Tootfinder

Will we ever see the shooter again, hear anything about him, or will it be like he hardly existed, like a second page of search results?
The truth itself seems to be in more danger than potus ever was.
#USpol #WHCD

The file-based markdown version of #logseq is officially put into maintenance-only mode or rather fully retired. The software has not even reached version 1.0 and will be stopped being developed.
Sadly that's what many warned of, when the devs started working on the database version.

I just had to … ¯\_(ツ)_/¯
#shimmyshimmyya

edit_wikibooks: Wikipedia book edits (2010)
Two bipartite user-page networks extracted from Wikipedia, about books. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 960 nodes and 1308 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

There are some fascinating topics in the 'Critical Approaches to Libraries Conference' CALC26 programme https://sites.google.com/view/calcconference/calc2026

[2026-03-27 Fri (UTC), 8 new articles found for physics.chem-ph Chemical Physics]
toXiv_bot_toot

does anybody happen to know if the results of the #europeana Linked Data Task Force have been published somewhere?
This page here https://pro.europeana.eu/index.php/projec…

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Time-resolving the birth of photoelectrons in strong-filed ionization with an isolated attosecond...
Kunlong Liu, Yidian Tian, Pengcheng Li

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Smoothing and spatial verification of global fields
Gregor Skok, Katarina Kosovelj
https://arxiv.org/abs/2412.00936 https://mastoxiv.page/@arXiv_physicsaoph_bot/113587890587543610
- Radiosonde-constrained reconstructions reveal a weakening Northern Hadley circulation
Matic Pikovnik, \v{Z}iga Zaplotnik
https://arxiv.org/abs/2503.05331 https://mastoxiv.page/@arXiv_physicsaoph_bot/114137034331234752
- Non-stationary time series attribution for heatwaves over Europe
Pascal Meurer, Sebastian Buschow, Svenja Szemkus, Petra Friederichs
https://arxiv.org/abs/2601.05841 https://mastoxiv.page/@arXiv_physicsaoph_bot/115881278944409798
- Enabling High-Accuracy Data Assimilation with Limited Ensembles via Machine Learning-Based Covari...
Zhou Yao, Zhilin Li, Li Zhao, Zeng Liu, Zhaokuan Lu, Seungnam Kim, Guangyao Wang
https://arxiv.org/abs/2605.11639 https://mastoxiv.page/@arXiv_physicsaoph_bot/116566237831166337
toXiv_bot_toot

📰 Vielen Dank für Eure #Spenden zur Unterstützung der Technikkosten, um alles am Laufen zu halten. Shoutout an alle #Steady-Förderer! 👍
Es geht los: Die vermaledeiten Amazon-Affiliate-Links werden seit Februar schrittweise gelöscht.
Hier kann man Steady-Mäzen werden:

KlimaCrew
Ich informiere über Zukunftsthemen, um Menschen vor Risiken zu warnen und Fortschritte sichtbar zu machen.

Introducing pixelation with applications
J. Daisie Rock
https://arxiv.org/abs/2603.25432 https://arxiv.org/pdf/2603.25432 https://arxiv.org/html/2603.25432
arXiv:2603.25432v1 Announce Type: new
Abstract: Motivated by the desire for a new kind of approximation, we define a type of localization called pixelation. We present how pixelation manifests in representation theory and in the study of sites and sheaves. A path category is constructed from a set, a collection of "paths" into the set, and an equivalence relation on the paths. A screen is a partition of the set that respects the paths and equivalence relation. For a commutative ring, we also enrich the path category over its modules (=linearize the category with respect to the ring) and quotient by an ideal generated by paths (possibly 0). The pixelation is the localization of a path category, or the enriched quotient, with respect to a screen. The localization has useful properties and serves as an approximation of the original category. As applications, we use pixelations to provide a new point of view of the Zariski topology of localized ring spectra, provide a parallel story to a ringed space and sheaves of modules, and construct a categorical generalization of higher Auslander algebras of type $A$.
toXiv_bot_toot

Crosslisted article(s) found for math.GN. https://arxiv.org/list/math.GN/new
[1/1]:
- Polish topologies on endomorphism monoids of linear orders
Serhii Bardyla, Luna Elliott

Diffusion in interacting two-dimensional systems under a uniform magnetic field
{\L}ukasz Iwanek, Marcin Mierzejewski, Adam S. Sajna
https://arxiv.org/abs/2603.25659 https://

Roger Dooley does not have a Wikipedia page. Which is odd, considering his interesting life.
Spanish galleon San José does have a Wikipedia page on which Dooley is not mentioned. Which is odd, considering Dooley discovered the wreck.
"Neptune's Fortune: The Billion-Dollar Shipwreck and the Ghosts of the Spanish Empire", a most interesting en well-researched book by by Julian Sancton.

Ahh I just noticed that when I import GPX tracks into #wanderer, it creates routes but no activity. That's why my stats page is empty (and as I found it out, I remember that I asked that... A long while ago).
Now I wonder, if I want to import and add the activities for 2026... Doing it retrospectively for 2025 is too much effort to me (with too little value). 🤔

RE: https://mastodon.social/@arstechnica/116618619694373891
“‘To cut and paste page by page, the text from each page would have been an hour’s worth of work, of mindless cutting and pasting. ChatGPT did it in about four seconds.’
To which the obv…

Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
Gabriel Melo, Leonardo Santiago, Peter Y. Lu
https://arxiv.org/abs/2604.21097 https://

[2026-03-27 Fri (UTC), 8 new articles found for physics.chem-ph Chemical Physics]
toXiv_bot_toot

