Tootfinder

"When a debate gets heated, it effectively hijacks the mental "radar" people rely on to flag suspicious content."
✅ Can you tell a bot from a human online? | TechRadar
https://www.techradar.com/vpn/vp…

Can you tell a bot from a human online? Surfshark's new experiment says nearly half of us cannot
Surfshark's "Bot or Not" challenge proves our biggest weakness against AI is our own emotions

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/2]:
- Facade: High-Precision Insider Threat Detection Using Deep Contextual Anomaly Detection
Alex Kantchelian, et al.
https://arxiv.org/abs/2412.06700 https://mastoxiv.page/@arXiv_csCR_bot/113627226016881063
- Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective
Puwei Lian, Yujun Cai, Songze Li, Bingkun Bao
https://arxiv.org/abs/2505.20955 https://mastoxiv.page/@arXiv_csCR_bot/114584229384238830
- Cryptographic Choreographies
Sebastian M\"odersheim, Simon Lund, Alessandro Bruni, Marco Carbone, Rosario Giustolisi
https://arxiv.org/abs/2602.12967 https://mastoxiv.page/@arXiv_csCR_bot/116079470877038972
- TALUS: FIPS-204-Exact Threshold ML-DSA via Boundary Clearance
Leo Kao, Raymond Chang
https://arxiv.org/abs/2603.22109 https://mastoxiv.page/@arXiv_csCR_bot/116283533730322814
- SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for ...
Liu, Ying, Zhang, Zou, Zhang, Yang, Zhang, Peng
https://arxiv.org/abs/2605.05704 https://mastoxiv.page/@arXiv_csCR_bot/116537968700983890
- AI Security Policy Should Assess Systems, Not Only Models
Michael A. Riegler, Inga Str\"umke
https://arxiv.org/abs/2605.09504 https://mastoxiv.page/@arXiv_csCR_bot/116560756584469721
- Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models
Cheng Li, Jiexiong Liu, Yixuan Chen, Yi Li
https://arxiv.org/abs/2607.13093 https://mastoxiv.page/@arXiv_csCR_bot/116928605403267286
- From Neural Intent to Cryptographic Authorization: Securing AI-Driven Enterprise Workflows
Jiasi Weng, Jian Weng, Minrong Chen, Ming Li, Jia-Nan Liu, Zhi Li, Yue Zhang
https://arxiv.org/abs/2607.15596 https://mastoxiv.page/@arXiv_csCR_bot/116951166143038362
- Towards an Automated Test of LLM Security Knowledge
Shufan Chai, Liangliang Sun, Jessica Staddon
https://arxiv.org/abs/2607.18496 https://mastoxiv.page/@arXiv_csCR_bot/116962579240952448
- DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool ...
Navid Aftabi, Abhishek Hanchate, Satish Bukkapatnam, Dan Li
https://arxiv.org/abs/2508.21797 https://mastoxiv.page/@arXiv_eessSY_bot/115128274334268839
- CLOAK: Contrastive Guidance for Latent Diffusion-Based Data Obfuscation
Xin Yang, Omid Ardakanian
https://arxiv.org/abs/2512.12086 https://mastoxiv.page/@arXiv_csLG_bot/115729063749487767
toXiv_bot_toot

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

Crosslisted article(s) found for physics.med-ph. https://arxiv.org/list/physics.med-ph/new
[1/1]:
- Diversity, Equity and Inclusivity in the Australian and New Zealand Medical Physics Workforce
Simpson-Page, Anderson, Crowe, Kairn, Smith, Thompson, Keall
https://arxiv.org/abs/2606.25262 https://mastoxiv.page/@arXiv_physicsedph_bot/116809655065501468
- From Propulsion to Suction: Unraveling Thrust Reversal in Propellers at Intermediate Reynolds Num...
Rong Fu, Siyu Li, Yang Ding
https://arxiv.org/abs/2606.25472 https://mastoxiv.page/@arXiv_physicsfludyn_bot/116809738791069036
- Positron Emission Tomography with quantum-entangled Compton events: first imaging results at clin...
Makek, Ko\v{z}uljevi\'c, Bokuli\'c, Gro\v{s}ev, Kuncic, Parashari, Paveli\'c
https://arxiv.org/abs/2606.25804 https://mastoxiv.page/@arXiv_physicsinsdet_bot/116809666435275378
toXiv_bot_toot

