Our Episode 4 guest, Leidy Klotz, is a Professor at the University of Virginia. He studies the science of design: how we transform things from how they are – to how we want them to be. Leidy wants to apply his work outside of academia. He wants address climate change and systemic inequality, Leidy also works directly with organizations including the World Bank.
Stream and subscribe at: sunelehmann.com
This episode’s guest is Dirk Brockmann. Dirk is a physicist and complex systems researcher. He’s a professor at the Department of Biology, Humboldt University of Berlin and the Robert Koch Institute, Berlin. Berfore returning to his native Germany, he was a professor at Northwestern University.
Read the full article at: sunelehmann.com
Martin Zumaya, Rita Guerrero, Eduardo Islas, Omar Pineda, Carlos Gershenson, Gerardo Iñiguez, Carlos Pineda
Mexico has kept electronic records of all taxable transactions since 2014. Anonymized data collected by the Mexican federal government comprises more than 80 million contributors (individuals and companies) and almost 7 billion monthly-aggregations of invoices among contributors between January 2015 and December 2018. This data includes a list of almost ten thousand contributors already identified as tax evaders, due to their activities fabricating invoices for non-existing products or services so that recipients can evade taxes. Harnessing this extensive dataset, we build monthly and yearly temporal networks where nodes are contributors and directed links are invoices produced in a given time slice. Exploring the properties of the network neighborhoods around tax evaders, we show that their interaction patterns differ from those of the majority of contributors. In particular, invoicing loops between tax evaders and their clients are over-represented. With this insight, we use two machine-learning methods to classify other contributors as suspects of tax evasion: deep neural networks and random forests. We train each method with a portion of the tax evader list and test it with the rest, obtaining more than 0.9 accuracy with both methods. By using the complete dataset of contributors, each method classifies more than 100 thousand suspects of tax evasion, with more than 40 thousand suspects classified by both methods. We further reduce the number of suspects by focusing on those with a short network distance from known tax evaders. We thus obtain a list of highly suspicious contributors sorted by the amount of evaded tax, valuable information for the authorities to further investigate illegal tax activity in Mexico. With our methods, we estimate previously undetected tax evasion in the order of $10 billion USD per year by about 10 thousand contributors.
Read the full article at: arxiv.org
CCS2021 is the flagship conference on Complex Systems promoted by the CSS. It brings under one umbrella a wide variety of leading researchers, practitioners and stakeholders with a direct interest in Complex Systems, from Physics to Computer Science, Biology, Social Sciences, Economics, and Technological and Communication Networks, among others.
Deadline for abstract submission: May 20, 2021
Notification to authors: June 20, 2021
Early Registration: July 15, 2021
Dates of the Conference: October 25-29, 2021
Link to submit: https://easychair.org/cfp/CCS2021
More info at: ccs2021.univ-lyon1.fr
Tallinn University seeks to hire two Research Fellows in Cultural Data Analytics, particularly in (1) Audiovisual Machine Learning, and (2) Cultural Dynamics, to work on ambitious, high-impact research at the CUDAN ERA Chair (chair holder Prof. Dr. Maximilian Schich). Start of the employment contract: 01.07.- 01.09.2021, duration of the contract is up to 31.12.2023. Deadline of submitting the application documents is 31st May, 2021.
Read the full article at: cudan.tlu.ee