Modeling the mutational dynamics of very short tandem repeats
Amos Onn (Chair of Experimental Medicine and Therapy Research, University of Regensburg, Bioinformatics Group, Faculty of Mathematics and Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig), Tzipy Marx (Department of Computer Science and Applied Mathematics, Weizmann Institute of Science), Liming Tao (Cellular Tissue Genomics, Genentech), Tamir Biezuner (Department of Computer Science and Applied Mathematics, Weizmann Institute of Science), Ehud Shapiro (Department of Computer Science and Applied Mathematics, Weizmann Institute of Science), Christoph A. Klein (Chair of Experimental Medicine and Therapy Research, University of Regensburg, Fraunhofer Institute for Toxicology and Experimental Medicine Regensburg), Peter F. Stadler (Bioinformatics Group, Faculty of Mathematics and Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Max Planck Institute for Mathematics in the Sciences, Institute for Theoretical Chemistry, University of Vienna, Facultad de Ciencias, Universidad Nacional de Colombia, Center for non-coding RNA in Technology and Health, University of Copenhagen, Santa Fe Institute)
https://arxiv.org/abs/2603.25628 https://arxiv.org/pdf/2603.25628 https://arxiv.org/html/2603.25628
arXiv:2603.25628v1 Announce Type: new
Abstract: Short tandem repeats (STRs) are low-entropy regions in the genome, consisting of a short (1-6 bp) unit that is consecutively repeated multiple times. They are known for high mutational instability, due to so-called stutter-mutations, in which the number of units in the run increases or descreases. In particular, STRs with repeat unit length of 1-2 bp are prone to mutate even within several cell divisions. The extremely rapid accumulation of variation makes them interesting phylogenetic markers for retrospective single-cell lineage reconstruction. Here we model their mutational dynamics at the level of individual repeat unit type and then aggregate length variations over many STR loci with the aim of obtaining a very fast ``molecular clock''. We calibrate our model based on several datasets with known lineage structure prepared from cultured cells. We find that the mutational dynamics of STRs are reasonably consistent for a given cell line, but vary among different ones. This suggests that the dynamics are not entirely explained by mutations in caretaker genes, rather, various other factors play a role -- possibly tissue origin and differentiation state. Further data and research is necessary to asses their relative effects.
toXiv_bot_toot

Rules of the game:
- Grab the nearest book.
- Turn to page 42
- Find the 2nd sentence
- Post the sentence in a toot with the hashtag & write the rules as a comment to it
- Don't look for your favourite, coolest or wittiest book. Go for the closest.

The mediocrity of well-paid people whose only job is to make the line go up is really something to behold.
#KLM #inspirationalUpsellHeader #inspirationalUpsellToggle…

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Toward nanophotonic platforms for solid-state $^{229}$Th nuclear clocks
Sandro Kraemer, et al.

Got myself a native USD scene loader for Godot using the Pixar library.
This is from Robin’s page who need snipped me: https://rystorm.com/blog/usd-example-assets-v1-1

[2026-03-27 Fri (UTC), no new articles found for physics.pop-ph Popular Physics]
toXiv_bot_toot

Justice Department settles lawsuit brought by Trump-Russia probe subject Carter Page (NBC News)
https://www.nbcnews.com/politics/justice-department/justice-department-settles-carter-page-lawsuit-trump-russia-probe-rcna341640
http://www.memeorandum.com/260423/p89#a260423p89

This account and accounts that I own ( @… , @… ) now have #AltText for both profile picture and header.
Available on Mastodon…

A note on Gurzadyan theorem
Christian Carimalo
https://arxiv.org/abs/2603.25323 https://arxiv.org/pdf/2603.25323 …

Replaced article(s) found for math.KT. https://arxiv.org/list/math.KT/new
[1/1]:
- On higher Du Bois singularities and $K$-regularity
Wanchun Shen
https://arxiv.org/abs/2504.12402 https://mastoxiv.page/@arXiv_mathAG_bot/114357763878794264
- Higher Koszul duality and $n$-affineness
James Pascaleff, Emanuele Pavia, Nicol\`o Sibilla
https://arxiv.org/abs/2504.16935 https://mastoxiv.page/@arXiv_mathAG_bot/114397398626428739
- Growth in noncommutative algebras and entropy in derived categories
Dmitri Piontkovski
https://arxiv.org/abs/2604.13373 https://mastoxiv.page/@arXiv_mathRA_bot/116413312400294881
toXiv_bot_toot

Lagged sea-surface-temperature precursors of the leading PM2.5 mode in China
Yuan Chen, Dan Zhao, Xu Li
https://arxiv.org/abs/2605.25436 https://arxiv.org/pdf/2605.25436 https://arxiv.org/html/2605.25436
arXiv:2605.25436v1 Announce Type: new
Abstract: Fine particulate matter(PM2.5) pollution in China is strongly modulated bymeteorological variability, yet its seasonal predictability from oceanic signals remains unclear. Here we identify the leading PM2.5 variability mode over China and show that it is preceded by coherent sea-surface-temperature anomaly clusters by more than one season. These oceanic precursors influence summer PM2.5 mainly by altering precipitation and lowlevel ventilation, and winter PM2.5 by modulating boundary-layer height and near-surface stagnation. Using the four largest precursor regions, a simple regression model achieves significant independent prediction skill for both summer and winter PM2.5 variability. Our results reveal a physical pathway linking sea-surface-temperature memory to regional aerosol pollution and provide a basis for seasonal air-quality risk assessment.
toXiv_bot_toot

webkb: WebKB graphs (1998)
Web graphs crawled from four Computer Science departments in 1998, with each page manually classified into one of 7 categories: course, department, faculty, project, staff, student, or other. All graphs included in a single .zip; also included are 'co-citation' graphs, which links i and j if they both point to some k. Edge weights count the number of links from i to j.
This network has 286 nodes and 1002 edges.
Tags: Informational, Web gra…

Josephson effects in an interaction-asymmetric junction across the BCS-BEC crossover
Tingyu Zhang, Hiroyuki Tajima
https://arxiv.org/abs/2603.25577 https://

Quantum statistics on atom-ion Feshbach resonances
Joachim Siemund, Fabian Thielemann, Jonathan Grieshaber, Wei Wu, Patrick Mullan, Panagiotis Giannakeas, Krzysztof Jachymski, Tobias Schaetz
https://arxiv.org/abs/2606.26995

