Efficient Super-Resolution of Near-Surface Climate Modeling Using the Fourier Neural Operator

Downscaling methods are critical in efficiently generating high-resolution atmospheric data. However, state-of-the-art statistical or dynamical downscaling techniques either suffer from the high computational cost of running a physical model or require high-resolution data to develop a downscaling tool. Here, we demonstrate a recently proposed zero-shot super-resolution method, the Fourier neural operator (FNO), to efficiently perform downscaling without the need for high-resolution data. Because the FNO learns dynamics in Fourier space, FNO is a resolution-invariant emulator; it can be trained at a coarse resolution and produces emulation at any high resolution. We applied FNO to downscale a 4-km resolution Weather Research and Forecasting (WRF) Model simulation of near-surface heat-related variables over the Great Lakes region. The FNO is driven by the atmospheric forcings and topographic features used in the WRF model at the same resolution. We incorporated a physics-constrained loss in FNO by using the Clausius–Clapeyron relation to better constrain the relations among the emulated states. Trained on merely 600 WRF snapshots at 4-km resolution, the FNO shows comparable performance with a widely-used convolutional network, U-Net, achieving averaged modified Kling–Gupta Efficiency of 0.88 and 0.94 on the test data set for temperature and pressure, respectively. We then employed the FNO to produce 1-km emulations to reproduce the fine climate features. Further, by taking the WRF simulation as ground truth, we show consistent performances at the two resolutions, suggesting the reliability of FNO in producing high-resolution dynamics. Our study demonstrates the potential of using FNO for zero-shot super-resolution in generating first-order estimation on atmospheric modeling

GAFlow: Incorporating Gaussian Attention into Optical Flow

Optical flow, or the estimation of motion fields from image sequences, is one of the fundamental problems in computer vision. Unlike most pixel-wise tasks that aim at achieving consistent representations of the same category, optical flow raises extra demands for obtaining local discrimination and smoothness, which yet is not fully explored by existing approaches. In this paper, we push Gaussian Attention (GA) into the optical flow models to accentuate local properties during representation learning and enforce the motion affinity during matching. Specifically, we introduce a novel Gaussian-Constrained Layer (GCL) which can be easily plugged into existing Transformer blocks to highlight the local neighborhood that contains fine-grained structural information. Moreover, for reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM) which not only inherits properties from Gaussian distribution to instinctively revolve around the neighbor fields of each point but also is empowered to put the emphasis on contextually related regions during matching. Our fully-equipped model, namely Gaussian Attention Flow network (GAFlow), naturally incorporates a series of novel Gaussian-based modules into the conventional optical flow framework for reliable motion analysis. Extensive experiments on standard optical flow datasets consistently demonstrate the exceptional performance of the proposed approach in terms of both generalization ability evaluation and online benchmark testing. Code is available at https://github.com/LA30/GAFlow.Comment: To appear in ICCV-202

Things versus People: Gender Differences in Vocational Interests and in Occupational Preferences

Occupational choices remain strongly segregated by gender, for reasons not yet fully understood. In this paper, we use detailed information on the cognitive requirements in 130 distinct learnable occupations in the Swiss apprenticeship system to describe the broad job content in these occupations along the things-versus-people dimension. We first show that our occupational classification along this dimension closely aligns with actual job tasks, taken from an independent data source on employers job advertisements. We then document that female apprentices tend to choose occupations that are oriented towards working with people, while male apprentices tend to favor occupations that involve working with things. In fact, our analysis suggests that this variable is by any statistical measure among the most important proximate predictors of occupational gender segregation. In a further step, we replicate this finding using individual-level data on both occupational aspirations and actual occupational choices for a sample of adolescents at the start of 8th grade and the end of 9th grade, respectively. Using these additional data, we finally show that the gender difference in occupational preferences is largely independent of a large number of individual, parental, and regional controls

Oblique strategies for ambient journalism

Alfred Hermida recently posited ‘ambient journalism’ as a new framework for para- and professional journalists, who use social networks like Twitter for story sources, and as a news delivery platform. Beginning with this framework, this article explores the following questions: How does Hermida define ‘ambient journalism’ and what is its significance? Are there alternative definitions? What lessons do current platforms provide for the design of future, real-time platforms that ‘ambient journalists’ might use? What lessons does the work of Brian Eno provide–the musician and producer who coined the term ‘ambient music’ over three decades ago? My aim here is to formulate an alternative definition of ambient journalism that emphasises craft, skills acquisition, and the mental models of professional journalists, which are the foundations more generally for journalism practices. Rather than Hermida’s participatory media context I emphasise ‘institutional adaptiveness’: how journalists and newsrooms in media institutions rely on craft and skills, and how emerging platforms can augment these foundations, rather than replace them