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@netzschleuder@social.skewed.de
2025-11-28 04:00:04

sp_hypertext: Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.
This network has 113 nodes and 2196 edges.
Tags: Social, Offline, Unweighted, Temporal
networks.skewed.de/net/sp_hype

sp_hypertext: Hypertext 2009 dynamic contact network. 113 nodes, 2196 edges. https://networks.skewed.de/net/sp_hypertext#intervals
@arXiv_csMA_bot@mastoxiv.page
2025-09-29 08:59:18

Effective Policy Learning for Multi-Agent Online Coordination Beyond Submodular Objectives
Qixin Zhang, Yan Sun, Can Jin, Xikun Zhang, Yao Shu, Puning Zhao, Li Shen, Dacheng Tao
arxiv.org/abs/2509.22596

@rachel@norfolk.social
2025-10-26 17:07:28

Just spent the last two days on a Remote Area First Aid course, run by JP of the Overland Guide Association. Really enjoyed it and definitely learned lots.
DRC ABCDE
#Motorcycles #Travel

JP knelt next to a patient whilst demonstrating using a silver blanket to apply a pelvis support
The most awesome box of props ever. There’s various false limbs in a plastic box, each covered in fake blood and bandages
Two people applying field dressings to an artificial limb
Some people stood over boxes containing false body parts that have deep wounds oozing blood that we were practicing packing.
@arXiv_grqc_bot@mastoxiv.page
2025-09-29 09:08:37

Thermodynamics of BTZ Black Holes in Bumblebee Gravity with Barrow Entropy with Cavity-Modification
H. Kaur, Prince A Ganai
arxiv.org/abs/2509.22129

@netzschleuder@social.skewed.de
2025-10-26 23:00:04

sp_hypertext: Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.
This network has 113 nodes and 20818 edges.
Tags: Social, Offline, Unweighted, Temporal
networks.skewed.de/net/sp_hype…

sp_hypertext: Hypertext 2009 dynamic contact network. 113 nodes, 20818 edges. https://networks.skewed.de/net/sp_hypertext#contacts
@arXiv_csET_bot@mastoxiv.page
2025-09-29 07:35:35

Bacterial Gene Regulatory Neural Network as a Biocomputing Library of Mathematical Solvers
Adrian Ratwatte, Samitha Somathilaka, Thanh Cao, Xu Li, Sasitharan Balasubramaniam
arxiv.org/abs/2509.21598

@pavelasamsonov@mastodon.social
2025-10-22 18:53:38

Outcomes-over-outputs is one thing — but even outcomes aren't always impactful.
Investing in any outcome carries opportunity cost. Any effort you expend means not spending that effort elsewhere.
And when there's a bottleneck in the system, effort upstream of the bottleneck has diminishing returns. Typically, that bottleneck is your process.
Since the process is made up of people, technical solutions won't help.

@arXiv_qbioNC_bot@mastoxiv.page
2025-12-12 08:16:59

Modeling, Segmenting and Statistics of Transient Spindles via Two-Dimensional Ornstein-Uhlenbeck Dynamics
C. Sun, D. Fettahoglu, D. Holcman
arxiv.org/abs/2512.10844 arxiv.org/pdf/2512.10844 arxiv.org/html/2512.10844
arXiv:2512.10844v1 Announce Type: new
Abstract: We develop here a stochastic framework for modeling and segmenting transient spindle- like oscillatory bursts in electroencephalogram (EEG) signals. At the modeling level, individ- ual spindles are represented as path realizations of a two-dimensional Ornstein{Uhlenbeck (OU) process with a stable focus, providing a low-dimensional stochastic dynamical sys- tem whose trajectories reproduce key morphological features of spindles, including their characteristic rise{decay amplitude envelopes. On the signal processing side, we propose a segmentation procedure based on Empirical Mode Decomposition (EMD) combined with the detection of a central extremum, which isolates single spindle events and yields a collection of oscillatory atoms. This construction enables a systematic statistical analysis of spindle features: we derive empirical laws for the distributions of amplitudes, inter-spindle intervals, and rise/decay durations, and show that these exhibit exponential tails consistent with the underlying OU dynamics. We further extend the model to a pair of weakly coupled OU processes with distinct natural frequencies, generating a stochastic mixture of slow, fast, and mixed spindles in random temporal order. The resulting framework provides a data- driven framework for the analysis of transient oscillations in EEG and, more generally, in nonstationary time series.
toXiv_bot_toot

@netzschleuder@social.skewed.de
2025-10-20 19:00:03

sp_hypertext: Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.
This network has 113 nodes and 20818 edges.
Tags: Social, Offline, Unweighted, Temporal
networks.skewed.de/net/sp_hype…

sp_hypertext: Hypertext 2009 dynamic contact network. 113 nodes, 20818 edges. https://networks.skewed.de/net/sp_hypertext#contacts
@netzschleuder@social.skewed.de
2025-10-19 01:00:04

sp_hypertext: Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.
This network has 113 nodes and 2196 edges.
Tags: Social, Offline, Unweighted, Temporal
networks.skewed.de/net/sp_hype…

sp_hypertext: Hypertext 2009 dynamic contact network. 113 nodes, 2196 edges. https://networks.skewed.de/net/sp_hypertext#intervals