Week Ending 10.4.2020
RESEARCH WATCH: 10.4.2020
This week was very active for "Computer Science - Artificial Intelligence", with 157 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 417 times, including in the article WHO guidance: Healthy people should wear masks only when 'taking care of' coronavirus patients in FOXNews.com. The paper author, De Kai, was quoted saying "I felt like this was pretty urgent". The paper also got the most social media traction with 5334 shares. On Twitter, @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 commented "> We present two models for the COVID-19 pandemic predicting the impact of u".
Leading researcher Yoshua Bengio (Université de Montréal) published "A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM".
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 362 shares. The investigators 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".
This week was active for "Computer Science - Computer Vision and Pattern Recognition", with 296 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 47 times, including in the article COVID-19 CT Analysis using Deep Learning in Towards Data Science. The paper author, Adam Bernheim (Icahn School of Medicine), was quoted saying "AI is of particular value in places where disease prevalence is high and availability of testing kits is limited". The paper got social media traction with 89 shares. A user, @primer_ai, tweeted "The website identifies emerging topics, grouping research papers by common concepts. One topic is “Deep Learning & Machine Learning” with a popular study coming from that used AI-based image analysis tools for the detection of coronavirus".
Leading researcher Chris Dyer (DeepMind) came out with "Learning to Segment Actions from Observation and Narration".
The paper shared the most on social media this week is "A neural network walks into a lab: towards using deep nets as models for human behavior" by Wei Ji Ma et al (May 2020) with 231 shares. @DEMelnikoff (David Melnikoff) tweeted "This is fabulous. Lucid and empowering, speaking as someone who thinks DNNs are neat, but doesn't (didn't) really see how they might illuminate the questions about human cognition that interest me most".
This week was very active for "Computer Science - Computers and Society", with 62 new papers.
The paper discussed most in the news over the past week was by a team at Boston University: "Anonymous Collocation Discovery: Harnessing Privacy to Tame the Coronavirus" by Ran Canetti et al (Mar 2020), which was referenced 63 times, including in the article Workplaces are turning to devices to monitor social distancing, but does the tech respect privacy? in GCN. The paper author, Ari Trachtenberg (Boston University), was quoted saying "When a person is tested positive for COVID-19, the person could choose (through the administrating medical professional) to voluntarily share their list of random numbers -- either their own generated numbers or the numbers that the app observed". The paper got social media traction with 63 shares. On Twitter, @alexcryptan posted "By now there are at least 5 academic initiatives: ● TCN ● Canetti et al. ● DP-3T (European) ● PACT ● MIT protocol".
Leading researcher Yoshua Bengio (Université de Montréal) published "A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM".
The paper shared the most on social media this week is by a team at Microsoft: "How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt" by Gabriel Kaptchuk et al (May 2020) with 126 shares. @jsrailton (John Scott-Railton) tweeted "IMPORTANT STUDY: up to 50% of Americans wouldn't use a contact tracing app with privacy & accuracy problems. Research by: et al. Quick THREAD".
This week was extremely active for "Computer Science - Human-Computer Interaction", with 62 new papers.
The paper discussed most in the news over the past week was "A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study" by Halgurd S. Maghdid et al (Mar 2020), which was referenced 12 times, including in the article Which tech companies will survive the pandemic-triggered recession? in InfoWorld. The paper got social media traction with 31 shares. A user, @yapp1e, tweeted "A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study. Coronaviruses are a famous family of viruses that causes illness in human or animals. The new type of corona viru", while @OfficialNerissa observed "Here's a novel framework to detect #Covid_19 using smart phone sensors such as the accelerometer. #WorkingFromHomeLife".
Leading researcher Yoshua Bengio (Université de Montréal) came out with "A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM".
The paper shared the most on social media this week is by a team at Microsoft: "How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt" by Gabriel Kaptchuk et al (May 2020)
The most influential Twitter user discussing papers is Vincent Rajkumar who shared "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) and said: "The original paper is here".
This week was extremely active for "Computer Science - Learning", with 550 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)
Leading researcher Yoshua Bengio (Université de Montréal) published "A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM".
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)
The most influential Twitter user discussing papers is Vincent Rajkumar who shared "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)
This week was active for "Computer Science - Multiagent Systems", with 21 new papers.
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 49 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 – 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 794 shares. The researchers develop an agent - based model for a fine - grained computational simulation of the ongoing COVID-19 pandemic in Australia. A user, @arthaey, tweeted "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 88 shares. The researchers evaluate the performance of multiple machine - learning methods in the emulation of agent - based models (ABMs).
The most influential Twitter user discussing papers is Vincent Rajkumar who shared "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)
This week was active for "Computer Science - Neural and Evolutionary Computing", with 48 new papers.
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 7 times, including in the article Why everyone uses transfer learning in Towards Data Science. The paper got social media traction with 146 shares. On Twitter, @billiout posted "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) came out with "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.
The paper shared the most on social media this week is by a team at Stanford University: "Machine Learning on Graphs: A Model and Comprehensive Taxonomy" by Ines Chami et al (May 2020) with 349 shares. @kerstingAIML (Kristian Kersting) tweeted "Nice overview & conceptualization of (differentiable) approaches to learning on graphs. It is really important to get overviews & unifying views. 🙏 Follow up could be on learning with graphs, showing also the strong connection to graph kernels (via WL & neural fingerprints etc.)".
This week was very active for "Computer Science - Robotics", with 82 new papers.
The paper discussed most in the news over the past week was "Series Elastic Force Control for Soft Robotic Fluid Actuators" by Chunpeng Wang et al (Apr 2020), which was referenced 5 times, including in the article Remotely Operated Robot Takes Straight Razor to Face of Brave Roboticist in Spectrum Online. The paper author, John Peter Whitney, was quoted saying "These traits and behaviors are especially interesting for applications where we must interact with delicate and uncertain environments". The paper was shared 2 times in social media.
Leading researcher Jianfeng Gao (Microsoft) published "RMM: A Recursive Mental Model for Dialog Navigation".