|Fig 3 of Riek at al. A: sketch showing lateral increase in |
sampling cross section which leads to averaging over
noise patterns in circled areas. B: differential histograms
at various positions of the beam detector.
- A fascinating paper by Riek at al called "Direct sampling of electric-field vacuum fluctuations" demonstrates direct detection of the vacuum fluctuations of electromagnetic radiation in free space by using tightly focused laser pulses lasting a few femtoseconds. This allows "an extreme time-domain approach to quantum physics, with nondestructive access to the quantum state of light. Operating at multiterahertz frequencies, such techniques might also allow time-resolved studies of intrinsic fluctuations of elementary excitations in condensed matter." The paper derives a theoretical 4.7% change in the total normalised noise bandwidth and then shows a 4% widening experimentally. This is really exciting stuff - though once again I'd have liked to see a lot more data so that the uncertainly could be narrowed. Probably there is no significant difference between 4% and 4.7% but it would be very interesting if there were.
- My friend Michael Stumpf and his colleagues have an nice comment paper on Systems Biology whose conclusions are well worth quoting Models are simplified (but not simplistic) representations of real systems, and this is precisely the property that makes them attractive to explore the consequences of our assumptions, and to identify where we lack understanding of the principles governing a biological system. Models are tools to uncover mechanisms that cannot be directly observed, akin to microscopes or nuclear magnetic resonance machines. Used and interpreted appropriately, with due attention paid to inherent uncertainties, the mathematical and computational modelling of biological systems allows the exploration of hypotheses. But the relevance of these models depends on the ability to assess, communicate, and, ultimately, understand their uncertainties.
- In their remarkable "Peer effects on worker output in the laboratory generalize to the field" Daniel Herbst and Alexandre Mas compare peer effects on productivity (workers become more productive is their co-workers are more productive) from lab and field studies. By conducting a meta-analysis of published lab and fields studies they find that the relevant parameter is 0.15 (0.04, 0.26) in the lab and 0.11 (0.03,0.18) in the field, which suggests that there is probably one consistent value 0.12 (0.06,0.18) (the figures in brackets are 95% confidence limits). But what I find almost equally fascinating is that, as you can see from their Fig 1 below this lies outside the published "95% confidence limits" of 18% (2/11) of the lab studies and 48% (11/23) of the field studies. Moral: these studies may be "accurate" but they are vastly overconfident.
|Fig 1 from Herbst and Mas|