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ref: work-0 tags: distilling free-form natural laws from experimental data Schmidt Cornell automatic programming genetic algorithms date: 12-30-2021 05:11 gmt revision:7 [6] [5] [4] [3] [2] [1] [head]

Distilling free-form natural laws from experimental data

  • The critical step was to use the full set of all pairs of partial derivatives ( δx/δy\delta x / \delta y ) to evaluate the search for invariants.
  • The selection of which partial derivatives are held to be independent / which variables are dependent is a bit of a trick too -- see the supplemental information.
    • Even yet, with a 4D data set the search for natural laws took ~ 30 hours.
  • This was via a genetic algorithm, distributed among 'islands' on different CPUs, with mutation and single-point crossover.
  • Not sure what the IL is, but it appears to be floating-point assembly.
  • Timeseries data is smoothed with Loess smoothing, which fits a polynomial to the data, and hence allows for smoother / more analytic derivative calculation.
    • Then again, how long did it take humans to figure out these invariants? (Went about it in a decidedly different way..)
    • Further, how long did it take for biology to discover similar 'design equations'?
      • The same algorithm has been applied to biological data - a metabolic pathway - with some success pub 2011.
      • Of course evolution had to explore a much larger space - proteins and regulatory pathways, not simpler mathematical expressions / linkages.

Since his Phd, Michael Schmidt has gone on to found Nutonian, which produced Eurequa software, apparently without dramatic new features other than being able to use the cloud for equation search. (Probably he improved many other detailed facets of the software..). Nutonian received $4M in seed funding, according to Crunchbase.

In 2017, Nutonian was acquired by Data Robot (for an undisclosed amount), where Michael has worked since, rising to the title of CTO.

Always interesting to follow up on the authors of these classic papers!

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ref: Szarowski-2003.09 tags: Michigan array silicon histology MEA cornell date: 01-28-2013 20:47 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-12914963[0] Brain responses to micro-machined silicon devices.

  • Used 2 different implants (rough & sharp corners, smooth), 2 different ways of inserting (slow, by hand).
    • Neither made much diff.
  • Measured GFAP = glial fibrillary acidic protein, a standard measure for assesing reactive gliosis [44,18,28,33,35].
    • Normally larger astrocytes were seen around larger blood vessels.
    • "At four weeks, a clear sheath of GFAP-positive astrocytes was observed"
    • GFAP labeled sheath seems to have plateaued at 6 weeks. (The sheath may be useful for our devices... )
  • Measured Vimentin, which is increased in reactive astrocytes and is not normally expressed in mature astrocytes [6,12,15,40].
    • In control animals vimentin only present in ependymal lining of the ventricles.
    • At 6 weeks, sites around both types of devices had a compact sheath of vimentin-positive astrocytes 50-100um.
    • Seemed to be a plateau as with GFAP .. though it seems to label a slightly distinct set of cells.
  • Also labeled reactive microglia with ED1 [4,19,27,36].
  • Quote: These data indicate that device insertion promotes two responses-an early response that is proportional to device size and a sustained response that is independent of device size, geometry, and surface roughness. The early response may be associated with the amount of damage generated during insertion. The sustained response is more likely due to tissue-device interactions.


[0] Szarowski DH, Andersen MD, Retterer S, Spence AJ, Isaacson M, Craighead HG, Turner JN, Shain W, Brain responses to micro-machined silicon devices.Brain Res 983:1-2, 23-35 (2003 Sep 5)

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ref: notes-0 tags: James DeMarsh PHF tlh24 Cornell date: 08-21-2007 16:35 gmt revision:0 [head]