Tootfinder

How Forbes, the Financial Times, Snopes, Trusted Media Brands, and other news publishers are updating on-site search with AI chatbots (Sara Guaglione/Digiday)
https://digiday.com/media/publishers-give-on-site-search-a-long…

More #xenophobia and anti-#chinese #racebaiting. TikTok isn’t doing anything more egregious than what Meta, X, Snapchat, or any number of popular social media platforms are doing. If these committee members …

US lawmakers vote 50-0 to force sale of TikTok despite angry calls from users
Lawmaker: TikTok must "sever relationship with the Chinese Communist Party."

How Forbes, the Financial Times, Snopes, Trusted Media Brands, and other news publishers are updating on-site search with AI chatbots (Sara Guaglione/Digiday)
https://digiday.com/media/publishers-give-on-site-search-a-long…

This https://arxiv.org/abs/2403.19650 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Consistency of JWST Black Hole Observations with NANOGrav Gravitational Wave Measurements
JWST observations have opened a new chapter in studies of supermassive black holes (SMBHs), stimulating discussion of two puzzles: the abundance of SMBHs in the early Universe and the fraction of dual AGNs. In this paper we argue that the answers to these puzzles may be linked to an interpretation of the data on the nHz gravitational wave (GWs) discovered by NANOGrav and other Pulsar Timing Arrays (PTAs) in terms of SMBH binaries losing energy by interactions with their environments as well as …

#Mathematics #Math #Maths teacher getting downvoted for pointing out actual rules of Maths - what a time to be alive!

This https://arxiv.org/abs/1911.01951 has been replaced.
link: https://scholar.google.com/scholar?q=a

The Bron-Kerbosch Algorithm with Vertex Ordering is Output-Sensitive
The Bron-Kerbosch algorithm is a well known maximal clique enumeration algorithm. So far it was unknown whether it was output sensitive or not. In this paper we partially answer this question by proving that the Bron-Kerbosch Algorithm with vertex ordering, first introduced and studied by Eppstein, Löffler and Strash in "Listing all maximal cliques in sparse graphs in near-optimal time. International Symposium on Algorithms and Computation. Springer, Berlin, Heidelberg, 2010" is output sensiti…

Backpropagation through space, time, and the brain
Benjamin Ellenberger, Paul Haider, Jakob Jordan, Kevin Max, Ismael Jaras, Laura Kriener, Federico Benitez, Mihai A. Petrovici
https://arxiv.org/abs/2403.16933

Backpropagation through space, time, and the brain
Effective learning in neuronal networks requires the adaptation of individual synapses given their relative contribution to solving a task. However, physical neuronal systems -- whether biological or artificial -- are constrained by spatio-temporal locality. How such networks can perform efficient credit assignment, remains, to a large extent, an open question. In Machine Learning, the answer is almost universally given by the error backpropagation algorithm, through both space (BP) and time (B…

This https://arxiv.org/abs/1911.01951 has been replaced.
link: https://scholar.google.com/scholar?q=a

The Bron-Kerbosch Algorithm with Vertex Ordering is Output-Sensitive
The Bron-Kerbosch algorithm is a well known maximal clique enumeration algorithm. So far it was unknown whether it was output sensitive or not. In this paper we partially answer this question by proving that the Bron-Kerbosch Algorithm with vertex ordering, first introduced and studied by Eppstein, Löffler and Strash in "Listing all maximal cliques in sparse graphs in near-optimal time. International Symposium on Algorithms and Computation. Springer, Berlin, Heidelberg, 2010" is output sensiti…

Backpropagation through space, time, and the brain
Benjamin Ellenberger, Paul Haider, Jakob Jordan, Kevin Max, Ismael Jaras, Laura Kriener, Federico Benitez, Mihai A. Petrovici
https://arxiv.org/abs/2403.16933

Backpropagation through space, time, and the brain
Effective learning in neuronal networks requires the adaptation of individual synapses given their relative contribution to solving a task. However, physical neuronal systems -- whether biological or artificial -- are constrained by spatio-temporal locality. How such networks can perform efficient credit assignment, remains, to a large extent, an open question. In Machine Learning, the answer is almost universally given by the error backpropagation algorithm, through both space (BP) and time (B…