Tootfinder

Reliability, Embeddedness, and Agency: A Utility-Driven Mathematical Framework for Agent-Centric AI Adoption
Faruk Alpay, Taylan Alpay
https://arxiv.org/abs/2508.12896 https://

Reliability, Embeddedness, and Agency: A Utility-Driven Mathematical Framework for Agent-Centric AI Adoption
We formalize three design axioms for sustained adoption of agent-centric AI systems executing multi-step tasks: (A1) Reliability > Novelty; (A2) Embed > Destination; (A3) Agency > Chat. We model adoption as a sum of a decaying novelty term and a growing utility term and derive the phase conditions for troughs/overshoots with full proofs. We introduce: (i) an identifiability/confounding analysis for $(α,β,N_0,U_{\max})$ with delta-method gradients; (ii) a non-monotone comparator (logistic-with…

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 …

An elementary derivation of 3-cycles for a quadratic map
Arpad Benyi, Ioan Casu
https://arxiv.org/abs/2510.12558 https://arxiv.org/pdf/2510.12558

An elementary derivation of 3-cycles for a quadratic map
We present an elementary derivation of the period-three cycles for the real quadratic map $x\mapsto x^2+c$, a fundamental model in one-dimensional discrete dynamics. Using symmetric polynomials, we obtain a complete algebraic characterization of 3-cycles and determine explicit conditions for their existence and stability, without reliance on computer algebra. Through conjugacy with the logistic map, we recover the classical threshold values of the logistic parameter corresponding to the emergen…

An Approach to Estimating Quadratic Logistic Model Parameters of Fractal Dimension Curves
Yanguang Chen
https://arxiv.org/abs/2508.19917 https://arxiv.org/…

An Approach to Estimating Quadratic Logistic Model Parameters of Fractal Dimension Curves
The fractal dimension curves of urban form and growth fall into two categories: One can be described by common logistic function, and the other can be described with quadratic logistic function. The approach to estimating the parameter of the ordinary logistic model has been developed. However, how to estimate the parameter of quadratic logistic model is still a problem. This paper is devoted to finding a nonlinear regressive approach for estimating parameter values of quadratic logistic model …

FedCVD : Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab, Youssif Abuzied, Dineo Mpanya, Turgay Celik, Swarun Kumar, Tamer ElBatt
https://arxiv.org/abs/2507.22963…

FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communi…

Fractional Logistic Growth with Memory Effects: A Tool for Industry-Oriented Modeling
M. O. Aibinu, A. Shoukat, F. M. Mahomed
https://arxiv.org/abs/2509.20389 https://

Fractional Logistic Growth with Memory Effects: A Tool for Industry-Oriented Modeling
The logistic growth model is a classical framework for describing constrained growth phenomena, widely applied in areas such as population dynamics, epidemiology, and resource management. This study presents a generalized extension using Atangana-Baleanu in Caputo sense (ABC)-type fractional derivatives. Proportional time delay is also included, allowing the model to capture memory-dependent and nonlocal dynamics not addressed in classical formulations. Free parameters provide flexibility for m…

Quadratic Growth Model with Discontinuity: A Link between Monostable and Bistable Traveling Waves
Wonhyung Choi, Junsik Bae, Yong-Jung Kim
https://arxiv.org/abs/2509.01715 https…

Quadratic Growth Model with Discontinuity: A Link between Monostable and Bistable Traveling Waves
We classify traveling waves and stationary solutions of a reaction-diffusion equation arising in population dynamics with Allee-type effects. The reaction term is given by a quadratic polynomial with a discontinuity at zero, which captures finite-time extinction for sub-threshold populations. This discontinuity induces a free boundary in the wave profile, a phenomenon that distinguishes the model from the classical logistic or Allen-Cahn equations. A complete scenario is presented that connects…

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
Jianfei Li, Kevin Kam Fung Yuen
https://arxiv.org/abs/2507.14022

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The CPC, based on expert knowledge judgment, is used to calculate the weights of evaluation criteria, including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), Cohen's Kappa (Kappa), and efficiency. Naive Bayes, Linear Support Vector Classification (LSVC), Random Forest, Logistic Regression, Extreme Gradient B…

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Joaqu\'in Mart\'inez-Minaya, Lore Zumeta-Olaskoaga, Dae-Jin Lee
https://arxiv.org/abs/2508.07708

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Compositional data (CoDa) plays an important role in many fields such as ecology, geology, or biology. The most widely used modeling approaches are based on the Dirichlet and the logistic-normal formulation under Aitchison geometry. Recent developments in the mathematical field on the simplex geometry allow to express the regression model in terms of coordinates and estimate its coefficients. Once the model is projected in the real space, we can employ a multivariate Gaussian regression to deal…