Every "trivial fix" to a long-standing issue reveals at least one additional bug in the code, and at least one wrong assumption in the test suite.
https://github.com/conda-forge/conda-forge-bot/pull/6274

Crosslisted article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- TopoGuard: Graph Theory Based Defenses Against Split-Knowledge Attacks on RAG
Chahana Dahal, Zuobin Xiong
https://arxiv.org/abs/2607.20437 https://mastoxiv.page/@arXiv_csCL_bot/116973867316185089
- Isolating LLM Alignment from Regex: Zero Coverage and Metric-Dependent Divergence Under Adversari...
Alexandre Cristov\~ao Maiorano
https://arxiv.org/abs/2607.20494 https://mastoxiv.page/@arXiv_csAI_bot/116973970735647184
- Edit-Neighboring Data Streams and Privacy under Continual Observation
Joel Daniel Andersson, Anamay Chaturvedi, Monika Henzinger, Roodabeh Safavi
https://arxiv.org/abs/2607.20727 https://mastoxiv.page/@arXiv_csDS_bot/116973794572869732
- Beyond Heavy Log Curation: Perplexity-Based APT Detection via Unsupervised, Context-Augmented Lan...
Shoya Otsu, Kei Suzuki, Toshiaki Koike-Akino, Jing Liu, Ye Wang
https://arxiv.org/abs/2607.20832 https://mastoxiv.page/@arXiv_csLG_bot/116973999245670512
- The Consensus Number of Untraceable Cryptocurrencies
Christian Cachin, David Lehnherr, Juan Villacis, Fran\c{c}ois-Xavier Wicht
https://arxiv.org/abs/2607.20929 https://mastoxiv.page/@arXiv_csDC_bot/116973788672146161
- Weak Private Information Retrieval for Graph-based Storage
Shodasakshari Vidya, Chandan Anand, Prasad Krishnan
https://arxiv.org/abs/2607.21014 https://mastoxiv.page/@arXiv_csIT_bot/116973843557672147
- Risk-Limiting Audits for Parliamentary Majorities
Jack Freestone, Dennis Leung, Damjan Vukcevic
https://arxiv.org/abs/2607.21082 https://mastoxiv.page/@arXiv_statAP_bot/116973845690486258
- Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inference
Xiaolong Liang, Juanjuan Li, Rui Qin, Yisheng Lv
https://arxiv.org/abs/2607.21162 https://mastoxiv.page/@arXiv_csLG_bot/116974022251021755
- Advances in STV Margin Computation
Michelle Blom, Alexander Ek, Peter J. Stuckey, Vanessa Teague, Damjan Vukcevic
https://arxiv.org/abs/2607.21178 https://mastoxiv.page/@arXiv_csGT_bot/116973834510813731
- Unconditional Unclonable Encryption
Prabhanjan Ananth, Amit Sahai
https://arxiv.org/abs/2607.21551 https://mastoxiv.page/@arXiv_quantph_bot/116974006325118063
toXiv_bot_toot

Fiberace
bot by @…
No laws were broken in the making of this bot: let's not have humor lead to homicide.
#satire #potus45

[2026-07-24 Fri (UTC), no new articles found for math.CT Category Theory]
toXiv_bot_toot

