Tootfinder

Quantifying mixed-state entanglement via partial transpose and realignment moments
Poetri Sonya Tarabunga, Tobias Haug
https://arxiv.org/abs/2507.13840 htt…

Quantifying mixed-state entanglement via partial transpose and realignment moments
Entanglement plays a crucial role in quantum information science and many-body physics, yet quantifying it in mixed quantum many-body systems has remained a notoriously difficult problem. Here, we introduce families of quantitative entanglement witnesses, constructed from partial transpose and realignment moments, which provide rigorous bounds on entanglement monotones. Our witnesses can be efficiently measured using SWAP tests or variants of Bell measurements, thus making them directly impleme…

Quantifier Instantiations: To Mimic or To Revolt?
Jan Jakub\r{u}v, Mikol\'a\v{s} Janota
https://arxiv.org/abs/2508.13811 https://arxiv.org/pdf/2508.138…

Quantifier Instantiations: To Mimic or To Revolt?
Quantified formulas pose a significant challenge for Satisfiability Modulo Theories (SMT) solvers due to their inherent undecidability. Existing instantiation techniques, such as e-matching, syntax-guided, model-based, conflict-based, and enumerative methods, often complement each other. This paper introduces a novel instantiation approach that dynamically learns from these techniques during solving. By treating observed instantiations as samples from a latent language, we use probabilistic con…

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
Soham Hans, Nikolos Gurney, Stacy Marsella, Sofia Hirschmann
https://arxiv.org/abs/2508.13240 https://

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adversaries is paramount. Traditional cyber defense strategies have largely focused on fortification, while some approaches attempt to anticipate attacker strategies by mapping them to cognitive vulnerabilities, yet they fall short in dynamically interpreting attacks in progress. In recognition of this gap, IARPA's ReSCIND…

https://fanf2.user.srcf.net/hermes/doc/qsmtp/draft-fanf-wtf8.html
Encoding a character to WTF-8 proceeds as follows:
* Determine the number of octets required from the character number and the first column of the table above. It is important to note that th…

Calibrated Semantic Diffusion: A p-Laplacian Synthesis with Learnable Dissipation, Quantified Constants, and Graph-Aware Calibration
Faruk Alpay, Hamdi Alakkad
https://arxiv.org/abs/2508.13658

Calibrated Semantic Diffusion: A p-Laplacian Synthesis with Learnable Dissipation, Quantified Constants, and Graph-Aware Calibration
We develop a calibrated diffusion framework by synthesizing three established concepts: linear Laplacian smoothing, nonlinear graph p-Laplacian flows, and a learnable dissipation term derived from a strongly convex potential. This synthesis provides a general model for graph-based diffusion with controllable dynamics. Our key theoretical results include a quantified two-regime decay analysis for $p>2$, which provides stronger, p-dependent transient bounds not captured by standard ISS templates,…

Quantifying How Much Has Been Learned from a Research Study
Jonas M. Mikhaeil, Donald P. Green
https://arxiv.org/abs/2508.14789 https://arxiv.org/pdf/2508.…

Quantifying How Much Has Been Learned from a Research Study
How much does a research study contribute to a scientific literature? We propose a learning metric to quantify how much a research community learns from a given study. To do so, we adopt a Bayesian perspective and assess changes in the community's beliefs once updated with a new study's evidence. We recommend the Wasserstein-2 distance as a way to describe how the research community's prior beliefs change to incorporate a study's findings. We illustrate this approach through stylized examples a…

OpenLB-UQ: An Uncertainty Quantification Framework for Incompressible Fluid Flow Simulations
Mingliang Zhong, Adrian Kummerl\"ander, Shota Ito, Mathias J. Krause, Martin Frank, Stephan Simonis
https://arxiv.org/abs/2508.13867

OpenLB-UQ: An Uncertainty Quantification Framework for Incompressible Fluid Flow Simulations
Uncertainty quantification (UQ) is crucial in computational fluid dynamics to assess the reliability and robustness of simulations, given the uncertainties in input parameters. OpenLB is an open-source lattice Boltzmann method library designed for efficient and extensible simulations of complex fluid dynamics on high-performance computers. In this work, we leverage the efficiency of OpenLB for large-scale flow sampling with a dedicated and integrated UQ module. To this end, we focus on non-intr…

Conformal Contraction for Robust Nonlinear Control with Distribution-Free Uncertainty Quantification
Sihang Wei, Melkior Ornik, Hiroyasu Tsukamoto
https://arxiv.org/abs/2507.13613

Conformal Contraction for Robust Nonlinear Control with Distribution-Free Uncertainty Quantification
We present a novel robust control framework for continuous-time, perturbed nonlinear dynamical systems with uncertainty that depends nonlinearly on both the state and control inputs. Unlike conventional approaches that impose structural assumptions on the uncertainty, our framework enhances contraction-based robust control with data-driven uncertainty prediction, remaining agnostic to the models of the uncertainty and predictor. We statistically quantify how reliably the contraction conditions …

Symmetric orthogonalization and probabilistic weights in resource quantification
G\"okhan Torun
https://arxiv.org/abs/2508.12949 https://arxiv.org/pdf…

Symmetric orthogonalization and probabilistic weights in resource quantification
Transforming non-orthogonal bases into orthogonal ones often sacrifices essential properties or physical meaning in quantum systems. Here, we demonstrate that Löwdin symmetric orthogonalization (LSO) outperforms the widely used Gram-Schmidt orthogonalization (GSO) in characterizing and quantifying quantum resources, with particular emphasis on coherence and superposition. We employ LSO both to construct an orthogonal basis from a non-orthogonal one and to obtain a non-orthogonal basis from an …

Quasiprobability Thermodynamic Uncertainty Relation
Kohei Yoshimura, Ryusuke Hamazaki
https://arxiv.org/abs/2508.14354 https://arxiv.org/pdf/2508.14354

Quasiprobability Thermodynamic Uncertainty Relation
We derive a quantum extension of the thermodynamic uncertainty relation where dynamical fluctuations are quantified by the Terletsky-Margenau-Hill quasiprobability, a quantum generalization of the classical joint probability. The obtained inequality plays a complementary role to existing quantum thermodynamic uncertainty relations, focusing on observables' change rather than exchange of charges through jumps and respecting initial coherence. Quasiprobabilities show anomalous behaviors that are …