Tootfinder

JPMorgan's recent data access deal with Plaid, if extended to other aggregators, could allow Plaid to use its cost advantage to further consolidate market share (Marc Rubinstein/Bloomberg)

With the impending arrival of Typhoon #OpongPH (#Bualoi) and the recent protests against corruption in government-funded flood control projects, the recent release of the 2025 World Risk Report, with its topical focus on flooding, is very timely.
And to the surprise of no one, the

"…poll in April showed a majority of Americans suddenly wanted to restrict that presidency in ways they hadn’t before…percentage who said a president has 'too much' power rose from 32% last year (under Biden) all the way up to 54% (under Trump)."
"All of which suggests Americans-or at least a majority of them-see something extraordinary happening right now."
"Republicans…try to diminish… 'No Kings' rallies…something that’s very real to l…

Republicans are ridiculing ‘No Kings.’ A striking new poll shows Americans aren’t laughing
New polling reinforces how much the thrust of the “No Kings” message has resonated with much of the American public.

Causal Understanding by LLMs: The Role of Uncertainty
Oscar Lithgow-Serrano, Vani Kanjirangat, Alessandro Antonucci
https://arxiv.org/abs/2509.20088 https://

Causal Understanding by LLMs: The Role of Uncertainty
Recent papers show LLMs achieve near-random accuracy in causal relation classification, raising questions about whether such failures arise from limited pretraining exposure or deeper representational gaps. We investigate this under uncertainty-based evaluation, testing whether pretraining exposure to causal examples improves causal understanding >18K PubMed sentences -- half from The Pile corpus, half post-2024 -- across seven models (Pythia-1.4B/7B/12B, GPT-J-6B, Dolly-7B/12B, Qwen-7B). We an…

Bell state measurements in quantum optics: a review of recent progress and open challenges
Luca Bianchi, Carlo Marconi, Davide Bacco
https://arxiv.org/abs/2509.18756 https://

"From dirty water to wildlife protections, the environmental cost of Brexit only gets higher"
#UK #UnitedKingdom #Wildlife

Scientists discover microplastics deep inside human bones #environment

Scientists discover microplastics deep inside human bones
Microplastics have been detected in human blood, brain tissue, and even bones, where they may weaken skeletal structure and accelerate cell aging. Recent studies suggest that these particles could worsen metabolic bone diseases like osteoporosis, a risk that’s especially concerning as fractures are projected to rise sharply in the coming decades.

A survey of moment bounds for $\zeta(s)$: from Heath Brown's work to the present
Alexandra Florea
https://arxiv.org/abs/2509.20335 https://arxiv.org/pd…

A survey of moment bounds for $ζ(s)$: from Heath Brown's work to the present
In this expository article, we review some of the ideas behind the work of Heath-Brown (D.~R. Heath-Brown, {\em Fractional moments of the Riemann Zeta Function}, J. Lond. Math. Soc., (2), {\bf 24}, no.1, (1981), 65--78) on upper and lower bounds for moments of the Riemann zeta-function, as well as the impact this work had on subsequent developments in the field. We survey recent results on the topic, which essentially recover the expected rate of growth for all moments - unconditionally for sma…

Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
Xiao Han, Zimo Zhao, Wanyu Wang, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao
https://arxiv.org/abs/2509.18942

Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
Recent advancements in Large Language Models (LLMs) have emphasized the critical role of fine-tuning (FT) techniques in adapting LLMs to specific tasks, especially when retraining from scratch is computationally infeasible. Fine-tuning enables LLMs to leverage task- or domain-specific data, producing models that more effectively meet the requirements of targeted applications. However, con- ventional FT approaches often suffer from catastrophic forgetting and suboptimal data efficiency, limiting…

Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
Deokjae Lee, Hyun Oh Song
https://arxiv.org/abs/2509.20214 https://

Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
We study weight-only post-training quantization (PTQ), which quantizes the weights of a large language model (LLM) without retraining, using little or no calibration data. Weight-only PTQ is crucial for reducing the memory footprint and latency of LLM inference, especially in memory-bound, small-batch inference scenarios, such as personalized inference on edge devices. Despite its importance, irregular weight distributions with heavy-tailed outliers in LLMs complicate quantization, recently mot…