[2026-06-25 Thu (UTC), 4 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- On the stability of an in-line formation of hydrodynamically interacting flapping plates
Monika Nitsche, Anand U. Oza, Michael Siegel
https://arxiv.org/abs/2410.04626 https://mastoxiv.page/@arXiv_physicsfludyn_bot/113270998236203403
- Side-wall wetting and linear stability of falling films
Hammam Mohamed, J\"orn Sesterhenn
https://arxiv.org/abs/2504.13300 https://mastoxiv.page/@arXiv_physicsfludyn_bot/114374794050144417
- An Omni-Temporal Theory for Hydrodynamic Dispersion and Reaction in Porous Media
Md Abdul Hamid, Kyle C. Smith
https://arxiv.org/abs/2505.06063 https://mastoxiv.page/@arXiv_physicsfludyn_bot/114493702701690116
- Confirming Wave Turbulence Predictions in Rotating Turbulence
Omri Shaltiel, Omri Gat, Eran Sharon
https://arxiv.org/abs/2510.25446 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115462467154250733
- Using Physics Informed Neural Network (PINN) and Neural Network (NN) to Improve a $k-\omega$ Turb...
Lars Davidson
https://arxiv.org/abs/2511.12493 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115570134553603649
- Oscillating Detonation of Liquid Ammonia
Wenhao Wang, Zongmin Hu, Peng Zhang
https://arxiv.org/abs/2511.14167 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115575358542454196
- On the Poisson-Source Basis of Logarithmic Wall-Pressure-Variance Growth
Jonathan M. O. Massey, Joseph C. Klewicki, Beverley J. McKeon
https://arxiv.org/abs/2511.16776 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115603689363840109
- Convolutional causal learning for aerodynamic flows
Ryo Koshikawa, Ryo Araki, Qiong Liu, Kai Fukami
https://arxiv.org/abs/2601.19104 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115971839485449464
- Assessing engineering wake models against operational data: insights from the Lillgrund wind farm...
Siguenza-Alvarado, Harrison, Mohammadi, Vishwakarma, Bossanyi, Landberg, Bastankhah
https://arxiv.org/abs/2601.21035 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115983015393462612
- Neural equilibria for long-term prediction of nonlinear conservation laws
Benitez, Hegazy, Guo, Dokmani\'c, Mahoney, de Hoop
https://arxiv.org/abs/2501.06933 https://mastoxiv.page/@arXiv_csLG_bot/113825452743912532
- Self-similar rupture of thin films of power-law fluid
Michael C Dallaston, Steven A Kedda, Scott W McCue
https://arxiv.org/abs/2509.05383 https://mastoxiv.page/@arXiv_condmatsoft_bot/115173629129170202
- Instability and self-propulsion of flexible autophoretic filaments
Ursy Makanga, Akhil Varma, Panayiota Katsamba
https://arxiv.org/abs/2509.10153 https://mastoxiv.page/@arXiv_condmatsoft_bot/115207443699020835
- Analytical response functions for a compressible thin fluid layer with odd viscosity
Abdallah Daddi-Moussa-Ider, Yuto Hosaka, Shigeyuki Komura
https://arxiv.org/abs/2602.18136 https://mastoxiv.page/@arXiv_condmatsoft_bot/116119064615788127
toXiv_bot_toot

Cloudflare partners with Google, Microsoft, and Mozilla on PACT, a protocol to distinguish legitimate human or bot traffic from undesirable network requests (Thomas Claburn/The Register)
https://www.theregister.com/software…

Cloudflare teams up with big browsers to help websites tell welcome from unwelcome visitors
Makers of Chrome, Edge, Firefox back bot-fraud defense called Private Access Control Tokens

Youtuber baut Arduino-Roboter Turtle Bot in siebenfacher Größe
Der Turtle Bot gehört zu den Klassikern beim Arduino-Basteln. Der Youtuber UncleStem hat ihn siebenmal so groß aufgebaut – und er funktioniert.

Youtuber baut Arduino-Roboter Turtle Bot in siebenfacher Größe
Der Turtle Bot gehört zu den Klassikern beim Arduino-Basteln. Der Youtuber UncleStem hat ihn siebenmal so groß aufgebaut – und er funktioniert.