Looking at Brom's awesome artworks:
#art

Automating Computational Chemistry Workflows via OpenClaw and Domain-Specific Skills
Mingwei Ding, Chen Huang, Yibo Hu, Yifan Li, Zitian Lu, Xingtai Yu, Duo Zhang, Wenxi Zhai, Tong Zhu, Qiangqiang Gu, Jinzhe Zeng
https://arxiv.org/abs/2603.25522 https://arxiv.org/pdf/2603.25522 https://arxiv.org/html/2603.25522
arXiv:2603.25522v1 Announce Type: new
Abstract: Automating multistep computational chemistry tasks remains challenging because reasoning, workflow specification, software execution, and high-performance computing (HPC) execution are often tightly coupled. We demonstrate a decoupled agent-skill design for computational chemistry automation leveraging OpenClaw. Specifically, OpenClaw provides centralized control and supervision; schema-defined planning skills translate scientific goals into executable task specifications; domain skills encapsulate specific computational chemistry procedures; and DPDispatcher manages job execution across heterogeneous HPC environments. In a molecular dynamics (MD) case study of methane oxidation, the system completed cross-tool execution, bounded recovery from runtime failures, and reaction network extraction, illustrating a scalable and maintainable approach to multistep computational chemistry automation.
toXiv_bot_toot

The Self-Replication Phase Diagram: Mapping Where Life Becomes Possible in Cellular Automata Rule Space
Don Yin
https://arxiv.org/abs/2603.25239 https://arxiv.org/pdf/2603.25239 https://arxiv.org/html/2603.25239
arXiv:2603.25239v1 Announce Type: new
Abstract: What substrate features allow life? We exhaustively classify all 262,144 outer-totalistic binary cellular automata rules with Moore neighbourhood for self-replication and produce phase diagrams in the $(\lambda, F)$ plane, where $\lambda$ is Langton's rule density and $F$ is a background-stability parameter. Of these rules, 20,152 (7.69%) support pattern proliferation, concentrated at low rule density ($\lambda \approx 0.15$--$0.25$) and low-to-moderate background stability ($F \approx 0.2$--$0.3$), in the weakly supercritical regime (Derrida coefficient $\mu = 1.81$ for replicators vs. $1.39$ for non-replicators). Self-replicating rules are more approximately mass-conserving (mass-balance 0.21 vs. 0.34), and this generalises to $k{=}3$ Moore rules. A three-tier detection hierarchy (pattern proliferation, extended-length confirmation, and causal perturbation) yields an estimated 1.56% causal self-replication rate. Self-replication rate increases monotonically with neighbourhood size under equalised detection: von Neumann 4.79%, Moore 7.69%, extended Moore 16.69%. These results identify background stability and approximate mass conservation as the primary axes of the self-replication phase boundary.
toXiv_bot_toot

JAX-SCM v1.0: a modern atmospheric single-column model for boundary layer research
Maximilian Pierzyna
https://arxiv.org/abs/2605.24544 https://arxiv.org/pdf/2605.24544 https://arxiv.org/html/2605.24544
arXiv:2605.24544v1 Announce Type: new
Abstract: We present JAX-SCM v1.0, an open-source atmospheric single-column model for boundary layer research, implemented in Python using the JAX computing library. The model solves for horizontal wind, potential temperature, and specific humidity, combined with prognostic turbulent kinetic energy and turbulent statistics parameterized by the Mellor-Yamada-Nakanishi-Niino level-2.5 (MYNN-2.5) turbulence closure. We verify the implementation against three well-established benchmark cases covering neutral (turbulent Ekman layer), stable (GABLS1), and convective (Wangara Day 33) conditions. Close agreement with reference solutions is demonstrated across all regimes. By building on JAX, the model benefits from just-in-time compilation and native GPU support. While JAX-SCM is not yet fully differentiable, basing it on JAX also lays the foundation for future integration with machine learning components. The model is designed for simplicity and modularity, lowering the barrier to entry for users and developers alike.
toXiv_bot_toot

Infinite-time surface flux for full-dimensional three-body breakup dynamics
Jinzhen Zhu
https://arxiv.org/abs/2606.26178 https://arxiv.org/pdf/2606.26178…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation
David Holzm\"uller, Francis Bach

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
P. Bern\'at Szab\'o, Zeno Sch\"atzle, Frank No\'e
https://arxiv.org/abs/2603.25381 https://arxiv.org/pdf/2603.25381 https://arxiv.org/html/2603.25381
arXiv:2603.25381v1 Announce Type: new
Abstract: A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated systems. Transferable deep-learning variational Monte Carlo (VMC) offers a promising route by efficiently solving the electronic Schr\"odinger equation jointly across molecular geometries at consistently high accuracy, yet its stochastic nature renders direct exploration of molecular configuration space nontrivial. Here, we present a framework for highly accurate ab initio exploration of PESs that combines transferable deep-learning VMC with a cost-effective estimation of energies, forces, and Hessians. By continuously sampling nuclear configurations during VMC optimization of electronic wave functions, we obtain transferable descriptions that achieve zero-shot chemical accuracy within chemically relevant distributions of molecular geometries. Throughout the subsequent characterization of molecular configuration space, the PES is evaluated only sparsely, with local approximations constructed by estimating VMC energies and forces at sampled geometries and aggregating the resulting noisy data using Gaussian process regression. Our method enables accurate and efficient exploration of complex PES landscapes, including structure relaxation, transition-state searches, and minimum-energy pathways, for both ground and excited states. This opens the door to studying bond breaking, formation, and large structural rearrangements in systems with pronounced multi-reference character.
toXiv_bot_toot

Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers
Minghui Qiu, Jun Chen, Lin Chen, Weifeng Chen, Shuxin Zhong, Zhidan Liu, Yu Zhang, Kaishun Wu
https://arxiv.org/abs/2605.24067 https://arxiv.org/pdf/2605.24067 https://arxiv.org/html/2605.24067
arXiv:2605.24067v1 Announce Type: new
Abstract: Most nowcasting systems, built on radar reflectivity, focus on current precipitation, ignoring the atmospheric precursors -- such as low-level convergence, turbulent eddies, and latent heating -- that offer a fleeting window to foresee storm birth. We introduce MeteoLogist, a physics-inspired radar intelligence framework that models the full life cycle of convection -- from its precursors to organized storm evolution. However, exploiting these precursors is non-trivial: they originate from multiple meteorological drivers -- thermodynamic, kinematic, and microphysical -- that evolve asynchronously (C1) and remain spatially fragmented (C2). To this end, MeteoLogist designs three tightly integrated components. The Physics-Tailored Encoders process radar echoes according to their intrinsic physical scales and semantics, forming thermodynamic, kinematic, and microphysical streams that capture distinct dynamical regimes. The Temporal-Phase Aligner addresses C1 by leveraging causal temporal attention to capture when and how different drivers interact and activate. The Cross-Field Spatial Aggregator addresses C2 through cross-regional fusion, aligning weak and scattered precursors across neighboring cells to expose upstream triggers and enforce spatial coherence. Evaluated on 3D-NEXRAD (2020--2022, US-wide), MeteoLogist boosts high-impact detection (CSI40) by 9.7% over strong baselines, and achieves a remarkable 37.67% gain during the storm-developing stage -- demonstrating true foresight in sensing storms before they appear. The code can be found in the supplementary material.
toXiv_bot_toot

