Tootfinder

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Towards Non-Abelian Quantum Signal Processing: Efficient Control of Hybrid Continuous- and Discrete-Variable Architectures
Robust quantum control is crucial for achieving operations below the quantum error correction threshold. Quantum Signal Processing (QSP) transforms a unitary parameterized by $θ$ into one governed by a polynomial function $f(θ)$, a feature that underpins key quantum algorithms. Originating from composite pulse techniques in NMR, QSP enhances robustness against systematic control errors. We extend QSP to a new class, non-abelian QSP, which utilizes non-commuting control parameters, $\hatθ_1, …

A structure preserving H-curl algebraic multigrid method for the eddy current equations
Raymond Tuminaro, Christian Glusa
https://arxiv.org/abs/2506.08284 …

A structure preserving H-curl algebraic multigrid method for the eddy current equations
A new algebraic multigrid method (AMG) is presented for solving the linear systems associated with the eddy current approximation to the Maxwell equations. This AMG method extends an idea proposed by Reitzinger and Schoberl. The main feature of the Reitzinger and Schoberl algorithm (RSAMG) is that it maintains null-space properties of the Curl-Curl operator throughout all levels of the AMG hierarchy. It does this by enforcing a commuting relationship involving grid transfers and the discrete gr…

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding
Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka
https://arxiv.org/abs/2507.03836

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding
Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. …

Graph neural networks for residential location choice: connection to classical logit models
Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Yuqi Zhou, Shenhao Wang
https://arxiv.org/abs/2507.21334

Graph neural networks for residential location choice: connection to classical logit models
Researchers have adopted deep learning for classical discrete choice analysis as it can capture complex feature relationships and achieve higher predictive performance. However, the existing deep learning approaches cannot explicitly capture the relationship among choice alternatives, which has been a long-lasting focus in classical discrete choice models. To address the gap, this paper introduces Graph Neural Network (GNN) as a novel framework to analyze residential location choice. The GNN-ba…

Blind Passive Beamforming for MIMO System
Wenhai Lai, Jiawei Yao, Kaiming Shen
https://arxiv.org/abs/2506.00987 https://arxiv.org/pdf…

Blind Passive Beamforming for MIMO System
Passive beamforming for the intelligent surface (IS)-aided multiple-input multiple-output (MIMO) communication is a difficult nonconvex problem. It becomes even more challenging under the practical discrete constraints on phase shifts. Unlike most of the existing approaches that rely on the channel state information (CSI), this work advocates a blind beamforming strategy without any CSI. Simply put, we propose a statistical method that learns the main feature of the wireless environment from th…

H${\beta}$ line shape and radius-luminosity relation in 2.5D FRADO
M. H. Naddaf, M. L. Martinez-Aldama, D. Hutsemekers, D. Savic, B. Czerny
https://arxiv.org/abs/2506.01159

H$β$ line shape and radius-luminosity relation in 2.5D FRADO
Galaxies with active galactic nuclei (AGN) exhibit broad emission lines as a key spectral feature. The shape of emission-line profiles depends on the complex dynamics of discrete clouds within a spatially extended region known as the Broad Line Region (BLR). The distribution of cloud positions within BLR, or the geometry of BLR indeed, is directly linked to measurements of time lags of BLR. In this paper, we convolve a large grid of physically-based simulations of cloud distributions in BLR wit…

TeV spectral bump of cosmic-ray protons and helium nuclei: the role of nearby supernova remnants
Sourav Bhadra, Satyendra Thoudam, Biman B Nath, Prateek Sharma
https://arxiv.org/abs/2506.18681

TeV spectral bump of cosmic-ray protons and helium nuclei: the role of nearby supernova remnants
The existence of nearby discrete cosmic-ray sources can lead to many interesting effects on the observed properties of cosmic rays. Recent measurements of cosmic rays with the CALET and the DAMPE experiments have revealed a bump-like new feature in the proton and helium spectra in the energy range of ~ (1-100) TeV/nucleon. The origin of the feature is not clearly understood. In this paper, considering an improved and more detailed analysis than previous works, and using an updated age and dista…

A Discrete Analog of Tutte's Barycentric Embeddings on Surfaces
\'Eric Colin de Verdi\`ere, Vincent Despr\'e, Lo\"ic Dubois
https://arxiv.org/abs/2507.13096

A Discrete Analog of Tutte's Barycentric Embeddings on Surfaces
Tutte's celebrated barycentric embedding theorem describes a natural way to build straight-line embeddings (crossing-free drawings) of a (3-connected) planar graph: map the vertices of the outer face to the vertices of a convex polygon, and ensure that each remaining vertex is in convex position, namely, a barycenter with positive coefficients of its neighbors. Actually computing an embedding then boils down to solving a system of linear equations. A particularly appealing feature of this metho…

Extropy Rate: Properties and Application in Feature Selection
Naveen Kumar, Vivek Vijay
https://arxiv.org/abs/2507.11242 https://arxi…

Extropy Rate: Properties and Application in Feature Selection
Extropy, a complementary dual of entropy, (proposed by Lad et al. \cite{lad2015extropy} in 2015) has attracted considerable interest from the research community. In this study, we focus on discrete random variables and define conditional extropy, establishing key properties of joint and conditional extropy such as bounds, uncertainty reduction due to additional information, and Lipschitz continuity. We further introduce the concept of extropy rate for a stochastic process of discrete random var…

The Pitfalls and Potentials of Adding Gene-invariance to Optimal Mixing
Anton Bouter, Dirk Thierens, Peter A. N. Bosman
https://arxiv.org/abs/2506.15222 ht…

The Pitfalls and Potentials of Adding Gene-invariance to Optimal Mixing
Optimal Mixing (OM) is a variation operator that integrates local search with genetic recombination. EAs with OM are capable of state-of-the-art optimization in discrete spaces, offering significant advantages over classic recombination-based EAs. This success is partly due to high selection pressure that drives rapid convergence. However, this can also negatively impact population diversity, complicating the solving of hierarchical problems, which feature multiple layers of complexity. While t…