https://www.fox.temple.edu/sites/fox/files/SHMM_isre.2018.0831.pdf
Indeed, the structural model helps decom-pose heterogeneous treatment effects by engagement segment, which, in turns, empowers businesses to target the most ef cient users to effectively meet the fi challenge of low engagement with mobile apps.
https://sites.temple.edu/pdames/files/2016/07/DamesTokekarKumarISRR2015.pdf
Let Xt = fx1;t;x2;t;:::;xnt;tg denote a realization of a RFS of target states at time t. A probability distribution of a RFS is characterized by a discrete distribution over the cardinality of the set and a family of densities for the elements of the set conditioned on the size, i.e.,
https://cis.temple.edu/~jiewu/teaching/Spring%202013/01-savvides-localization-wireless-sensor-networks-fine-grained.pdf
We advocate a distributed algorithm, which we show to be more power ef-ficient than a centralized solution. Furthermore, our approach is based on an iterative process, where increasingly more nodes are able to resolve their location.
https://cis.temple.edu/~latecki/Papers/GIFT-IEEEMM2017.pdf
In summary, we call the feature in (12) Aggregated Contextual Activation (ACA). Next, we will introduce some improvements of (12) concerning its retrieval accuracy and computational ef-ficiency. 1) Improving Accuracy: Similar to diffusion process, the proposed ACA requires an accurate estimation of the context in the data manifold.