[2026-06-26 Fri (UTC), 2 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Complementary Eigen-Zundel Interpretation Reconciles Thermodynamics and Spectroscopy of Excess Protons in Aqueous HF Solutions
Louis Lehmann, Florian N. Br\"unig, Jonathan Scherlitzki, Morten Lehmann, Martin Kaupp, Beate Paulus, Roland R. Netz
https://arxiv.org/abs/2603.25371 https://arxiv.org/pdf/2603.25371 https://arxiv.org/html/2603.25371
arXiv:2603.25371v1 Announce Type: new
Abstract: Aqueous solutions of HF and HCl behave very differently at intermediate concentrations: HCl dissociates completely, whereas HF remains only partially dissociated and forms bifluoride (HF$_2^-$). This should lead to different excess-proton spectra in HF and HCl solutions, in contrast to experimental reports. Using ab initio molecular dynamics, we show that in HF the proton is not firmly bound to F$^-$, as suggested by textbook chemistry, but dynamically shared with a hydrating water molecule. This is rationalized by a modified Eigen-state description which also explains the formation of HF$_2^-$. The similar vibrational spectra of HF and HCl solutions are explained by a complementary Zundel picture in terms of almost identical excess proton transfer free-energy profiles for HF and HCl. These results reconcile thermodynamic and spectroscopic observations and provide a unified microscopic picture of excess protons in aqueous solution.
toXiv_bot_toot

Volador 1.0: A Data-Driven Air-Sea Full-Coupling Regional Forecast Model with Submesoscale-Permitting Based on MOE-Swin-Transformer Framework
Yuhang Zhu, Jianxin Wang, Yu-kun Qian, Yineng Li, Yahui Liu, Yankun Gong, Shilin Tang, Shiqiu Peng, Tao Song
https://arxiv.org/abs/2605.24032 https://arxiv.org/pdf/2605.24032 https://arxiv.org/html/2605.24032
arXiv:2605.24032v1 Announce Type: new
Abstract: A data-driven air-sea full-coupling regional forecast model with submesoscale-permitting, named "Volador 1.0", is developed for the South China Sea (SCS). The model features a Swin-Transformer framework integrated with a Mixture-of-Experts (MoE) system, a latent space interaction architecture based on Cross-Grid Bidirectional Cross-Attention, and a fast-slow dual-branch architecture. Both the three-month hindcast test and the 15-day operational real-time forecasting demonstrate that Volador 1.0 has a very encouraging and promising performance in 0-72h forecasting of temperature and salinity in the 0-500m upper ocean as well as the sea surface height with root-mean-square-error (RMSE) or mean absolute error (MAE) smaller than or at least comparable to those from the reanalysis datasets REDOS V2.0 and GLORYS12 and the state-of-the-art regional numerical model Regional Ocean Modeling System (ROMS). In particular, Volador 1.0 demonstrates its capability of capturing/forecasting submesoscale processes including internal waves, with an energy spectrum well representing sub- to mesoscale energy cascade as expected by the classical turbulence theory. Further analysis based on ablation experiments shows that the air-sea full-coupling framework, which takes into account the dynamic exchanges of momentum and heat fluxes between the atmosphere and the ocean, indeed helps improve the model's performance compared to the non-full-coupling one. Volador 1.0, though still subject to refinement in the coming future with a large space for improvement, blazes a path for an accurate, fine and fast marine environment forecasting, and thus could help promote our capability of disaster prevention and mitigation in the SCS as well as in other coastal regions where these innovative techniques can be applied.
toXiv_bot_toot

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Scattering theory for cavity-assisted spin-motion-photon interactions
Seigo Kikura, Aruku Senoo, Akihisa Goban, Shinichi Sunami

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Regime-Calibrated Demand Priors for Ride-Hailing Fleet Dispatch and Repositioning
Indar Kumar, Akanksha Tiwari

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates
Toshifumi Mori, Kei-ichi Okazaki, Kang Kim, Nobuyuki Matubayasi
https://arxiv.org/abs/2603.25237 https://arxiv.org/pdf/2603.25237 https://arxiv.org/html/2603.25237
arXiv:2603.25237v1 Announce Type: new
Abstract: In complex molecular systems, the reaction coordinate (RC) that characterizes transition pathways is essential to understand underlying molecular mechanisms. This review surveys a framework for identifying the RC by applying deep learning to the committor, which provides the most reliable measure of the progress along a transition path. The inputs to the neural network are collective variables (CVs) expressed as functions of atomic coordinates of the system, and the corresponding RC is predicted as the output by training the network on the committor as the learning target. Because deep learning models typically operate in a black-box manner, it is difficult to determine which input variables govern the predictions. The incorporation of eXplainable Artificial Intelligence (XAI) techniques enables quantitative assessment of the contributions of individual input variables to the predictions. This approach allows the identification of CVs that play dominant roles and demonstrates that the committor distribution on the surface using important CVs is separated by well-defined boundaries. The framework provides an explainable deep learning strategy for assigning a molecular mechanism from the RC and is applicable to a wide range of complex molecular systems.
toXiv_bot_toot

Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
Zachary James, Joseph Guinness, Arthur DeGaetano
https://arxiv.org/abs/2605.24009 https://arxiv.org/pdf/2605.24009 https://arxiv.org/html/2605.24009
arXiv:2605.24009v1 Announce Type: new
Abstract: Convective available potential energy (CAPE) is an important variable for forecasting severe weather and understanding deep convection and precipitation. The latest versions of the Global Forecast System (GFS) and related Global Ensemble Forecast System (GEFS) have exhibited a bias towards underestimating CAPE values during the summertime. We train an artificial intelligence (AI) diffusion model to improve the skill and uncertainty quantification of afternoon 6-hour lead time ensemble forecasts over the United States. Our model takes a GFS CAPE forecast as input and outputs an ensemble that significantly outperforms both GFS and GEFS 6-hour forecasts on root mean square error, continuous ranked probability score, and Brier score. We propose a two-stage training pipeline to leverage both a larger historical GFS forecast dataset and a smaller historical GEFS dataset, despite the two using initialization and parameterization schemes that vary over time. We also show that classifier-free guidance can be used to control the skill and spread of the forecasts. We then demonstrate the versatility of our framework by adding aerosol optical depths (AODs) of black carbon, organic carbon, dust, sea salt, and sulfates as additional input features. Aerosols can invigorate or suppress convection depending on atmospheric conditions. Our AI models effectively incorporate aerosols to produce improved CAPE forecasts. We interpret the model components by using permutation feature importance to rank the influence of the different AODs and find that black carbon, organic carbon, and sulfate aerosols have a greater impact on the model's CAPE predictions than sea salt and dust aerosols.
toXiv_bot_toot

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Atomic-referenced Hz-linewidth lasers via fiber interferometric stabilization
Changmin Ahn, Hansol Jeong, Seoyeon Yang, Junyong Choi, Igju Jeon, Hanseb Moon, Jungwon Kim

A sustainable photocatalytic pathway for concurrent hydrogen and value-added chemical production utilizing microalgae as bio-scavenger in water
Ho Truong Nam Hai, Augusto Ducati Luchessi, Kaveh Edalati
https://arxiv.org/abs/2603.24924 https://arxiv.org/pdf/2603.24924 https://arxiv.org/html/2603.24924
arXiv:2603.24924v1 Announce Type: new
Abstract: Microalgae are an abundant bioorganic material source and play a significant role in life on Earth by conducting photosynthesis for carbon dioxide (CO2) capture and its conversion to oxygen (O2). In this study, a combination of microalgae as a negative-CO2-emitting sacrificial agent with the traditional photocatalytic water-splitting process using brookite TiO2, as a model photocatalyst, is introduced as a new strategy to maximize green hydrogen (H2) production while converting microalgae to valuable products, like methane (CH4) and carbon monoxide (CO). The process, under optimal conditions, produces up to 0.990 mmol/g.h of H2 without cocatalyst addition and 3.200 mmol/g.h with platinum (Pt) cocatalyst, which is 13 times higher than the production rate without microalgae. The strategy of using microalgae in photocatalysis has high potential in green H2 production, as it not only eliminates valuable hole sacrificial agents, like alcohol, but also produces other useful compounds, like CH4 and CO. Moreover, this sustainable process contributes to CO2 capture and conversion during microalgae cultivation.
toXiv_bot_toot

Quantification of atmospheric carbon dioxide from the Geostationary Operational Environmental Satellite (GOES East)
Aaron Sonabend-W, Sean Campbell, John Platt, Christopher Van Arsdale, Anna M. Michalak
https://arxiv.org/abs/2605.23991 https://arxiv.org/pdf/2605.23991 https://arxiv.org/html/2605.23991
arXiv:2605.23991v1 Announce Type: new
Abstract: There is a growing urgency to track greenhouse gasses with the resolution, precision and accuracy needed to support independent verification of $CO_2$ fluxes at local to global scales. The current generation of space-based sensors, however, only provides sparse observations in space and time. This challenge has fueled interest in the potential use of data from existing missions originally developed for other applications for inferring global greenhouse gas variability. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-East), operational since 2017, provides full coverage of much of the western hemisphere at 10-minute intervals from geostationary orbit at 16 wavelengths at an approximately 2$km^2$ spatial resolution. Here, we leverage this high spatial coverage and temporal revisit to develop a single-pixel, physics-guided neural network to estimate dry-air column $CO_2$ mole fraction ($XCO_2$). The model employs a time series of GOES-East's 16 spectral bands, ECMWF ERA5 lower tropospheric meteorology, MODIS surface reflectance, solar and satellite viewing geometry, and day of year. Training used collocated GOES-East and OCO-2/OCO-3 observations. We also present case studies illustrating the use of the model to observe $XCO_2$ enhancements over urban areas and drawdown over agricultural regions. Overall, while the precision of GOES-East derived $XCO_2$ can never rival that of dedicated instruments, the unprecedented combination of contiguous geographic coverage, 10-minute temporal frequency, and multi-year record offers the potential to observe aspects of atmospheric $CO_2$ variability currently unseen from space.
toXiv_bot_toot

Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors
Sherly Alfonso-S\'anchez, Cristi\'an Bravo, Kristina G. Stankova
https://arxiv.org/abs/2604.21893

Experimental and theoretical studies of hyperfine structures in $^{21}$Na
Junho Won, Jeongsu Ha, Deuk Soon Ahn, Sunghoon Ahn, Vivek Chavan, Anastasiia Chekhovska, Gyoungmo Gu, Kevin Insik Hahn, Seongjin Heo, Jangyong Huh, Dahee Kim, Do Gyun Kim, Dong Geon Kim, Jung Bog Kim, Sunji Kim, Yeong Seok Kim, Yung Hee Kim, Zeren Korkulu, Donghyeon Kwak, Jens Lassen, Jin Ho Lee, Jung Woo Lee, Chaeyeong Lim, Joochun Park, Ben Ohayon, Sung Jong Park, Xesus Pereira-Lopez, Peter Plattner, Sung Jae P…

