Tootfinder

RE: https://loma.ml/display/373ebf56-595c02ae-e4e712bd0deeaf55
Sonderbare Überschrift. Wenn die Ministerin davon ausgehen würde, dass ihre Anordnung gegen Gesetze verstoßen könnte, wäre sie rechtswidrig.

Preliminary Nucleus Size Estimate for #Kreutz Sungrazer C/2026 A1 (MAPS): https://iopscience.iop.org/article/10.3847/2515-5172/ae5220 -> "JWST imagery of the approaching Kreutz sungrazer C/2026 A1 (MAPS) appears sufficient to distinguish the comet’s nucleus from the surrounding dust. Coma model fitting indicates the morphology is compatible with a ∼0.4 km diameter nucleus—likely larger than those of the minor Kreutz fragments routinely discovered with coronagraphs, and comparable to or slightly smaller than that of C/2011 W3 (Lovejoy). However, since C/2026 A1 is the only Kreutz sungrazer whose nucleus has been directly observed, these comparisons should be treated with caution" -> https://groups.io/g/comets-ml/message/35035 / https://scicomm.xyz/@qicheng@cometary.org/116245251072721081

on my blog!
one page of async Rust
https://dotat.at/@/2026-02-16-async.html
what is the least boilerplate needed to make a minimal async runtime?

Two Years of Building AI in Firefox | Tarek Ziadé
https://blog.ziade.org/2025/12/05/two-years-of-ai-at-mozilla/?trk=feed_main-feed-card_feed-article-content

On 16WW Data Collections and Graphs - Open for research home #dataset - https://m.earth.org.uk/note-on-data.html

On 16WW Data Collections and Graphs
Open for research home #dataset

Tschüss, Core ML: Apple plant neues KI-Framework
Einem Bericht zufolge wird Apple seinen KI-Ansatz für Entwickler überdenken. Auf Core ML folgt „Core AI” – mit moderneren Methoden.
https://www.…

Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML

"Anthropic is a company started by people who left OpenAI. What did they do there, why did they leave, how was Anthropic supposed to be different, and how is it actually different?"
(Original title: Anthropic is untrustworthy)
https://anthropic.ml/

🪪 The Science of Detecting LLM-Generated Text
#llm #ml

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Molina feat. ML Buch:
🎵 Organs
#Molina #MLBuch
https://corpusmolina.bandcamp.com/track/organs-feat-ml-buch
https://open.spotify.com/track/3XrEOFbHz26fgR8UTmPxmP

I realise on the fediverse this is maybe asking for a flaming, but yesterday out of sheer curiosity I tried Claude for a simpleish coding task that I'd been putting off (largely inspired by @… 's latest on #theclimatebrink). The performance of Claude was seriously impressive. I am convinced the AI cycle is more than hype (and have been for a while), the chatbots have been a huge attention hogger, misleadingly so, while the serious work has been done elsewhere. (We are developing ML tools to supplement parts of our climate model workflows).
Now I'm wondering if there is any serious EU competition to Anthropic? - Mistral's codestral perhaps?
Because this kind of performance changes everything and we can't afford to lag behind...
#AIcoding #ML
Edit: here is the climate brink post I mentioned
https://www.theclimatebrink.com/p/the-ai-augmented-scientist

Prospects for #Comet C/2026 A1 MAPS - a comet with a narrow tail in the evening twilight, with uncertain but potentially high brightness? https://hdr-astrophotography.com -> Tail simulations for comet MAPS: https://groups.io/g/comets-ml/message/34888 - in order not to depend on a single prediction, i have simulated different scenarii.

Fuzzing the brain: automated stress testing for the safety of ML-driven neurostimulation https://iopscience.iop.org/article/10.1088/1741-2552/ae4927 Curiously, the paper does not mention known risks such as seizures like those that already happened with the Dobelle and Orion…

LED Lighting: Mini Reviews - Real-world testing! - https://m.earth.org.uk/LED-lighting.html

LED Lighting: Mini Reviews
Real-world testing!

Just had a "software engineer specializing on full stack AI/ML development" show up in the ngscopeclient dev chat, give like two lines of what looked like human generated feedback on the CI/build setup, then descend into AI slop completely misunderstanding the infrastructure.
What is the world coming to...

