Variational Bayes Lower Bound derivation In Attias paper

I am reading the paper titled "A Variational Bayesian Framework for Graphical Models" by Hagai Attias ( http://www.gatsby.ucl.ac.uk/publications/papers/03-2000.pdf ). I do not follow how Hagai got equation (2). I realize that one can write $$P(Y|m)$$ as $$\int P(Y, \theta) d(\theta)$$. Then taking log on both sides and dividing and multiplying by $$q(X) q(\theta)$$, by applying Jensens inequality one gets $$\int d(\theta) q(X)q(\theta) log(\frac{P(Y,\theta)}{q(X)Q(\theta)})$$ on the LHS. What throws me off is Hagai also has X in there. So the RHS is really $$P(Y,X|m)$$ isnt it, because the $$X$$ has not been marginalized in the integral? Also not sure what is the difference between $$X$$ and $$\theta$$ is in this paper. The author says $$\theta$$ are simply additional hidden nodes, what is the difference then and what is the need to have X and $$\theta$$ as separate nodes?

Similarly, i have the same confusion with equation 3. Expanding equation 2 i get $$\int log(\frac{P(Y,X|\theta)}{q(X)q(\theta)}) q(X)q(\theta)d(\theta) + \int log(\frac{P(\theta)}{q(X)q(\theta))})q(X)q(\theta)d(\theta)$$ . How is this equation the same as equation 3? The first term in equation 3 is missing a $$q(\theta)$$ in the denominator and the KL term should be with respect to $$q(X,\theta)$$ instead of $$q(\theta)$$ isnt it?