Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The Rise of the Creative Class

Inclusion and democracy.

Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

Graph Signal Processing: Overview, Challenges, and Applications

Sampson, Robert J. 2012. Great American city: Chicago and the enduring neighborhood effect. Chicago: University of Chicago Press. ISBN-13: 9780226734569. pp. 552, $27.50 cloth. Robert J. Sampson’s ...

Great American city: Chicago and the enduring neighborhood effect

Measuring Sensitivity to Social Distancing Behavior during the COVID-19 Pandemic

Big Buildings and Big Data: Do Energy Disclosure Policies Impact Energy Use over Time?

https://www.nature.com/articles/s41598-023-29283-8.pdf

Author Correction: Measuring sensitivity to social distancing behavior during the COVID-19 pandemic

New streams of data enable us to associate physical objects with rich multi-dimensional data on the urban environment. This study presents how open data integration can contribute to deeper insights into urban ecology. We analyze street trees in New York City (NYC) with cross-domain data integration methods by combining crowd-sourced tree census data - which includes geolocation, species, size, and condition of each street tree - with pollen activity and allergen severity, neighborhood demographics, and spatial-temporal data on tree condition from NYC 311 complaints. We further integrate historical data on neighborhood asthma hospitalization rates by Zip Code and in-situ air quality monitoring data (PM 2.5) to investigate how street trees impact local air quality and the prevalence of respiratory illnesses. The results indicate although the number of trees contributes to better air quality, species with severe allergens may increase local asthma hospitalization rates in vulnerable populations.

Constantine E. Kontokosta

Papers

Measuring Sensitivity to Social Distancing Behavior during the COVID-19 Pandemic

Big Buildings and Big Data: Do Energy Disclosure Policies Impact Energy Use over Time?

Author Correction: Measuring sensitivity to social distancing behavior during the COVID-19 pandemic

Measuring the Impact of Urban Street Trees on Air Quality and Respiratory Illness.