Implementation of the multigrid Gaussian-Plane-Wave algorithm with GPU acceleration in PySCF
Rui Li, Xing Zhang, Qiming Sun, Yuanheng Wang, Junjie Yang, Garnet Kin-Lic Chan
https://arxiv.org/abs/2603.24881 https://arxiv.org/pdf/2603.24881 https://arxiv.org/html/2603.24881
arXiv:2603.24881v1 Announce Type: new
Abstract: We introduce a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting (FFTDF) approach for efficient Fock builds and nuclear gradient evaluations within Kohn-Sham density functional theory, as implemented in the GPU4PySCF module of PySCF. Our CUDA kernels employ a grid-based parallelization strategy for contracting Gaussian basis function pairs and achieve up to 80% of the FP64 peak performance on NVIDIA GPUs, with no loss of efficiency for high angular momentum (up to f-shell) functions. Benchmark calculations on molecules and solids with up to 1536 atoms and 20480 basis functions show up to 25x speedup on an H100 GPU relative to the CPU implementation on a 28-core shared memory node. For a 256-water cluster, the ground-state energy and nuclear gradients can be computed in ~30 seconds on a single H100 GPU. This implementation serves as an open-source foundation for many applications, such as ab initio molecular dynamics and high-throughput calculations.
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[2026-05-26 Tue (UTC), 6 new articles found for physics.ao-ph Atmospheric and Oceanic Physics]
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Permeation of hydrogen across graphdiyne: molecular dynamics vs. quantum simulations and role of membrane motion
Mateo Rodr\'iguez, Jos\'e Campos-Mart\'inez, Marta I. Hern\'andez
https://arxiv.org/abs/2603.24827 https://arxiv.org/pdf/2603.24827 https://arxiv.org/html/2603.24827
arXiv:2603.24827v1 Announce Type: new
Abstract: Previous research based on electronic structure calculations and molecular dynamics (MD) simulations have demonstrated that graphdiyne (GDY) is a very suitable two-dimensional membrane for the separation of small molecules in a gas mixture of different species. However, quantum effects may play a role in the dynamics of these permeation processes when light molecules are the ones involved in the crossing of the GDY subnanometric pores. In this work we report rigorous quantum-mechanical calculations together with equivalent MD simulations of the transport of H2 molecules through a static GDY membrane, as a case study for the validity of the application to these problems of classical dynamics. The force fields employed are based on an improved Lennard-Jones formulation, with parameters optimized by means of accurate ab initio calculations. It is found that, although quantum effects are still significant at the temperatures of interest (between 250 and 350 K), MD simulations are able to reasonably reproduce the dependence of the quantum permeances with the temperature. Moreover, MD permeances computed with quantum corrections through Feynman-Hibbs effective potentials provide a lower bound to quantum permeances, while the pure classical counterpart gives an upper bound, thus leading to a well delimited range of confidence of the permeation results. Furthermore, within MD simulations it is possible to incorporate the thermal motion of the GDY layer and in this situation it is observed an enhancement of the permeances with respect to the fixed membrane case, due to a significant reduction of the permeation barriers when the GDY atoms are allowed to vibrate. It seems apparent therefore, that modeling the membrane motion is crucial to provide reliable simulations of the gas transport features.
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Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Non-equilibrium pathway to mesoscale ordering in ethanol-water binary liquid
Xinyue Jiang, Yating Shang, Jianhui Li, Zhaoyong Zou, Yanxia Zuo, Yuqun Xie

Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions
Di Wu, Ling Liang, Haizhao Yang
https://arxiv.org/abs/2604.21849

Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
Vivienne Pelletier, Vedant Bhat, Daniel J. Rivera, Steven A. Wilson, Christopher L. Muhich
https://arxiv.org/abs/2603.24752 https://arxiv.org/pdf/2603.24752 https://arxiv.org/html/2603.24752
arXiv:2603.24752v1 Announce Type: new
Abstract: Machine-learned interatomic potentials (MLIPs), particularly graph neural network (GNN)-based models, offer a promising route to achieving near-density functional theory (DFT) accuracy at significantly reduced computational cost. However, their practical deployment is often limited by the large volumes of expensive quantum mechanical training data required. In this work, we introduce a transfer learning framework, Transfer-PaiNN (T-PaiNN), that substantially improves the data efficiency of GNN-MLIPs by leveraging inexpensive classical force field data. The approach consists of pretraining a PaiNN MLIP architecture on large-scale datasets generated from classical molecular simulations, followed by fine-tuning (dubbed autotuning) using a comparatively small DFT dataset. We demonstrate the effectiveness of autotuning T-PaiNN on both gas-phase molecular systems (QM9 dataset) and condensed-phase liquid water. Across all cases, T-PaiNN significantly outperforms models trained solely on DFT data, achieving order-of-magnitude reductions in mean absolute error while accelerating training convergence. For example, using the QM9 data set, error reductions of up to 25 times are observed in low-data regimes, while liquid water simulations show improved predictions of energies, forces, and experimentally relevant properties such as density and diffusion. These gains arise from the model's ability to learn general features of the potential energy surface from extensive classical sampling, which are subsequently refined to quantum accuracy. Overall, this work establishes transfer learning from classical force fields as a practical and computationally efficient strategy for developing high-accuracy, data-efficient GNN interatomic potentials, enabling broader application of MLIPs to complex chemical systems.
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Emergent conservation in atmospheric chemical mechanisms
Beatriz Lucia G. Rodriguez, Patrick Obin Sturm, Daniel Getter, Sam J. Silva
https://arxiv.org/abs/2605.27271 https://

Self-calibrated multiparameter measurement of three-dimensional microwave fields
Yupeng Wang, Xinghan Wang, Aishik Panja, Md. Ehsanuzzaman, Chuan-Hsun Li, Qi-Yu Liang
https://arxiv.org/abs/2605.26098

Automating Computational Chemistry Workflows via OpenClaw and Domain-Specific Skills
Mingwei Ding, Chen Huang, Yibo Hu, Yifan Li, Zitian Lu, Xingtai Yu, Duo Zhang, Wenxi Zhai, Tong Zhu, Qiangqiang Gu, Jinzhe Zeng
https://arxiv.org/abs/2603.25522

From Vintage Mythology to Topological Physics: Unveiling a Universal Structural Attractor in Alcoholic Beverage Aging
Xinyue Jiang, Heng Yang, Zhiyin Jiu, Youxi Luo, Lin Chen, Yuqun Xie
https://arxiv.org/abs/2605.25472

There Will Be a Scientific Theory of Deep Learning
Jamie Simon, Daniel Kunin, Alexander Atanasov, Enric Boix-Adser\`a, Blake Bordelon, Jeremy Cohen, Nikhil Ghosh, Florentin Guth, Arthur Jacot, Mason Kamb, Dhruva Karkada, Eric J. Michaud, Berkan Ottlik, Joseph Turnbull
https://arxiv.org/abs/2604.21691…

