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@servelan@newsie.social
2025-12-24 04:40:05

US Consumer Confidence: weakened for a fifth consecutive month as perceptions of business conditions were negative, and apprehensions about jobs and income deepened
conference-board.org/topics/co

@castarco@hachyderm.io
2025-11-16 12:05:17
Content warning: "long" rant about american sci-fi tv series and "neuro-archy"

I have the distinct impression that we could use most American "sci-fi" TV series (which seem to have a kink for post-apocalyptical scenographies) as a diagnostic tool for the autism spectrum.
For a moment, let's leave aside the tons of right-wing propaganda "hidden" in plain sight, and their excessive reliance on boring & worn out tropes (religious & cultish bullshit, irrational lack of communication & excess of anti-social behaviour, all vs all, ultra-low-iq characters*, psychotic & irrationally treacherous characters*, ultra-inconsistent character development used to justify "unexpected" plot twists, rampant anti-intellectualism...).
What could be used as a diagnosis tool is the incredible amount of strong inconsistencies that we can find in them**. It throws me out of the story every single time; and I suspect that it takes a certain kind of "uncommon personality" to feel that way about it, because otherwise these series wouldn't be so popular without real widespread criticism beyond cliches like "too slow", "it loses steam towards the end of the season", etc.
Many of those plots start in a gold mine of potentially powerful ideas... yet they consistently provide us with dirt & clay instead, while side-lining the "good stuff" as if it was too complicated for the populace.
Do you feel strongly about it? Do you feel like you can't verbalize it without being criticised as "too negative", or "too picky", or an "unbearable snob"? Do you wonder why it seems like nobody around shares your discomfort with these stories?
* : I feel this is a bit like the chicken & egg problem. Has the media conditioned part of American society to behave like dumb psychopaths as if it was something "natural", or is the media reflecting what was already there? Also, could we use other societies as models for these stories... just for a change? Please?
** : Just a tiny example: a "brilliant" engineer who builds a bridge out of fence parts and who doesn't bother to perform the most basic tests before trying it in a real setting and suffer the consequences: the bridge failing and her falling into the void. Bonus points for anyone who knows what I'm talking about.

@arXiv_mathOC_bot@mastoxiv.page
2025-11-14 09:30:00

The Age-Structured Chemostat with Substrate Dynamics as a Control System
Iasson Karafyllis, Dionysis Theodosis, Miroslav Krstic
arxiv.org/abs/2511.09963 arxiv.org/pdf/2511.09963 arxiv.org/html/2511.09963
arXiv:2511.09963v1 Announce Type: new
Abstract: In this work we study an age-structured chemostat model with a renewal boundary condition and a coupled substrate equation. The model is nonlinear and consists of a hyperbolic partial differential equation and an ordinary differential equation with nonlinear, nonlocal terms appearing both in the ordinary differential equation and the boundary condition. Both differential equations contain a non-negative control input, while the states of the model are required to be positive. Under an appropriate weak solution framework, we determine the state space and the input space for this model. We prove global existence and uniqueness of solutions for all admissible initial conditions and all allowable control inputs. To this purpose we employ a combination of Banach's fixed-point theorem with implicit solution formulas and useful solution estimates. Finally, we show that the age-structured chemostat model gives a well-defined control system on a metric space.
toXiv_bot_toot

