Tootfinder

Replaced article(s) found for nucl-ex. https://arxiv.org/list/nucl-ex/new
[1/1]:
- No-quenching baseline for energy loss signals in oxygen-oxygen collisions
Jannis Gebhard, Aleksas Mazeliauskas, Adam Takacs

This https://arxiv.org/abs/2408.07148 has been replaced.
initial toot: https://mastoxiv.page/@a…

Gate-defined Kondo lattices with valley-helical quantum dot arrays
Kondo physics and heavy-fermion behavior have been predicted and observed in moiré materials. The electric tunability of moiré materials allows an in-situ study of Kondo lattices' phase diagrams, which is not possible with their intermetallic counterparts. However, moiré platforms rely on twisting, which introduces twisting angle disorder and undesired buckling. Here we propose device layouts for one- and two-dimensional gate-defined superlattices in Bernal bilayer graphene where localized s…

A Comparative Study of NAFNet Baselines for Image Restoration
Vladislav Esaulov, M. Moein Esfahani
https://arxiv.org/abs/2506.19845 https://

A Comparative Study of NAFNet Baselines for Image Restoration
We study NAFNet (Nonlinear Activation Free Network), a simple and efficient deep learning baseline for image restoration. By using CIFAR10 images corrupted with noise and blur, we conduct an ablation study of NAFNet's core components. Our baseline model implements SimpleGate activation, Simplified Channel Activation (SCA), and LayerNormalization. We compare this baseline to different variants that replace or remove components. Quantitative results (PSNR, SSIM) and examples illustrate how each m…

Rules, agents and order
Amalia Puente, Diego Radillo-Ochoa, C. A. Terrero-Escalante
https://arxiv.org/abs/2505.23985 https://arxiv.or…

Rules, agents and order
Complex systems often exhibit highly structured network topologies that reflect functional constraints. In this work, we investigate how, under varying combinations of system-wide selection rules and special agents, different classes of random processes give rise to global order, with a focus restricted to finite-size networks. Using the large-$N$ Erdos-Renyi model as a null baseline, we contrast purely random link-adding processes with goal-directed dynamics, including variants of the chip-fir…

🌡️ Global mercury levels in rivers have doubled since Industrial Revolution, research reveals
https://phys.org/news/2025-06-global-mercury-rivers-industrial-revolution.html

Global mercury levels in rivers have doubled since Industrial Revolution, research reveals
Mercury levels in the world's rivers have more than doubled since the pre-industrial era, according to new research from Tulane University that establishes the first known global baseline for riverine mercury pollution.

Practical GPU Choices for Earth Observation: ResNet-50 Training Throughput on Integrated, Laptop, and Cloud Accelerators
Ritvik Chaturvedi
https://arxiv.org/abs/2508.18206 https…

Practical GPU Choices for Earth Observation: ResNet-50 Training Throughput on Integrated, Laptop, and Cloud Accelerators
This project implements a ResNet-based pipeline for land use and land cover (LULC) classification on Sentinel-2 imagery, benchmarked across three heterogeneous GPUs. The workflow automates data acquisition, geospatial preprocessing, tiling, model training, and visualization, and is fully containerized for reproducibility. Performance evaluation reveals up to a 2x training speed-up on an NVIDIA RTX 3060 and a Tesla T4 compared to the Apple M3 Pro baseline, while maintaining high classification a…

RIP, Mark Snow: listen to the composer talk about scoring X-Files and more
https://cdm.link/rip-mark-snow/?utm_source=flipboard&utm_content=muz4now/magazine/music

RIP, Mark Snow: listen to the composer talk about scoring X-Files and more - CDM Create Digital Music
Mark Snow, the mind-bogglingly prolific composer behind scores for Blue Bloods, Smallville, and the X-Files has died at age 78. From TV to film to games, Snow's take on electronic music scoring has left a mark on many of us. Here he is talking about his take on music, and some reflections on that unforgettable X-Files score.

An Ejection Event Captured by VLBI During the Outburst of Swift J1727.8$-$1613
Hongmin Cao, Jun Yang, S\'andor Frey, Callan M. Wood, James C. A. Miller-Jones, Krisztina \'E. Gab\'anyi, Giulia Migliori, Marcello Giroletti, Lang Cui, Tao An, Xiaoyu Hong, Weihua Wang
https://arxiv.org/abs/2506.18817

An Ejection Event Captured by VLBI During the Outburst of Swift J1727.8$-$1613
We observed a newly-discovered Galactic black hole X-ray binary Swift J1727.8$-$1613 with the European Very Long Baseline Interferometry Network (EVN) at 5 GHz. The observation was conducted immediately following a radio quenching event detected by the Karl G. Jansky Very Large Array (VLA). The visibility amplitude evolution over time reveals a large-amplitude radio flare and is consistent with an ejection event. The data can be interpreted either as a stationary component (i.e., the radio core…

Activity in White Dwarf Debris Disks I: Spitzer Legacy Reveals Variability Incompatible with the Canonical Model
Hiba Tu Noor, Jay Farihi, Scott J. Kenyon, Roman R. Rafikov, Mark C. Wyatt, Kate Y. L. Su, Carl Melis, Andrew Swan, Thomas G. Wilson, Boris T. G\"ansicke, Amy Bonsor, Laura K. Rogers, Seth Redfield, Mukremin Kilic
https://a…

Activity in White Dwarf Debris Disks I: Spitzer Legacy Reveals Variability Incompatible with the Canonical Model
This study presents all available, multi-epoch 3.6 and 4.5 $μ$m photometry from Spitzer Space Telescope observations of white dwarf debris disks, including weekly cadence observations of 16 relatively bright systems, and 5 h staring-mode observations for five of these. Significant variability is detected in 85 per cent of disks and across all timescales probed, from minutes to weeks to years, where the largest flux changes correlate with the longest time baselines, and the infrared excesses pe…

This https://arxiv.org/abs/2505.23585 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

On-Policy RL with Optimal Reward Baseline
Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement learning algorithms often suffer from training instability due to loose on-policy constraints and computational inefficiency due to auxiliary models. In this work, we propose On-Policy RL with Optimal reward baseline (OPO), a novel and simplified reinforcement learning algorithm designed to address these challenges. O…