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David Gillman

Publications and source records attributed to David Gillman.

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Slow Convergence of Ising and Spin Glass Models with Well-Separated Frustrated Vertices

The ferromagnetic Ising model on an $n\times n$ square lattice region $\Lambda$ with mixed boundary conditions can exhibit a phase transition as temperature varies. For this spin system, if we fix the spins on the top and bottom sides of the square to be $+$ and the left and right sides to be~$-$, a standard Peierls argument based on energy shows that below some critical temperature~$t_c$, any local Markov chain $\mathcal{M}$ requires time exponential in $n$ to mix. Spin glasses are magnetic alloys that generalize the Ising model by specifying the strength of nearest neighbor interactions on the lattice, including whether they are ferromagnetic or antiferromagnetic. Whenever a face of the lattice is bounded by an odd number of edges with ferromagnetic interactions, the face is considered {\it frustrated} because the local competing objectives cannot be simultaneously satisfied. We consider spin glasses with exactly four well-separated frustrated faces that are symmetric around the center of the lattice region under $90$ degree rotations. We show that local Markov chains require exponential time for all spin glasses in this class. This argument extends to the ferromagnetic Ising model with mixed boundary conditions described above, which behaves like spin glasses with frustrated faces on the boundary. The standard Peierls argument breaks down when the frustrated faces are on the interior of $\Lambda$ and yields weaker results when they are on the boundary of $\Lambda$ but not near the corners. We show that there is a universal temperature $T$ below which $\mathcal{M}$ will be slow for all spin glasses with four well-separated frustrated faces. Our argument shows that there is an exponentially small cut indicated by the {\it free energy}, carefully exploiting both entropy and energy to establish a small bottleneck in the state space to establish slow mixing.

cs.DM

Leveraging Minute-by-Minute Soccer Match Event Data to Adjust Team's Offensive Production for Game Context

In soccer, game context can result in skewing offensive statistics in ways that might misrepresent how well a team has played. For instance, in England's 1-2 loss to France in the 2022 FIFA World Cup quarterfinal, England attempted considerably more shots (16 to France's 8) and more corners (5 to 2), potentially suggesting they played better despite the loss. However, these statistics were largely accumulated when France was ahead and more willing to concede offensive initiative to England. To explore how game context influences offensive performance, we analyze minute-by-minute event-sequenced match data from 15 seasons across five major European leagues. Using count-response Generalized Additive Modeling, we consider features such as score and red card differential, home/away status, pre-match win probabilities, and game minute. Moreover, we leverage interaction terms to test several intuitive hypotheses about how these features might cooperate in explaining offensive production. The selected model is then applied to project offensive statistics onto a standardized "common denominator" scenario: a tied home game with even men on both sides. The adjusted numbers - in contrast to regular game totals that disregard game context - offer a more contextualized comparison, reducing the likelihood of misrepresenting the relative quality of play.

stat.AP

Splittable Spanning Trees and Balanced Forests in Dense Random Graphs

We consider the probability that a spanning tree chosen uniformly at random from a graph can be partitioned into a fixed number $k$ of trees of equal size by removing $k-1$ edges. In that case, the spanning tree is called {\em splittable}. Splittable spanning trees are useful in algorithms for sampling {\em balanced forests}, forests whose components are of equal size, and for sampling partitions of a graph into components of equal size, with applications in redistricting, network algorithms, and image decomposition. Cannon et al.~recently showed that spanning trees on grid and grid-like graphs on $n$ vertices are splittable into $k$ equal sized components with probability at least $n^{-2k}$, leading to the first rigorous sampling algorithm for balanced forests in any class of graphs. Focusing on the complementary case of dense random graphs, we show that random spanning trees have inverse polynomial probability of being splittable; specifically, a random spanning tree is splittable with probability at least $n^{(-k/2)}$ for both the $G(n,p)$ and $G(n,m)$ models when $p = \Omega(1/\log n)$, giving the first dense class of graphs where partitions of equal size can be sampled efficiently. In addition, we present an infinite family of graphs with properties that have been conjectured to ensure splittability (i.e., Hamiltonian subgraphs of the triangular lattice) and prove that random spanning trees are not splittable with more than exponentially small probability. As a consequence, we show that a family of widely-used Markov chain algorithms for sampling equal-size partitions will fail on this family of graphs if their state spaces are restricted to equal-size partitions. Moreover, we show these algorithms will be inefficient if their state spaces are generalized to include any unbalanced partitions, suggesting barriers for sampling balanced partitions in sparse graphs.

cs.DS

Predicting readmission risk from doctors' notes

We develop a model using deep learning techniques and natural language processing on unstructured text from medical records to predict hospital-wide $30$-day unplanned readmission, with c-statistic $.70$. Our model is constructed to allow physicians to interpret the significant features for prediction.

stat.ML

Untwisting Heegaard diagrams in 3-space

We show that if $V^3$ is a handlebody in $\R^3$, with curves $J_1, ..., J_g \subset \partial V$ which are the attaching curves for a Heegaard splitting of a homology sphere, then there exists a homeomorphism $h\colon V \to V$ so that each of the curves $h(J_i)$ bounds an orientable surface in $\R^3 - int(V)$. This leads to a new characterization of homology spheres and also contradicts a remark of Haken (in 1969) regarding the Poincaré homology sphere.

math.GT