Zobrazeno 1 - 10
of 16 174
pro vyhledávání: '"A. Minato"'
Autor:
Fukushima, Kenji, Minato, Shuhei
In a unified perturbative treatment from the high-density side, we compute the speed of sound and the trace anomaly as functions of the chemical potential $\mu$ for the two-color diquark superfluid, the pion-condensed high-isospin matter, and the 2SC
Externí odkaz:
http://arxiv.org/abs/2411.03781
Autor:
Minato, Futoshi
Nuclear microscopic structural models that treat two-body effective interactions self-consistently becomes available, one of which is second-random-phase-approximation (SRPA). SRPA can be used to study evolutions from 1 particle-1 hole (1p1h) to 2 pa
Externí odkaz:
http://arxiv.org/abs/2411.01709
Autor:
Minato, Hiroaki
This paper presents a Bayesian multilevel modeling approach for estimating well-level oil and gas production capacities across small geographic areas over multiple time periods. Focusing on a basin, which is a geologically and economically distinct d
Externí odkaz:
http://arxiv.org/abs/2408.11167
This paper introduces a novel method for finding integer sets that satisfy the Pythagorean theorem by leveraging the Higher-Order Binary Optimization (HOBO) formulation. Unlike the Quadratic Unconstrained Binary Optimization (QUBO) formulation, which
Externí odkaz:
http://arxiv.org/abs/2408.11076
Autor:
Minato, Yuichiro
The Quantum Approximate Optimization Algorithm (QAOA) has shown promise in solving combinatorial optimization problems by leveraging quantum computational power. We propose a simple approach, the Two-Step QAOA, which aims to improve the effectiveness
Externí odkaz:
http://arxiv.org/abs/2408.05383
In this study, we introduce HOBOTAN, a new solver designed for Higher Order Binary Optimization (HOBO). HOBOTAN supports both CPU and GPU, with the GPU version developed based on PyTorch, offering a fast and scalable system. This solver utilizes tens
Externí odkaz:
http://arxiv.org/abs/2407.19987
Autor:
Minato, Yuichiro
In the field of quantum computing, combinatorial optimization problems are typically addressed using QUBO (Quadratic Unconstrained Binary Optimization) solvers. However, these solvers are often insufficient for tackling higher-order problems. In this
Externí odkaz:
http://arxiv.org/abs/2407.16106
In this paper, we propose a two-phase training approach where pre-trained large language models are continually pre-trained on parallel data and then supervised fine-tuned with a small amount of high-quality parallel data. To investigate the effectiv
Externí odkaz:
http://arxiv.org/abs/2407.03145
Autor:
Minato, Futoshi, Iwamoto, Osamu
Fission fragment yield evaluations are one of the important nuclear data studies. Fission accompanies various physical observables such as prompt fission neutron, prompt fission gamma, and delayed-neutrons. When evaluating fission fragment yields, a
Externí odkaz:
http://arxiv.org/abs/2404.17728
Matroids are often represented as oracles since there are no unified and compact representations for general matroids. This paper initiates the study of binary decision diagrams (BDDs) and zero-suppressed binary decision diagrams (ZDDs) as relatively
Externí odkaz:
http://arxiv.org/abs/2404.14670