Tootfinder

Opt-in global Mastodon full text search. Join the index!

No exact results. Similar results found.
@cosmos4u@scicomm.xyz
2026-02-24 03:32:01

#CoronaDischarges Glow on Trees Under Thunderstorms: agupubs.onlinelibrary.wiley.co -> Thunderstorms conjure ghostly coronae in treetops, observed outdoors for the first time: news.agu.org/press-release/thu - the weak electric discharges may set off ultraviolet sparkles over large swaths of forest under storms, potentially impacting canopy health (this is fringe geophysics but some amateur astronomers are going after corona discharges - esp. in webcam images from mountain sites - with a vengeance, so here goes).

@kexpmusicbot@mastodonapp.uk
2025-11-25 06:10:05

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Cortex:
🎵 Back to My World
#Cortex
tradviberecords.bandcamp.com/t
open.spotify.com/track/3DkMbfq

@rainerzufall_le@mastodon.social
2026-02-21 20:20:22

"Auch [Tradwives] sind ein postpatriarchales Phänomen, weil sie zwei Dinge verbinden, die im klassischen Patriarchat unvereinbar waren: Hausarbeit machen und öffentlich sein. Eine Influencerin hat das mal gut beschrieben: Das Einkommen ihres Mannes reicht nicht für die Familie, also verdient sie mit Haushalt Geld und kann für ihr Kind da sein. Das ist ihre Antwort auf das Versäumnis der liberalen Gesellschaft, Familie und Beruf miteinander zu vereinbaren." Hmm.

@memeorandum@universeodon.com
2026-02-20 13:11:07

Women don't want to just be 'girlbosses' or 'tradwives' (Kimberly Ross/Washington Examiner)
washingtonexaminer.com/premium
memeorandum.com/260220/p14#a26

@davidbody@fosstodon.org
2026-01-21 19:13:28

Kind of an odd way to assure someone a poll is "completely anonymous," but I decided to answer Yes so I could take the poll. It was all questions about US national politics and approval/disapproval of various politicians and billionaires. I tried to answer truthfully, but on a few of them "strongly disapprove" is such an understatement that it almost doesn't feel truthful.

A screenshot from a phone with a header that reads "National Opinion Su...".  The first question reads

Hello, we are calling on behalf of a market research firm. We are conducting a brief survey about political issues in your area and would like your opinion. Your responses are completely anonymous, and we are not selling anything. Is this DAVID?

The choices are Yes or No.
@boris@cosocial.ca
2025-12-23 17:30:28

Railway is technically a PaaS (like Heroku). “serverless” because you don’t have to maintain the operating system. They have evolved buildpacks to Railpacks which will create containers on the fly.
OR you can use it as a Docker host
They are hosting their own bare metal servers. Recommended!
…

@stargazer@woof.tech
2026-01-24 22:50:28

"I have always believed in deeds, not words."
As the ongoing Russian attacks keep crippling our infrastructure (as seen from a cold unpowered home with a failing cell connection), I am left with few options. One of them, ruminating before sleep on a strange recurring phenomenon. If not careful, we become what we fight.
We're not infallible. It's the values we keep, the tradeoffs we make to succeed that shape us.
Be vigilant. Watch yourself.
And stay sa…

A bald man in dark clothes with an intense but otherwise neutral eyes - Kane, the leader of NOD from the game Command and Conquer
A dark mode digital map of northern Ukraine (including Kyiv, Kharkiv and Dnipro). Top label - "monitor 24.01.2026 02:08", "49 x unanned drones". Russian bombing drones marked in yellow (Shahed) mostly swarming on Kyiv and Kharkiv. Two ballistic missiles marked in red traversing the northern part to SW, roughly towards Moldova.
@arXiv_csLG_bot@mastoxiv.page
2026-02-25 10:43:21

Ski Rental with Distributional Predictions of Unknown Quality
Qiming Cui, Michael Dinitz
arxiv.org/abs/2602.21104 arxiv.org/pdf/2602.21104 arxiv.org/html/2602.21104
arXiv:2602.21104v1 Announce Type: new
Abstract: We revisit the central online problem of ski rental in the "algorithms with predictions" framework from the point of view of distributional predictions. Ski rental was one of the first problems to be studied with predictions, where a natural prediction is simply the number of ski days. But it is both more natural and potentially more powerful to think of a prediction as a distribution p-hat over the ski days. If the true number of ski days is drawn from some true (but unknown) distribution p, then we show as our main result that there is an algorithm with expected cost at most OPT O(min(max({eta}, 1) * sqrt(b), b log b)), where OPT is the expected cost of the optimal policy for the true distribution p, b is the cost of buying, and {eta} is the Earth Mover's (Wasserstein-1) distance between p and p-hat. Note that when {eta} < o(sqrt(b)) this gives additive loss less than b (the trivial bound), and when {eta} is arbitrarily large (corresponding to an extremely inaccurate prediction) we still do not pay more than O(b log b) additive loss. An implication of these bounds is that our algorithm has consistency O(sqrt(b)) (additive loss when the prediction error is 0) and robustness O(b log b) (additive loss when the prediction error is arbitrarily large). Moreover, we do not need to assume that we know (or have any bound on) the prediction error {eta}, in contrast with previous work in robust optimization which assumes that we know this error.
We complement this upper bound with a variety of lower bounds showing that it is essentially tight: not only can the consistency/robustness tradeoff not be improved, but our particular loss function cannot be meaningfully improved.
toXiv_bot_toot

