Tootfinder

watchOS 26: Probleme mit der Aktionstaste bei der Apple Watch Ultra
Apples Top-Uhr Apple Watch Ultra verfügt über einen frei belegbaren Zusatzdrücker. Seit watchOS 26 ist der aber nicht immer zuverlässig. So geht das Debugging.
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NFL Christmas streaming guide: Cowboys-Commanders, Lions-Vikings and Broncos-Chiefs https://www.nytimes.com/athletic/6918169/2025/12/25/cowboys-commanders-lions-vikings-broncos-chiefs-how-to-watch/

Burning games on strong path products
Sally Ambrose, Evan Angelone, Jacob Chen, Daniel Ma, Arturo Ortiz San Miguel, Wraven Watanabe, Stephen Whitcomb, Shanghao Wu
https://arxiv.org/abs/2509.20572

Burning games on strong path products
Burning and cooling are diffusion processes on graphs in which burned (or cooled) vertices spread to their neighbors with a new source picked at discrete time steps. In burning, the one tries to burn the graph as fast as possible, while in cooling one wants to delay cooling as long as possible. We consider $d$-fold strong products of paths, which generalize king graphs. The propagation of these graphs is radial, and models local spread of contagion in an arbitrary number of dimensions. We red…

I read about a major update of #jellyfin and that one should do this planned with some care.
So I sat down, prepared for an update full of hassles, debugging, restoring, ...
> docker down / pull / validate / up / done
Took me about 5-10 min.
Very well done Jellyfin!
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Jellyfin 10.11.0 | Jellyfin
We are pleased to bring you Jellyfin 10.11.0, our new stable release. This is probably one of, if not the, biggest and most impactful releases we've done yet, with some massive backend changes to improve performance and long-term expandability and maintainability. This release has been a long time coming, with over 6 months of development and another 6 months of RC testing, throwing our planned 6-month release schedule completely out of whack, but we definitely think the results are worth it - …

pop-cosmos: Star formation over 12 Gyr from generative modelling of a deep infrared-selected galaxy catalogue
Sinan Deger, Hiranya V. Peiris, Stephen Thorp, Daniel J. Mortlock, Gurjeet Jagwani, Justin Alsing, Boris Leistedt, Joel Leja
https://arxiv.org/abs/2509.20430

pop-cosmos: Star formation over 12 Gyr from generative modelling of a deep infrared-selected galaxy catalogue
We study star formation over 12 Gyr using pop-cosmos, a generative model trained on 26-band photometry of 420,000 COSMOS2020 galaxies (IRAC Ch.1 $<26$). The model learns distributions over 16 SPS parameters via score-based diffusion, matching observed colours and magnitudes. We compute the star formation rate density (SFRD) to $z=3.5$ by directly integrating individual galaxy SFRs. The SFRD peaks at $z=1.3\pm0.1$, with peak value $0.08\pm0.01$ M$_{\odot}$ yr$^{-1}$ Mpc$^{-3}$. We classify star-…

Glasfaser: Von Laufzeitfallen und untergeschobenen Verträgen
Manche Glasfaser-Anbieter tricksen bei Mindestlaufzeiten oder „Beratungsprotokollen“. Wie man das durchschaut und aus dubiosen Nummern herauskommt.
h…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
https://arxiv.org/abs/2506.10892 https://mastoxiv.page/@arXiv_csLG_bot/114675526577078472
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
https://arxiv.org/abs/2506.20494 https://mastoxiv.page/@arXiv_csLG_bot/114749113025183688
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
https://arxiv.org/abs/2506.20584 https://mastoxiv.page/@arXiv_csLG_bot/114749118923266356
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
https://arxiv.org/abs/2506.22376 https://mastoxiv.page/@arXiv_csLG_bot/114771735361664528
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
https://arxiv.org/abs/2507.18242 https://mastoxiv.page/@arXiv_csLG_bot/114913322736512937
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
https://arxiv.org/abs/2508.17702 https://mastoxiv.page/@arXiv_csLG_bot/115095014405732247
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
https://arxiv.org/abs/2508.19009 https://mastoxiv.page/@arXiv_csLG_bot/115100269482762688
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011 https://mastoxiv.page/@arXiv_csLG_bot/115100270137397046
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
https://arxiv.org/abs/2508.20705 https://mastoxiv.page/@arXiv_csLG_bot/115111565155687451
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
https://arxiv.org/abs/2509.25977 https://mastoxiv.page/@arXiv_csLG_bot/115298721327100391
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
https://arxiv.org/abs/2510.01384 https://mastoxiv.page/@arXiv_csLG_bot/115309690976554356
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
https://arxiv.org/abs/2510.09775 https://mastoxiv.page/@arXiv_csLG_bot/115372387779061015
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
https://arxiv.org/abs/2510.14386 https://mastoxiv.page/@arXiv_csLG_bot/115389079527543821
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
https://arxiv.org/abs/2510.16882 https://mastoxiv.page/@arXiv_csLG_bot/115412243355962887
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
https://arxiv.org/abs/2510.23117 https://mastoxiv.page/@arXiv_csLG_bot/115451891042176876
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
https://arxiv.org/abs/2510.23501 https://mastoxiv.page/@arXiv_csLG_bot/115451942159737549
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
https://arxiv.org/abs/2511.00040 https://mastoxiv.page/@arXiv_csLG_bot/115490555013124989
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
https://arxiv.org/abs/2511.04469 https://mastoxiv.page/@arXiv_csLG_bot/115507943827841017
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
https://arxiv.org/abs/2511.09902 https://mastoxiv.page/@arXiv_csLG_bot/115547587245365920
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
toXiv_bot_toot

TF-Restormer: Complex Spectral Prediction for Speech Restoration
Ui-Hyeop Shin, Jaehyun Ko, Woocheol Jeong, Hyuing-Min Park
https://arxiv.org/abs/2509.21003 https://

TF-Restormer: Complex Spectral Prediction for Speech Restoration
Speech restoration in real-world conditions is challenging due to compounded distortions such as clipping, band-pass filtering, digital artifacts, noise, and reverberation, and low sampling rates. Existing systems, including vocoder-based approaches, often sacrifice signal fidelity, while diffusion models remain impractical for streaming. Moreover, most assume a fixed target sampling rate, requiring external resampling that leads to redundant computations. We present TF-Restormer, an encoder-de…

Fuck the United States of America.
And Israel, of course.
Thank you for coming to my TED talk. https://norden.social/@stephie_hamburg/115555340426223090

It's time for ✨Decal Patterns✨
Reference from Apache ECharts https://echarts.apache.org/handbook/en/best-practices/aria/#decal-patterns — Github issue here