Tootfinder

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
B. Paroli, F. Borghi, M. A. C. Potenza, P. Milani
https://arxiv.org/abs/2506.19642

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
Threshold logic gates (TLGs) have been proposed as artificial counterparts of biological neurons with classification capabilities based on a linear predictor function combining a set of weights with the feature vector. The linearity of TLGs limits their classification capabilities requiring the use of networks for the accomplishment of complex tasks. A generalization of the TLG model called receptron, characterized by input-dependent weight functions allows for a significant enhancement of clas…

Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness
Thai T. Vu, John Le
https://arxiv.org/abs/2507.15145 https:/…

Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness
This paper proposes a communication-efficient, event-triggered inference framework for cooperative edge AI systems comprising multiple user devices and edge servers. Building upon dual-threshold early-exit strategies for rare-event detection, the proposed approach extends classical single-device inference to a distributed, multi-device setting while incorporating proportional fairness constraints across users. A joint optimization framework is formulated to maximize classification utility under…

Three-level qualitative classification of financial risks under varying conditions through first passage times
Carlos Bouthelier-Madre, Carlos Escudero
https://arxiv.org/abs/2507.08101

Three-level qualitative classification of financial risks under varying conditions through first passage times
This work focuses on financial risks from a probabilistic point of view. The value of a firm is described as a geometric Brownian motion and default emerges as a first passage time event. On the technical side, the critical threshold that the value process has to cross to trigger the default is assumed to be an arbitrary continuous function, what constitutes a generalization of the classical Black-Cox model. Such a generality favors modeling a wide range of risk scenarios, including those chara…

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
Taeho Jo, Eun Hye Lee, Alzheimer's Disease Sequencing Project
https://arxiv.org/abs/2506.00662

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncertainty estimation in AD classification from whole-genome sequencing (WGS). We combined a transformer that preserves single-nucleotide polymorphism (SNP) sequence structure with a concurrent random forest using flattened genotypes. An uncertainty threshold separated samples into …

Efficient Lines Detection for Robot Soccer
Jo\~ao G. Melo, Jo\~ao P. Mafaldo, Edna Barros
https://arxiv.org/abs/2507.19469 https://arxiv.org/pdf/2507.19469…

Efficient Lines Detection for Robot Soccer
Self-localization is essential in robot soccer, where accurate detection of visual field features, such as lines and boundaries, is critical for reliable pose estimation. This paper presents a lightweight and efficient method for detecting soccer field lines using the ELSED algorithm, extended with a classification step that analyzes RGB color transitions to identify lines belonging to the field. We introduce a pipeline based on Particle Swarm Optimization (PSO) for threshold calibration to opt…