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Explaining the results of Machine learning algorithms is crucial given the rapid growth and potential applicability of these methods in critical domains including healthcare, defense, autonomous driving, etc. In this paper, we address this problem in the context of Markov Logic Networks (MLNs) which are highly expressive statistical relational models that combine firstorder logic with probabilistic graphical models. MLNs in general are known to be interpretable models, i.e., MLNs can be understood more easily by humans as compared to models learned by approaches such as deep learning. However, at the same time, it is not straightforward to obtain humanunderstandable explanationsmore »

A bstract We present a measurement of the CabibboKobayashiMaskawa unitarity triangle angle ϕ 3 (also known as γ ) using a modelindependent Dalitz plot analysis of B + → D ( $$ {K}_S^0 $$ K S 0 h + h − ) h + , where D is either a D 0 or $$ \overline{D} $$ D ¯ 0 meson and h is either a π or K . This is the first measurement that simultaneously uses Belle and Belle II data, combining samples corresponding to integrated luminosities of 711 fb − 1 and 128 fb − 1 , respectively.more »Free, publiclyaccessible full text available February 1, 2023

Free, publiclyaccessible full text available November 1, 2022

Free, publiclyaccessible full text available October 1, 2022