Brian Brubach, Assistant Professor in the Computer Science Department at Wellesley College, joins us today to discuss his work “Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives".
Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives
by Brian Brubach, Aravind Srinivasan, and Shawn Zhao
Aside from victory questions like “can black force a checkmate on white in 5 moves?” many novel questions can be asked about a game of chess. Some questions are trivial (e.g. “How many pieces does white have?") while more computationally challenging questions can contribute interesting results in computational complexity theory.
In this episode, Josh Brunner, Master's student in Theoretical Computer Science at MIT, joins us to discuss his recent paper Complexity of Retrograde and Helpmate Chess Problems: Even Cooperative Chess is Hard.
Complexity of Retrograde and Helpmate Chess Problems: Even Cooperative Chess is Hard
by Josh Brunner, Erik D. Demaine, Dylan Hendrickson, and Juilian Wellman
1x1 Rush Hour With Fixed Blocks is PSPACE Complete
by Josh Brunner, Lily Chung, Erik D. Demaine, Dylan Hendrickson, Adam Hesterberg, Adam Suhl, Avi Zeff
Eil Goldweber, a graduate student at the University of Michigan, comes on today to share his work in applying formal verification to systems and a modification to the Paxos protocol discussed in the paper Significance on Consecutive Ballots in Paxos.
Works Mentioned :
Previous Episode on Paxos
On the Significance on Consecutive Ballots in Paxos by: Eli Goldweber, Nuda Zhang, and Manos Kapritsos
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Today on the show we have Adrian Martin, a Postdoctorial researcher from the Univeristy of Pompeu Fabra in Barcelona, Spain. He comes on the show today to discuss his research from the paper “Convolutional Neural Networks can be Decieved by Visual Illusions.”
Workes Mentioned in Paper:
“Convolutional Neural Networks can be Decieved by Visual Illusions.” by Alexander Gomez-Villa, Adrian Martin, Javier Vazquez-Corral, and Marcelo Bertalmio
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Have you ever wanted to hear what an earthquake sounds like? Today on the show we have Omkar Ranadive, Computer Science Masters student at NorthWestern University, who collaborates with Suzan van der Lee, an Earth and Planetary Sciences professor at Northwestern University, on the crowd-sourcing project Earthquake Detective.
Paper: Applying Machine Learning to Crowd-sourced Data from Earthquake Detective
by Omkar Ranadive, Suzan van der Lee, Vivan Tang, and Kevin Chao
Earthquake Detective: https://www.zooniverse.org/projects/vivitang/earthquake-detective
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Byzantine fault tolerance (BFT) is a desirable property in a distributed computing environment. BFT means the system can survive the loss of nodes and nodes becoming unreliable. There are many different protocols for achieving BFT, though not all options can scale to large network sizes.
Kyle shared some initial reactions to the announcement about Alpha Fold 2's celebrated performance in the CASP14 prediction. By many accounts, this exciting result means protein folding is now a solved problem.
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Above all, everyone wants voting to be fair. What does fair mean and how can we measure it? Kenneth Arrow posited a simple set of conditions that one would certainly desire in a voting system. For example, unanimity - if everyone picks candidate A, then A should win!
Yet surprisingly, under a few basic assumptions, this theorem demonstrates that no voting system exists which can satisfy all the criteria.
This episode is a discussion about the structure of the proof and some of its implications.
Works MentionedA Difficulty in the Concept of Social Welfare by Kenneth J. Arrow Three Brief Proofs of Arrows Impossibility Theorem by John Geanakoplos Thank you to our sponsors! Better Help is much more affordable than traditional offline counseling, and financial aid is available! Get started in less than 24 hours. Data Skeptic listeners get 10% off your first month when you visit: betterhelp.com/dataskeptic Let Springboard School of Data jumpstart your data career! With 100% online and remote schooling, supported by a vast network of professional mentors with a tuition-back guarantee, you can't go wrong. Up to twenty $500 scholarships will be awarded to Data Skeptic listeners. Check them out at springboard.com/dataskeptic and enroll using code: DATASK
As the COVID-19 pandemic continues, the public (or at least those with Twitter accounts) are sharing their personal opinions about mask-wearing via Twitter. What does this data tell us about public opinion? How does it vary by demographic? What, if anything, can make people change their minds?
Today we speak to, Neil Yeung and Jonathan Lai, Undergraduate students in the Department of Computer Science at the University of Rochester, and Professor of Computer Science, Jiebo-Luoto to discuss their recent paper. Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic.
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Niclas Boehmer, second year PhD student at Berlin Institute of Technology, comes on today to discuss the computational complexity of bribery in elections through the paper “On the Robustness of Winners: Counting Briberies in Elections.”
“On the Robustness of Winners: Counting Briberies in Elections.” by Niclas Boehmer, Robert Bredereck, Piotr Faliszewski. Rolf Niedermier
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Clement Fung, a Societal Computing PhD student at Carnegie Mellon University, discusses his research in security of machine learning systems and a defense against targeted sybil-based poisoning called FoolsGold.
The Limitations of Federated Learning in Sybil Settings
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Simson Garfinkel, Senior Computer Scientist for Confidentiality and Data Access at the US Census Bureau, discusses his work modernizing the Census Bureau disclosure avoidance system from private to public disclosure avoidance techniques using differential privacy. Some of the discussion revolves around the topics in the paper Randomness Concerns When Deploying Differential Privacy. WORKS MENTIONED: “Calibrating Noise to Sensitivity in Private Data Analysis” by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith https://link.springer.com/chapter/10.1007/11681878_14 "Issues Encountered Deploying Differential Privacy" by Simson L Garfinkel, John M Abowd, and Sarah Powazek https://dl.acm.org/doi/10.1145/3267323.3268949 "Randomness Concerns When Deploying Differential Privacy" by Simson L. Garfinkel and Philip Leclerc https://arxiv.org/abs/2009.03777 Check out: https://simson.net/page/Differential_privacy Thank you to our sponsor, BetterHelp. Professional and confidential in-app counseling for everyone. Save 10% on your first month of services with www.betterhelp.com/dataskeptic
Computer Science research fellow of Cambridge University, Heidi Howard discusses Paxos, Raft, and distributed consensus in distributed systems alongside with her work “Paxos vs. Raft: Have we reached consensus on distributed consensus?”
She goes into detail about the leaders in Paxos and Raft and how The Raft Consensus Algorithm actually inspired her to pursue her PhD.
Paxos vs Raft paper: https://arxiv.org/abs/2004.05074
Leslie Lamport paper “part-time Parliament”
Leslie Lamport paper "Paxos Made Simple"
Twitter : @heidiann360
Linhda joins Kyle today to talk through A.C.I.D. Compliance (atomicity, consistency, isolation, and durability). The presence of these four components can ensure that a database’s transaction is completed in a timely manner. Kyle uses examples such as google sheets, bank transactions, and even the game rummy cube. Thanks to this week's sponsors: Monday.com - Their Apps Challenge is underway and available at monday.com/dataskeptic
Brilliant - Check out their Quantum Computing Course, I highly recommend it! Other interesting topics I’ve seen are Neural Networks and Logic. Check them out at Brilliant.org/dataskeptic
Neil Johnson joins us to discuss the paper The online competition between pro- and anti-vaccination views.
Mashbat Suzuki joins us to discuss the paper How Many Freemasons Are There? The Consensus Voting Mechanism in Metric Spaces.
Check out Mashbat’s and many other great talks at the 13th Symposium on Algorithmic Game Theory (SAGT 2020)