Week Ending 5.10.2020
RESEARCH WATCH: 5.10.2020
This week was active for "Computer Science - Artificial Intelligence", with 101 new papers.
The paper discussed most in the news over the past week was "Universal Masking is Urgent in the COVID-19 Pandemic: SEIR and Agent Based Models, Empirical Validation, Policy Recommendations" by De Kai et al (Apr 2020), which was referenced 18 times, including in the article Western countries want to reopen. Renewed outbreaks in China and South Korea show the continued risk in CNN. The paper author, Vitamin D. Kai, was quoted saying "I felt like this was pretty urgent". The paper also got the most social media traction with 1383 shares. A Twitter user, @chrish_99, said "Masks more effective than lockdown at suppressing spread. Mandate mask wearing and end the lockdown? Even non medical masks are recommended", while @gastronomy observed "> We present two models for the COVID-19 pandemic predicting the impact of u".
Leading researcher Pieter Abbeel (University of California, Berkeley) published "Plan2Vec: Unsupervised Representation Learning by Latent Plans" The researchers introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning.
The paper shared the most on social media this week is by a team at UC Berkeley: "Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems" by Sergey Levine et al (May 2020) with 243 shares. The authors aim to provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms : reinforcement learning algorithms that utilize previously collected data, without additional online data collection. @popular_ML (Popular ML resources) tweeted "The most popular ArXiv tweet in the last 24h".
Over the past week, 200 new papers were published in "Computer Science - Computer Vision and Pattern Recognition".
The paper discussed most in the news over the past week was by a team at University of Waterloo: "COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images" by Linda Wang et al (Mar 2020), which was referenced 23 times, including in the article An AI Assist for Spotting COVID-19 in X RaysAI & Surroundings in GreenGround.it. The paper author, Alexander Wong (University of Waterloo), was quoted saying "The hope is that the AI can help radiologists to more rapidly and accurately differentiate between COVID-19 infections and other forms of infections (especially important since flus are prevalent still this time of year), and more importantly, reduce the burden for radiologists but enabling other front-line health workers with less expertise to better make diagnosis". The paper got social media traction with 150 shares. On Twitter, @hoangdta said "This AI acquired 100% sensitivity on Covid-19 cases detection ! The dataset is quite small, I would love to see scientists join force to tackle this problem. It would be HUGE if we can exploit the power of deep learning in the battle against Covid-19".
Leading researcher Chris Dyer (DeepMind) published "Learning to Segment Actions from Observation and Narration".
The paper shared the most on social media this week is "The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America" by Benjamin Charles Germain Lee et al (May 2020) with 163 shares. @Psythor (James O'Malley) tweeted "AI has just automated a job I used to have 11 years ago. When I was doing my MA, I worked at in a news clippings department, which would analyse scans of local papers for mentions of companies etc. Iād spend 8 hours non-stop dragging boxes around different headlines and stories".
This week was active for "Computer Science - Computers and Society", with 36 new papers.
The paper discussed most in the news over the past week was "Digital tools against COVID-19: Framing the ethical challenges and how to address them" by Urs Gasser et al (Apr 2020), which was referenced 1 time, including in the article Which Covid-19 Data Can You Trust? in Harvard Business Review. The paper got social media traction with 84 shares. A Twitter user, @techforgoodtv, commented "#ContactTracing looks set to go ahead in the UK - which has opted not to use the tools developed by and š From a #TechForGood perspective, a #TrackingApp can be problematic. Here's how to frame and address the ethical challenges posed".
This week was very active for "Computer Science - Human-Computer Interaction", with 43 new papers.
The paper shared the most on social media this week is by a team at Pennsylvania State University: "CODA-19: Reliably Annotating Research Aspects on 10,000+ CORD-19 Abstracts Using Non-Expert Crowd" by Ting-Hao 'Kenneth' Huang et al (May 2020) with 57 shares. The authors introduce CODA-19, a human - annotated dataset that denotes the Background, Purpose, Method, Finding/Contribution, and Other for 10,966 English abstracts in the COVID-19 Open Research Dataset. @WilliamWangNLP (William Wang) tweeted "This is very cool: how to leverage the crowd to label scientific literature that may require domain knowledge".
This week was very active for "Computer Science - Learning", with 351 new papers.
The paper discussed most in the news over the past week was by a team at University of Waterloo: "COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images" by Linda Wang et al (Mar 2020)
Leading researcher Pieter Abbeel (University of California, Berkeley) published "Plan2Vec: Unsupervised Representation Learning by Latent Plans" The investigators introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning.
The paper shared the most on social media this week is by a team at UC Berkeley: "Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems" by Sergey Levine et al (May 2020)
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 48 times, including in the article Social Distancing Has Become the Norm. What Have We Learned? in Wired News. The paper author, Mikhail Prokopenko (The University of Sydney), was quoted saying "If we want to control the spread of COVID-19 ā rather than letting the disease control us ā 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 674 shares. The researchers develop an agent - based model for a fine - grained computational simulation of the ongoing COVID-19 pandemic in Australia. A Twitter user, @arthaey, commented "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)".
The paper shared the most on social media this week is "Using Machine Learning to Emulate Agent-Based Simulations" by Claudio Angione et al (May 2020) with 63 shares. The authors evaluate the performance of multiple machine - learning methods in the emulation of agent - based models (ABMs).
Over the past week, 29 new papers were published in "Computer Science - Neural and Evolutionary Computing".
The paper discussed most in the news over the past week was "The Cost of Training NLP Models: A Concise Overview" by Or Sharir et al (Apr 2020), which was referenced 5 times, including in the article When the chips are down, thank goodness for software engineers: AI algorithms 'outpace Moore's law' in The Register. The paper got social media traction with 140 shares. A user, @billiout, tweeted "According to the following study from training a single BIG NLP model can cost about $10k. That's unacceptable! Both for the environmental burden as well as for the independent researchers who don't have access to these resources.#NLProc".
Leading researcher Danielle S. Bassett (University of Pennsylvania) published "Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example" The investigators demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and they explain the associated learning mechanism with new theory. This paper was also shared the most on social media with 56 tweets.
This week was active for "Computer Science - Robotics", with 54 new papers.
The paper discussed most in the news over the past week was by a team at UC Berkeley: "X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions" by Michael Danielczuk et al (Apr 2020), which was referenced 2 times, including in the article Reinforcement Learning With Augmented Data Is So Superior It Beats Google & DeepMind Hands-down in Analytics India Magazine. The paper got social media traction with 5 shares.