Active-Learning Inspired Ab Initio Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
Sarvesh Chaudhari, Cristobal Mendez, Rushil Choudhary, Tathagata Banerjee, Maciej Olszewski, Jadrien Paustian, Jaehong Choi, Zhaslan Baraissov, Raul Hernandez, David Muller, Britton Plourde, Gregory Fuchs, Valla Fatemi, Tomas Arias

Active-Learning Inspired Ab Initio Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
Surface oxides are associated with two-level systems (TLSs) that degrade the performance of niobium-based superconducting quantum computing devices. To address this, we introduce a predictive framework for selecting metal capping layers that inhibit niobium oxide formation. Using DFT-calculated oxygen interstitial and vacancy energies as thermodynamic descriptors, we train a logistic regression model on a limited set of experimental outcomes to successfully predict the likelihood of oxide forma…

LLM-Augmented and Fair Machine Learning Framework for University Admission Prediction
Mohammad Abbadi, Yassine Himeur, Shadi Atalla, Dahlia Mansoor, Wathiq Mansoor
https://arxiv.org/abs/2509.22560

LLM-Augmented and Fair Machine Learning Framework for University Admission Prediction
Universities face surging applications and heightened expectations for fairness, making accurate admission prediction increasingly vital. This work presents a comprehensive framework that fuses machine learning, deep learning, and large language model techniques to combine structured academic and demographic variables with unstructured text signals. Drawing on more than 2,000 student records, the study benchmarks logistic regression, Naive Bayes, random forests, deep neural networks, and a stac…

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
Tavor Z. Baharav, Spyros Dragazis, Aldo Pacchiano
https://arxiv.org/abs/2510.01020 https:/…

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
We study the problem of sequentially testing individuals for a binary disease outcome whose true risk is governed by an unknown logistic model. At each round, a patient arrives with feature vector $x_t$, and the decision maker may either pay to administer a (noiseless) diagnostic test--revealing the true label--or skip testing and predict the patient's disease status based on their feature vector and prior history. Our goal is to minimize the total number of costly tests required while guarante…

Verification of the NOAA Space Weather Prediction Center solar flare forecast (1998-2024)
Enrico Camporeale, Thomas E. Berger
https://arxiv.org/abs/2508.01114 https://

Verification of the NOAA Space Weather Prediction Center solar flare forecast (1998-2024)
The NOAA Space Weather Prediction Center (SWPC) issues the official U.S. government forecast for M-class and X-class solar flares, yet the skill of these forecasts has never been comprehensively verified. In this study, we evaluate the SWPC probabilistic flare forecasts over a 26-year period (1998-2024), comparing them to several zero-cost and statistical baselines including persistence, climatology, Naive Bayes, and logistic regression. We find that the SWPC model does not outperform these bas…

Evaluating the performance of Bayesian cumulative logistic models in randomised controlled trials: a simulation study
Chris J. Selman, Katherine J. Lee, Steven Y. C. Tong, Mark Jones, Robert K. Mahar
https://arxiv.org/abs/2507.21473

Evaluating the performance of Bayesian cumulative logistic models in randomised controlled trials: a simulation study
Background: The proportional odds (PO) model is the most common analytic method for ordinal outcomes in randomised controlled trials. While parameter estimates obtained under departures from PO can be interpreted as an average odds ratio, they can obscure differing treatment effects across the distribution of the ordinal categories. Extensions to the PO model exist and this work evaluates their performance under deviations to the PO assumption. Methods: We evaluated the bias, coverage and mea…

Cluster Analysis for Globally Coupled Map using Optimal Transport Distance and Complexity of Attractor-ruin
Koji Wada, Takao Namiki
https://arxiv.org/abs/2510.00538 https://

Cluster Analysis for Globally Coupled Map using Optimal Transport Distance and Complexity of Attractor-ruin
In this paper, we show the results of the strength of attractorruins for a globally coupled map. The globally coupled map (GCM) is a discrete dynamical system, and here we consider a model in which the logistic map is globally coupled. An attractor-ruin is a set in which the attractor is destabilized by a change in parameters, which is characterized by a Milnor attractor. Intermittent phenomena called chaotic itinerancy, in which orbits transition between attractor-ruin, have been observed in v…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- A mixture logistic model for panel data with a Markov structure
Yu-Hsiang Cheng, Tzee-Ming Huang

Instability of inverse probability weighting methods and a remedy for non-ignorable missing data
Pengfei Li, Jing Qin, Yukun Liu
https://arxiv.org/abs/2507.17137 https://…

Instability of inverse probability weighting methods and a remedy for non-ignorable missing data
Inverse probability weighting (IPW) methods are commonly used to analyze non-ignorable missing data under the assumption of a logistic model for the missingness probability. However, solving IPW equations numerically may involve non-convergence problems when the sample size is moderate and the missingness probability is high. Moreover, those equations often have multiple roots, and identifying the best root is challenging. Therefore, IPW methods may have low efficiency or even produce biased re…