Institution
Google (United States)
Recent research
- Physics & SpaceOpen access
The fundamental indistinguishability of elementary particles remains an ontological enigma within the Standard Model. We propose a solution based on pure Geometrodynamics, hypothesizing that baryonic and leptonic matter are stable, non-evaporating Micro-Black Holes (Geons) formed...
- Physics & SpaceOpen access
The fundamental indistinguishability of elementary particles remains an ontological enigma within the Standard Model. We propose a solution based on pure Geometrodynamics, hypothesizing that baryonic and leptonic matter are stable, non-evaporating Micro-Black Holes (Geons) formed...
- BiologyOpen access
Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography
Abstract The difficulty of particle picking in cryo-electron tomography remains a barrier to routine in situ structure determination. Machine learning is well-suited to overcome this bottleneck with efficient algorithms that generalize across molecular species. To spur new algori...
- Society & EconomicsOpen access
Reviving, reproducing, and revisiting Axelrod’s second tournament
Abstract Direct reciprocity, typically studied using the Iterated Prisoner’s Dilemma ( IPD ), is central to explaining cooperation. In the 1980s Robert Axelrod ran two computer tournaments in which Tit for Tat ( TFT ) emerged as the winner. Yet the archival record is incomplete:...
- Climate & EnvironmentOpen access
Galactic Seed Engine Theory: A Mechanical Fluid-Dynamic Model of Galactic Evolution
The Galactic Seed Engine Theory (GSET) provides a placeholder-free alternative to mainstream cosmological models by replacing abstract patches—such as dark matter halos and gravitational singularities—with localized, self-regulating fluid-dynamic and magnetohydrodynamic (MHD) loo...
- Climate & EnvironmentOpen access
Galactic Seed Engine Theory: A Mechanical Fluid-Dynamic Model of Galactic Evolution
The Galactic Seed Engine Theory (GSET) provides a placeholder-free alternative to mainstream cosmological models by replacing abstract patches—such as dark matter halos and gravitational singularities—with localized, self-regulating fluid-dynamic and magnetohydrodynamic (MHD) loo...
- Climate & EnvironmentOpen access
The models that produce most existing forest height maps minimize mean individual prediction errors quantified by metrics such as Root Mean Square Error (RMSE). However, when predictor data do not explain all height variability in the training sample, this objective function lead...