning has demonstrated remarkable capabilities in learning from unstructured data, modern tree-based ensemble models remain superior in extracting relevant information and learning from structured datasets. While several efforts have been made to accelerate tree-based models, the inherent characteristics of the models pose significant challenges for conventional accelerators. Recent research leveraging content-addressable memory (CAM) offers a promising solution for acceleratin…

roadnet: U.S. roads (CA, PA, TX)
Road networks from three US states (CA, PA, TX), in which edges are stretches of road and vertices are intersections of roads.
This network has 1393383 nodes and 3843320 edges.
Tags: Transportation, Roads, Unweighted
https://networks.skewed.de/net/roadnet

Dual-Alignment Knowledge Retention for Continual Medical Image Segmentation
Yuxin Ye, Yan Liu, Shujian Yu
https://arxiv.org/abs/2507.03638 https://

Dual-Alignment Knowledge Retention for Continual Medical Image Segmentation
Continual learning in medical image segmentation involves sequential data acquisition across diverse domains (e.g., clinical sites), where task interference between past and current domains often leads to catastrophic forgetting. Existing continual learning methods fail to capture the complex dependencies between tasks. We introduce a novel framework that mitigates forgetting by establishing and enhancing complex dependencies between historical data and the network in the present task. Our fram…

Set Shaping Theory and the Foundations of Redundancy-Free Testable Codes
Aida Koch, Alix Petit
https://arxiv.org/abs/2507.03444 https://

Set Shaping Theory and the Foundations of Redundancy-Free Testable Codes
To render a sequence testable, namely capable of identifying and detecting errors, it is necessary to apply a transformation that increases its length by introducing statistical dependence among symbols, as commonly exemplified by the addition of parity bits. However, since the decoder does not have prior knowledge of the original symbols, it must treat the artificially introduced symbols as if they were independent. Consequently, these additional symbols must be transmitted, even though their …

I have contributed a public statement in the currently open consultation on EU data retention plans: https://www.mayrhofer.eu.org/post/on-mass-data-retention/
You might want to do so as well.

On data retention laws: Why this is (still) not a good balance between mass surveillance and law enforcement | René Mayrhofer
The latest effort to legalize mass data retention of communication traffic metadata is still problematic. I try to make my most pressing argument why the balance between preventing harm and causing potential harm is not met.

Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy
Yu Feng, Weikai Lin, Yuge Cheng, Zihan Liu, Jingwen Leng, Minyi Guo, Chen Chen, Shixuan Sun, Yuhao Zhu
https://arxiv.org/abs/2506.05682

Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy
3D Gaussian Splatting (3DGS) has vastly advanced the pace of neural rendering, but it remains computationally demanding on today's mobile SoCs. To address this challenge, we propose Lumina, a hardware-algorithm co-designed system, which integrates two principal optimizations: a novel algorithm, S^2, and a radiance caching mechanism, RC, to improve the efficiency of neural rendering. S2 algorithm exploits temporal coherence in rendering to reduce the computational overhead, while RC leverages th…

Insights from the "Red devil" AT 2022fpx: A dust-reddened TDE family excluded by their apparent red color?
Zheyu Lin, Ning Jiang, Yibo Wang, Xu Kong, Shifeng Huang, Zesen Lin, Chen Qin, Tianyu Xia
https://arxiv.org/abs/2507.04834

Insights from the "Red devil" AT 2022fpx: A dust-reddened TDE family excluded by their apparent red color?
We report unnoticed but intriguing features in the peculiar nuclear transient AT 2022fpx, and investigate its type. These features include the constantly red optical color of $g-r>0$, stable soft X-ray flare ($kT\sim100$ eV) in the past $\sim$550 days, prominent mid-infrared echo peaked at $\sim$$10^{43.3}$ erg s$^{-1}$ and the confirmation of a weak AGN by weak flares in pre-event WISE mid-infrared light curves with no contemporary optical, radio or X-ray counterparts. The combination of the o…

roadnet: U.S. roads (CA, PA, TX)
Road networks from three US states (CA, PA, TX), in which edges are stretches of road and vertices are intersections of roads.
This network has 1090920 nodes and 3083796 edges.
Tags: Transportation, Roads, Unweighted
https://networks.skewed.de/net/roadnet