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Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task—accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots. 

Politicians have used the web and social media to circumvent the gatekeeping behavior of traditional mass media by directly communicating with supporters in their accounts. This paper is aimed at understanding communication strategies used by politicians and campaigns, focusing on the role of gender cues in their visual self-presentation and their impact on election outcomes. Previous research has discussed the importance of visual portrayals of leaders in campaigns. These studies, however, have been mainly based on manual coding and are limited in scale and scope. This paper aims to fill the research gap by introducing a multi task method that infers perceived gender-stereotypical visual traits from social media images. We analyze 77,861 photographs collected from the Facebook accounts of 554 US politicians who ran in the 2018 elections. Regression analyses discover the positive association of the masculinity trait for electoral outcomes. We also identify an empirical evidence that the effect of gender stereotypes could vary according to the gender and party combinations of the candidates in a race. In the intersectional analysis, we found that the win of female democrats against the same gendered opponent was positively correlated with the femininity trait score. This study provides methodological foundations and empirical contributions to the understanding of politicians’ campaign behaviors via photographs shared on social media and their relation to electoral success.

 

Abstract When an overhand knot tied in an elastic rod is tightened, it can undergo a sudden change in shape through snap buckling. In this article, we use a combination of discrete differential geometry (DDG)-based simulations and tabletop experiments to explore the onset of buckling as a function of knot topology, rod geometry, and friction. In our setup, two open ends of an overhand knot are slowly pulled apart, which leads to snap buckling in the knot loop. We call this phenomenon “inversion” since the loop appears to move dramatically from one side of the knot to the other. This inversion occurs due to the coupling of elastic energy between the braid (the portion of the knot in self-contact) and the loop (the portion of the knot with two ends connected to the braid). A numerical framework is implemented that combines discrete elastic rods with a constraint-based method for frictional contact to explore inversion in overhand knots. The numerical simulation robustly captures inversion in the knot and is found to be in good agreement with experimental results. In order to gain physical insight into the inversion process, we also develop a simplified model of the knot that does not require simulation of self-contact, which allows us to visualize the bifurcation that results in snap buckling. 

Abstract Wearing masks reduces the spread of COVID-19, but compliance with mask mandates varies across individuals, time, and space. Accurate and continuous measures of mask wearing, as well as other health-related behaviors, are important for public health policies. This article presents a novel approach to estimate mask wearing using geotagged Twitter image data from March through September, 2020 in the United States. We validate our measure using public opinion survey data and extend the analysis to investigate county-level differences in mask wearing. We find a strong association between mask mandates and mask wearing—an average increase of 20%. Moreover, this association is greatest in Republican-leaning counties. The findings have important implications for understanding how governmental policies shape and monitor citizen responses to public health crises. 

This paper introduces the Multimodal Chile & Venezuela Protest Event Dataset (MMCHIVED). MMCHIVED contains city-day event data using a new source of data, text and images shared on social media. These data enables the improved measurement of theoretically important variables such as protest size, protester and state violence, protester demographics, and emotions. In Venezuela, MMCHIVED records many more protests than existing datasets. In Chile, it records slightly more events than the Armed Conflict Location and Events Dataset (ACLED). These extra events are from small cities far from Caracas and Santiago, an improvement of coverage over datasets that rely on newspapers, and the paper confirms they are true positives. While MMCHIVED covers protest events in Chile and Venezuela, the approach used in the paper is generalizable and could generate protest event data in 107 countries containing 97.14% of global GDP and 82.7% of the world's population.