@arXiv_csLG_bot@mastoxiv.page
2026-02-25 16:07:37

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[1/6]:
- Towards Attributions of Input Variables in a Coalition
Xinhao Zheng, Huiqi Deng, Quanshi Zhang
arxiv.org/abs/2309.13411
- Knee or ROC
Veronica Wendt, Jacob Steiner, Byunggu Yu, Caleb Kelly, Justin Kim
arxiv.org/abs/2401.07390
- Rethinking Disentanglement under Dependent Factors of Variation
Antonio Almud\'evar, Alfonso Ortega
arxiv.org/abs/2408.07016 mastoxiv.page/@arXiv_csLG_bot/
- Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
Etrit Haxholli, Yeti Z. Gurbuz, Ogul Can, Eli Waxman
arxiv.org/abs/2411.00759 mastoxiv.page/@arXiv_csLG_bot/
- Predicting Subway Passenger Flows under Incident Situation with Causality
Xiannan Huang, Shuhan Qiu, Quan Yuan, Chao Yang
arxiv.org/abs/2412.06871 mastoxiv.page/@arXiv_csLG_bot/
- Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
arxiv.org/abs/2501.08219 mastoxiv.page/@arXiv_csLG_bot/
- Universality of Benign Overfitting in Binary Linear Classification
Ichiro Hashimoto, Stanislav Volgushev, Piotr Zwiernik
arxiv.org/abs/2501.10538 mastoxiv.page/@arXiv_csLG_bot/
- Safe Reinforcement Learning for Real-World Engine Control
Julian Bedei, Lucas Koch, Kevin Badalian, Alexander Winkler, Patrick Schaber, Jakob Andert
arxiv.org/abs/2501.16613 mastoxiv.page/@arXiv_csLG_bot/
- A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers
Roman Tarasov, Petr Mokrov, Milena Gazdieva, Evgeny Burnaev, Alexander Korotin
arxiv.org/abs/2502.01310
- Improving the Convergence of Private Shuffled Gradient Methods with Public Data
Shuli Jiang, Pranay Sharma, Zhiwei Steven Wu, Gauri Joshi
arxiv.org/abs/2502.03652 mastoxiv.page/@arXiv_csLG_bot/
- Using the Path of Least Resistance to Explain Deep Networks
Sina Salek, Joseph Enguehard
arxiv.org/abs/2502.12108 mastoxiv.page/@arXiv_csLG_bot/
- Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, Efstratios Gavves
arxiv.org/abs/2502.17028 mastoxiv.page/@arXiv_csLG_bot/
- Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
Sharan Vaswani, Reza Babanezhad
arxiv.org/abs/2503.00229 mastoxiv.page/@arXiv_csLG_bot/
- Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
Yan Li, Zhenyu Zhang, Zhengang Wang, Pengfei Chen, Pengfei Zheng
arxiv.org/abs/2503.04398 mastoxiv.page/@arXiv_csLG_bot/
- A Survey on Federated Fine-tuning of Large Language Models
Wu, Tian, Li, Sun, Tam, Zhou, Liao, Xiong, Guo, Li, Xu
arxiv.org/abs/2503.12016 mastoxiv.page/@arXiv_csLG_bot/
- Towards Trustworthy GUI Agents: A Survey
Yucheng Shi, Wenhao Yu, Jingyuan Huang, Wenlin Yao, Wenhu Chen, Ninghao Liu
arxiv.org/abs/2503.23434 mastoxiv.page/@arXiv_csLG_bot/
- CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Chao Yang, Xiannan Huang, Shuhan Qiu, Yan Cheng
arxiv.org/abs/2504.13961 mastoxiv.page/@arXiv_csLG_bot/
- Regularity and Stability Properties of Selective SSMs with Discontinuous Gating
Nikola Zubi\'c, Davide Scaramuzza
arxiv.org/abs/2505.11602 mastoxiv.page/@arXiv_csLG_bot/
- RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
Alonso Urbano, David W. Romero, Max Zimmer, Sebastian Pokutta
arxiv.org/abs/2505.13289 mastoxiv.page/@arXiv_csLG_bot/
- RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
Yilang Zhang, Bingcong Li, Georgios B. Giannakis
arxiv.org/abs/2505.18877 mastoxiv.page/@arXiv_csLG_bot/
- SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Bechler-Speicher, Zerio, Huri, Vestergaard, Gilad-Bachrach, Jess, Bhatt, Sazonovs
arxiv.org/abs/2505.19193 mastoxiv.page/@arXiv_csLG_bot/
toXiv_bot_toot