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
P. Bern\'at Szab\'o, Zeno Sch\"atzle, Frank No\'e
https://arxiv.org/abs/2603.25381

FPGA-based disturbance-observer servo for broadband noise suppression in laser frequency stabilization
Meung Ho Seo, Jae Hoon Lee, Young-Ho Park, Hyun-Gue Hong, Seji Kang, Myoung-Sun Heo, Sang-Bum Lee, Taeg Yong Kwon, Sangwon Seo, Sang Eon Park
https://arxiv.org/abs/2605.25471

Crosslisted article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Hybrid Quantum-Classical Corrective Diffusion Modeling for Meteorological Downscaling
Rui Wang, Edoardo Pasetto, Amer Delilbasic, Morris Riedel, Kristel Michielsen, Gabriele Cavallaro
https://arxiv.org/abs/2605.23403 https://mastoxiv.page/@arXiv_csLG_bot/116634262222667592
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Complementary Eigen-Zundel Interpretation Reconciles Thermodynamics and Spectroscopy of Excess Protons in Aqueous HF Solutions
Louis Lehmann, Florian N. Br\"unig, Jonathan Scherlitzki, Morten Lehmann, Martin Kaupp, Beate Paulus, Roland R. Netz
https://arxiv.org/abs/2603.25371

The Pseudospectral Method for the Dirac Equation with Confining Potential
Dengshan Liu, Huihui Xie, Pengxiang Du, Jian Li, Tomoya Naito
https://arxiv.org/abs/2605.25390 https://…

A Kernel Nonconformity Score for Multivariate Conformal Prediction
Louis Meyer, Wenkai Xu
https://arxiv.org/abs/2604.21595 https://arxiv.org/pdf/2604.21595…

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates
Toshifumi Mori, Kei-ichi Okazaki, Kang Kim, Nobuyuki Matubayasi
https://arxiv.org/abs/2603.25237

Minimally Destructive Fast Imaging of Single Atoms in an Optical Tweezer Array with Coherent Excitation
Rei Yokoyama, Takumi Kashimoto, Kosuke Shibata, Yuki Kawamura, Toshi Kusano, Chih-Han Yeh, Reiji Asano, Yuma Nakamura, Tetsushi Takano, Yosuke Takasu, Yoshiro Takahashi
https://arxiv.org/abs/2605.24175

A sustainable photocatalytic pathway for concurrent hydrogen and value-added chemical production utilizing microalgae as bio-scavenger in water
Ho Truong Nam Hai, Augusto Ducati Luchessi, Kaveh Edalati
https://arxiv.org/abs/2603.24924

[2026-05-26 Tue (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
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A single algorithm for both restless and rested rotting bandits
Julien Seznec, Pierre M\'enard, Alessandro Lazaric, Michal Valko
https://arxiv.org/abs/2604.21432 https://

Implementation of the multigrid Gaussian-Plane-Wave algorithm with GPU acceleration in PySCF
Rui Li, Xing Zhang, Qiming Sun, Yuanheng Wang, Junjie Yang, Garnet Kin-Lic Chan
https://arxiv.org/abs/2603.24881

A high-flux atomic strontium oven with light-driven flux modulation
Kenneth M. Hughes, Jesse S. Schelfhout, Charu Mishra, Timothy Leese, Elliot Bentine, Christopher J. Foot
https://arxiv.org/abs/2603.25567

Permeation of hydrogen across graphdiyne: molecular dynamics vs. quantum simulations and role of membrane motion
Mateo Rodr\'iguez, Jos\'e Campos-Mart\'inez, Marta I. Hern\'andez
https://arxiv.org/abs/2603.24827

Atmosphere as a steam engine
Anastassia Makarieva, Andrei Nefiodov
https://arxiv.org/abs/2605.23875 https://arxiv.org/pdf/2605.23875 https://arxiv.org/html/2605.23875
arXiv:2605.23875v1 Announce Type: new
Abstract: Earth's atmosphere operates a steam cycle in which water vapor evaporates from the surface, expands, condenses, and returns as precipitation. The Clausius-Clapeyron law relates the incremental expansion work of saturated water vapor to latent heat converted at a Carnot efficiency corresponding to the temperature difference between evaporation and condensation. We generalize this relation to an atmospheric column with condensation occurring over a range of heights and derive the expansion work per mole of precipitated water. This includes the gravitational work associated with lifting moist air to the mean condensation height, the expansion work generated by condensation, and a correction for incomplete condensation. Using GPCP v3.3 precipitation and observational constraints on condensation height, we estimate the global steam-engine power as $W_v=4.4\pm0.9$ W/m2, close to an independent estimate of total atmospheric power, $W=W_P W_K\simeq4.3\pm0.6$ W/m2, obtained from the gravitational power of precipitation and kinetic energy generation by horizontal pressure gradients diagnosed from MERRA-2. Kinetic energy generation is $W_K\simeq3.2\pm0.3$ W/m2, of which at least two thirds is generated in the lower atmosphere. The smaller upper-atmospheric contribution, dominated by temperature-related pressure gradients, is comparable to Lorenz available potential energy generation. The agreement between steam-engine and atmospheric power is linked to condensation and precipitation fallout. By removing water from the atmospheric gas phase and enabling column-mass redistribution, precipitation maintains surface pressure gradients that drive cross-isobaric flow in the frictional lower atmosphere. The steam-engine framework thus provides a thermodynamic basis for condensation-induced atmospheric dynamics and identifies a major lower-atmospheric power pathway associated with water phase transitions.
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Radiative Association of Ag and H: Formation of AgH from Ab Initio Calculations
Lin Jiang, Yu Wang, Yukun Yang, Xuanbing Qiu, Yali Tian, Guqing Guo, Ling Liu, Chuanliang Li, Yong Wu
https://arxiv.org/abs/2603.25297

CLT-Optimal Parameter Error Bounds for Linear System Identification
Yichen Zhou, Stephen Tu
https://arxiv.org/abs/2604.21270 https://arxiv.org/pdf/2604.212…

Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
Vivienne Pelletier, Vedant Bhat, Daniel J. Rivera, Steven A. Wilson, Christopher L. Muhich
https://arxiv.org/abs/2603.24752

