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I am trying to fit a nonlinear model using nlm(). My application of nlm() is a bit complicated. Here is the story behind the model being fit: The observer is trying to detect a signal corrupted by noise. On each trial, the observer gets stim=signal+rnorm(). In the simulation below I have 500 trials. Each row of stim is a new trial. On each trial, if the cross-correlation between the stim and the

Hi all (and Mr. Dunlap in particular), I have a question about the credit (and ultimately credit2) scheduler that I hope you can help me with. I have read the white paper "Scheduler development update" and as much material on the credit scheduler as I can find, but I am still not completely clear on how I should think about the cap. Example scenario: Server hardware: 2