Institute of Science Tokyo

Under a controlled protocol that fixes the Boltzmann machine model, simulated-annealing sampler, and learning-rate design, the study compares Ising ({−1,+1}) and QUBO ({0,1}) variable encodings, exploits the identity that the Fisher information matrix equals the covariance of sufficient statistics to visualize empirical moments, and finds that QUBO induces larger cross terms between first- and second-order statistics, creating more small-eigenvalue directions and lowering spectral entropy, which explains slower convergence under stochastic gradient descent, whereas natural gradient descent, which rescales updates by the Fisher information matrix metric, achieves similar convergence across encodings due to reparameterization invariance.

Under a controlled protocol that fixes the Boltzmann machine model, simulated-annealing sampler, and learning-rate design, the study compares Ising ({−1,+1}) and QUBO ({0,1}) variable encodings, exploits the identity that the Fisher information matrix equals the covariance of sufficient statistics to visualize empirical moments, and finds that QUBO induces larger cross terms between first- and second-order statistics, creating more small-eigenvalue directions and lowering spectral entropy, which explains slower convergence under stochastic gradient descent, whereas natural gradient descent, which rescales updates by the Fisher information matrix metric, achieves similar convergence across encodings due to reparameterization invariance.

Using 5,613 autopsy cases from the Mayo Clinic Brain Bank collected between 1998 and 2023, this study fine-tuned a large language model to convert narrative gross descriptions into semi-quantitative scores for 39 features (extraction accuracy 0.95 on 200 manually annotated feature-level test examples), then classified seven neuropathologic diagnostic categories with a CatBoost classifier and a second fine-tuned LLM, both including age at death, sex, and brain weight; on a held-out test set of 562 cases CatBoost reached accuracy 0.73, kappa 0.65, and macro-average AUC 0.92, while the text-based LLM reached accuracy 0.75 and kappa 0.68, with macro-average sensitivity 0.66 for both, PSP sensitivity 0.92 and 0.93, MSA sensitivity 0.87 and 0.92, but AD-LBD sensitivity only 0.21 and 0.

Using 5,613 autopsy cases from the Mayo Clinic Brain Bank collected between 1998 and 2023, this study fine-tuned a large language model to convert narrative gross descriptions into semi-quantitative scores for 39 features (extraction accuracy 0.95 on 200 manually annotated feature-level test examples), then classified seven neuropathologic diagnostic categories with a CatBoost classifier and a second fine-tuned LLM, both including age at death, sex, and brain weight; on a held-out test set of 562 cases CatBoost reached accuracy 0.73, kappa 0.65, and macro-average AUC 0.92, while the text-based LLM reached accuracy 0.75 and kappa 0.68, with macro-average sensitivity 0.66 for both, PSP sensitivity 0.92 and 0.93, MSA sensitivity 0.87 and 0.92, but AD-LBD sensitivity only 0.21 and 0.