Tootfinder

Comparative Studies of Quantum Annealing, Digital Annealing, and Classical Solvers for Reaction Network Pathway Analysis and mRNA Codon Selection
Milind Upadhyay, Mark Nicholas Jones
https://arxiv.org/abs/2509.09862

Comparative Studies of Quantum Annealing, Digital Annealing, and Classical Solvers for Reaction Network Pathway Analysis and mRNA Codon Selection
For various optimization problems, the classical time to solution is super-polynomial and intractable to solve with classical bit-based computing hardware to date. Digital and quantum annealers have the potential to identify near-optimal solutions for such optimization problems using a quadratic unconstrained binary optimization (QUBO) problem formulation. This work benchmarks two use cases to evaluate the utility of QUBO solvers for combinatorial optimization problems, in order to determine if…

A Digital Twin Framework for Metamorphic Testing of Autonomous Driving Systems Using Generative Model
Tony Zhang, Burak Kantarci, Umair Siddique
https://arxiv.org/abs/2510.07133

A Digital Twin Framework for Metamorphic Testing of Autonomous Driving Systems Using Generative Model
Ensuring the safety of self-driving cars remains a major challenge due to the complexity and unpredictability of real-world driving environments. Traditional testing methods face significant limitations, such as the oracle problem, which makes it difficult to determine whether a system's behavior is correct, and the inability to cover the full range of scenarios an autonomous vehicle may encounter. In this paper, we introduce a digital twin-driven metamorphic testing framework that addresses th…

Forensic Similarity for Speech Deepfakes
Viola Negroni, Davide Salvi, Daniele Ugo Leonzio, Paolo Bestagini, Stefano Tubaro
https://arxiv.org/abs/2510.02864 https://

Forensic Similarity for Speech Deepfakes
In this paper, we introduce a digital audio forensics approach called Forensic Similarity for Speech Deepfakes, which determines whether two audio segments contain the same forensic traces or not. Our work is inspired by prior work in the image domain on forensic similarity, which proved strong generalization capabilities against unknown forensic traces, without requiring prior knowledge of them at training time. To achieve this in the audio setting, we propose a two-part deep-learning system c…