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Public health beliefs predict support for climate action
Person holding phone checking air pollution levels outside wearing a mask.

Image: iStock/humonia

Public health beliefs predict support for climate action

New research from the Annenberg Public Policy Center examines the relationship between health-related beliefs about climate change and support for climate policy proposals.

From the Annenberg Public Policy Center

Redlining and rentals
An aerial view of the Park Forest housing development outside of Chicago in the 1950s.

Aerial view of a Park Forest neighborhood in 1952 that captures the neat rows of homes that characterized the post-World War II housing boom in the planned community.

(Image: Owen Kent via the Chicago Historical Society)

Redlining and rentals

Historian Brent Cebul in the School of Arts & Sciences is working on a new digital mapping project looking at the impact of Federal Housing Administration policies on the availability of affordable rental housing post-World War II. 

Kristen de Groot

The anthropology of plastics in India
An image of people picking through a dump with a handful of skyscrapers along the horizon

Children inspect plastic waste in a scrapyard with skyscrapers on the horizon line.

(Image: Sidharth Chitalia)

The anthropology of plastics in India

Doctoral candidate Adwaita Banerjee uses ethnographic research to document the ecological transition of the Deonar dumping ground, where thousands of Dalits and Muslim migrants mine the area for plastic that can be resold and recycled.

Kristina García

A first, physical system to learn nonlinear tasks without a traditional computer processor
Contrastive local learning network.

University of Pennsylvania physics and engineering researchers have created a contrastive local learning network, an analog system that is fast, low-power, scalable, and able to learn nonlinear tasks.

(Image: Erica Moser)

A first, physical system to learn nonlinear tasks without a traditional computer processor

Physics and engineering researchers created a contrastive local learning network that is fast, low-power, and scalable.