Tootfinder

Just picked up this #Salter Improved Family Scale to gift a friend who's getting married and is into old-fashioned stuff and British stuff.
But, it doesn't quite sit at 0. Does anyone have any experience on how to tare a machine like this? There's no knob as far as I can see. The plate on top also doesn't seem like it can be removed without force.
More pictures below. …

Challenges and Practices in Quantum Software Testing and Debugging: Insights from Practitioners
Jake Zappin, Trevor Stalnaker, Oscar Chaparro, Denys Poshyvanyk
https://arxiv.org/abs/2506.17306

Challenges and Practices in Quantum Software Testing and Debugging: Insights from Practitioners
Quantum software engineering is an emerging discipline with distinct challenges, particularly in testing and debugging. As quantum computing transitions from theory to implementation, developers face issues not present in classical software development, such as probabilistic execution, limited observability, shallow abstractions, and low awareness of quantum-specific tools. To better understand current practices, we surveyed 26 quantum software developers from academia and industry and conducte…

Pedro the #Math Cat 🐈
Fits \(\implies\) Sits
#Cats #CatsOfMastodon

Local feature filtering for scalable and well-conditioned Random Feature Methods
Jan Willem van Beek, Victorita Dolean, Ben Moseley
https://arxiv.org/abs/2506.17626

Local feature filtering for scalable and well-conditioned Random Feature Methods
In recent years, machine learning has emerged as a powerful tool for solving partial differential equations (PDEs), with randomized neural networks showing remarkable potential. These networks, typically shallow, differ from conventional approaches by randomizing all parameters except those in the final layer. Notably, by integrating techniques inspired by domain decomposition, they achieve improved local accuracy and computational efficiency. Following \cite{chen:2022:BTM}, we refer to these a…

TRUST: Transparent, Robust and Ultra-Sparse Trees
Albert Dorador
https://arxiv.org/abs/2506.15791 https://arxiv.org/pdf/2506.15791

TRUST: Transparent, Robust and Ultra-Sparse Trees
Piecewise-constant regression trees remain popular for their interpretability, yet often lag behind black-box models like Random Forest in predictive accuracy. In this work, we introduce TRUST (Transparent, Robust, and Ultra-Sparse Trees), a novel regression tree model that combines the accuracy of Random Forests with the interpretability of shallow decision trees and sparse linear models. TRUST further enhances transparency by leveraging Large Language Models to generate tailored, user-friendl…

A clear sunny Winter solstice day, so why not Chicken katsu udon soup with roasted rice tea for lunch at Tokyo Canteen in Kingston?
The soup had citrus-pickled vegetables in it (daikon, carrot, cauliflower and Brussels sprouts) - which was a pleasant surprise (I just expected dashi or miso or something).
#food #canberra

Golden Partition Zone: Rethinking Neural Network Partitioning Under Inversion Threats in Collaborative Inference
Rongke Liu, Youwen Zhu
https://arxiv.org/abs/2506.15412

Golden Partition Zone: Rethinking Neural Network Partitioning Under Inversion Threats in Collaborative Inference
In collaborative inference, intermediate features transmitted from edge devices can be exploited by adversaries to reconstruct original inputs via model inversion attacks (MIA). While existing defenses focus on shallow-layer protection, they often incur significant utility loss. A key open question is how to partition the edge-cloud model to maximize resistance to MIA while minimizing accuracy degradation. We first show that increasing model depth alone does not guarantee resistance. Through th…

I'm not saying any new shit:
"I have almost reached the regrettable conclusion that the Negro's great stumbling block in his stride toward freedom is not the White Citizen's Counciler or the Ku Klux Klanner, but the white moderate, who is more devoted to "order" than to justice; who prefers a negative peace which is the absence of tension to a positive peace which is the presence of justice; who constantly says: "I agree with you in the goal you seek, but I cannot agree with your methods of direct action"; who paternalistically believes he can set the timetable for another man's freedom; who lives by a mythical concept of time and who constantly advises the Negro to wait for a "more convenient season." Shallow understanding from people of good will is more frustrating than absolute misunderstanding from people of ill will. Lukewarm acceptance is much more bewildering than outright rejection."
- Letter from a Birmingham Jail, MLK

High-Efficiency, High-Fidelity Charge Initialization of Shallow Nitrogen Vacancy Centers in Diamond
Marjana Mahdia, Artur Lozovoi, Jared Rovny, Zhiyang Yuan, Carlos A. Meriles, Nathalie P. de Leon
https://arxiv.org/abs/2506.00707

High-Efficiency, High-Fidelity Charge Initialization of Shallow Nitrogen Vacancy Centers in Diamond
Nitrogen vacancy (NV) centers in diamond exhibit long spin coherence times, optical initialization, and optical spin readout under ambient conditions, making them excellent quantum sensors. However, the conventional scheme for charge state initialization based on off-resonant green excitation results in significant state preparation errors, typically around 30%. One method for improving charge state initialization fidelity is to use multicolor excitation, which has been demonstrated to achieve …

Someday, I want to erase everything and vanish offline, I’ve said it before and I’ll say it again.
I’m sick of being trapped in this endless cycle of digital puppetry and mental sludge, where every click feeds some shallow algorithm designed to keep you hooked and dumbed down, turning people into another batch of screen-zombies chasing cheap hits and empty dopamine hits.
I want out of this rat race of constant noise and manufactured crises, where reality is warped into a carnival…

This https://arxiv.org/abs/2505.22598 has been replaced.
initial toot: https://mastoxiv.page/@arX…

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and procedures, a wide theoretical understanding is still lacking, with the risk of suboptimal implementations. As a first step to address this gap, we provide here a complete analytic study of the widely-used Sequential Tempering procedure applied to a shallow MA…

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Luca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
https://arxiv.org/abs/2505.22598

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and procedures, a wide theoretical understanding is still lacking, with the risk of suboptimal implementations. As a first step to address this gap, we provide here a complete analytic study of the widely-used Sequential Tempering procedure applied to a shallow MA…