RE: https://social.mitexleo.one/@ml/116162576720495326
unboosted: it’s a fraud

RE: https://loma.ml/display/373ebf56-32c1ad61-8d704fa04291d97b
Ahhhhja. CD und DVD sind also "analoge Medien". Ich glaube die DACs in den Abspielgeräten möchten da Einspruch einlegen.

AWS implemented a 15% price hike for EC2 Capacity Blocks for ML on January 3; Amazon says it "reflects the supply/demand patterns we expect this quarter" (Corey Quinn/The Register)
https://www.theregister.com/2026/01/05/aws_price_increase/

AWS raises GPU prices 15% on a Saturday, hopes you weren't paying attention
: An anomaly or the beginning of a new trend? My bet's on the latter

The F is silent in ML

Выписываю вам рецепт на блины :blobcat_doctor:
Rp.: Farinae Tritici 500,0
Ovi gallini 140,0
Natrii chloridi 10,0
Natrii hydrocarbonatis 2,0
Lactis vaccini 1000 ml
Aquae coctae 300 ml
Olei Helianthi 90 ml
M. f. pasta
D. S. Внутрь, предварительно обжарив на сильно разогретой смазанной маслом сковороде 10 секунд с одной стороны, 5 секунд - с другой. Подавать со сметаной, вареньем, сгущенным молоком или сливочным маслом.

> Concretely, we expect to add ML-KEM and ML-DSA APIs that are only available with #LibreSSL/#BoringSSL/AWS-LC, and not with #OpenSSL.
(from #Cryptography and #Qt simultaneously, right? (Qt rejected LibreSSL support.)
#Python

Lazypost, is there a good tool to fix (as much as possible) somewhat broken audio from a CD that has some read errors (I've fixed surface of CD and read with EAC to extract as much info as possible, you can hear everything but it sounds a bit like a crackling record).
I am not looking for CD reading software (EAC already spent 70 hours on extracting the one track I'm interested in).
I am looking for fixing errors in a ripped lossless audio track, for example through interpolation and/or specifically tuned ML algorithms.
If helpful, I have access to Logic Pro and to Adobe tools.
(Please only recommend software you personally have used and that you like, thank you.)

#Kubernetes 1.35 Released with In-Place Pod Resize and AI-Optimized Scheduling
https://www.infoq.com/news/2025/12/kubernetes-1-35/

'Artemis I: Flight Day 13' From the #NASA_App :
https://apod.nasa.gov/apod/ap260131.html

The #Kreutz #comet #MAPS "has become more than 1 mag brighter between March 6 and 9" and stands at 10.5 mag. right now: https://groups.io/g/comets-ml/message/35019 and https://www.facebook.com/photo/?fbid=2374472359736249 with the latest picture by Jäger & Rhemann here - less than 4 weeks til perihelion, and the brightness continues to rise with a strong n~8 ...

Whoa. The schedule for #FOSSnorth is live: https://foss-north.se/2026/schedule.html
If you want to hear my new talk on how to disobey with

Machine Learning techniques are upending multiple scientific fields. Operational 5-day forecasting of air quality in 1 minute in this paper from Chinese researchers.
This is awesome work with very clear public health implications.
EDIT for clarity: I am.not suggesting LLMs have anything to do with this work, but many people hear AI and imagine LLMs. And many of them.are perhaps rightly sceptical of AI as a result.
But AI or ML techniques can be useful for lots of things, not just chatbots. And we should probably invest more in those.

https://www.nature.com/articles/s41586-026-10234-y

https://vvtat.lrv.lt/lt/pavojingi-produktai/pavojingi-produktai/cbd-aliejus-30-3000-mg-10-ml-bruksninis-kodas-4779044360444-RCw/

MadSpace -- Event Generation for the Era of GPUs and ML
Theo Heimel, Olivier Mattelaer, Ramon Winterhalder
https://arxiv.org/abs/2602.06895 https://arxiv.o…

How an appeal changed the way the USPTO assesses AI patents under the US Patent Act, signaling a shift towards more favorable treatment of AI and ML inventions (Matthew Carey/Bloomberg Law)
https://news.bloomberglaw.com/tech-and

AI Patent Eligibility Has Shifted With One Machine-Learning Case
Opinion: A patent case involving inventor Guillaume Desjardins has evolved into a cornerstone of modern patent eligibility practice for AI technologies in just four months.

