Tootfinder

What is SSSS?
SSSS stands for
"Secondary Security Screening Selection"
by the U.S. Department of Homeland Security
and is displayed on boarding passes as a four-letter code.
The symbol prompts officers to conduct additional security screenings on a passenger.
Travelers can receive an SSSS on their boarding pass for both domestic and international flights to and from the U.S.
Those flagged with the designation have reported their carry-on…

Mrs. Betty Bowers (@mrsbettybowers.bsky.social)
Being penalized for political speech is not unique to the Trump administration. When I was a writer for the WhiteHouse.Org website, which mercilessly spoofed the George W. Bush administration, I constantly got "SSSS" written on my boarding passes. My carry-on was placed on the gangway and searched.

🇺🇦 Auf radioeins läuft...
Morcheeba:
🎵 Never Undo
#NowPlaying #Morcheeba
https://morcheeba.bandcamp.com/track/never-undo
https://open.spotify.com/track/5fuEPYYSRCyprhMa5qFG9F

The Gauss-Markov Adjunction: Categorical Semantics of Residuals in Supervised Learning
Moto Kamiura
https://arxiv.org/abs/2507.02442 https://

The Gauss-Markov Adjunction: Categorical Semantics of Residuals in Supervised Learning
Enhancing the intelligibility and interpretability of machine learning is a crucial task in responding to the demand for Explicability as an AI principle, and in promoting the better social implementation of AI. The aim of our research is to contribute to this improvement by reformulating machine learning models through the lens of category theory, thereby developing a semantic framework for structuring and understanding AI systems. Our categorical modeling in this paper clarifies and formalize…

Random Flights and Anomalous Diffusion: A Non-Markovian Take on Lorentz Processes
Lorenzo Facciaroni, Costantino Ricciuti, Enrico Scalas, Bruno Toaldo
https://arxiv.org/abs/2507.02796

Random Flights and Anomalous Diffusion: A Non-Markovian Take on Lorentz Processes
We study Lorentz processes in two different settings. Both cases are characterized by infinite expectation of the free-flight times, contrary to what happens in the classical Gallavotti-Spohn models. Under a suitable Boltzmann-Grad type scaling limit, they converge to non-Markovian random-flight processes with superdiffusive behavior. A further scaling limit yields another non Markovian process, i.e., a superdiffusion obtained by a suitable time-change of Brownian motion. Furthermore, we obtain…

Dealing with separation problem in hidden Markov models with covariates based on a penalized maximum likelihood approach
Luca Brusa, Fulvia Pennoni, Francesco Bartolucci, Romina Peruilh Bagolini
https://arxiv.org/abs/2507.02425

Dealing with separation problem in hidden Markov models with covariates based on a penalized maximum likelihood approach
A penalized maximum likelihood estimation approach is proposed for discrete-time hidden Markov models where covariates affect the observed responses and serial dependence is considered. The proposed penalized maximum likelihood method addresses the issue of latent state separation that typically occurs when this model is applied to binary and categorical response variables with a limited number of categories, resulting in extremely large estimates of the support points of the latent variable as…

Recursive Privacy-Preserving Estimation Over Markov Fading Channels
Jie Huang, Fanlin Jia, Xiao He
https://arxiv.org/abs/2506.02725 https://

Recursive Privacy-Preserving Estimation Over Markov Fading Channels
In industrial applications, the presence of moving machinery, vehicles, and personnel, contributes to the dynamic nature of the wireless channel. This time variability induces channel fading, which can be effectively modeled using a Markov fading channel (MFC). In this paper, we investigate the problem of secure state estimation for systems that communicate over a MFC in the presence of an eavesdropper. The objective is to enable a remote authorized user to accurately estimate the states of a d…

This https://arxiv.org/abs/2503.21224 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Efficient Learning for Entropy-Regularized Markov Decision Processes via Multilevel Monte Carlo
Designing efficient learning algorithms with complexity guarantees for Markov decision processes (MDPs) with large or continuous state and action spaces remains a fundamental challenge. We address this challenge for entropy-regularized MDPs with Polish state and action spaces, assuming access to a generative model of the environment. We propose a novel family of multilevel Monte Carlo (MLMC) algorithms that integrate fixed-point iteration with MLMC techniques and a generic stochastic approximat…

Steady-State Coherences under Partial Collective non-Markovian Decoherence
S. L. Wu, W. Ma, Zhao-Ming Wang, P. Brumer, Lian-Ao Wu
https://arxiv.org/abs/2507.02557

Steady-State Coherences under Partial Collective non-Markovian Decoherence
Steady-state coherence in open quantum systems is crucial for quantum technologies, yet its behavior is not fully understood due to the interplay between collective and individual decoherence. While collective decoherence is thought to induce steady-state coherence, experiments often fail to observe this because of individual decoherence. We study a system of two harmonic oscillators coupled to both individual and collective environments, introducing a tunable parameter to adjust the decoherenc…

What, exactly, are we celebrating tomorrow?
Our cutting off healthcare to the needy to enrich the greedy?
Our new concentration camps?
Our dying economy?
Our ditching due process?
Our corruption?
Our broken legislature?
Our corrupt SCOTUS?
Our ridiculous buffoon president?
#FourthOfJuly
-- Mrs Betty Bowers

Context-aware gate set tomography: Improving the self-consistent characterization of trapped-ion universal gate sets by leveraging non-Markovianity
Pablo Vi\~nas, Alejandro Bermudez
https://arxiv.org/abs/2507.02542

Context-aware gate set tomography: Improving the self-consistent characterization of trapped-ion universal gate sets by leveraging non-Markovianity
To progress in the characterization of noise for current quantum computers, gate set tomography (GST) has emerged as a self-consistent tomographic protocol that can accurately estimate the complete set of noisy quantum gates, state preparations, and measurements. In its original incarnation, GST improves the estimation precision by applying the gates sequentially, provided that the noise makes them a set of fixed completely-positive and trace preserving (CPTP) maps independent of the history of…