Tootfinder

Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
Akira SaiToh, Arezoo Modiri, Amit Sawant, Robabeh Rahimi
https://arxiv.org/abs/2506.04328

Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
Among the genetic algorithms generally used for optimization problems in the recent decades, quantum-inspired variants are known for fast and high-fitness convergence and small resource requirement. Here the application to the patient scheduling problem in proton therapy is reported. Quantum chromosomes are tailored to possess the superposed data of patient IDs and gantry statuses. Selection and repair strategies are also elaborated for reliable convergence to a clinically feasible schedule alt…

Verification of the Release-Acquire Semantics
Parosh Abdulla, Elli Anastasiadi, Mohamed Faouzi Atig, Samuel Grahn
https://arxiv.org/abs/2506.08238 https://…

Verification of the Release-Acquire Semantics
The Release-Acquire (RA) semantics and its variants are some of the most fundamental models of concurrent semantics for architectures, programming languages, and distributed systems. Several steps have been taken in the direction of testing such semantics, where one is interested in whether a single program execution is consistent with a memory model. The more general verification problem, i.e., checking whether all allowed program runs are consistent with a memory model, has still not been stu…

The iterated Golub-Kahan-Tikhonov method
Davide Bianchi, Marco Donatelli, Davide Furch\`i, Lothar Reichel
https://arxiv.org/abs/2507.12307 https://

The iterated Golub-Kahan-Tikhonov method
The Golub-Kahan-Tikhonov method is a popular solution technique for large linear discrete ill-posed problems. This method first applies partial Golub-Kahan bidiagonalization to reduce the size of the given problem and then uses Tikhonov regularization to compute a meaningful approximate solution of the reduced problem. It is well known that iterated variants of this method often yield approximate solutions of higher quality than the standard non-iterated method. Moreover, it produces more accur…

A solvable microscopic model for the propagation of light in a dielectric medium
Richard Dengler
https://arxiv.org/abs/2506.17415 https://

A solvable microscopic model for the propagation of light in a dielectric medium
This work examines a dielectric medium consisting of small, polarizable classical atoms arranged on a cubic lattice. Using a regularized variant of the discrete dipole approximation (DDA) method, we derive the solution for the propagation of light in this medium in the long wavelength limit. Under typical conditions, the atoms remain effectively stationary, but acquire momentum. The analytical solution is used to calculate the mechanical contribution to the the signal momentum from the electrom…

A Reduced Cost Two-component Relativistic Equation-of-Motion Coupled Cluster Method for Ionization Potential
Somesh Chamoli, Malaya K. Nayak, Achintya Kumar Dutta
https://arxiv.org/abs/2506.06805

A Reduced Cost Two-component Relativistic Equation-of-Motion Coupled Cluster Method for Ionization Potential
We report an efficient implementation of the ionization potential (IP) variant of the equation-of-motion coupled cluster (IP-EOM-CC) method based on the exact two-component atomic mean field (X2CAMF) framework, utilizing Cholesky decomposition (CD) and frozen natural spinors (FNS). The CD approximation significantly reduces memory demands, whereas the FNS approximation lowers the number of floating-point operations. Together, these techniques make the method computationally efficient for accura…

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?
Bhakti Khera, Rezvan Alamian, Pascal A. Scherz, Stephan M. Goetz
https://arxiv.org/abs/2507.10576

Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?
The legal field already uses various large language models (LLMs) in actual applications, but their quantitative performance and reasons for it are underexplored. We evaluated several open-source and proprietary LLMs -- including GPT-series, Anthropic, Deepseek and Llama-3, variants -- on parts of the European Qualifying Examination (EQE) for future European Patent Attorneys. OpenAI o1 led with 0.82 accuracy and 0.81 F1 score, whereas (Amazon Web Services) AWS Llama 3.1 8B lagged at 0.50 accura…

BUT System for the MLC-SLM Challenge
Alexander Polok, Jiangyu Han, Dominik Klement, Samuele Cornell, Jan \v{C}ernock\'y, Luk\'a\v{s} Burget
https://arxiv.org/abs/2506.13414

BUT System for the MLC-SLM Challenge
We present a two-speaker automatic speech recognition (ASR) system that combines DiCoW -- a diarization-conditioned variant of Whisper -- with DiariZen, a diarization pipeline built on top of Pyannote. We first evaluate both systems in out-of-domain (OOD) multilingual scenarios without any fine-tuning. In this scenario, DiariZen consistently outperforms the baseline Pyannote diarization model, demonstrating strong generalization. Despite being fine-tuned on English-only data for target-speaker …