RE: https://mastodon.social/@firefoxwebdevs/115849251057488746
A Killswitch should of course kill all ML/AI functionality and people could then reactivate certain specific features of they want to, it's really not that hard. Just cause you consi…

LED Lighting: Mini Reviews - Real-world testing! - https://m.earth.org.uk/LED-lighting.html

LED Lighting: Mini Reviews
Real-world testing!

I'm installing and tweaking #Immich, a really neat "Google Photos" like app that runs locally. It uses ML to do face detection and other neat features. I've been tuning my server and needed a better way to measure performance, so I vibe wrote a helper script. If you run Immich, this might be useful to you:

GitHub - rubix1138/immich_eta: Script that queries the Immich API and calculates and ETA
Script that queries the Immich API and calculates and ETA - rubix1138/immich_eta

Prune, Don't Rebuild: Efficiently Tuning $\alpha$-Reachable Graphs for Nearest Neighbor Search
Tian Zhang, Ashwin Padaki, Jiaming Liang, Zack Ives, Erik Waingarten
https://arxiv.org/abs/2602.08097 https://arxiv.org/pdf/2602.08097 https://arxiv.org/html/2602.08097
arXiv:2602.08097v1 Announce Type: new
Abstract: Vector similarity search is an essential primitive in modern AI and ML applications. Most vector databases adopt graph-based approximate nearest neighbor (ANN) search algorithms, such as DiskANN (Subramanya et al., 2019), which have demonstrated state-of-the-art empirical performance. DiskANN's graph construction is governed by a reachability parameter $\alpha$, which gives a trade-off between construction time, query time, and accuracy. However, adaptively tuning this trade-off typically requires rebuilding the index for different $\alpha$ values, which is prohibitive at scale. In this work, we propose RP-Tuning, an efficient post-hoc routine, based on DiskANN's pruning step, to adjust the $\alpha$ parameter without reconstructing the full index. Within the $\alpha$-reachability framework of prior theoretical works (Indyk and Xu, 2023; Gollapudi et al., 2025), we prove that pruning an initially $\alpha$-reachable graph with RP-Tuning preserves worst-case reachability guarantees in general metrics and improved guarantees in Euclidean metrics. Empirically, we show that RP-Tuning accelerates DiskANN tuning on four public datasets by up to $43\times$ with negligible overhead.
toXiv_bot_toot

A look at the state of AI agents, the evolution of thinking models, the staggering need for inference compute in the coming years, automated research, and more (Eric Jang/Evjang.com)
https://evjang.com/2026/02/04/rocks.html

A clear detection of #comet C/2025 R3 (PANSTARRS) on the morning of March 2 "despite the 99% illuminated Moon, morning twilight and the comet at an altitude of 4.2 degrees": https://groups.io/g/comets-ml/message/34974 -> "The fact that the comet is showing this much detail under such bad conditions with such bad equipment and software bodes well for what we expect will be a very nice comet in the next few months."

from my link log —
The HP-35.
http://codex99.com/design/the-hp35.html
saved 2020-12-13 https://dotat.at/:/F9KEM.html

LED Lighting: Mini Reviews - Real-world testing! - https://m.earth.org.uk/LED-lighting.html

LED Lighting: Mini Reviews
Real-world testing!

I can't hide that AMD cosplaying as Nvidia was a nasty surprise to me. But I am just a lowly gamer. Mad respect to the Linux hackers for knowing that corporations are not their friends, and - respectfully to AMD - bringing the goods only to Linux 🙂 ->
Modder gets FSR Redstone ML-powered Frame Generation running on RDNA 3 GPUs - VideoCardz.com

https://mondays.pizza/stickers-club.html

The *other* potentially quite interesting April #comet C/2025 R3 (PANSTARRS) has been recovered in morning twilight: https://groups.io/g/comets-ml/message/34973 - it "has brightened nicely since my last observation on February 9 when it was in evening twilight at magnitude 13.3, magnitude calculations on the mornings of February 27/28 found the comet at magnitude 11.1 and 10.7 but I am sure the coma is a lot bigger and not fully reflected in these magnitude measurements."

