Week Ending 05.05.19
RESEARCH WATCH: 05.05.19
Over the past week, 212 new papers were published in "Computer Science".
The paper discussed most in the news over the past week was "Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges"by Philip Daian et al (Apr 2019), which was referenced 37 times, including in the article Bots exploiting blockchains for profit in Cornell University. The paper author, Philip Daian, was quoted saying "The miners have a tremendous amount of power". The paper got social media traction with 390 shares. A Twitter user, @Miles_Kellerman, posted "Interesting paper by et al. on front-running in dig. currencies: Can confirm from my field research that dig. curr. exchs VERY slow to set up surveillance. also find only 50% of top 10 have surveillance in place - shockingly low".
Leading researcher Aaron Courville (Université de Montréal) published "Improved Conditional VRNNs for Video Prediction".
The paper shared the most on social media this week is "Real numbers, data science and chaos: How to fit any dataset with a single parameter" by Laurent Boué (Apr 2019) with 861 shares. @lqgist (Astroboy) tweeted "While the work is fascinating the claim that a "single parameter can fit any data" is totally false. The paper says "complete values of parameter α that may extend to tens of thousands of digits". The net information contained in that "one" parameter is unbounded!".
Over the past week, 81 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was "Mirroring to Build Trust in Digital Assistants" by Katherine Metcalf et al (Apr 2019), which was referenced 3 times, including in the article 7 Indicators Of The State-Of-Artificial Intelligence (AI), April 2019 in Forbes.com. The paper got social media traction with 25 shares. On Twitter, @roseveleth commented "The setup of this study is kind of hilarious to me: subjects sit in a room and talk to what they think is a digital assistant. In fact it's just an Apple employee in the other room. via".
Leading researcher Sergey Levine (University of California, Berkeley) published "PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings".
The paper shared the most on social media this week is by a team at Carnegie Mellon University: "Unsupervised Data Augmentation" by Qizhe Xie et al (Apr 2019) with 326 shares. The authors propose to apply data augmentation to unlabeled data in a semi - supervised learning setting. @AI_Kho_ (Andrei Khobnia) tweeted "Training SOTA model just from 20 labeled examples. Impressive! Would like to reproduce this. Hope authors will release the code. #NLP #NLProc #ML #DS #DataScience #MachineLearning".
Over the past week, 143 new papers were published in "Computer Science - Computer Vision and Pattern Recognition".
The paper discussed most in the news over the past week was "Fooling automated surveillance cameras: adversarial patches to attack person detection" by Simen Thys et al (Apr 2019), which was referenced 35 times, including in the article A week in security (April 22 – 28) in Malware Bytes. The paper author, Wiebe Van Ranst, was quoted saying "The idea behind this work is to be able to circumvent security systems that use a person detector to generate an alarm when a person enters the view of a camera". The paper also got the most social media traction with 13902 shares. A user, @pwang, tweeted "This is going to be a major Tshirt trend over the next couple of years: Innocent-looking shirts with various patterns that are specifically designed to trick neural networks. Adversarial hats will also be a thing, for face detectors".
Leading researcher Aaron Courville (Université de Montréal) came out with "Improved Conditional VRNNs for Video Prediction".
The paper shared the most on social media this week is by a team at University of Oxford: "Surprising Effectiveness of Few-Image Unsupervised Feature Learning" by Yuki M. Asano et al (Apr 2019) with 103 shares. @AjdDavison (Andrew Davison) tweeted "I like the ideas in this paper... says that the early feature layers of a CNN can not only be learned in an unsupervised way, but very effectively just from a single image. Seems to confirm the strong generality of low level natural image statistics".
Over the past week, 20 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was "Discrimination through optimization: How Facebooks ad delivery can lead to skewed outcomes" by Muhammad Ali et al (Apr 2019), which was referenced 52 times, including in the article All the Ways Hiring Algorithms Can Introduce Bias in Harvard Business Review. The paper author, Alan Mislove (Computer science professor at Northeastern University), was quoted saying "All advertising is based on auctions all over the web, and I don’t know how you fix that without just saying we don’t have those kinds of ads". The paper also got the most social media traction with 1311 shares. The investigators demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platforms own predictions about the relevance of ads to different groups of users. A Twitter user, @TimKarr, observed "April 4, 2019: Academic paper published analyzing FB's algorithms to find they're built using historically discriminatory data. The algorithms deliver results biased against people based on race & gender, & perpetuate discrimination in advertising".