Binding Energy of Muonic Beryllium: Perturbative versus All--Order Calculations
Shikha Rathi, Ulrich D. Jentschura, Paul Indelicato, Ben Ohayon
https://arxiv.org/abs/2603.25278 …

The physics of AI weather models
George Craig, Tobias Selz, Matthias Beylich, Kirsten I. Tempest
https://arxiv.org/abs/2605.23778 https://arxiv.org/pdf/2605.23778 https://arxiv.org/html/2605.23778
arXiv:2605.23778v1 Announce Type: new
Abstract: Could it be that AI weather models are solving physical equations, although they may not be the equations used by conventional NWP models? We compute correlations of forecast skill and Centered Kernel Alignment, providing evidence that different AI weather models represent the atmosphere in similar ways, despite differences in architecture and capacity. We argue that the architecture and training of the AI models constrains the form of the physical laws that they might simulate. In particular, we propose that the models implement a particle description of the atmosphere, where the latent variables at each mesh point correspond to the position of a particle in the high dimensional latent space. We hypothesize that the movement of the particles follows a gradient flow in the latent space towards a minimum of a learned free energy functional. Analysis of the GraphCast and Aurora models show that they make changes on large spatial scales in the early processor layers and move to smaller scale with increasing layer depth, consistent with the gradient flow hypothesis.
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Portable laser-cooled ytterbium beam clock based on an ultra-narrow optical transition
R. F. Offer, E. Klantsataya, A. P. Hilton, A. Strathearn, N. Bourbeau H\'ebert, C. J. Billington, S. Watzdorf, S. K. Scholten, B. White, M. Nelligan, T. M. Stace, A. N. Luiten
https://arxiv.org/abs/2603.25261…

Calibeating Prediction-Powered Inference
Lars van der Laan, Mark Van Der Laan
https://arxiv.org/abs/2604.21260 https://arxiv.org/pdf/2604.21260

Precipitation diffusion downscaling and application to out-of-distribution simulations with and without stratospheric aerosol injection
Cameron Dong, James W. Hurrell, Elizabeth A. Barnes
https://arxiv.org/abs/2605.23776 https://arxiv.org/pdf/2605.23776 https://arxiv.org/html/2605.23776
arXiv:2605.23776v1 Announce Type: new
Abstract: Stratospheric aerosol injection (SAI), a possible climate engineering strategy where reflective particles are injected into the stratosphere, has been explored to mitigate global warming and its associated risks, such as the intensification of extreme precipitation events. However, current Earth system models (ESMs) often used to simulate SAI and other climate change scenarios are too coarse to properly assess such risks. Traditional statistical downscaling methods, used to project higher resolution impacts, may be biased and unrealistic. To address this, we train a deep learning diffusion downscaler to generate 0.25{\deg} contiguous United States (CONUS) daily precipitation using historical and future climate simulations from the Mesoscale Atmosphere-Ocean Interaction in Seasonal-to-Decadal Climate Prediction (MESACLIP) project, then apply the diffusion downscaler to out-of-distribution CESM2 simulations with and without SAI. The diffusion model generates realistic downscaled precipitation using either MESACLIP or CESM2 inputs. It also faithfully recreates the climate change projections of extreme precipitation in MESACLIP. Diffusion-downscaled projections of the future CESM2 SAI scenarios suggest that SAI could nearly cut in half the CONUS-average increase in yearly max precipitation, compared to the non-SAI scenario. However, there is considerable regional variation and internal variability, with SAI modeled to only slightly reduce increases in extreme precipitation frequency in the Mid Atlantic and the Pacific Northwest, but mitigating most intensification in other regions. Future application of diffusion downscaling to a wider variety of SAI scenarios would provide valuable insight into how proposed SAI strategies may affect precipitation variability on fine spatial scales for regional impact assessments.
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Self-energy corrections to the ionization energies in sodium-like ions: comparison of the \textit{ab initio} QED and model-QED-operator approaches
P. Yang, A. V. Malyshev, E. A. Prokhorchuk, I. I. Tupitsyn, V. M. Shabaev, D. P. Usov
https://arxiv.org/abs/2603.25212

Sensing T-violating nuclear moments of paramagnetic ions in crystals
Aleksandar Radak, Mingyu Fan, Bassam Nima, Yuiki Takahashi, Amar Vutha
https://arxiv.org/abs/2603.24907 http…

Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction
Ziyang Wei, Wanrong Zhu, Jingyang Lyu, Wei Biao Wu
https://arxiv.org/abs/2604.21203 h…

Absence of Far-Detuned Attractive Optical Traps for Alkali Rydberg Atoms
Gabriel E. Patenotte, Youngshin Kim, Samuel Gebretsadkan, Kang-Kuen Ni
https://arxiv.org/abs/2603.24789 …

Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model
Manuel Fernandez V, Ludovic Stephan, Yizhe Zhu
https://arxiv.org/abs/2604.20907 https://…

Near-deterministic loading of optical tweezer arrays via repulsive barricade potentials
Archie C. Baldock, Alex J. Matthies, Luke Caldwell, Hannah J. Williams
https://arxiv.org/abs/2604.22406

[2026-04-27 Mon (UTC), 1 new article found for physics.atom-ph Atomic Physics]
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[2026-05-25 Mon (UTC), 3 new articles found for physics.ao-ph Atmospheric and Oceanic Physics]
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Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Smoothing and spatial verification of global fields
Gregor Skok, Katarina Kosovelj

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Photoelectron spectroscopy of 3s3p doubly excited helium dressed with strong near-infrared laser ...
Fushitani, Liu, Ono, Amaike, Yamazaki, Kato, Matsuda, Owada, Yabashi, Hik…

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Bright-state source cancellation in dissipative shortcut Raman atom optics
Asad Ali, Saif Al-Kuwari, M. I. Hussain, H. Kuniyil, M. T. Rahim, Saeed Haddadi

Collisions and Stopping of Fast Charged Particles in Matter
Francesc Salvat
https://arxiv.org/abs/2606.25847 https://arxiv.org/pdf/2606.25847

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Continuous operation of a coherent 3,000-qubit system
Neng-Chun Chiu, et al.

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Weak wave turbulence as a precursor to universal coarsening in a homogeneous Bose gas
Fischer, Gazo, Morris, Maslov, Zhang, Etrych, Martirosyan, Eigen, Hadzibabic