ATOTActual Take Off Time (aviation)
ATOTAdvanced Training of Trainers (various locations)
ATOTAll-Terrain Open Transport (Star Wars vehicle)
ATOTAnandtech Off Topic
ATOTAt Time of Test
ATOTActual Time Over Target
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References in periodicals archive ?
Before presenting our Author-Topic over Time (AToT) model, we first describe two related generative models: AT model and ToT model.
From the above generative process, one can see that AToT model is parameterized as follows:
If one wants to differentiate the contributions of the first author and reprint author from those of other coauthors, it is very easy for AToT model to set different weights for different authors.
For inference, the task is to estimate the sets of the following unknown parameters in the AToT model: (1) [PHI] = [{[[phi].sub.k]}.sup.K.sub.k=1], [THETA] = [{[partial derivative]}.sup.A.sub.a=1], and [PSI] = [{[[psi].sub.k]}.sup.K.sub.k=1] and (2) the corresponding topic and author assignments z , xm n for each word token [w.sub.m,n].
Detailed derivation of Gibbs sampling for AToT is provided in the appendix.
With (2)-(6), Gibbs sampling algorithm for AToT model is summarized in Algorithm 1.
The overall distributed architecture for AToT model is shown in Figure 5.
Qiao et al., "Author-topic over time (AToT): A dynamic users' interest model," in Proceedings of the 2nd International Conference on Ubiquitous Context-Awareness and Wireless Sensor Network, pp.