Tootfinder

Cosmology of Cubic Poincar\'e Gauge gravity
Sebastian Bahamonde, Rebecca Briffa, Konstantinos Dialektopoulos, Damianos Iosifidis, Jackson Levi Said
https://arxiv.org/abs/2506.17017

Cosmology of Cubic Poincaré Gauge gravity
In this paper, we study flat FLRW cosmology for a Poincaré gauge theory containing cubic invariants that is free from ghosts in arbitrary backgrounds in the axial and vector sectors of the torsion tensor. The new degrees of freedom can be related to hypermomentum but continue to be dynamical even in vacuum. These extra degrees of freedom open a more natural way in which to construct potential gravitational models that provide possible ways to modify astrophysical and cosmological physics. In t…

Attitude Estimation Using Scalar Measurements
Hassan Alnahhal, Sifeddine Benahmed, Soulaimane Berkane, Tarel Hamel
https://arxiv.org/abs/2506.07068 https:/…

Attitude Estimation Using Scalar Measurements
This paper revisits the problem of orientation estimation for rigid bodies through a novel framework based on scalar measurements. Unlike traditional vector-based methods, the proposed approach enables selective utilization of only the reliable axes of vector measurements while seamlessly incorporating alternative scalar modalities such as Pitot tubes, barometers with range sensors, and landmark-based constraints. The estimation problem is reformulated within a linear time-varying (LTV) framewo…

This https://arxiv.org/abs/2505.24137 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

Energy-Oriented Computing Architecture Simulator for SNN Training
With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to their unique potential in energy efficiency and biomimetic neural processing. However, current hardware development for efficient SNN train…

Numerical modeling of isochoric heating experiments using the TROLL code in the warm dense matter regime
S\'ebastien Rassou, Marie Bonneau, Christophe Rousseaux, Xavier Vaisseau, Witold Cayzac, Adrien Denoeud, Fr\'ed\'eric Perez, Tom Beaumont, Morris Demoulins, Jean-Christophe Pain
https://arxiv.org/abs/2506.08912

Numerical modeling of isochoric heating experiments using the TROLL code in the warm dense matter regime
Experiments of isochoric heating by protons of solid material were recently performed at LULI laser facilities. In these experiments, protons, produced from target normal sheath acceleration (TNSA) of Au foil with the PICO2000 laser, deposit their energy into an aluminum or copper foil initially at room temperature and solid density. The heated material reaches the warm dense matter regime with temperature in the rear face of the material between 1 and 5 eV. The temperature is inferred by strea…

AI-Augmented OTDR Fault Localization Framework for Resilient Rural Fiber Networks in the United States
Sabab Al Farabi (Department of Industrial Engineering, Lamar University, Beaumont, Texas, USA)
https://arxiv.org/abs/2506.03041

AI-Augmented OTDR Fault Localization Framework for Resilient Rural Fiber Networks in the United States
This research presents a novel framework that combines traditional Optical Time-Domain Reflectometer (OTDR) signal analysis with machine learning to localize and classify fiber optic faults in rural broadband infrastructures. The proposed system addresses a critical need in the expansion of middle-mile and last-mile networks, particularly in regions targeted by the U.S. Broadband Equity, Access, and Deployment (BEAD) Program. By enhancing fault diagnosis through a predictive, AI-based model, th…

Viscoelasticity of biomimetic scale beams from trapped complex fluids
Pranta Rahman Sarkar, Outi Tammisola, Ranajay Ghosh
https://arxiv.org/abs/2505.21760 …

Viscoelasticity of biomimetic scale beams from trapped complex fluids
We investigate the nonlinear viscoelastic behavior of a biomimetic scale-covered beam in which shear-dependent complex fluids are trapped between overlapping scales under bending loads. These fluids mimic biological mucus and slime layers commonly enveloping the skins found in nature. An energy-based analytical model is developed to quantify the interplay between substrate elasticity, scale geometry, and fluid rheology at multiple length scales. Constant strain rate and oscillatory bending are …

This https://arxiv.org/abs/2503.18782 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Adhesion differentials control the rheology of biomimetic emulsions
Animal morphogenesis involves complex tissue deformation processes, which require tight control over tissue rheology. Yet, it remains insufficiently understood how tissue rheology results from the interplay between cellular packing and cellular forces, such as cortical tension, cell pressure, and cell-cell adhesion. Here, we follow a biomimetic approach to study this interplay, focusing on the cellular adhesion hierarchy. We mimic adhesive cells with oil droplets whose adhesion strength and spe…

Energy-Oriented Computing Architecture Simulator for SNN Training
Yunhao Ma (Pengcheng Laboratory, Southern University of Science and Technology), Wanyi Jia (Pengcheng Laboratory, Southern University of Science and Technology), Yanyu Lin (Pengcheng Laboratory), Wenjie Lin (Pengcheng Laboratory), Xueke Zhu (Pengcheng Laboratory), Huihui Zhou (Pengcheng Laboratory), Fengwei An (Southern University of Science and Technology)

Energy-Oriented Computing Architecture Simulator for SNN Training
With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to their unique potential in energy efficiency and biomimetic neural processing. However, current hardware development for efficient SNN train…