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Inflation Expectations and Political Polarization: Evidence from the Cooperative Election Study
Get PDFUsing a unique, nationally representative survey from the 2022 midterm elections, we investigate the partisan divide in beliefs about inflation and monetary policy. We find that party identity is predictive of inflation forecasts even after conditioning on beliefs about both past inflation and the Federal Reserve’s long-run inflation target. Partisan forecast differences are driven by respondents who express low generalized trust in others and have a high degree of political knowledge; high-trust and low- knowledge partisans make similar forecasts all else equal. This finding is consistent with the literature in political psychology that examines the endorsement of conspiracy theories and political misinformation. We argue that the partisan divide in consumer inflation surveys is consistent with strategic responses by partisans
Disordered Flanking Regions of Viral Short Linear Motifs potentially enhance Antibody Recognition
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Metal-Organic Frameworks for Single-Atom Heterogeneous Photocatalysis
No full textMetal-organic frameworks (MOFs) are a class of materials composed of organic and inorganic subunits linked to create a network solid. Due to their ultrahigh porosity and incredible tunability, MOFs have been an immense field of research over the past twenty years. Alongside work in gas separation and storage, these projects have found particular success in catalytic applications. With the goal of improving the efficiency and lifespan of single-atom metal sites, numerous examples of MOF-supported catalysts have been developed. With carbon emissions a particular pressing concern today, one goal has been the conversion of CO2 to higher value small molecules using the energy of the sun. By supporting catalytic sites and photosensitizers within the pores of MOFs, photoexcited states have been shown to facilitate the transfer of electrons and protons to the catalytic site to reduce CO2 to carbon monoxide and water. This work aims to explore current literature that is pursuing an artificial photosynthetic system utilizing MOFs that would enable a new source of chemical feedstocks or a renewable cycle of fuel generation
Incorporating Nonspatial Policing Information into Spatial Models
No full textThe incorporation of nonspatial variables into inherently spatial models is a challenge which has become increasingly relevant in recent years, especially in the field of policing. Experts in the field have proposed multiple ways of dealing with the combination of spatial and nonspatial data into spatial models, such as the k-Groups model developed by Quick et al. [2015] and the Two-Stage model proposed by Kelling and Haran [2022]. In this paper, we aim to establish a baseline understanding of the field of spatial statistics, explain existing methods of incorporating nonspatial data into spatial models, and compare and contrast the two selected strategies. To do so, we utilize a police use of force data set from Minneapolis, Minnesota. We use predictive Gaussian Processes to improve computational speeds and Markov Chain Monte Carlo Bayesian computing methods to obtain estimates for model parameters. We then estimate the intensity of use of force incidents across Minneapolis. The k-Groups method models the intensity of use of force incidents as a function of spatial variables differentiating by groups formed with combinations of categorical nonspatial variables. The Two-Stage method models (1) the spatial intensity of use of force events and (2) the probability of our mark (weapon or no weapon) influenced by a combination of our nonspatial variables, spatial variables, and their interaction. Finally, we discuss the benefits and drawbacks of each method
Safety First: The Effects of Hyperparameter Tuning in Safe Reinforcement Learning
No full textIn this paper, we study and report on the differences between two conventional reinforcement learning (RL) algorithms and their respective safety constrained versions. Specifically, we focus on two simulated environments: one in which a moving, car-like agent learns to minimize travel time toward some destination and another in which a humanoid agent learns to run. Both of these environments originate from the Farama Foundation’s Gymnasium, and we accessed them through OmniSafe, an infrastructural framework for safe RL research. While baseline RL algorithms tend to incentivize agents to pursue riskier strategies to maximize reward, we found that constrained RL algorithms still allow for learning while simultaneously constraining safety violations. We performed experiments in which we changed various hyperparameters to determine which hyperparameters most positively impacted the performance of the algorithms. The most impactful hyperparameter was the number of steps simulated per epoch. By increasing this value, we observed a drastic improvement in the performance of our safety algorithms
Believe, Advocate and Uplift The Black Maternal Health Crisis in Minnesota’s Hennepin and Ramsey Counties
No full textNarratives relaying the birthing experiences of Sha-Asia Washington, Tori Bowie, Serena Williams, Mariah Carey, as well as my sister underscore the shared experiences of Black women navigating maternal health systems. A long-standing pattern of structural racism, characterized by systematic biases in public policies, institutional practices, and cultural depictions that perpetuate racial disparities, has created significant barriers to health for Black mothers compared to white mothers. This structural racism plays a pivotal role in the heightened rates of maternal reduced life expectancy observed among Black mothers