ILP Recommender System: Explainable and More

In this paper, we explore the use of ILP thoroughly in generating explainable, negative, group and context-aware recommendation. ILP provides recommendation rules in if-then logical format that allows us to form a clear and concise explanation to accompany the suggested items. It can indirectly derive negative rules which tell us not to recommend certain products to users. It also emphasizes the use of universal representations which enables us to suggest the items to a group of users who share the same interest. Additionally, ILP requires no re-training if new contexts (e.g., location, time and mood) are added to the system to generate context-aware recommendations (CARS), only predicates and settings are simply specified. In this paper, we also propose the explainability evaluation in terms of transparency by comparing the items/features appearing in the explanation with the features presented in the user's review. The negative, group and dynamic recommendations can be evaluated using the standard measurement

Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery

We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently governs noisy spatial-temporal observed data with few reliable terms. Since the naive use of the BIC for model selection has been known to yield an undesirable overfitted PDE, the UBIC penalizes the found PDE not only by its complexity but also the quantified uncertainty, derived from the model supports' coefficient of variation in a probabilistic view. We also introduce physics-informed neural network learning as a simulation-based approach to further validate the selected PDE flexibly against the other discovered PDE. Numerical results affirm the successful application of the UBIC in identifying the true governing PDE. Additionally, we reveal an interesting effect of denoising the observed data on improving the trade-off between the BIC score and model complexity. Code is available at https://github.com/Pongpisit-Thanasutives/UBIC.Comment: 17 pages, 15 figure

Adaptive Uncertainty-Penalized Model Selection for Data-Driven PDE Discovery

Thanasutives P., Morita T., Numao M., et al. Adaptive Uncertainty-Penalized Model Selection for Data-Driven PDE Discovery. IEEE Access 12, 13165 (2024); https://doi.org/10.1109/ACCESS.2024.3354819.We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to discover the stable governing partial differential equation (PDE) composed of a few important terms. Since the naive use of the BIC for model selection yields an overfitted PDE, the UBIC penalizes the found PDE not only by its complexity but also by its quantified uncertainty. Representing the PDE as the best subset of a few candidate terms, we use Bayesian regression to compute the coefficient of variation (CV) of the posterior PDE coefficients. The PDE uncertainty is then derived from the obtained CV. The UBIC follows the premise that the true PDE shows relatively lower uncertainty when compared with overfitted PDEs. Thus, the quantified uncertainty is an effective indicator for identifying the true PDE. We also introduce physics-informed neural network learning as a simulation-based approach to further validate the UBIC-selected PDE against the other potential PDE. Numerical results confirm the successful application of the UBIC for data-driven PDE discovery from noisy spatio-temporal data. Additionally, we reveal a positive effect of denoising the observed data on improving the trade-off between the BIC score and model complexity

PAMPs(Pathogen-associated molecular patterns)刺激がマクロファージにFasL感受性を誘導する分子機構とその生理学的意義の検討

取得学位：博士(理学)，学位授与番号：博甲第585号，学位授与年月日：平成15年3月31日,学位授与年：200