The paper shared the most on social media this week is "Characterizing Attention Cascades in WhatsApp Groups" by Josemar Alves Caetano et al (May 2019) with 224 shares. The investigators characterize and analyze how attention propagates among the participants of a WhatsApp group. @alexvespi (Alessandro Vespignani) tweeted "Characterizing Attention Cascades in WhatsApp Groups “attention cascades in more than 1.7 million messages posted in 120 groups over one year”".
Over the past week, 18 new papers were published in "Computer Science - Human-Computer Interaction".
The paper discussed most in the news over the past week was "Mirroring to Build Trust in Digital Assistants" by Katherine Metcalf et al (Apr 2019), which was referenced 3 times, including in the article 7 Indicators Of The State-Of-Artificial Intelligence (AI), April 2019 in Forbes.com. The paper got social media traction with 25 shares. A user, @roseveleth, tweeted "The setup of this study is kind of hilarious to me: subjects sit in a room and talk to what they think is a digital assistant. In fact it's just an Apple employee in the other room. via".
Over the past week, 154 new papers were published in "Computer Science - Learning".
The paper discussed most in the news over the past week was "On Arrhythmia Detection by Deep Learning and Multidimensional Representation" by K. S. Rajput et al (Mar 2019), which was referenced 32 times, including in the article FDA clears Biofourmis' software for ECG-based arrhythmia detection in Mobihealthnews. The paper author, Kuldeep Singh Rajput, was quoted saying "Comprehensive diagnosis of a patient's cardiac health requires longer continuous monitoring and full characterization of multiple arrhythmias. We are on a mission of predicting and preventing serious medical events using software-based therapeutic intervention and RhythmAnalytics TM is an integral part of our Digital Therapeutics platform that would enable prescription of the right dose, to the right patient at the right time". The paper got social media traction with 25 shares. On Twitter, @ksingh_rajput posted "RhythmAnalytics, our #DeepLearning platform for ECG interpretation outperforms Cardiologists analyzing ECG's. Watch out this space for some exciting news... #biofourmis #CardioTwitter #Cardiology #arrhythmias".
Leading researcher Oriol Vinyals (DeepMind) came out with "Graph Matching Networks for Learning the Similarity of Graph Structured Objects", which had 66 shares over the past 4 days. The investigators address the challenging problem of retrieval and matching of graph structured objects, and make two key contributions. @0x464D tweeted "seems incredibly relevant to my current intersection of ML and RE interests. I chuckled a bit when I saw as an author and the first figure and realized this wouldn't just deal with my abstract problem". This paper was also shared the most on social media with 419 tweets. @0x464D (Florian Magin) tweeted "seems incredibly relevant to my current intersection of ML and RE interests. I chuckled a bit when I saw as an author and the first figure and realized this wouldn't just deal with my abstract problem".
Over the past week, 13 new papers were published in "Computer Science - Multiagent Systems".
Over the past week, 12 new papers were published in "Computer Science - Neural and Evolutionary Computing".
The paper discussed most in the news over the past week was by a team at Stanford University: "Wave Physics as an Analog Recurrent Neural Network" by Tyler W. Hughes et al (Apr 2019), which was referenced 6 times, including in the article Putin Will Put Russia Behind an Web Curtain – NEWPAPER24 in Newpaper24. The paper also got the most social media traction with 256 shares. The authors identify a mapping between the dynamics of wave physics, and the computation in recurrent neural networks. On Twitter, @KordingLab said "This is the beginning of practically conceptualizing physics as computation", while @yieldthought said "So you can train any differentiable system with backpropagation - such as one modelled by the wave equation. Doesn’t have to be a NN to use NN techniques. Fascinating future here".
This week was active for "Computer Science - Robotics", with 52 new papers.
The paper discussed most in the news over the past week was "STANCE: Locomotion Adaptation over Soft Terrain" by Shamel Fahmi et al (Apr 2019), which was referenced 1 time, including in the article Video Friday: Watch This 'RoboCop' Make a Traffic Stop in Spectrum Online. The paper was shared 2 times in social media. The investigators propose a novel soft terrain adaptation algorithm called STANCE : Soft Terrain Adaptation and Compliance Estimation. On Twitter, @shamelfahmi commented "This work is currently under review. The preprint could be found in".
Leading researcher Sergey Levine (University of California, Berkeley) published "PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings".