I will discuss how to place conformal boundary conditions on free higher derivative theories of scalars and spinors as well as the zoo of boundary renormalization group flows that connect the different boundary conditions.  Historically, there are connections to Poisson’s ratio and classical equations governing the bending of thin steel plates.  As these higher derivative theories are often invoked in the context of cosmology, there may be cosmological applications for the boundary conditions discussed here.
 

In the critical beta-splitting model of a random $n$-leaf rooted tree, clades (subtrees) are recursively split into sub-clades, and a clade of $m$ leaves is split into sub-clades containing $i$ and $m-i$ leaves with probabilities $\propto 1/(i(m-i))$. This model turns out to have interesting properties. There is a canonical embedding into a continuous-time model ($\operatorname{CTCS}(n)$). There is an inductive construction of $\operatorname{CTCS}(n)$ as $n$ increases, analogous to the stick-breaking constructions of the uniform random tree and its limit continuum random tree. We study the heights of leaves and the limit fringe distribution relative to a random leaf. In addition to familiar probabilistic methods, there are analytic methods (developed by co-author Boris Pittel), based on explicit recurrences, which often give more precise results. So this model provides an interesting concrete setting in which to compare and contrast these methods. Many open problems remain.
Preprints at https://arxiv.org/abs/2302.05066 and https://arxiv.org/abs/2303.02529

More than 30 year ago Jerrum studied the planted clique problem and proved that under worst-case initialization Metropolis fails to recover planted cliques of size $\ll n^{1/2}$ in the Erdős-Rényi graph $G(n,1/2)$. This result is classically cited in the literature of the problem, as the "first evidence" that finding planted cliques of size much smaller than square root $n$ is "algorithmically hard". Cliques of size $\gg n^{1/2}$ are easy to find using simple algorithms. In a recent work we show that the Metropolis process actually fails to find planted cliques under worst-case initialization for cliques up to size almost linear in $n$. Thus the algorithm fails well beyond the $\sqrt{n}$ "conjectured algorithmic threshold". We also prove that, for a large parameter regime, that the Metropolis process fails also under "natural initialization". Our results resolve some open questions posed by Jerrum in 1992. Based on joint work with Zongchen Chen and Iias Zadik.