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@arXiv_eessSP_bot@mastoxiv.page
2024-04-18 06:54:21

Robust and tractable multidimensional exponential analysis
H. N. Mhaskar, S. Kitimoon, Raghu G. Raj
arxiv.org/abs/2404.11004

@arXiv_csMS_bot@mastoxiv.page
2024-06-13 07:29:59

A square root algorithm faster than Newton's method for multiprecision numbers, using floating-point arithmetic
Fabio Romano
arxiv.org/abs/2406.07751

@arXiv_csRO_bot@mastoxiv.page
2024-06-12 07:28:22

Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Under Dynamic Lighting and Weather Conditions Using Electric Vehicles
Michael Khalfin, Jack Volgren, Matthew Jones, Luke LeGoullon, Joshua Siegel, Chan-Jin Chung
arxiv.org/abs/2406.06899

@cheeaun@mastodon.social
2024-06-07 15:53:07

The "Catch up on this thread" notifications on #Threads are pretty annoying.
It seems trying to notify you a post from a following that you missed, buried under the chronological Following timeline but not going to be resurfaced by the algorithm in the 'For You' timeline 🤔

@kernellogger@fosstodon.org
2024-04-27 06:08:10

How Allegro reduced latency outliers by 82% by switching to #XFS:
blog.allegro.tech/2024/03/kafk
"'Using a com…

@arXiv_csCV_bot@mastoxiv.page
2024-05-10 08:29:58

This arxiv.org/abs/2405.03352 has been replaced.
initial toot: mastoxiv.page/@arXiv_csCV_…

@j12t@social.coop
2024-05-03 22:21:46

Opaque algorithms are a funny thing.
Over on #Threads, my "Following" feed does consistently have more things in it that are interesting to me than my "For you" feed.
I've been observing this for a few weeks now, and it's consistent.
Seem the cost function might be optimizing for something other than what I like? Or is the signal not good enough because …

@arXiv_csDC_bot@mastoxiv.page
2024-05-09 06:52:34

Probabilistic Byzantine Fault Tolerance (Extended Version)
Diogo Avel\~as, Hasan Heydari, Eduardo Alchieri, Tobias Distler, Alysson Bessani
arxiv.org/abs/2405.04606

@arXiv_csDS_bot@mastoxiv.page
2024-04-30 07:20:33

Private graph colouring with limited defectiveness
Aleksander B. G. Christiansen, Eva Rotenberg, Teresa Anna Steiner, Juliette Vlieghe
arxiv.org/abs/2404.18692 arxiv.org/pdf/2404.18692
arXiv:2404.18692v1 Announce Type: new
Abstract: Differential privacy is the gold standard in the problem of privacy preserving data analysis, which is crucial in a wide range of disciplines. Vertex colouring is one of the most fundamental questions about a graph. In this paper, we study the vertex colouring problem in the differentially private setting.
To be edge-differentially private, a colouring algorithm needs to be defective: a colouring is d-defective if a vertex can share a colour with at most d of its neighbours. Without defectiveness, the only differentially private colouring algorithm needs to assign n different colours to the n different vertices. We show the following lower bound for the defectiveness: a differentially private c-edge colouring algorithm of a graph of maximum degree {\Delta} > 0 has defectiveness at least d = {\Omega} (log n / (log c log {\Delta})).
We also present an {\epsilon}-differentially private algorithm to {\Theta} ( {\Delta} / log n 1 / {\epsilon})-colour a graph with defectiveness at most {\Theta}(log n).

@j12t@social.coop
2024-04-30 03:14:53

Whittled down the list of people I’m following. With no algorithm in the fediverse that shows me what it thinks I should be interested in, I better pick myself how I fill my feed.