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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…

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…

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…

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…

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…