Tootfinder

Unitary Quantum Cellular Automata for Density Classification
Pedro C. S. Costa, Yuval R. Sanders, Pedro Paulo Balbi, Gavin K. Brennen
https://arxiv.org/abs/2510.06947 https://…

Unitary Quantum Cellular Automata for Density Classification
We investigate the density classification task (DCT) -- determining the majority bit in a one-dimensional binary lattice -- within a quantum cellular automaton (CA) framework. While there is no one-dimensional two-state, radius $r \geq 1$, deterministic CA with periodic boundary conditions that solves the DCT perfectly, we explore whether a unitary quantum model can succeed. We employ the Partitioned Unitary Quantum Cellular Automaton (PUQCA), a number-conserving model, and, via evolutionary se…

Classification of small binary bibraces via bilinear maps
Roberto Civino, Valerio Fedele
https://arxiv.org/abs/2510.05848 https://arxiv.org/pdf/2510.05848

Classification of small binary bibraces via bilinear maps
We classify small binary bibraces, using the correspondence with alternating algebras over the field F2, up to dimension eight, also determining their isomorphism classes. These finite-dimensional algebras, defined by an alternating bilinear multiplication and nilpotency of class two, can be represented by subspaces of skew-symmetric matrices, with classification corresponding to GL(m, F_2)-orbits under congruence. Our approach combines theoretical invariants, such as rank sequences and the ide…

Topological Signatures of ReLU Neural Network Activation Patterns
Vicente Bosca, Tatum Rask, Sunia Tanweer, Andrew R. Tawfeek, Branden Stone
https://arxiv.org/abs/2510.12700 htt…

Topological Signatures of ReLU Neural Network Activation Patterns
This paper explores the topological signatures of ReLU neural network activation patterns. We consider feedforward neural networks with ReLU activation functions and analyze the polytope decomposition of the feature space induced by the network. Mainly, we investigate how the Fiedler partition of the dual graph and show that it appears to correlate with the decision boundary -- in the case of binary classification. Additionally, we compute the homology of the cellular decomposition -- in a regr…

Classification of $g$-modes for neutron stars with a strong transition: Novel universal relation including slow stable twin stars
M. C. Rodriguez, Jos\'e C. Jim\'enez, Ignacio F. Ranea-Sandoval
https://arxiv.org/abs/2510.06463

Classification of $g$-modes for neutron stars with a strong transition: Novel universal relation including slow stable twin stars
We investigate the behavior of the non-radial gravity-pulsation discontinuity mode ($g$-mode) in hybrid compact stars with a strong first-order phase transition, which can give rise to twin-star configurations in some cases. These modes are of utmost relevance since they can be potentially excited in isolated as well as binary neutron star systems in the inspiral phase, thus allowing us to indirectly detect the presence of a deconfinement transition. In order to do this, we consider four catego…

Robust Functional Logistic Regression
Berkay Akturk, Ufuk Beyaztas, Han Lin Shang
https://arxiv.org/abs/2510.12048 https://arxiv.org/pdf/2510.12048

Robust Functional Logistic Regression
Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables. However, many methods used for parameter estimation in functional logistic regression are sensitive to outliers, which may lead to inaccurate parameter estimates and inferior classification accuracy. We propose a robust estimation procedure for functional logistic regression, in which the observations of the functional predictor are projected onto a set …

PAC Learnability in the Presence of Performativity
Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov
https://arxiv.org/abs/2510.08335 https://arxiv.org/p…

PAC Learnability in the Presence of Performativity
Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, becomes increasingly prevalent. Unfortunately, since models are usually trained solely based on samples from the original (unshifted) distribution, this performative shift may lead to decreased test-time performance. In this paper, we study the question of whether and when performative binary classification problems are…

Quantum Reservoir Computing for Credit Card Default Prediction on a Neutral Atom Platform
Giacomo Vitali, Chiara Vercellino, Paolo Viviani, Olivier Terzo, Bartolomeo Montrucchio, Valeria Zaffaroni, Francesca Cibrario, Christian Mattia, Giacomo Ranieri, Alessandro Sabatino, Francesco Bonazzi, Davide Corbelletto
https://arxiv.org/abs/2510.04…

Quantum Reservoir Computing for Credit Card Default Prediction on a Neutral Atom Platform
In this paper, we define and benchmark a hybrid quantum-classical machine learning pipeline by performing a binary classification task applied to a real-world financial use case. Specifically, we implement a Quantum Reservoir Computing (QRC) layer within a classical routine that includes data preprocessing and binary classification. The reservoir layer has been executed on QuEra's Aquila, a 256-qubit neutral atom simulator, using two different types of encoding: position and local detuning. In …

A Multilingual Framework for Dysarthria: Detection, Severity Classification, Speech-to-Text, and Clean Speech Generation
Ananya Raghu, Anisha Raghu, Nithika Vivek, Sofie Budman, Omar Mansour
https://arxiv.org/abs/2510.03986

A Multilingual Framework for Dysarthria: Detection, Severity Classification, Speech-to-Text, and Clean Speech Generation
Dysarthria is a motor speech disorder that results in slow and often incomprehensible speech. Speech intelligibility significantly impacts communication, leading to barriers in social interactions. Dysarthria is often a characteristic of neurological diseases including Parkinson's and ALS, yet current tools lack generalizability across languages and levels of severity. In this study, we present a unified AI-based multilingual framework that addresses six key components: (1) binary dysarthria de…