I am tackeling my next project in moving away from #BigTech and #USTech companies. This one is one that is really hard for me, and that pains me a bit: I'm moving away from #GitHub - it's…

I so hope this blows up in his face spectacularly.
Note that I do not believe that any LLM can become as skilled as the least skilled #InfoSec professional using conventional tools, so I’m quite confident this will not reach any useful goal.
I’m saying he needs to learn a lesson, and it would be great if it were useful for others as well. @…

On 16WW Data Collections and Graphs - Open for research home #dataset - https://m.earth.org.uk/note-on-data.html

On 16WW Data Collections and Graphs
Open for research home #dataset

Hierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels
Anupam Sharma, Harish Katti, Prajwal Singh, Shanmuganathan Raman, Krishna Miyapuram
https://arxiv.org/abs/2602.20932 https://arxiv.org/pdf/2602.20932 https://arxiv.org/html/2602.20932
arXiv:2602.20932v1 Announce Type: new
Abstract: An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies have explored EEG-based classification of objects or concepts, often for passive viewing of briefly presented image or video stimuli, with limited classes. Because EEG exhibits a low signal-to-noise ratio, recognizing fine-grained representations across a large number of classes remains challenging; however, abstract-level object representations may exist. In this work, we investigate whether EEG captures object representations across multiple hierarchical levels, and propose episodic analysis, in which a Machine Learning (ML) model is evaluated across various, yet related, classification tasks (episodes). Unlike prior episodic EEG studies that rely on fixed or randomly sampled classes of equal cardinality, we adopt hierarchy-aware episode sampling using WordNet to generate episodes with variable classes of diverse hierarchy. We also present the largest episodic framework in the EEG domain for detecting observed text from EEG signals in the PEERS dataset, comprising $931538$ EEG samples under $1610$ object labels, acquired from $264$ human participants (subjects) performing controlled cognitive tasks, enabling the study of neural dynamics underlying perception, decision-making, and performance monitoring.
We examine how the semantic abstraction level affects classification performance across multiple learning techniques and architectures, providing a comprehensive analysis. The models tend to improve performance when the classification categories are drawn from higher levels of the hierarchy, suggesting sensitivity to abstraction. Our work highlights abstraction depth as an underexplored dimension of EEG decoding and motivates future research in this direction.
toXiv_bot_toot

from my link log —
psychec: ML-style type inference for C.
https://github.com/ltcmelo/psychec
saved 2019-09-09 https://dotat.at/:/GN6G4.html

GitHub - ltcmelo/psychec: A compiler frontend for the C programming language
A compiler frontend for the C programming language - ltcmelo/psychec

I wonder if part of the reason that Mozilla are so confused about what their actual user-base want is that all the people who want to turn off the AI bullshit are also the ones who turn off telemetry so the bulk of people with
also have telemetry turned off so are invisible to Mozilla.
🤔

LED Lighting: Mini Reviews - Real-world testing! - https://m.earth.org.uk/LED-lighting.html

LED Lighting: Mini Reviews
Real-world testing!

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
toXiv_bot_toot

On 16WW Data Collections and Graphs - Open for research home #dataset - https://m.earth.org.uk/note-on-data.html

On 16WW Data Collections and Graphs
Open for research home #dataset

On 16WW Data Collections and Graphs - Open for research home #dataset - https://m.earth.org.uk/note-on-data.html

On 16WW Data Collections and Graphs
Open for research home #dataset

On 16WW Data Collections and Graphs - Open for research #dataset - https://m.earth.org.uk/note-on-data.html

On 16WW Data Collections and Graphs
Open for research #dataset

The new #Kreutz #comet discovered unusually early has been named C/2026 A1 (MAPS): #photosphere on 4 April and becoming a bright comet.

The new #Kreutz #comet is now C/2026 A1 (MAPS): http://www.cbat.eps.harvard.edu/iau/cbet/005600/CBET005658.txt and #perihelion is highly uncertain but there is hope as https://www.facebook.com/groups/227002358661288/posts/1670699484291561 and https://groups.io/g/comets-ml/topic/117373975#msg34667 explain. And if MAPS makes it to perihelion and is (very) bright then coronagraphs on the ground might be able to catch it against the inner corona as the following table - calculated with https://ssd.jpl.nasa.gov/horizons/app.html#/ - shows.