Week Ending 3.29.2020
RESEARCH WATCH: 3.29.2020
Over the past week, 57 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was by a team at Google: "Learning to Walk in the Real World with Minimal Human Effort" by Sehoon Ha et al (Feb 2020), which was referenced 39 times, including in the article AI-Powered Robot Teaches Itself To Walk In Short Time in EFY. The paper author, Jie Tan (Google), was quoted saying "Now is still the early days of research. Next, we plan to test our learning system on a wide range of robots and in a more diverse set of environments". The paper got social media traction with 137 shares. The researchers develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort. A user, @popular_ML, tweeted "The most popular ArXiv tweet in the last 24h", while @hafiz_coolman observed "You can download the article from this link. On the right, there is PDF in hyperlink. It is nice to know what is the challenges and technique used".
The paper shared the most on social media this week is by a team at University of Waterloo: "COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images" by Linda Wang et al (Mar 2020) with 91 shares.
This week was active for "Computer Science - Computer Vision and Pattern Recognition", with 232 new papers.
The paper discussed most in the news over the past week was "Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis" by Ophir Gozes et al (Mar 2020), which was referenced 20 times, including in the article New Study Finds that RADLogics AI-Powered Solution Achieves High Accuracy for Detecting COVID-19 on CT in Benzinga.com. The paper got social media traction with 50 shares. A user, @AtoAndyKing, tweeted "But (exactly) the same figure for the Chinese only experiment I think? My concerns are more that (1) these are all hospitalised patients, so presumably severe symptoms and easy to detect using other means, (2) is it realistic to use CT as a diagnostic tool for #COVID19?".
Leading researcher Kyunghyun Cho (New York University) came out with "Understanding the robustness of deep neural network classifiers for breast cancer screening".
The paper shared the most on social media this week is "Neural Contours: Learning to Draw Lines from 3D Shapes" by Difan Liu et al (Mar 2020) with 125 shares. The authors introduce a method for learning to generate line drawings from 3D models. @__dpdp__ (dpdp) tweeted "Some smart person: port this to Blender Now. #b3d".
Over the past week, 29 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was by a team at Massachusetts Institute of Technology: "Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic" by Ramesh Raskar et al (Mar 2020), which was referenced 2 times, including in the article Techie collective to whip together official WHO-backed COVID-19 app within a week to meet 'urgent, global need' in The Register. The paper got social media traction with 56 shares. On Twitter, @TechnicallA posted "best tracking of infected Covid19 - CZECH Republic is number one in mobile phones per population / infected separate / I thing", while @rzanardelli posted "MIT's Private Kit: Safe Paths - Can we slow the spread without giving up individual privacy? Participatory sharing and (privacy-safe) broadcasting are gaining track! this is huge!!!".
The paper shared the most on social media this week is "Mapping the Landscape of Artificial Intelligence Applications against COVID-19" by Joseph Bullock (Sasha) et al (Mar 2020) with 63 shares. The researchers present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, medical and epidemiological applications. @janbeger (Jan Beger) tweeted "#ML and can support the response against #COVID19 in a broad set of domains. In particular, emerging applications in diagnosis, clinical outcome prediction, drug discovery and development, epidemiology, and infodemiology".
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 "This PIN Can Be Easily Guessed" by Philipp Markert et al (Mar 2020), which was referenced 9 times, including in the article How Secure Are 4- and 6-Digit Mobile Phone PINs in Homeland Security News Wire. The paper author, Maximilian Golla, was quoted saying "Since users only have ten attempts to guess the PIN on the iPhone anyway, the blacklist does not make it any more secure". The paper got social media traction with 33 shares. The investigators provide the first comprehensive study of user - chosen 4- and 6-digit PINs (n=1220) collected on smartphones with participants being explicitly primed for the situation of device unlocking. A user, @stshank, tweeted "That quote was from the preprint of a paper to be published later this year from researchers at Ruhr University Bochum, Max Planck Institute for Security and Privacy, and George Washington University".
This week was very active for "Computer Science - Learning", with 301 new papers.
The paper discussed most in the news over the past week was by a team at Google: "Learning to Walk in the Real World with Minimal Human Effort" by Sehoon Ha et al (Feb 2020)
Leading researcher Kyunghyun Cho (New York University) came out with "Understanding the robustness of deep neural network classifiers for breast cancer screening".
The paper shared the most on social media this week is "A Survey of Deep Learning for Scientific Discovery" by Maithra Raghu et al (Mar 2020) with 521 shares. @gabriel_hussy (Gabriel Hussy 🔁) tweeted "With the rapid increase of data in scientific domains, there are exciting opportunities for deep learning for science. But a challenge is knowing where to start. With this survey, we aim to address this, giving a broad overview of key deep learning metho".
Over the past week, 16 new papers were published in "Computer Science - Multiagent Systems".
The paper discussed most in the news over the past week was by a team at The University of Sydney: "Modelling transmission and control of the COVID-19 pandemic in Australia" by Sheryl L. Chang et al (Mar 2020), which was referenced 40 times, including in the article Confused about what to do about coronavirus? This data says just stay home in ABC Online. The paper author, Mikhail Prokopenko (The University of Sydney), was quoted saying "If we want to control the spread of COVID-19 – rather than letting the disease control us – at least eighty per cent of the Australian population must comply with strict social distancing measures for at least four months". The paper also got the most social media traction with 319 shares. The researchers develop an agent - based model for a fine - grained computational simulation of the ongoing COVID-19 pandemic in Australia. On Twitter, @arthaey observed "This paper models 80-90% social distancing compliance is needed, & only works while we KEEP doing it, until a vaccine: (blue line is 70% compliance, red 80%, yellow 90%; spikes later are when social distancing is lifted)".
Over the past week, 32 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 Google: "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch" by Esteban Real et al (Mar 2020), which was referenced 3 times, including in the article Addressing Drawbacks Of AutoML With AutoML-Zero in Analytics India Magazine. The paper also got the most social media traction with 1134 shares. On Twitter, @tomvarsavsky observed "One of the most interesting results I've seen in ML in the last 5 years. Evolving programs using a generic search space and generic mutations leads to the discovery of not only SGD and two layer NNs but also rand init, ReLU, Grad Norm. Can someone find a hidden inductive bias?".
This week was active for "Computer Science - Robotics", with 57 new papers.
The paper discussed most in the news over the past week was by a team at Google: "Learning to Walk in the Real World with Minimal Human Effort" by Sehoon Ha et al (Feb 2020)
Leading researcher Abhinav Gupta (Carnegie Mellon University) published "Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects" The authors take a step towards a more physical understanding of actions. This paper was also shared the most on social media with 31 tweets.