most citedUncovering socioeconomic gaps in mobility reduction during the COVID-19 pandemic using location data

37 citations · 70 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CV2020

Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation

Raul Gomez, Yahui Liu, Marco De Nadai +3

Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into…

cs.CV20208 cited

Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach

Yahui Liu, Marco De Nadai, Deng Cai +4

Manipulating visual attributes of images through human-written text is a very challenging task. On the one hand, models have to learn the manipulation without the ground truth of t…

physics.soc-ph202037 cited

Uncovering socioeconomic gaps in mobility reduction during the COVID-19 pandemic using location data

Samuel P. Fraiberger, Pablo Astudillo, Lorenzo Candeago +8

Using smartphone location data from Colombia, Mexico, and Indonesia, we investigate how non-pharmaceutical policy interventions intended to mitigate the spread of the COVID-19 pand…

cs.SI20201 cited

Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks

Gevorg Yeghikyan, Felix L. Opolka, Mirco Nanni +2

A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and constructi…

cs.CY2020

Mobile phone data and COVID-19: Missing an opportunity?

Nuria Oliver, Emmanuel Letouzé, Harald Sterly +21

This paper describes how mobile phone data can guide government and public health authorities in determining the best course of action to control the COVID-19 pandemic and in asses…

cs.CV2020

GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling

Yahui Liu, Marco De Nadai, Jian Yao +3

Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images. Recent studies have shown remarkable suc…