Special Interest Groups and the Australia-United States Free Trade Agreement

In the political economy model of Grossman and Helpman (1995), two incumbent governments attempt to negotiate a free trade agreement (FTA), while special interest groups in each country influence negotiations by offering financial contributions to their governments. As a consequence, a set of politically sensitive industries is excluded from the proposed FTA. Using the empirical methodology of Gawande, Sanguinetti, and Bohara (2001), this paper shows that the Grossman-Helpman (1995) model successfully predicts the set of excluded industries for the recently implemented Australia-United States FTA. It is also shown that the set of exclusions favours Australian interest groups, which could indicate that the gains from the FTA are lower for the government of Australia than for the government of the United States.

Nonordinary edge criticaliy of two-dimensional quantum critical magnets

Based on large-scale quantum Monte Carlo simulations, we examine the correlations along the edges of two-dimensional semi-infinite quantum critical Heisenberg spin-$1/2$ systems. In particular, we consider coupled quantum spin-dimer systems at their bulk quantum critical points, including the columnar-dimer model and the plaquette-square lattice. The alignment of the edge spins strongly affects these correlations and the corresponding scaling exponents, with remarkably similar values obtained for various quantum spin-dimer systems. We furthermore observe subtle effects on the scaling behavior from perturbing the edge spins that exhibit the genuine quantum nature of these edge states. Our observations furthermore challenge recent attempts that relate the edge spin criticality to the presence of symmetry-protected topological phases in such quantum spin systems.Comment: 9 pages, 11 figures, v2: as publishe

Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature

While Graph Neural Networks (GNNs) have been successfully leveraged for learning on graph-structured data across domains, several potential pitfalls have been described recently. Those include the inability to accurately leverage information encoded in long-range connections (over-squashing), as well as difficulties distinguishing the learned representations of nearby nodes with growing network depth (over-smoothing). An effective way to characterize both effects is discrete curvature: Long-range connections that underlie over-squashing effects have low curvature, whereas edges that contribute to over-smoothing have high curvature. This observation has given rise to rewiring techniques, which add or remove edges to mitigate over-smoothing and over-squashing. Several rewiring approaches utilizing graph characteristics, such as curvature or the spectrum of the graph Laplacian, have been proposed. However, existing methods, especially those based on curvature, often require expensive subroutines and careful hyperparameter tuning, which limits their applicability to large-scale graphs. Here we propose a rewiring technique based on Augmented Forman-Ricci curvature (AFRC), a scalable curvature notation, which can be computed in linear time. We prove that AFRC effectively characterizes over-smoothing and over-squashing effects in message-passing GNNs. We complement our theoretical results with experiments, which demonstrate that the proposed approach achieves state-of-the-art performance while significantly reducing the computational cost in comparison with other methods. Utilizing fundamental properties of discrete curvature, we propose effective heuristics for hyperparameters in curvature-based rewiring, which avoids expensive hyperparameter searches, further improving the scalability of the proposed approach

Macroalgal distribution shifts during the climate change induced glacier retreat in Potter Cove, West Antarctic Peninsula, 1993-2016

The West Antarctic Peninsula is one of the fastest changing regions on earth due to global warming. Consequent retreat of Fourcade Glacier in Potter Cove (PC), King George Is-land, released 1.5 km2 of newly ice-free water areas in the last 60 years. Macroalgae, important primary producers in the benthic Antarctic ecosystem, were evidenced to col-onize some of these areas. To reconstruct the spatial process of this shift, macroalgal transect data sets, first recorded in 1993 and last in 2016, as well as environmental pre-dictors are available owing to many years of field studies in PC. Species Distribution Models (SDMs) can statistically link species samples with raster predictors to project distribution maps. Macroalgal data is therefore prepared for modeling and grouped in three time steps, 1993-1996, 2008-2012 and 2016. Physical predictors for macroalgal dis-tribution used in this thesis are: Probability of hard substrate occurrence, suspended par-ticulate matter, bathymetry and slope. For each time step, SDMs are calculated for the species H. grandifolius, the grouped Desmarestia genus, remaining Phaeophyceae (brown algae) and Rhodophyceae (red algae). Community Maps, generated by classifying model results, are presented for the three time steps. They reveal information on species-richness and composition. Additionally a change raster is calculated for area gain and loss during a period between two time steps. In 1993-1996, 2.77 km2 of the study area was populated by algae. This area increased by 1.38 km2 to 4.15 km2 in 2016. 2008-2012 results confirm this growth. 0.5 km2 of this gain is located in areas, which were covered by glacier ice in 1956. Species of the class Rhodophyceae show as pioneers in exploring new habitable areas. Complexity and vertical distribution of macroalgal community is reduced in newly ice-free water areas near the glacier due to increased sediment input. Keywords: Macroalgae, Habitat shift, Species Distribution Models, Community maps, Change detection, Glacier retreat, Potter Cove, Antarctic Peninsul