Tootfinder

> "We need an accessible database to act as a repository for all election advertising across all advertising platforms on the internet."

I need a few incoming webmentions for testing (okay, this sounds like a really fishy attempt to generate likes … I promise that it is not 😂). So would you all please like and repost this piece I recently wrote? You can also read it, of course.
https://matthiasott.com/articles/webspace-inv…

Webspace Invaders · Matthias Ott
There’s something happening on the Web at the moment that almost feels like watching that old arcade game Space Invaders play out across our servers. Bots and scrapers marching in formation, attacking our servers wave after wave, systematically requesting page after page, relentlessly filling their data stores while we watch our access logs fill up.

This is what democracy looks like -- There is an uncountable number of repositories of power all across the United States. (pbump)
https://www.pbump.net/o/this-is-what-democracy-looks-like/
http://www.memeorandum.com/260203/p145#a260203p145

On February 1, 1960, four Black freshmen from the Agricultural and Technical College of North Carolina,
(today known as North Carolina A&T State University),
sat down at the segregated lunch counter at the Woolworth’s in downtown Greensboro and asked to be served.
When staff refused to serve them, they refused to leave.
These four students,
now known as the Greensboro Four,
included Ezell Blair Jr. (who later changed his name to Jibreel Khazan),

Crossmodal interaction of flashes and beeps across time and number follows Bayesian causal inference #SiFI

Crossmodal interaction of flashes and beeps across time and number follows Bayesian causal inference - Psychonomic Bulletin & Review
Multisensory perception requires the brain to dynamically infer causal relationships between sensory inputs across various dimensions, such as temporal and spatial attributes. Traditionally, Bayesian Causal Inference (BCI) models have generally provided a robust framework for understanding sensory processing in unidimensional settings where stimuli across sensory modalities vary along one dimension such as spatial location, or numerosity (Samad et al., PloS one, 10 (2), e0117178, 2015). However…

Repost: CLANS Workshop 'The Trade in Religious Imagination in Late Antiquity' (24 April) | Call for Abstracts https://networks.h-net.org/group/announcements/20143177/repost-clans-workshop-trade-religious-imaginatio…

Repost: CLANS Workshop 'The Trade in Religious Imagination in Late Antiquity' (24 April) | Call for Abstracts https://networks.h-net.org/group/announcements/20143177/repost-clans-workshop-trade-religious-imaginatio…

T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
Dongik Park, Hyunwoo Ryu, Suahn Bae, Keondo Park, Hyung-Sin Kim
https://arxiv.org/abs/2602.21043 https://arxiv.org/pdf/2602.21043 https://arxiv.org/html/2602.21043
arXiv:2602.21043v1 Announce Type: new
Abstract: Imputing missing values in multivariate time series remains challenging, especially under diverse missing patterns and heavy missingness. Existing methods suffer from suboptimal performance as corrupted temporal features hinder effective cross-variable information transfer, amplifying reconstruction errors. Robust imputation requires both extracting temporal patterns from sparse observations within each variable and selectively transferring information across variables--yet current approaches excel at one while compromising the other. We introduce T1 (Time series imputation with 1-to-1 channel-head binding), a CNN-Transformer hybrid architecture that achieves robust imputation through Channel-Head Binding--a mechanism creating one-to-one correspondence between CNN channels and attention heads. This design enables selective information transfer: when missingness corrupts certain temporal patterns, their corresponding attention pathways adaptively down-weight based on remaining observable patterns while preserving reliable cross-variable connections through unaffected channels. Experiments on 11 benchmark datasets demonstrate that T1 achieves state-of-the-art performance, reducing MSE by 46% on average compared to the second-best baseline, with particularly strong gains under extreme sparsity (70% missing ratio). The model generalizes to unseen missing patterns without retraining and uses a consistent hyperparameter configuration across all datasets. The code is available at https://github.com/Oppenheimerdinger/T1.
toXiv_bot_toot

Robust forecast aggregation via additional queries
Rafael Frongillo, Mary Monroe, Eric Neyman, Bo Waggoner
https://arxiv.org/abs/2512.05271 https://arxiv.org/pdf/2512.05271 https://arxiv.org/html/2512.05271
arXiv:2512.05271v1 Announce Type: new
Abstract: We study the problem of robust forecast aggregation: combining expert forecasts with provable accuracy guarantees compared to the best possible aggregation of the underlying information. Prior work shows strong impossibility results, e.g. that even under natural assumptions, no aggregation of the experts' individual forecasts can outperform simply following a random expert (Neyman and Roughgarden, 2022).
In this paper, we introduce a more general framework that allows the principal to elicit richer information from experts through structured queries. Our framework ensures that experts will truthfully report their underlying beliefs, and also enables us to define notions of complexity over the difficulty of asking these queries. Under a general model of independent but overlapping expert signals, we show that optimal aggregation is achievable in the worst case with each complexity measure bounded above by the number of agents $n$. We further establish tight tradeoffs between accuracy and query complexity: aggregation error decreases linearly with the number of queries, and vanishes when the "order of reasoning" and number of agents relevant to a query is $\omega(\sqrt{n})$. These results demonstrate that modest extensions to the space of expert queries dramatically strengthen the power of robust forecast aggregation. We therefore expect that our new query framework will open up a fruitful line of research in this area.
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Repost: CLANS Workshop 'The Trade in Religious Imagination in Late Antiquity' (24 April) | Call for Abstracts
https://ift.tt/Nbxd7IR
H-Net Job Guide Weekly Report for H-NC: 15 February - 22 February H-Net Job Guide 02/25/2026 -…
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