Living Labs Roundtable / NYC Climate Week 2020/ Part 2 of 2

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The Netherlands' Consulate General in New York hosted a webinar September 24 2020. Featured presenters included Saskia Muller of Buiksloterham Circular Lab in Amsterdam and Prof. Masoud Ghandehari of New York University Tandon School of Engineering and the Center for Urban Science & Progress. Professor Ghandehari's presentation is included here.

Living Labs
NL+US Virtual Meeting
September 24, 2020
Masoud Ghandehari
New York University
www.masoudghandehari.com

Data Domain
Phenomenology
Models
First Principles
Living Labs
Resolution, Coverage, Diversity
Integration
Platforms
Interdisciplinary
Stakeholders
Insights
Insights
Analytics
Platforms

Complex&
Interdependent
Expected
Outcomes
Natural Systems Constructed Systems Human Systems
Predictive Analytics Scalable PlatformsActionable Insights
courtesy Brandignity
CHALLENGES & POTENTIALS

Some Living Labs Articles
in past 3 years

Surface temperatures in New York City:
Geospatial data enables the accurate
prediction of radiative heat transfer
Ghandehari M. Emig T., Aghamohamadnia M.
NATURE Scientific Reports, 8, pp. 1-10, 2018

Location based assessment of burden of disease
Jay Street Metrotech Subway Station
Hourly count of number of people and particulate matter concentration
People
Pollution
Pedestrian count and particulate matter concentration
at one subway exit in NYC
Ling Yang, Masoud Ghandehari, Giancarlo Fortini, WengFeng Li
People-Centric Cognitive Internet of Things for the Quantitative Analysis
of Environmental Exposure, 2017

Published 2019
Corresponding Author Masoud Ghandehari, NYU Tandon
Andrew Caplin, Department of Economics, NYU Arts and Science
George Thorsten, Department of Environmental Health, NYU Langone
Advancing Environmental Exposure Assessment
Science to Benefit Society

Some federally funded Projects
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110-140 mph
Integrated Socio-technical Framework to Evaluate and Enhance Resilience
in Islanded Communities
A Critical Resilient Infrastructure Systems Protection Project (CRISP 2.0)

Inspecting dynamics of networks opens up a new dimension in the understanding or mechanisms behind real-world systems. Involving the time factor may help identifying previously hidden (or otherwise hard to recognize) phenomenon and/or patterns compared to static analysis, like individuals periodically changing between groups within a community. Concentrating on edge dynamics, we defined a set of dynamic network models with various rules (including creating new and relinking edges randomly, by using assortative mixing or preferential attachment strategies) to analyize the evolution of different network properties. Starting from an initial network created by classical network models (like the Erdos-Renyi model) we examined the evolution of basic structural network properties (including density, clustering, average path length, number of components, degree distribution and betweennes centralities). The structure of the snapshot network (i.e., the network that is actually observed in a given instant of time) and the cumulative network (i.e., the network that is constructed by collecting and aggregating several samples of snapshot networks over a period of time) is inherently different, but we also found that certain properties have a strong dependence on the sampling windows length: we made experiments through computer simulations with various aggregation time windows and found that it has a great impact on the results. In our presentation, we would like to briefly introduce the key findings of our previous results regarding to the elementary dynamic network models, and compare the theoretical results obtained from evaluating different empirical data sets. The selected data sets used for the comparison include political event data compiled from English-language news reports and a dataset created to analyze internet-mediated sexual encounters in Brazil.

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一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单

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一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单

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Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Subhajit Sahu

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

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SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation" Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults Description: Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project. Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas

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Living Labs Roundtable / NYC Climate Week 2020/ Part 2 of 2

1. Living Labs NL+US Virtual Meeting September 24, 2020 Masoud Ghandehari New York University www.masoudghandehari.com

2. Data Domain Phenomenology Models First Principles Living Labs Resolution, Coverage, Diversity Integration Platforms Interdisciplinary Stakeholders Insights Insights Analytics Platforms

3. Complex& Interdependent Expected Outcomes Natural Systems Constructed Systems Human Systems Predictive Analytics Scalable PlatformsActionable Insights courtesy Brandignity CHALLENGES & POTENTIALS

4. Some Living Labs Articles in past 3 years

5. NATURE Scientific Report June 2017

6. Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer Ghandehari M. Emig T., Aghamohamadnia M. NATURE Scientific Reports, 8, pp. 1-10, 2018

7. Location based assessment of burden of disease Jay Street Metrotech Subway Station Hourly count of number of people and particulate matter concentration People Pollution Pedestrian count and particulate matter concentration at one subway exit in NYC Ling Yang, Masoud Ghandehari, Giancarlo Fortini, WengFeng Li People-Centric Cognitive Internet of Things for the Quantitative Analysis of Environmental Exposure, 2017

8. Published 2019 Corresponding Author Masoud Ghandehari, NYU Tandon Andrew Caplin, Department of Economics, NYU Arts and Science George Thorsten, Department of Environmental Health, NYU Langone Advancing Environmental Exposure Assessment Science to Benefit Society

9. Some federally funded Projects in past 3 years

10.

11. 110-140 mph Integrated Socio-technical Framework to Evaluate and Enhance Resilience in Islanded Communities A Critical Resilient Infrastructure Systems Protection Project (CRISP 2.0)

12. Governors Island

13.

14. Living Labs

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