Tootfinder

So the basic idea is that we first compute a "level" for whatever interaction, by adding beneficial modifiers and subtracting harmful ones. Imagine most modifiers are smallish integers like 2 or -3 (though they can be non-integers too). Each level can be thought of as making things twice as good/bad, although this only applies directly when they're balanced. The actual formula starts with a 50/50 chance of "success" at level 0, and then each positive level halves the chance of failure, or if the levels are negative, each negative level halves the chance of success (note that halving the chance of failure is not the same as doubling the chance of success).
The intuitive explanation is that you start with a coin flip. Then if the level is positive, you flip that many additional coins and succeed if any single coin succeeds, but it the level is negative, you have to flip that many additional coins and succeed only if *all* flips succeed.
For example, if I have a dagger with 5 crit chance, and I attack an opponent with no armor modifiers, I'd have to win any 1 of 6 coin flips to score a crit (p = 1 - (1/(2^6)) = 63/64. Increasing my crit modifier by 1 ups my chances only slightly, to 127/128. This is obviously pretty poor return, indicating that the 5 I already have is very strong. If the opponent had armor with -3 to crits, the interaction is now level 2, so the crit chance is 7/8, which is still pretty good. We can see from these examples that the basic system
rewards a small level advantage a lot, but the rewards diminish rapidly. The system has a few avenues for tweaking how it works though, that can let us modify this. There's also a potential benefit (though sometimes drawback) that no matter what the level gap, there's an effective limit to how much the interaction swings.

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
Night Tapes:
🎵 pacifico
#NightTapes
https://samsararcds.bandcamp.com/album/pacifico-night-songs
https://open.spotify.com/track/2UKsvC3OHquxXYSibXWlCG

Isoperimetric inequality on Finsler metric measure manifolds with non-negative weighted Ricci curvature
Xinyue Cheng, Yalu Feng, Liulin Liu
https://arxiv.org/abs/2507.08571

Isoperimetric inequality on Finsler metric measure manifolds with non-negative weighted Ricci curvature
In this paper, we define the volume entropy and the second Cheeger constant and prove a sharp isoperimetric inequality involving the volume entropy on Finsler metric measure manifolds with non-negative weighted Ricci curvature ${\rm Ric}_{\infty}$. As an application, we prove a Cheeger-Buser type inequality for the first eigenvalue of Finsler Laplacian by using the volume entropy and the second Cheeger constant.

wiki_users: Wikipedia user interaction (2011)
A network derived from interactions between editors of the English language Wikipedia, as derived from the edit histories of 563 wiki pages related to politics. A positive sign indicates positive links such as trust or similarities, and a negative sign indicates distrust or disagreement.
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Edge Radial Electric Field in Positive and Negative Triangularity Plasmas in the TCV Tokamak
S. Rien\"acker, P. Hennequin, L. Vermare, C. Honor\'e, R. Bouffet-Klein, S. Coda, B. Labit, B. Vincent, K. E. Thome, O. Krutkin, A. Balestri, Y. Nakeva, the TCV team
https://arxiv.org/abs/2507.08682

Edge Radial Electric Field in Positive and Negative Triangularity Plasmas in the TCV Tokamak
We present the first edge $E_r$ measurements in negative triangularity (NT) TCV plasmas. The Doppler backscattering measurements of $v_\perp \approx E_r/B$ reveal a significant impact of triangularity on the $E_r$ well: In Ohmic, NBI, and ECRH heated discharges, the $E_r$ well and associated $E_r \times B$ shear are stronger in NT-shaped plasmas compared to their positive triangularity (PT) counterpart. This suggests a connection to the concomitant NT performance gain relative to PT L-mode.

“Minister Melissa Depraetere bevestigt dit: ‘Het capaciteitstarief in zijn huidige vorm is op sommige momenten inderdaad een drempel om flexibiliteit aan te bieden wanneer dit gewenst is. Op een zonnige zondagnamiddag met negatieve prijzen is het naar marktwerking toe niet ideaal dat men zou worden afgestraft om het eigen vermogen af te nemen om bijvoorbeeld een elektrische wagen te laden aan vol vermogen.’”

Minister Depraetere: capaciteitstarief botst met flexibele energiecontracten
Minister Depraetere heeft in het Vlaams Parlement erkend dat het huidige capaciteitstarief op gespannen voet staat met de nieuwe, flexibele energiecontracten die onder meer gebruikmaken van dynamische stroomprijzen.

Quench dynamics of negativity Hamiltonians
Riccardo Travaglino, Colin Rylands, Pasquale Calabrese
https://arxiv.org/abs/2506.09561 https://

Quench dynamics of negativity Hamiltonians
In this paper, we investigate the quench dynamics of the negativity and fermionic negativity Hamiltonians in free fermionic systems. We do this by generalizing a recently developed quasiparticle picture for the entanglement Hamiltonians to tripartite geometries. We obtain analytic expressions for these quantities which are then extensively checked against previous results and numerics. In particular, we find that the standard negativity Hamiltonian contains both non-local hopping terms and four…

AMRScan: A hybrid R and Nextflow toolkit for rapid antimicrobial resistance gene detection from sequencing data
Kaitao Lai
https://arxiv.org/abs/2507.08062

AMRScan: A hybrid R and Nextflow toolkit for rapid antimicrobial resistance gene detection from sequencing data
AMRScan is a hybrid bioinformatics toolkit implemented in both R and [Nextflow](https://www.nextflow.io/) for the rapid and reproducible detection of antimicrobial resistance (AMR) genes from next-generation sequencing (NGS) data. The toolkit enables users to identify AMR gene hits in sequencing reads by aligning them against reference databases such as CARD using BLAST. The R implementation provides a concise, script-based approach suitable for single-sample analysis, teaching, and rapid pro…

allen nichtbinären geistern und körpern, alles gute zum nonbinary day! wir sind auf weitere weise wunderschön. #nonbinary

The full formula for the probability of "success" is:
p = {
1/(2^(-n 1)) if n is negative, or
1 - (1/(2^(n 1))) if n is zero or positive
}
(Both branches have the same value when n is 0, so the behavior is smooth around the origin.)
How can we tweak this?
First, we can introduce fixed success and/or failure chances unaffected by level, with this formula only taking effect if those don't apply. For example, you could do 10% failure, 80% by formula, and 10% success to keep things from being too sure either way even when levels are very high or low. On the other hand, this flattening makes the benefit of extra advantage levels even less exciting.
Second, we could allow for gradations of success/failure, and treat the coin pools I used to explain that math like dice pools a bit. An in-between could require linearly more success flips to achieve the next higher grade of success at each grade. For example, simple success on a crit role might mean dealing 1.5x damage, but if you succeed on 2 of your flips, you get 9/4 damage, or on 4 flips 27/8, or on 7 flips 81/16. In this world, stacking crit levels might be a viable build, and just giving up on armor would be super dangerous. In the particular case I was using this for just now, I can't easily do gradations of success (that's the reason I turned to probabilities in the first place) but I think I'd favor this approach when feasible.
The main innovation here over simple dice pools is how to handle situations where the number of dice should be negative. I'm almost certain it's not a truly novel innovation though, and some RPG fan can point out which system already does this (please actually do this, I'm an RPG nerd too at heart).
I'll leave this with one more tweak we could do: what if the number 2 in the probability equation were 3, or 2/3? I think this has a similar effect to just scaling all the modifiers a bit, but the algebra escapes me in this moment and I'm a bit lazy. In any case, reducing the base of the probability exponent should let you get a few more gradations near 50%, which is probably a good thing, since the default goes from 25% straight to 50% and then to 75% with no integer stops in between.