Tootfinder

🇺🇦 Auf radioeins läuft...
Fever Ray:
🎵 Mama's Hand
#NowPlaying #FeverRay
https://hobonoisemusic.bandcamp.com/track/fever-ray-mamas-hand-hobo-noise-remix
https://open.spotify.com/track/0EpME4IMeFu53YJc2IkVDC

Joint Bayesian calibration and map-making for intensity mapping experiments
Zheng Zhang, Philip Bull, Mario G. Santos, Ainulnabilah Nasirudin
https://arxiv.org/abs/2509.10992 ht…

Joint Bayesian calibration and map-making for intensity mapping experiments
Line intensity maps have high dynamic range, and demand careful spectral and spatial calibration. We present a Bayesian framework for joint calibration and map-making using Gibbs sampling, which provides access to the full joint posterior of calibration and sky map parameters. Our data model incorporates instrumental noise based on the radiometer equation, capturing the coupling between noise level and system temperature without assuming a fixed noise amplitude. To enable unbiased and fast esti…

GW231109_235456: A Sub-threshold Binary Neutron Star Merger in the LIGO-Virgo-KAGRA O4a Observing Run?
Wanting Niu, Chad Hanna, Carl-Johan Haster, Shomik Adhicary, Pratyusava Baral, Amanda Baylor, Bryce Cousins, Jolien D. E. Creighton, Heather Fong, Yun-Jing Huang, Rachael Huxford, Prathamesh Joshi, James Kennington, Alvin K. Y. Li, Ryan Magee, Duncan Meacher, Cody Messick, Soichiro Morisaki, Cort Posnansky, Surabhi Sachdev, Shio Sakon, Urja Shah, Divya Singh, Ron Tapia, Leo Tsukada, A…

GW231109_235456: A Sub-threshold Binary Neutron Star Merger in the LIGO-Virgo-KAGRA O4a Observing Run?
We present a sub-threshold search for gravitational-wave inspirals from binary neutron stars using data from the first part of the fourth observing run of the LIGO-Virgo-KAGRA Collaboration. To enhance sensitivity to this targeted population, we incorporate a redshift-corrected population model based on radio observations of Galactic double neutron star systems. The search identifies a significant trigger with a false-alarm rate of about one per 50 years and a network signal-to-noise ratio of 9…

Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Johan Andreas Balle Rubak, Khuram Naveed, Sanyam Jain, Lukas Esterle, Alexandros Iosifidis, Ruben Pauwels
https://arxiv.org/abs/2509.06553

Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Objectives: Federated learning (FL) may mitigate privacy constraints, heterogeneous data quality, and inconsistent labeling in dental diagnostic AI. We compared FL with centralized (CL) and local learning (LL) for tooth segmentation in panoramic radiographs across multiple data corruption scenarios. Methods: An Attention U-Net was trained on 2066 radiographs from six institutions across four settings: baseline (unaltered data); label manipulation (dilated/missing annotations); image-quality man…

A Multi-Scale Feature Extraction and Fusion UNet for Pathloss Prediction in UAV-Assisted mmWave Radio Networks
Sajjad Hussain
https://arxiv.org/abs/2509.09606 https://

A Multi-Scale Feature Extraction and Fusion UNet for Pathloss Prediction in UAV-Assisted mmWave Radio Networks
Accurate pathloss prediction is essential for the design and optimization of UAV-assisted millimeter-wave (mmWave) networks. While deep learning approaches have shown strong potential, their generalization across diverse environments, robustness to noisy inputs, and sensitivity to UAV altitude remain underexplored. To address these challenges, we propose a UNet-based deep learning architecture that combines multi-scale feature extraction, convolution-based feature fusion, and an atrous spatial …

Black-hole X-ray binary Swift J1727.8$-$1613 shows Type-B quasi-periodic oscillations across the hard-intermediate and soft-intermediate states
Pei Jin, Mariano M\'endez, Federico Garc\'ia, Diego Altamirano, Federico M. Vincentelli
https://arxiv.org/abs/2510.10353

Black-hole X-ray binary Swift J1727.8$-$1613 shows Type-B quasi-periodic oscillations across the hard-intermediate and soft-intermediate states
We present a timing analysis of \textit{Insight}-HXMT observations of the black-hole X-ray binary Swift J1727.8$-$1613 across a bright soft X-ray flare on 2023 September 19 (MJD 60206). At the peak of the flare, the source undergoes a brief transition from the hard-intermediate state (HIMS) into the soft-intermediate state (SIMS), marked by the simultaneous appearance of three discrete radio jet ejections, a drop in broadband noise in the 2$-$10 keV band, and the presence of a narrow quasi-peri…

🇺🇦 Auf radioeins läuft...
The Art Of Noise:
🎵 Peter Gunn
#NowPlaying #TheArtOfNoise
https://djhazemnabil.bandcamp.com/track/peter-gunn-the-twang-mix
https://open.spotify.com/track/1MeMfXuGPbO4qWVFZ1MeAV

Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
Derek Jiu, Kiran Nijjer, Nishant Chinta, Ryan Bui, Ben Liu, Kevin Zhu
https://arxiv.org/abs/2509.25265

Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
Deep learning models are increasingly used for radiographic analysis, but their reliability is challenged by the stochastic noise inherent in clinical imaging. A systematic, cross-task understanding of how different noise types impact these models is lacking. Here, we evaluate the robustness of state-of-the-art convolutional neural networks (CNNs) to simulated quantum (Poisson) and electronic (Gaussian) noise in two key chest X-ray tasks: semantic segmentation and pulmonary disease classificati…

🇺🇦 Auf radioeins läuft...
The Art Of Noise:
🎵 Peter Gunn
#NowPlaying #TheArtOfNoise
https://djhazemnabil.bandcamp.com/track/peter-gunn-the-twang-mix
https://open.spotify.com/track/1MeMfXuGPbO4qWVFZ1MeAV

🇺🇦 Auf radioeins läuft...
Noiseshaper feat. Juggla:
🎵 All A Dem A Do
#NowPlaying #Noiseshaper #Juggla
https://open.spotify.com/track/2NP4D0ddCfGZq21VFRay5s