Classical finite element methods discretize scalar functions using piecewise polynomials. Vector finite elements, such as those developed by Raviart-Thomas, Nédélec, and Brezzi-Douglas-Marini in the 1970s and 1980s, have since undergone significant theoretical advancements and found wide-ranging applications. Subsequently, Bossavit recognized that these finite element spaces are specific instances of Whitney’s discrete differential forms, which inspired the systematic development of Finite Element Exterior Calculus (FEEC). These discrete topological structures and patterns also emerge in fields like Topological Data Analysis.

In this talk, we present an overview of discrete and finite element differential forms motivated by applications from topological hydrodynamics, alongside recent advancements in tensorial finite elements. The Bernstein-Gelfand-Gelfand (BGG) sequences encode the algebraic and differential structures of tensorial problems, such as those encountered in solid mechanics, differential geometry, and general relativity. Discretization of the BGG sequences extends the periodic table of finite elements, originally developed for Whitney forms, to include Christiansen’s finite element interpretation of Regge calculus and various distributional finite elements for fluids and solids as special cases. This approach further illuminates connections between algebraic and geometric structures, generalized continuum models, finite elements, and discrete differential geometry.

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Migrant communities shape cross-border investment to their country of origin by reducing

information frictions and attitudes bias. Whether these benefits spill over to locals depends

not only on the size of the diaspora but also on the intensity of interaction between migrants

and locals in the host country. I present a theoretical model with agent-based simulation to

study how homophily between migrants and locals affects information and attitude diffusion

in the host society. I implement varying homophily preferences in a Schelling-style

segregation model and compare two diffusion processes: (i) a simple susceptible–infected

(SI) model for information diffusion; (ii) an adoption-threshold model for attitude diffusion.

For information diffusion, preliminary results indicate that higher homophily slows the

spread and confines diffusion within the migrant group, especially under high segregation. In

the attitude model, adoption varies non-monotonically with homophily. I also provide an

initial analysis of how these patterns interact with different migrant population shares and

seeding rules.

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Major depressive disorder (MDD) is a heterogeneous mental disorder. International guidelines present overall symptom severity as the key dimension for clinical characterisation. However, additional layers of heterogeneity may reside within severity levels related to how symptoms interact with one-another in a patient, called symptom dynamics. We investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same diagnosis and overall symptom severity. We show that examining symptom dynamics provides information about the person-specific psychopathological expression of patients beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may serve as a promising new dimension for clinical characterisation. Areas of opportunity are outlined for the field of precision psychiatry in uncovering disorder evolution patterns (e.g., spontaneous recovery; critical worsening) and the identification of granular treatment effects by moving toward investigations that leverage symptom dynamics as their foundation. Future work aimed at investigating the cascading dynamics underlying depression onset and maintenance using the large-scale (N > 5.5 million) CIPA Study are outlined. 

A 1977 conjecture of Erdős and Hajnal asserts that for every hereditary class of graphs not containing all graphs, every graph in the class has a polynomial-sized clique or stable set. Fox, Pach, and Suk and independently Chernikov, Starchenko, and Thomas asked whether this conjecture holds for every class of graphs of bounded VC-dimension. In joint work with Alex Scott and Paul Seymour, we resolved this question in the affirmative. The talk will introduce the Erdős–Hajnal conjecture and discuss some ideas behind the proof of the bounded VC-dimension case.