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📷 Zeiss IKON Super Ikonta 533/16
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#filmphotography #Photography #blackandwhite

Easy Adaptation: An Efficient Task-Specific Knowledge Injection Method for Large Models in Resource-Constrained Environments
Dong Chen, Zhengqing Hu, Shixing Zhao, Yibo Guo
https://arxiv.org/abs/2512.17771 https://arxiv.org/pdf/2512.17771 https://arxiv.org/html/2512.17771
arXiv:2512.17771v1 Announce Type: new
Abstract: While the enormous parameter scale endows Large Models (LMs) with unparalleled performance, it also limits their adaptability across specific tasks. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a critical approach for effectively adapting LMs to a diverse range of downstream tasks. However, existing PEFT methods face two primary challenges: (1) High resource cost. Although PEFT methods significantly reduce resource demands compared to full fine-tuning, it still requires substantial time and memory, making it impractical in resource-constrained environments. (2) Parameter dependency. PEFT methods heavily rely on updating a subset of parameters associated with LMs to incorporate task-specific knowledge. Yet, due to increasing competition in the LMs landscape, many companies have adopted closed-source policies for their leading models, offering access only via Application Programming Interface (APIs). Whereas, the expense is often cost-prohibitive and difficult to sustain, as the fine-tuning process of LMs is extremely slow. Even if small models perform far worse than LMs in general, they can achieve superior results on particular distributions while requiring only minimal resources. Motivated by this insight, we propose Easy Adaptation (EA), which designs Specific Small Models (SSMs) to complement the underfitted data distribution for LMs. Extensive experiments show that EA matches the performance of PEFT on diverse tasks without accessing LM parameters, and requires only minimal resources.
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#science #collectif
https://www.

Sclérose en plaques : l’espoir vient du collectif
Un candidat médicament sérieux apparaît pour la sclérose en plaques. Au-delà de l’espoir possible pour les patients, l’organisation scientifique à l’origine de l’avancée propose une leçon de politique scientifique : pas de génie, beaucoup de collectif.

More than 160 Texas faith leaders urge school boards to oppose setting aside time for prayer, Bible readings – Houston Public Media https://www.houstonpublicmedia.org/article

More than 160 Texas faith leaders urge school boards to oppose setting aside time for prayer, Bible readings
The letter, circulated by a coalition of religious freedom organizations, comes less than two months before a deadline for public school boards and charter school governing bodies to vote on whether to adopt such policies, as mandated by Senate Bill 11, passed last year.

Federal agents raiding schools and abducting children sounds far-fetched. 
But considering the recent trends of immigration enforcement nationwide,
California officials are treating this as a real possibility. 
The California Attorney General’s office has released updated guidelines for school staff, students, and families
on what to do should Immigration and Customs Enforcement (ICE) try to step onto school grounds.
It is intended to expand and standardize pro…

Correlation of Rankings in Matching Markets
R\'emi Castera, Patrick Loiseau, Bary S. R. Pradelski
https://arxiv.org/abs/2512.05304 https://arxiv.org/pdf/2512.05304 https://arxiv.org/html/2512.05304
arXiv:2512.05304v1 Announce Type: new
Abstract: We study the role of correlation in matching markets, where multiple decision-makers simultaneously face selection problems from the same pool of candidates. We propose a model in which a candidate's priority scores across different decision-makers exhibit varying levels of correlation dependent on the candidate's sociodemographic group. Such differential correlation can arise in school choice due to the varying prevalence of selection criteria, in college admissions due to test-optional policies, or due to algorithmic monoculture, that is, when decision-makers rely on the same algorithms and data sets to evaluate candidates. We show that higher correlation for one of the groups generally improves the outcome for all groups, leading to higher efficiency. However, students from a given group are more likely to remain unmatched as their own correlation level increases. This implies that it is advantageous to belong to a low-correlation group. Finally, we extend the tie-breaking literature to multiple priority classes and intermediate levels of correlation. Overall, our results point to differential correlation as a previously overlooked systemic source of group inequalities in school, university, and job admissions.
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I'm originally from Texas. There are times I'm not really proud to say that.
Why is it always the Decalogue they want posted? Why isn't ever the Beatitudes?
And if posting the Decalogue makes people behave, why aren't churches the safest places around?
https://www.friendlyath…

Texas families file class action lawsuit to halt Ten Commandments display in every school district
As Attorney General Ken Paxton urges districts to disobey federal rulings, church/state watchdogs are pushing for a statewide injunction

What was A practical skill very important when you were a teen (12-18), that is still still very useful today but most current teens no longer are able to do?
Think concrete stuff like remounting a bicycle chain, not abstract stuff like critical thinking.
For me it was definitely going to places without modern navigation aids.
Just look at a map and write down the rough route and asking directions if I couldn't find it.
And if I needed a train, I'd just look at t…

As recently as Thursday, Vladimir Putin told a Kremlin demographic conference that increasing births was “crucial” for Russia.
Putin has launched initiatives to encourage people to have more children -- from free school meals for large families to awarding Soviet-style “hero-mother” medals to women with 10 or more children.
“Many of our grandmothers and great-grandmothers had seven, eight, and even more children,” Putin said in 2023.
“Let’s preserve and revive these wonderful…

Russia's population is getting smaller and older. Putin sees that as a national security threat
Russia has grappled with a shrinking and aging population for decades, with President Vladimir Putin viewing it as a threat to national security. Yet despite efforts like cash incentives and family-friendly policies, the number of deaths now outpaces births. Economic uncertainty, restrictions on immigration and the war in Ukraine are making the crisis worse. Desperate to reverse the trend, Russian authorities are also rolling out a wave of restrictive laws, such as banning "child-free propagand…

Invariant Price of Anarchy: a Metric for Welfarist Traffic Control
Ilia Shilov, Mingjia He, Heinrich H. Nax, Emilio Frazzoli, Gioele Zardini, Saverio Bolognani
https://arxiv.org/abs/2512.05843 https://arxiv.org/pdf/2512.05843 https://arxiv.org/html/2512.05843
arXiv:2512.05843v1 Announce Type: new
Abstract: The Price of Anarchy (PoA) is a standard metric for quantifying inefficiency in socio-technical systems, widely used to guide policies like traffic tolling. Conventional PoA analysis relies on exact numerical costs. However, in many settings, costs represent agents' preferences and may be defined only up to possibly arbitrary scaling and shifting, representing informational and modeling ambiguities. We observe that while such transformations preserve equilibrium and optimal outcomes, they change the PoA value. To resolve this issue, we rely on results from Social Choice Theory and define the Invariant PoA. By connecting admissible transformations to degrees of comparability of agents' costs, we derive the specific social welfare functions which ensure that efficiency evaluations do not depend on arbitrary rescalings or translations of individual costs. Case studies on a toy example and the Zurich network demonstrate that identical tolling strategies can lead to substantially different efficiency estimates depending on the assumed comparability. Our framework thus demonstrates that explicit axiomatic foundations are necessary in order to define efficiency metrics and to appropriately guide policy in large-scale infrastructure design robustly and effectively.
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