@arXiv_csLG_bot@mastoxiv.page
2025-12-22 13:54:24

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[1/5]:
- Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization a...
Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert Nowak, Yixuan Li
arxiv.org/abs/2306.09158
- Sparse, Efficient and Explainable Data Attribution with DualXDA
Galip \"Umit Yolcu, Moritz Weckbecker, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
arxiv.org/abs/2402.12118 mastoxiv.page/@arXiv_csLG_bot/
- HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs
Sun, Que, {\AA}rrestad, Loncar, Ngadiuba, Luk, Spiropulu
arxiv.org/abs/2405.00645 mastoxiv.page/@arXiv_csLG_bot/
- On the Identification of Temporally Causal Representation with Instantaneous Dependence
Li, Shen, Zheng, Cai, Song, Gong, Chen, Zhang
arxiv.org/abs/2405.15325 mastoxiv.page/@arXiv_csLG_bot/
- Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
Yang Li, Daniel Agyei Asante, Changsheng Zhao, Ernie Chang, Yangyang Shi, Vikas Chandra
arxiv.org/abs/2405.15877 mastoxiv.page/@arXiv_csLG_bot/
- Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric
Yan Shvartzshnaider, Vasisht Duddu
arxiv.org/abs/2409.03735 mastoxiv.page/@arXiv_csLG_bot/
- Low-Rank Filtering and Smoothing for Sequential Deep Learning
Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig
arxiv.org/abs/2410.06800 mastoxiv.page/@arXiv_csLG_bot/
- Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
Xiaoyu Tao, Tingyue Pan, Mingyue Cheng, Yucong Luo, Qi Liu, Enhong Chen
arxiv.org/abs/2410.18686 mastoxiv.page/@arXiv_csLG_bot/
- Fairness via Independence: A (Conditional) Distance Covariance Framework
Ruifan Huang, Haixia Liu
arxiv.org/abs/2412.00720 mastoxiv.page/@arXiv_csLG_bot/
- Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
Simon Frieder, et al.
arxiv.org/abs/2412.15184 mastoxiv.page/@arXiv_csLG_bot/
- Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy
Ishank Juneja, Carlee Joe-Wong, Osman Ya\u{g}an
arxiv.org/abs/2501.10290 mastoxiv.page/@arXiv_csLG_bot/
- Towards Human-Guided, Data-Centric LLM Co-Pilots
Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat, Anders Boyd, Mihaela van der Schaar
arxiv.org/abs/2501.10321 mastoxiv.page/@arXiv_csLG_bot/
- Regularized Langevin Dynamics for Combinatorial Optimization
Shengyu Feng, Yiming Yang
arxiv.org/abs/2502.00277
- Generating Samples to Probe Trained Models
Eren Mehmet K{\i}ral, Nur\c{s}en Ayd{\i}n, \c{S}. \.Ilker Birbil
arxiv.org/abs/2502.06658 mastoxiv.page/@arXiv_csLG_bot/
- On Agnostic PAC Learning in the Small Error Regime
Julian Asilis, Mikael M{\o}ller H{\o}gsgaard, Grigoris Velegkas
arxiv.org/abs/2502.09496 mastoxiv.page/@arXiv_csLG_bot/
- Preconditioned Inexact Stochastic ADMM for Deep Model
Shenglong Zhou, Ouya Wang, Ziyan Luo, Yongxu Zhu, Geoffrey Ye Li
arxiv.org/abs/2502.10784 mastoxiv.page/@arXiv_csLG_bot/
- On the Effect of Sampling Diversity in Scaling LLM Inference
Wang, Liu, Chen, Light, Liu, Chen, Zhang, Cheng
arxiv.org/abs/2502.11027 mastoxiv.page/@arXiv_csLG_bot/
- How to use score-based diffusion in earth system science: A satellite nowcasting example
Randy J. Chase, Katherine Haynes, Lander Ver Hoef, Imme Ebert-Uphoff
arxiv.org/abs/2505.10432 mastoxiv.page/@arXiv_csLG_bot/
- PEAR: Equal Area Weather Forecasting on the Sphere
Hampus Linander, Christoffer Petersson, Daniel Persson, Jan E. Gerken
arxiv.org/abs/2505.17720 mastoxiv.page/@arXiv_csLG_bot/
- Train Sparse Autoencoders Efficiently by Utilizing Features Correlation
Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev, Daniil Gavrilov, Nikita Balagansky
arxiv.org/abs/2505.22255 mastoxiv.page/@arXiv_csLG_bot/
- A Certified Unlearning Approach without Access to Source Data
Umit Yigit Basaran, Sk Miraj Ahmed, Amit Roy-Chowdhury, Basak Guler
arxiv.org/abs/2506.06486 mastoxiv.page/@arXiv_csLG_bot/
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