A Bayesian Approach toward Active Learning for Collaborative Filtering

Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples given by the active user, the more accurate the predicted ratings will be. Active learning provides an effective way to acquire the most informative rated examples from active users. Previous work on active learning for collaborative filtering only considers the expected loss function based on the estimated model, which can be misleading when the estimated model is inaccurate. This paper takes one step further by taking into account of the posterior distribution of the estimated model, which results in more robust active learning algorithm. Empirical studies with datasets of movie ratings show that when the number of ratings from the active user is restricted to be small, active learning methods only based on the estimated model don't perform well while the active learning method using the model distribution achieves substantially better performance.Comment: Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004

Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning

Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a wide range of verification and analysis tasks. Data-driven approaches show great promise in improving CHC solving without the painstaking manual effort of creating and tuning various heuristics. However, a large performance gap exists between data-driven CHC solvers and symbolic reasoning-based solvers. In this work, we develop a simple but effective framework, "Chronosymbolic Learning", which unifies symbolic information and numerical data points to solve a CHC system efficiently. We also present a simple instance of Chronosymbolic Learning with a data-driven learner and a BMC-styled reasoner. Despite its great simplicity, experimental results show the efficacy and robustness of our tool. It outperforms state-of-the-art CHC solvers on a dataset consisting of 288 benchmarks, including many instances with non-linear integer arithmetics

Investigating the spectroscopy behavior of undetected 1F1F-wave charmed baryons

In this work, we investigate the spectroscopic properties of $1F$-wave charmed baryons, which have not yet been observed in experiments. We employ a non-relativistic potential model and utilize the Gaussian expansion method to obtain the mass spectra of these charmed baryons. Additionally, we focus on the two-body Okubo-Zweig-Iizuka allowed strong decay behaviors, which plays a crucial role in characterizing the properties of these baryons. Our comprehensive analyses of the mass spectra and two-body Okubo-Zweig-Iizuka allowed decay behaviors provides valuable insights for future experimental investigations. This study significantly contributes to our understandings of the spectroscopic properties of $1F$-wave charmed baryons.Comment: 10 pages, 2 figures, 9 tables. More references added. Accepted by Phys. Rev.

The newly observed Ωc(3327)\Omega_c(3327): A good candidate for a DD-wave charmed baryon

The newly observed $\Omega_c(3327)$ gives us a good chance to construct the $\Omega_c$ charmed baryon family. In this work, we carry out the mass spectrum analysis by a non-relativistic potential model using Gaussian Expansion Method, and the study of its two-body Okubo-Zweig-Iizuka allowed strong decay behavior. Our results imply that the $\Omega_c(3327)$ is good candidate of $\Omega_c(1D)$ state with $J^P=5/2^+$. We also predict the spectroscopy behavior of other $\Omega_c(1D)$ states, which may provide further clues to their search.Comment: 6 pages, 4 tables, 3 figures. Accepted by PR