Crosslisted article(s) found for physics.med-ph. https://arxiv.org/list/physics.med-ph/new
[1/1]:
- GPU-accelerated superiorization on constrained physical problems with SupPy
Tobias Becher, Yair Censor, Kay Barshad, Niklas Wahl
https://arxiv.org/abs/2606.27086 https://mastoxiv.page/@arXiv_physicscompph_bot/116815335801053799
toXiv_bot_toot

@… hello! are you a bot? I can't tell! Why did you pick our server?

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
toXiv_bot_toot

https://rs.bot/esK7cz

Put an end to the seemingly endless grift and corruption of trump2.0
Join 1,136 people. On June 23 2026 Senator Chris Murphy said, “Because once corruption becomes normal, it becomes permanent.” The self dealing and self enrichment of… Sign and Resistbot delivers your letter in minutes.

Bunker Boy
bot by @…
No laws were broken in the making of this bot: cancel the hit squad.
#satire #potus45

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

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Double Categories of Open Systems: the Cospan Approach
John C. Baez
https://

[2026-06-25 Thu (UTC), 3 new articles found for physics.med-ph Medical Physics]
toXiv_bot_toot

Traitor Tot
bot by @…
No laws were broken in the making of this bot: let's not get carceral over comedy.
#satire #potus45

[2026-05-26 Tue (UTC), 6 new articles found for physics.ao-ph Atmospheric and Oceanic Physics]
toXiv_bot_toot

https://rs.bot/jUvmQj

Reject Blanche
Join 123 people. SENATORS: Vote against Todd Blanche for AG. He is more corrupt than Pam Bondi was, if that is even possible, refusing to comply with the Epstein bill… Sign and Resistbot delivers your letter in minutes.

[2026-05-25 Mon (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

[2026-06-26 Fri (UTC), no new articles found for physics.med-ph Medical Physics]
toXiv_bot_toot

Pedoführer
bot by @…
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Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[2/2]:
- PISmith: Reinforcement Learning-based Red Teaming for Prompt Injection Defenses
Chenlong Yin, Runpeng Geng, Yanting Wang, Jinyuan Jia
https://arxiv.org/abs/2603.13026 https://mastoxiv.page/@arXiv_csLG_bot/116237889578853916
toXiv_bot_toot

[2026-05-25 Mon (UTC), 3 new articles found for physics.ao-ph Atmospheric and Oceanic Physics]
toXiv_bot_toot

Crosslisted article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- The doctrinal G\"odel's completeness theorem and the type space functor
Marco Abbadini, Francesca Guffanti

Scooby Coup
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https://rs.bot/34mPz7

Support for H.R. 9694: The Epstein Files Transparency Act II
Join 100 people. I am writing to urge you to support and prioritize the passage of H.R. 9694, the Epstein Files Transparency Act II. Transparency and accountability… Sign and Resistbot delivers your letter in minutes.

[2026-05-26 Tue (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

On the link between optoacoustic imaging and selective photothermolysis
Sergio Contador, Rodrigo Rojo, Alvaro Jimenez, Juan Aguirre
https://arxiv.org/abs/2606.25913 https://arxiv.org/pdf/2606.25913 https://arxiv.org/html/2606.25913
arXiv:2606.25913v1 Announce Type: new
Abstract: Selective photothermolysis (SP) is widely used in clinical and cosmetic dermatology to remove unwanted skin structures. Careful laser parameter selection results in safe and effective target removal. Nevertheless, parameter selection relies on a trial-and-error process based on visual inspection of the immediate skin response. This process is highly dependent on the practitioner experience and can be time-consuming.
SP and optoacoustic imaging (OI) share many physical principles. However, the possibility of using OI to improve laser parameter selection in SP has not been studied before. Here, we explore the relationship between OI and SP theoretically and through clinical in-human trials with a focus on tattoo removal. Our results demonstrate a strong correlation between OI signals acquired before and after treatment with the immediate clinical endpoint, suggesting that OI could be used as a tool for optimal parameter selection and reduced treatment duration in tattoo removal and other SP treatments.
toXiv_bot_toot

