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A Ferrers rook graph is a graph whose vertices correspond to the dots in a Ferrers diagram, and where two vertices are adjacent if they are in the same row or the same column.  We propose a conjectural formula for the gonality of Ferrers rook graphs, and prove this conjecture for a few infinite families of Ferrers diagrams.  We also prove the conjecture for all Ferrers diagrams F with |F| ≤ 8. 

Abstract. Wind-driven redistribution of snow on sea ice alters itstopography and microstructure, yet the impact of these processes on radarsignatures is poorly understood. Here, we examine the effects of snowredistribution over Arctic sea ice on radar waveforms and backscattersignatures obtained from a surface-based, fully polarimetric Ka- and Ku-bandradar at incidence angles between 0∘ (nadir) and 50∘.Two wind events in November 2019 during the Multidisciplinary drifting Observatory forthe Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- andKu-band radar waveforms and backscatter coefficients at nadir are observed,coincident with surface topography changes measured by a terrestrial laserscanner. At both frequencies, redistribution caused snow densification atthe surface and the uppermost layers, increasing the scattering at theair–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previousair–snow interfaces, buried beneath newly deposited snow. The additionalscattering from previous air–snow interfaces could therefore affect therange retrieved from Ka- and Ku-band satellite altimeters. With increasingincidence angles, the relative scattering contribution of the air–snowinterface decreases, and the snow–sea ice interface scattering increases.Relative to pre-wind event conditions, azimuthally averaged backscatter atnadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scanarea at all incidence angles and polarizations, in response to increasingwind speed and changes in wind direction. Our results show that snowredistribution and wind compaction need to be accounted for to interpretairborne and satellite radar measurements of snow-covered sea ice. 

Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that influence both potential causes and potential effects. Unmeasured or latent confounders can bias causal estimates, and this has motivated interest in measuring potential confounders from observed text. For example, an individual’s entire history of social media posts or the content of a news article could provide a rich measurement of multiple confounders.Yet, methods and applications for this problem are scattered across different communities and evaluation practices are inconsistent.This review is the first to gather and categorize these examples and provide a guide to data-processing and evaluation decisions. Despite increased attention on adjusting for confounding using text, there are still many open problems, which we highlight in this paper.