Tootfinder

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.

A model-agnostic likelihood for the reinterpretation of the $\boldsymbol{B^{ }\to K^{ } \nu \bar{\nu}}$ measurement at Belle II
Collaboration, Abumusabh, Adachi, Aggarwal, Ahmed, Ahn, Akopov, Alghamdi, Alhakami, Aloisio, Althubiti, Amos, Ky, Asner, Atmacan, Ayad, Babu, Bae, Baghel, Bambade, Banerjee, Barrett, Bartl, Baudot, Baur, Beaubien, Becherer, Becker, Bennett, Bernlochner, Bertacchi, Bertemes, Bertholet, Bessner, Bettarini, Bhardwaj, Bhuyan, Bianchi, Biswas, Bodrov, Bondar, Bonvi…

A model-agnostic likelihood for the reinterpretation of the $\boldsymbol{B^{+}\to K^{+} ν\barν}$ measurement at Belle II
We recently measured the branching fraction of the $B^{+}\rightarrow K^{+}ν\barν$ decay using 362 fb$^{-1}$ of on-resonance $e^+e^-$ collision data, under the assumption of Standard Model kinematics, providing the first evidence for this decay. To facilitate future reinterpretations and maximize the scientific impact of this measurement, we hereby publicly release the full analysis likelihood along with all necessary material required for reinterpretation under arbitrary theoretical models se…

One of the goals I've set for further development of #Python eclasses in #Gentoo was to avoid needless complexity. Unfortunately, the subject matter sometimes requires them. However, many of the functions added lately were already manually done in ebuilds for years.
We've started disabling plugin autoloading years ago. First we just did that for individual packages that caused issues. Then, for these where tests ended up being really slow. Finally, pretty much anywhere `python_test()` was declared. Doing it all manually was particularly cumbersome — all I needed for `EPYTEST_PLUGINS` is a good idea how to generalize it.
Similarly, `EPYTEST_XDIST` was added after we have been adding manually `epytest -p xdist -n "$(makeopts_jobs)" --dist=worksteal` — and while at it, I've added `EPYTEST_JOBS` to override the job count.
Perhaps `EPYTEST_TIMEOUT` wasn't that common. However, it was meant to help CI systems that could otherwise get stuck on hanging test.
Similarly, "standard library" version (like `3.9`) matching to `python_gen_cond_dep` was added after a long period of explicitly stating `python3_9 pypy3`. As an extra benefit, this also resolved the problem that at the time `pypy3` could mean different Python versions.

Honey bees remove 80% of pollen—leaving native bees with nothing #evironment

Honey bees remove 80% of pollen—leaving native bees with nothing
Feral honey bees, once celebrated for their agricultural value, are now threatening native ecosystems in Southern California by monopolizing pollen sources and overwhelming native pollinators. A new study reveals they remove up to 80% of pollen in a single day, severely disrupting food sources for over 700 species of native bees. Despite their benefits to agriculture, these invasive bees dominate nearly all bee biomass in the region and even produce lower-quality offspring when pollinating nati…

Understanding the Error Sensitivity of Privacy-Aware Computing
Mat\'ias Mazzanti (University of Buenos Aires), Esteban Mocskos (University of Buenos Aires), Augusto Vega (IBM T. J. Watson Research Center), Pradip Bose (IBM T. J. Watson Research Center)
https://arxiv.org/abs/2506.07957

Understanding the Error Sensitivity of Privacy-Aware Computing
Homomorphic Encryption (HE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit from HE by enabling third-party computation and services on sensitive data. In other words, HE constitutes the "Holy Grail" of cryptography: data remains encrypted all the time, being protected while in …

Robotic Multimodal Data Acquisition for In-Field Deep Learning Estimation of Cover Crop Biomass
Joe Johnson, Phanender Chalasani, Arnav Shah, Ram L. Ray, Muthukumar Bagavathiannan
https://arxiv.org/abs/2506.22364

Robotic Multimodal Data Acquisition for In-Field Deep Learning Estimation of Cover Crop Biomass
Accurate weed management is essential for mitigating significant crop yield losses, necessitating effective weed suppression strategies in agricultural systems. Integrating cover crops (CC) offers multiple benefits, including soil erosion reduction, weed suppression, decreased nitrogen requirements, and enhanced carbon sequestration, all of which are closely tied to the aboveground biomass (AGB) they produce. However, biomass production varies significantly due to microsite variability, making …

This https://arxiv.org/abs/2506.02120 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Random-key genetic algorithms
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regar…

Random-key genetic algorithms
Mariana A. Londe, Luciana S. Pessoa, Carlos E. Andrade, Jos\'e F. Gon\c{c}alves, Mauricio G. C. Resende
https://arxiv.org/abs/2506.02120

Random-key genetic algorithms
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regar…