Mean Semivariance Optimization VS PMPT

Mean Semivariance optimization defines semivariance, variance only below the benchmark/required rate of return, as:

$$(1/T).\sum_{t=1}^{T} [Min(R_{it}-B,0)]^2$$

where $$B$$ is the benchmark rate, $$R_{i}$$ is the asset returns for asset $$i$$, and $$T$$ is the number of observations.

Post Modern Portfolio theory however, (https://en.wikipedia.org/wiki/Post-modern_portfolio_theory), defines downside risk (which according to my understanding should be equal to semivariance), as :

$$\sqrt {{\int_{-\infty}^{t}} ({t-r})^2 f(r)dr}$$

where $$t$$ is the benchmark here, $$r$$ is the random variable representing the return for the distribution of annual returns $$f(r)$$, and $$f(r)$$ is the distribution for the annual returns.

Assuming $$t$$ is equal to $$B$$, would these two be equal, and can anyone prove so?