Tootfinder

Dissociative associated J/$\psi$ and dimuon production in Ap ultraperipheral collisions via double parton scattering
Bruna O. Stahlh\"ofer, Edgar Huayra, Emmanuel G. de Oliveira
https://arxiv.org/abs/2510.05420

Dissociative associated J/$ψ$ and dimuon production in Ap ultraperipheral collisions via double parton scattering
We study the dissociative associated production of a J/$ψ$ meson and a dimuon via double parton scattering (DPS) in nucleus--proton ultraperipheral collisions. This new channel, characterized by a rapidity gap, is sensitive to the photon and gluon distributions of the proton. We derive a DPS pocket formula for this process, together with a corresponding expression for the effective cross section. Furthermore, we demonstrate the kinematic dependence of the effective cross section and present pr…

Spatial trends in Ediacaran Bilaterian trails
Brittany A. Laing, Luis A. Buatois, M. Gabriela M\'angano, Glenn A. Brock, Zoe Vestrum, Luke C. Strotz, Lyndon Koens
https://arxiv.org/abs/2509.01104

Spatial trends in Ediacaran Bilaterian trails
The Savannah Hypothesis and the Cambrian Information Revolution invoke the development of spatially heterogeneous resource distribution during the Ediacaran-Cambrian transition as a key driver of infaunalization and sensory evolution of mobile bilaterians, respectively. However, difficulties in detecting historical resource distribution hinders the ability to tests these theories. If external conditions crucial to organism fitness (e.g. nutrient distribution, oxygen availability) became increas…

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR
Chenxin Jiang, Changhu Wang, Jingyi Jessica Li
https://arxiv.org/abs/2507.20598

Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR
Differential expression (DE) analysis is a key task in RNA-seq studies, aiming to identify genes with expression differences across conditions. A central challenge is balancing false discovery rate (FDR) control with statistical power. Parametric methods such as DESeq2 and edgeR achieve high power by modeling gene-level counts using negative binomial distributions and applying empirical Bayes shrinkage. However, these methods may suffer from FDR inflation when model assumptions are mildly viola…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees
Meshi Bashari, Yonghoon Lee, Roy Maor Lotan, Edgar Dobriban, Yaniv Romano

Temperature-dependent Photoluminescence and Raman Spectroscopy of Selenium Thin Film Solar Cells
Rasmus S. Nielsen, Axel G. Medaille, Arnau Torrens, Oriol Segura-Blanch, Se\'an R. Kavanagh, David O. Scanlon, Aron Walsh, Edgardo Saucedo, Marcel Placidi, Mirjana Dimitrievska
https://arxiv.org/abs/2507.23647…

Temperature-dependent Photoluminescence and Raman Spectroscopy of Selenium Thin Film Solar Cells
Selenium is experiencing renewed interest as a elemental semiconductor for a range of optoelectronic and energy applications due to its irresistibly simple composition and favorable wide bandgap. However, its high volatility and low radiative efficiency make it challenging to assess structural and optoelectronic quality, calling for advanced, non-destructive characterization methods. In this work, we employ a closed-space encapsulation strategy to prevent degradation during measurement and enab…

Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees
Meshi Bashari, Yonghoon Lee, Roy Maor Lotan, Edgar Dobriban, Yaniv Romano
https://arxiv.org/abs/2509.20345

Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees
The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference. This paper introduces a GEneral Synthetic-Powered Inference (GESPI) framework that wraps around any statistical inference procedure to safely enhance sample efficiency by combining synthetic and real data. Our framework leverages high-quality synthetic data to boost statistical power, …