Tootfinder

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Trapped-ion laser cooling in structured light fields
Zhenzhong Xing, Karan K. Mehta
https://arxiv.org/abs/2411.08844 https://mastoxiv.page/@arXiv_physicsatomph_bot/113480313003086521
- Lorentz and CPT violation and the (anti-)hydrogen molecular ion
Graham M. Shore
https://arxiv.org/abs/2412.09730 https://mastoxiv.page/@arXiv_physicsatomph_bot/113661506443273739
- Bremsstrahlung induced atomic processes
Singh, Kumar, Chatterjee, Swami, Kaur, Jha, Oswal, Singh, Nandi
https://arxiv.org/abs/2501.02967 https://mastoxiv.page/@arXiv_physicsatomph_bot/113786077273931578
- Clarifying Self Seed in 391-nm N$_2^{ }$ Air Lasing by Vectorially Structured Light Fields
Jingsong Gao, Yang Wang, Jiahao Dong, Yi Liu, Chengyin Wu, Hongbing Jiang, Yunquan Liu, Meng Han
https://arxiv.org/abs/2502.01414 https://mastoxiv.page/@arXiv_physicsatomph_bot/113944386182439139
- Linear dichroism signals due to the alignment in the ground state of hydrogen-like atoms and thei...
M. V. Petrenko, A. S. Pazgalev, A. K. Vershovskii
https://arxiv.org/abs/2504.20716 https://mastoxiv.page/@arXiv_physicsatomph_bot/114425750205554960
- Sub-Doppler cooling of bosonic strontium in a two-color MOT
Negyedi, Deutschle, Jessen, Fort\'agh, S\'ark\'any
https://arxiv.org/abs/2507.02693 https://mastoxiv.page/@arXiv_physicsatomph_bot/114794127138414131
- Molecular Quantum Control Algorithm Design by Reinforcement Learning
Anastasia Pipi, Xuecheng Tao, Arianna Wu, Prineha Narang, David R. Leibrandt
https://arxiv.org/abs/2410.11839 https://mastoxiv.page/@arXiv_quantph_bot/113316450427865660
- Single-atom resolved collective spectroscopy of a one-dimensional atomic array
Britton Hofer, Damien Bloch, Giulio Biagioni, Nathan Bonvalet, Antoine Browaeys, Igor Ferrier-Barbut
https://arxiv.org/abs/2412.02541 https://mastoxiv.page/@arXiv_quantph_bot/113593627161222722
- Mutual friction and vortex Hall angle in a strongly interacting Fermi superfluid
Nicola Grani, et al.
https://arxiv.org/abs/2503.21628 https://mastoxiv.page/@arXiv_condmatquantgas_bot/114238880586578320
- Lorentz and CPT violation and the (anti-)hydrogen molecular ion II -- hyperfine-Zeeman spectrum
Graham M Shore
https://arxiv.org/abs/2504.19015 https://mastoxiv.page/@arXiv_hepph_bot/114420102398265292
- Cooling of an optically levitated nanoparticle via measurement-free coherent feedback
Bruno Melo, Daniel Veldhuizen, Gregoire F. M. Tomassi, Nadine Meyer, Romain Quidant
https://arxiv.org/abs/2506.21341 https://mastoxiv.page/@arXiv_quantph_bot/114754750333187473
toXiv_bot_toot

Sen. Alex Padilla grants the Internet Archive federal depository status, making it a part of a network of over 1,100 libraries that archive government documents (Morgan Sung/KQED)
https://www.kqed.org/news/12049420/sf-based…

SF-Based Internet Archive Is Now a Federal Depository Library. What Does That Mean?
The Internet Archive, thanks to its designation by California Sen. Alex Padilla, joins a network of over 1,100 libraries that make government documents accessible to the public.

Sen. Alex Padilla grants the Internet Archive federal depository status, making it a part of a network of over 1,100 libraries that archive government documents (Morgan Sung/KQED)
https://www.kqed.org/news/12049420/sf-based…

SF-Based Internet Archive Is Now a Federal Depository Library. What Does That Mean?
The Internet Archive, thanks to its designation by California Sen. Alex Padilla, joins a network of over 1,100 libraries that make government documents accessible to the public.

