Tootfinder

Continued Rapid Radio Brightening of the Tidal Disruption Event AT2018hyz
Yvette Cendes, Edo Berger, Paz Beniamini, Ramandeep Gill, Tatsuya Matsumoto, Kate D. Alexander, Michael F. Bietenholz, Aprajita Hajela, Collin T. Christy, Ryan Chornock, Sebastian Gomez, Mark A. Gurwell, Garrett K. Keating, Tanmoy Laskar, Raffaella Margutti, Ramprasad Rao, Natalie Velez, Mark H. Wieringa

Continued Rapid Radio Brightening of the Tidal Disruption Event AT2018hyz
We present ongoing radio observations of the tidal disruption event (TDE) AT2018hyz, which was first detected in the radio at 972 days after disruption, following multiple non-detections from earlier searches. The new observations presented here span approximately 1370-2160 days and 0.88-240 GHz. We find that the light curves continue to rise at all frequencies during this time period, following a power law of about F ~ t^3 (compared to F_nu ~ t^5.7 at 972-1400 days), and reaching a peak lumino…

Interval-Censored Survival Analysis of Grapevine Phenology: Thermal Controls on Flowering and Fruit Ripening
Sara Behnamian, Fatemeh Fogh
https://arxiv.org/abs/2510.09702 https:…

Interval-Censored Survival Analysis of Grapevine Phenology: Thermal Controls on Flowering and Fruit Ripening
European grapevine (\textit{Vitis vinifera} L.) is a climate-sensitive perennial whose flowering and ripening govern yield and quality. Phenological records from monitoring programs are typically collected at irregular intervals, so true transition dates are interval-censored, and many site-years are right-censored. We develop a reproducible workflow that treats phenology as a time-to-event outcome: Status \& Intensity observations from the USA-NPN are converted to interval bounds, linked to NA…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/1]:
- DeepTV: A neural network approach for total variation minimization
Andreas Langer, Sara Behnamian

Crosslisted article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Interval-Censored Survival Analysis of Grapevine Phenology: Thermal Controls on Flowering and Fru...
Sara Behnamian, Fatemeh Fogh

Optimal Finsler-Hadwiger inequalities
Beniamin Bogosel
https://arxiv.org/abs/2508.06285 https://arxiv.org/pdf/2508.06285

Optimal Finsler-Hadwiger inequalities
Various inequalities exist between the area of a triangle, the perimeter squared $(a+b+c)^2$ and the isoperimetric deficit $Q=(a-b)^2+(b-c)^2+(c-a)^2$. The direct and reverse Finsler-Hadwiger inequalities correspond to the best linear inequalities between the three quantities mentioned above. In this paper, the sharpest inequalities between these three quantities are found explicitly. The techniques used involve Blaschke-Santaló diagrams and constrained optimization problems.

Shape-to-Music: A Musical Representation for Structural Topologies of Mechanical Metamaterials
Sofia Cassara, Buminhan Sansa, Saltuk Yildiz, Waris Khan, Pinar Acar
https://arxiv.org/abs/2509.09020

Shape-to-Music: A Musical Representation for Structural Topologies of Mechanical Metamaterials
Mechanical metamaterials are increasingly attracting interest in engineering applications due to their unique mechanical properties and lightweight nature. This study develops a novel sound-based representation to characterize the topologies of mechanical metamaterials, including spinodal designs and porous cellular structures. Two distinct frameworks are introduced: an image-based approach, where material topologies are divided into grids and their relative densities are mapped to evolving mel…

Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space
M. Hadi Sepanj, Benyamin Ghojogh, Paul Fieguth
https://arxiv.org/abs/2509.07289 https://

Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space
Self-supervised learning (SSL) has emerged as a powerful paradigm for representation learning by optimizing geometric objectives--such as invariance to augmentations, variance preservation, and feature decorrelation--without requiring labels. However, most existing methods operate in Euclidean space, limiting their ability to capture nonlinear dependencies and geometric structures. In this work, we propose Kernel VICReg, a novel self-supervised learning framework that lifts the VICReg objective…

dciWebMapper2: Enhancing the dciWebMapper framework toward integrated, interactive visualization of linked multi-type maps, charts, and spatial statistics and analysis
Sarigai Sarigai, Liping Yang, Katie Slack, Carolyn Fish, Michaela Buenemann, Qiusheng Wu, Yan Lin, Joseph A. Cook, David Jacobs
https://arxiv.org/abs/2509.07897

dciWebMapper2: Enhancing the dciWebMapper framework toward integrated, interactive visualization of linked multi-type maps, charts, and spatial statistics and analysis
As interactive web-based geovisualization becomes increasingly vital across disciplines, there is a growing need for open-source frameworks that support dynamic, multi-attribute spatial analysis and accessible design. This paper introduces dciWebMapper2, a significant expansion of the original dciWebMapper framework, designed to enable exploratory analysis across domains such as climate justice, food access, and social vulnerability. The enhanced framework integrates multiple map types, includi…

FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
Sara Behnamian, Rasoul Khaksarinezhad, Andreas Langer
https://arxiv.org/abs/2509.08670

FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
We present FractalPINN-Flow, an unsupervised deep learning framework for dense optical flow estimation that learns directly from consecutive grayscale frames without requiring ground truth. The architecture centers on the Fractal Deformation Network (FDN) - a recursive encoder-decoder inspired by fractal geometry and self-similarity. Unlike traditional CNNs with sequential downsampling, FDN uses repeated encoder-decoder nesting with skip connections to capture both fine-grained details and long…

The redshift distribution of Einstein Probe transients supports their relation to gamma-ray bursts
Brendan O'Connor, Paz Beniamini, Eleonora Troja, Malte Busmann, Simone Dichiara, Ramandeep Gill, Jonathan Granot, Michael Moss, Xander Hall, Antonella Palmese, Niccolo Passaleva, Yu-Han Yang
https://arxiv.org/abs/2509.07141

The redshift distribution of Einstein Probe transients supports their relation to gamma-ray bursts
The launch of the \textit{Einstein Probe} unleashed a new era of high-energy transient discovery in the largely unexplored soft X-ray band. The \textit{Einstein Probe} has detected a significant number of fast X-ray transients that display no gamma-ray emission, complicating their robust association to more common gamma-ray bursts. To explore their possible connection, we analyzed the redshift distribution of both \textit{Einstein Probe} fast X-ray transients and long duration gamma-ray bursts.…