@arXiv_csLG_bot@mastoxiv.page
2026-02-25 16:08:18

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[5/6]:
- Watermarking Degrades Alignment in Language Models: Analysis and Mitigation
Apurv Verma, NhatHai Phan, Shubhendu Trivedi
arxiv.org/abs/2506.04462 mastoxiv.page/@arXiv_csCL_bot/
- Sensory-Motor Control with Large Language Models via Iterative Policy Refinement
J\^onata Tyska Carvalho, Stefano Nolfi
arxiv.org/abs/2506.04867 mastoxiv.page/@arXiv_csAI_bot/
- ICE-ID: A Novel Historical Census Dataset for Longitudinal Identity Resolution
de Carvalho, Popov, Kaatee, Correia, Th\'orisson, Li, Bj\"ornsson, Sigur{\dh}arson, Dibangoye
arxiv.org/abs/2506.13792 mastoxiv.page/@arXiv_csAI_bot/
- Feedback-driven recurrent quantum neural network universality
Lukas Gonon, Rodrigo Mart\'inez-Pe\~na, Juan-Pablo Ortega
arxiv.org/abs/2506.16332 mastoxiv.page/@arXiv_quantph_b
- Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs
Cook, Sapora, Ahmadian, Khan, Rocktaschel, Foerster, Ruis
arxiv.org/abs/2506.18777 mastoxiv.page/@arXiv_csAI_bot/
- Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Jiechen Chen, Bipin Rajendran, Osvaldo Simeone
arxiv.org/abs/2506.21324 mastoxiv.page/@arXiv_csNE_bot/
- Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm
Pierre Boudart (SIERRA), Pierre Gaillard (Thoth), Alessandro Rudi (PSL, DI-ENS, Inria)
arxiv.org/abs/2507.05306 mastoxiv.page/@arXiv_statML_bo
- Characterizing State Space Model and Hybrid Language Model Performance with Long Context
Saptarshi Mitra, Rachid Karami, Haocheng Xu, Sitao Huang, Hyoukjun Kwon
arxiv.org/abs/2507.12442 mastoxiv.page/@arXiv_csAR_bot/
- Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Da...
Ayush Roy, Samin Enam, Jun Xia, Won Hwa Kim, Vishnu Suresh Lokhande
arxiv.org/abs/2507.19575 mastoxiv.page/@arXiv_csCV_bot/
- TASER: Table Agents for Schema-guided Extraction and Recommendation
Nicole Cho, Kirsty Fielding, William Watson, Sumitra Ganesh, Manuela Veloso
arxiv.org/abs/2508.13404 mastoxiv.page/@arXiv_csAI_bot/
- Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
Nuno Costa, Julija Zavadlav
arxiv.org/abs/2509.02060 mastoxiv.page/@arXiv_qbioBM_bo
- PCPO: Proportionate Credit Policy Optimization for Aligning Image Generation Models
Jeongjae Lee, Jong Chul Ye
arxiv.org/abs/2509.25774 mastoxiv.page/@arXiv_csCV_bot/
- Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned I...
Didrik Bergstr\"om, Deniz G\"und\"uz, Onur G\"unl\"u
arxiv.org/abs/2510.06868 mastoxiv.page/@arXiv_csIT_bot/
- MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile...
Chengshu Li, et al.
arxiv.org/abs/2510.18316 mastoxiv.page/@arXiv_csRO_bot/
- A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffs
Roxanne Holden, Luana Ruiz
arxiv.org/abs/2510.20954 mastoxiv.page/@arXiv_statML_bo
- Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agents
Bazinska, Mathys, Casucci, Rojas-Carulla, Davies, Souly, Pfister
arxiv.org/abs/2510.22620 mastoxiv.page/@arXiv_csCR_bot/
- Uncertainty Calibration of Multi-Label Bird Sound Classifiers
Raphael Schwinger, Ben McEwen, Vincent S. Kather, Ren\'e Heinrich, Lukas Rauch, Sven Tomforde
arxiv.org/abs/2511.08261 mastoxiv.page/@arXiv_csSD_bot/
- Two-dimensional RMSD projections for reaction path visualization and validation
Rohit Goswami (Institute IMX and Lab-COSMO, \'Ecole polytechnique f\'ed\'erale de Lausanne)
arxiv.org/abs/2512.07329 mastoxiv.page/@arXiv_physicsch
- Distribution-informed Online Conformal Prediction
Dongjian Hu, Junxi Wu, Shu-Tao Xia, Changliang Zou
arxiv.org/abs/2512.07770 mastoxiv.page/@arXiv_statML_bo
- Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
Ang Lv, Jin Ma, Yiyuan Ma, Siyuan Qiao
arxiv.org/abs/2512.23447 mastoxiv.page/@arXiv_csCL_bot/
toXiv_bot_toot