Collective Memory in Contemporary Fiction Films
https://ift.tt/7BnhLWG
updated: Thursday, December 18, 2025 - 11:01pmfull name / name of organization: Karine Bertrand,…
via Input 4 RELCFP ht…
With the emergence of more processors with 64 cores or more, I'm thinking more about whether it makes sense to implement a hypercube virtualised on a single chip with a single vector of memory, or as a literal hypercube of 64 (say) RP2350s. I understand the problems of transferring data across a hypercube, but I don't have a good feeling of how the bus contention on a multicore processor scales. What should I read?
Myanmar releases six journalists from prison, all detained following the military's 2021 coup, but continues to hold at least 27 journalists behind bars (Committee to Protect Journalists)
https://cpj.org/2025/12/myanmar-releases-six-journalists-in-pre-el…
Myanmar shut down a major online scam center on October 21 as part of operations starting in September to curb cross-border online scams and illegal gambling (Associated Press)
https://apnews.com/article/myanmar-scam-centers-cybercr…
Memory Retrieval and Consolidation in Large Language Models through Function Tokens
Shaohua Zhang, Yuan Lin, Hang Li
https://arxiv.org/abs/2510.08203 https://
We've reached the point in the evolution of cyber scamming where military units are blowing up buildings to stop the digital crimes.
Stragglers from Myanmar scam center raided by army cross into Thailand as buildings are blown up
https://apnews.com/artic…
Complex Gaussianity and spatio-frequential memory effect of random wave processes
Guillaume Bal, Anjali Nair
https://arxiv.org/abs/2510.09402 https://arxiv…
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.
toXiv_bot_toot
Obstructions for normally spanned sets of vertices
Nicola Lorenz, Max Pitz
https://arxiv.org/abs/2510.04367 https://arxiv.org/pdf/2510.04367