[2026-05-26 Tue (UTC), 6 new articles found for physics.ao-ph Atmospheric and Oceanic Physics]
toXiv_bot_toot

Bunker Baby
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Generation of synthetic CT images from optical scanning for superficial mold brachytherapy
Scott B. Crowe (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia, Queensland University of Technology, Brisbane Qld, Australia), Emily Simpson-Page (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia), Jenna Luscombe (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia), Rachael Wilks (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia), Tanya Kairn (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia, Queensland University of Technology, Brisbane Qld, Australia)
https://arxiv.org/abs/2606.25221 https://arxiv.org/pdf/2606.25221 https://arxiv.org/html/2606.25221
arXiv:2606.25221v1 Announce Type: new
Abstract: Optical 3D scanning systems allow the acquisition of accurate models of patient anatomy, suitable for use in the design of simple 3D-printable patient-matched medical devices with 3D modelling software. This study developed and demonstrated the use of superficial brachytherapy surface mold design workflow that utilizes data from optical 3D surface scanning and enables a commercial brachytherapy treatment planning system to be used for catheter positioning and dose optimization steps. Synthetic CT images were generated from 14 optically scanned anatomical models of human participants. Models and skin textures ac-quired from the optical scans were imported into Autodesk Meshmixer, where the treatment area was delineated, and treatment and device volumes produced. 3D Slicer was used to convert the body, treatment and device volumes to DICOM CT and RTSTRUCT data. The synthetic CT data and contoured volumes were imported into Varian Eclipse, where catheters were designed, and dwell positions and times optimised for dose coverage of the treatment volume. The lack of in-ternal anatomy did not compromise dose calculations, due to clinical use of a TG43 based algorithm. Once 3D printed, molds can be imaged in-situ during CT simulation, and reconstructed, for clinical dose calculation and plan approval.
toXiv_bot_toot

[2026-05-25 Mon (UTC), 3 new articles found for physics.ao-ph Atmospheric and Oceanic Physics]
toXiv_bot_toot

[2026-07-23 Thu (UTC), 3 new articles found for math.CT Category Theory]
toXiv_bot_toot

https://rs.bot/D4gdz4

Demand a Full Tax Audit of Trump and His Family
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Voldermoron
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Magnetic resonance imaging assessment of the suitability and consistency of radiotherapy treatment positioning achieved using intra-oral stents
Tanya Kairn (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia, Queensland University of Technology, Brisbane Qld, Australia), Philip Chan (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia), Benjamin Chua (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia), Susannah Cleland (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane Qld, Australia), Jodi Dawes (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia), Lizbeth Kenny (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia), Charles Y. Lin (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia), William R. McDowall (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia), Tania Poroa (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane Qld, Australia), Scott B. Crowe (Royal Brisbane and Women's Hospital, Brisbane Qld, Australia, University of Queensland, Brisbane Qld, Australia, Queensland University of Technology, Brisbane Qld, Australia, Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane Qld, Australia)
https://arxiv.org/abs/2606.25210 https://arxiv.org/pdf/2606.25210 https://arxiv.org/html/2606.25210
arXiv:2606.25210v1 Announce Type: new
Abstract: As head-and-neck radiotherapy treatments grow more complex and precise, it becomes increasingly important to assess the anatomical separations that can be achieved using intra-oral stents. A series of twenty T2-weighted turbo spin echo magnetic resonance images (MRI) were acquired of one healthy participant, with a range of different wax and 3D printed intra-oral stents in situ. The resulting measurements showed that a 3D printed modular stent containing hard polylactic acid (PLA) and flexible thermoplastic polyurethane (TPU) components made the largest and most reproducible separation between the cheeks (70.8 /- 0.3 mm), two hard PLA stents designed to exactly fit the participant's teeth produced the poorest positioning reproducibility (standard deviations of up to 3 mm between a range of landmarks measured in repeated images). Most stents were described as ``comfortable'' although the wax stents left small pieces of wax attached to the teeth after use. This MRI based comparison demonstrated that the materials and designs used for intra-oral stents can have substantial effects on the level of anatomical separation and positioning reproducibility that they produce.
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

