Tootfinder

Multi-mode feedback cooling of the collective modes of a Bose-Einstein condensate
Ryan J. Thomas, Jordan A. McMahon, Zain Mehdi, Stuart S. Szigeti, Simon A. Haine, Samuel Legge, John D. Close, Joseph J. Hope
https://arxiv.org/abs/2506.19739

Multi-mode feedback cooling of the collective modes of a Bose-Einstein condensate
We experimentally demonstrate cavity-free feedback cooling of the three lowest-lying collective modes of a Bose-Einstein condensate in a prolate harmonic trap. Using shadowgraph imaging as an \textit{in situ} probe of the atomic density, we measure the time-dependent centers-of-mass and widths of the condensate in two dimensions and use these measurements to damp oscillations in the two visible dipole modes and the low-frequency quadrupole mode. By inducing oscillations in the condensate, we sh…

Duration and properties of the embedded phase of star formation in 37 nearby galaxies from PHANGS-JWST
Lise Ramambason, M\'elanie Chevance, Jaeyeon Kim, Francesco Belfiore, J. M. Diederik Kruijssen, Andrea Romanelli, Amirnezam Amiri, M\'ed\'eric Boquien, Ryan Chown, Daniel A. Dale, Simthembile Dlamini, Oleg V. Egorov, Ivan Gerasimov, Simon C. O. Glover, Kathryn Grasha, Hamid Hassani, Hwihyun Kim, Kathryn Kreckel, Hannah Koziol, Adam K. Leroy, Jos\'e Eduardo M\'endez…

Duration and properties of the embedded phase of star formation in 37 nearby galaxies from PHANGS-JWST
Light reprocessed by dust grains emitting in the infrared allows the study of the physics at play in dusty, embedded regions, where ultraviolet and optical wavelengths are attenuated. Infrared telescopes such as JWST have made it possible to study the earliest feedback phases, when stars are shielded by cocoons of gas and dust. This phase is crucial for unravelling the effects of feedback from young stars, leading to their emergence and the dispersal of their host molecular clouds. Here we show…

SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
Hanwen Liu, Qihan Zhang, Ryan Marcus, Ibrahim Sabek
https://arxiv.org/abs/2508.17556 https://a…

SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
Query optimization is a crucial problem in database systems that has been studied for decades. Learned query optimizers (LQOs) can improve performance over time by incorporating feedback; however, they suffer from cold-start issues and often require retraining when workloads shift or schemas change. Recent LLM-based query optimizers leverage pre-trained and fine-tuned LLMs to mitigate these challenges. Nevertheless, they neglect LLMs' in-context learning and execution records as feedback for co…