A Learning-based Domain Decomposition Method
Rui Wu, Nikola Kovachki, Burigede Liu
https://arxiv.org/abs/2507.17328 https://arxiv.org…

A Learning-based Domain Decomposition Method
Recent developments in mechanical, aerospace, and structural engineering have driven a growing need for efficient ways to model and analyse structures at much larger and more complex scales than before. While established numerical methods like the Finite Element Method remain reliable, they often struggle with computational cost and scalability when dealing with large and geometrically intricate problems. In recent years, neural network-based methods have shown promise because of their ability …

ARSP: Automated Repair of Verilog Designs via Semantic Partitioning
Bingkun Yao, Ning Wang, Xiangfeng Liu, Yuxin Du, Yuchen Hu, Hong Gao, Zhe Jiang, Nan Guan
https://arxiv.org/abs/2508.16517

ARSP: Automated Repair of Verilog Designs via Semantic Partitioning
Debugging functional Verilog bugs consumes a significant portion of front-end design time. While Large Language Models (LLMs) have demonstrated great potential in mitigating this effort, existing LLM-based automated debugging methods underperform on industrial-scale modules. A major reason for this is bug signal dilution in long contexts, where a few bug-relevant tokens are overwhelmed by hundreds of unrelated lines, diffusing the model's attention. To address this issue, we introduce ARSP, a t…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 8361 nodes and 15751 edges.
Tags: Social, Collaboration…

Domain Adaptation via Feature Refinement
Savvas Karatsiolis, Andreas Kamilaris
https://arxiv.org/abs/2508.16124 https://arxiv.org/pdf/2508.16124

Domain Adaptation via Feature Refinement
We propose Domain Adaptation via Feature Refinement (DAFR2), a simple yet effective framework for unsupervised domain adaptation under distribution shift. The proposed method synergistically combines three key components: adaptation of Batch Normalization statistics using unlabeled target data, feature distillation from a source-trained model and hypothesis transfer. By aligning feature distributions at the statistical and representational levels, DAFR2 produces robust and domain-invariant feat…

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Bremsstrahlung induced atomic processes
Singh, Kumar, Chatterjee, Swami, Kaur, Jha, Oswal, Singh, Nandi
https://arxiv.org/abs/2501.02967 https://mastoxiv.page/@arXiv_physicsatomph_bot/113786077273931578
- Probing Instantaneous Single-Molecule Chirality in the Planar Ground State of Formic Acid
D. Tsitsonis, et al.
https://arxiv.org/abs/2503.13318 https://mastoxiv.page/@arXiv_physicsatomph_bot/114182276400669831
- Electronic structure calculation for superheavy elements Livermorium (Lv, Z=116) and Tennessine (...
V. A. Dzuba, V. V. Flambaum, G. K. Vong
https://arxiv.org/abs/2505.22895 https://mastoxiv.page/@arXiv_physicsatomph_bot/114595619538348153
- Fast programmable entanglement of Barium ion qubits using Rydberg states and AC-Stark shifts
Adam R. Vernon, Mitch Peaks
https://arxiv.org/abs/2506.00611 https://mastoxiv.page/@arXiv_physicsatomph_bot/114618331124166707
- Mitigating higher-band heating in Floquet-Hubbard lattices via two-tone driving
Yuanning Chen, Zijie Zhu, Konrad Viebahn
https://arxiv.org/abs/2410.12308 https://mastoxiv.page/@arXiv_condmatquantgas_bot/113321883265330530
- Constructing Quantum Many-Body Scars from Hilbert Space Fragmentation
Fan Yang, Matteo Magoni, Hannes Pichler
https://arxiv.org/abs/2506.10806 https://mastoxiv.page/@arXiv_quantph_bot/114675529198570764
toXiv_bot_toot

On Zero-Shot Reinforcement Learning
Scott Jeen
https://arxiv.org/abs/2508.16496 https://arxiv.org/pdf/2508.16496 …

On Zero-Shot Reinforcement Learning
Modern reinforcement learning (RL) systems capture deep truths about general, human problem-solving. In domains where new data can be simulated cheaply, these systems uncover sequential decision-making policies that far exceed the ability of any human. Society faces many problems whose solutions require this skill, but they are often in domains where new data cannot be cheaply simulated. In such scenarios, we can learn simulators from existing data, but these will only ever be approximately cor…

Sequential Cohort Selection
Hortence Phalonne Nana, Christos Dimitrakakis
https://arxiv.org/abs/2508.16386 https://arxiv.org/pdf/2508.16386

Sequential Cohort Selection
We study the problem of fair cohort selection from an unknown population, with a focus on university admissions. We start with the one-shot setting, where the admission policy must be fixed in advance and remain transparent, before observing the actual applicant pool. In contrast, the sequential setting allows the policy to be updated across stages as new applicant data becomes available. This is achieved by optimizing admission policies using a population model, trained on data from previous a…