(RM Jr of worm fame was just a kid then.)
AFAIK the only group to discover Ken’s hack was us in PWB/UNIX. One of the other guys noticed C prepreprocessor had gotten bigger, looked at binary namelist, found symbol not in source code. I got onto Ken’s system, found the code, very clever.
(RM Jr of worm fame was just a kid then.)
More chortling, then (must have been Bob): uhh, NSA really doesn’t have sense of humor.
We did audit, agreed with that, BUT:
1) Many terminals had yellow stickies with root password.
2) They’d reused unused lab space w/o adequate HVAC, room got hot, so they often left door open.
As usual, good tech helps, but human error/laziness must always be guarded against.
Same thing happened later with workstations & then PCs: user depts got impatient with central IT.
Just as happened later with workstations/PCs, BTL Directors found that running own computer center well was a pain. The main computer centers then offered to do facilities management, with good HVAC,
I recall centralized mainframe service bureaus of 1960s,reborrn as cloud🙂
More from Software
@JuliaLMarcus @Iplaywithgerms This paper gives documentation on software (with causal reasoning, assumptions reviewed in appendix) for a parametric approach to estimating either "total effects" or "controlled direct effects" with competing events and time-varying
@Iplaywithgerms Total effects capture paths by which treatment affects competing event (e.g. protective total effect of lifesaving treatment on dementia may be wholly/partially due to effect on survival). Controlled direct effects do not capture these paths
@Iplaywithgerms More detailed reasoning on the difference and tradeoffs between total and controlled direct effects and causal reasoning in the point treatment context provided here along with description of some estimators and
@Iplaywithgerms If you are familiar with more robust approaches like IPW or even better TMLE for time-varying treatment, these are trivially adapted to go after the controlled direct effect by simply treating competing events like loss to follow-up (censoring). e.g.
@Iplaywithgerms Examples of IPW estimation of the total effect of a time-varying treatment described in Appendix D of this paper:
https://t.co/RNhcgTBMkb
And here
https://t.co/rMWmwFBWwV
Others in reference lists of above papers.
@Iplaywithgerms Total effects capture paths by which treatment affects competing event (e.g. protective total effect of lifesaving treatment on dementia may be wholly/partially due to effect on survival). Controlled direct effects do not capture these paths
@Iplaywithgerms More detailed reasoning on the difference and tradeoffs between total and controlled direct effects and causal reasoning in the point treatment context provided here along with description of some estimators and
@Iplaywithgerms If you are familiar with more robust approaches like IPW or even better TMLE for time-varying treatment, these are trivially adapted to go after the controlled direct effect by simply treating competing events like loss to follow-up (censoring). e.g.
@Iplaywithgerms Examples of IPW estimation of the total effect of a time-varying treatment described in Appendix D of this paper:
https://t.co/RNhcgTBMkb
And here
https://t.co/rMWmwFBWwV
Others in reference lists of above papers.
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