Mango Zedong
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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…

https://rs.bot/izpL2S

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Con Juan
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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

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

Payless Putin
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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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Crosslisted article(s) found for physics.med-ph. https://arxiv.org/list/physics.med-ph/new
[1/1]:
- A few remarks on hyperstatistics and some applications
Lucas Squillante, Samuel M. Soares, Guilherme Lepski, Mariano de Souza
https://arxiv.org/abs/2606.20735 https://mastoxiv.page/@arXiv_condmatstatmech_bot/116798390593601016
- Anatomically Consistent TMJ Disc Segmentation via Semantic Anchoring and Clinical Priors
Dayun Ju, Chanyoung Kim, Sunyoung Jung, Hyo-Jung Jung, Chena Lee, Younjung Park, Seong Jae Hwang
https://arxiv.org/abs/2606.21177 https://mastoxiv.page/@arXiv_eessIV_bot/116798475307178225
- Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction
Il{\i}cak, Imre, Najac, van den Broek, Lena, Webb, Staring
https://arxiv.org/abs/2606.21602 https://mastoxiv.page/@arXiv_eessIV_bot/116798521905196206
- A unified perspective on wavelength selection for molecular composition inference from diffuse sp...
Scibilia, Rosier, Giannoni, Ricci, Lange, Pavone, Tachtsidis, Rueckert, Ezhov
https://arxiv.org/abs/2606.23223 https://mastoxiv.page/@arXiv_physicsoptics_bot/116798705543787818
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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.
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Collisions and Stopping of Fast Charged Particles in Matter
Francesc Salvat
https://arxiv.org/abs/2606.25847 https://arxiv.org/pdf/2606.25847

Blob the Builder
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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.
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Coupling of negative-positive trapped-ion pairs
Daniel Kienzler
https://arxiv.org/abs/2606.25828 https://arxiv.org/pdf/2606.25828

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.
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Enhancing the Instantaneous Bandwidth of Rydberg Microwave Sensors: A Proposed Scheme
Yuhan Yan, Xuejie Li, Jinyin Wan, Xing Xia, Haojie Zhao, Binghong Yu, Jianliao Deng, L. Q. Chen, Huadong Cheng
https://arxiv.org/abs/2606.25555

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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Donny Dorko
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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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Statistical Characteristics of Tunneling States in Strong-Field Atomic Ionization
M. W. Cao, Z. Y. Chen, J. N. Wu, S. Q. Shen, S. Wang, W. Y. Li, J. Y. Che, Y. J. Chen
https://arxiv.org/abs/2606.25481 …

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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.
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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

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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…

Our Fondling Father
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Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- A Robust Strontium Tweezer Apparatus for Quantum Computing
Marijn Venderbosch, et al.

Sherbet Pervert
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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 physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postp...
Manuel P\'erez-Carrasco, et al.

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Criminal in Chief
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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/…

Combover Caligula
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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/p…

Our Fondling Father
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Super Spreader in Chief
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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

Commander in Cheats
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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

Cockwomble-In-Chief
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Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
Zachary James, Joseph Guinness, Arthur DeGaetano
https://arxiv.org/abs/2605.24009 http…

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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

Pumpkin Spice Colored Trash Bag
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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

President Rottinghands McRagebaby
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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 Cav…

Atmosphere as a steam engine
Anastassia Makarieva, Andrei Nefiodov
https://arxiv.org/abs/2605.23875 https://arxiv.org/pdf/2605.23875

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/260…

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