Log20: Fully automated optimal placement of log printing statements under specified overhead threshold Zhao et al., SOSP’17 Logging has become an overloaded term. In this paper logging is used in the context of recording information about the execution of a piece of software, for the purposes of aiding troubleshooting. For these kind of logging statements … Continue reading Log20: Fully automated optimal placement of log printing statements under specified overhead threshold
Year: 2017
My VM is lighter (and safer) than your container
My VM is lighter (and safer) than your container Manco et al., SOSP’17 Can we have the improved isolation of VMs, with the efficiency of containers? In today’s paper choice the authors investigate the boundaries of Xen-based VM performance. They find and eliminate bottlenecks when launching large numbers of lightweight VMs (both unikernels and minimal … Continue reading My VM is lighter (and safer) than your container
DeepXplore: automated whitebox testing of deep learning systems
DeepXplore: automated whitebox testing of deep learning systems Pei et al., SOSP’17 The state space of deep learning systems is vast. As we’ve seen with adversarial examples, that creates opportunity to deliberately craft inputs that fool a trained network. Forget adversarial examples for a moment though, what about the opportunity for good old-fashioned bugs to … Continue reading DeepXplore: automated whitebox testing of deep learning systems
Same stats, different graphs: generating datasets with varied appearance and identical statistics through simulated annealing
Same stats, different graphs: generating datasets with varied appearance and identical statistics through simulated annealing Matejka & Fitzmaurice et al., CHI’17 Today’s paper choice is inspired by the keynote that Prof. Miriah Meyer gave at the recent Velocity conference in London, ‘Why an interactive picture is worth a thousand numbers.’ She made a wonderful and … Continue reading Same stats, different graphs: generating datasets with varied appearance and identical statistics through simulated annealing
Occupy the cloud: distributed computing for the 99%
Occupy the cloud: distributed computing for the 99% Jonas et al., SoCC’17 ‘Occupy the cloud’ won the best vision paper award at the recent ACM Symposium on Cloud Computing event. In the spirit of a vision paper, you won’t find detailed implementation and evaluation information here, but hopefully you’ll find something to make you think. … Continue reading Occupy the cloud: distributed computing for the 99%
Learning networking by reproducing research results
Learning networking by reproducing research results Yan & McKeown et al., SIGCOMM’17 Students taking Stanford’s Advanced Topics in Networking class have to select a networking research paper and reproduce a result from it as part of a three-week pair project. At the end of the process, they publish their findings on the course’s public Reproducing … Continue reading Learning networking by reproducing research results
The QUIC transport protocol: design and Internet-scale deployment
The QUIC transport protocol: design and Internet-scale deployment Langley et al., SIGCOMM’17 QUIC is a transport protocol designed from the ground up by Google improve the performance of HTTPS traffic. The chances are you’ve already used it - QUIC is deployed in Chrome, in the YouTube mobile app, and in the Google Search app on … Continue reading The QUIC transport protocol: design and Internet-scale deployment
Why does the neocortex have columns, a theory of learning the structure of the world
Why does the neocortex have columns, a theory of learning the structure of the world Hawkins et al., bioRxiv preprint, 2017 Yesterday we looked at the ability of the HTM sequence memory model to learn sequences over time, with a model that resembles what happens in a single layer of the neocortex. But the neocortex … Continue reading Why does the neocortex have columns, a theory of learning the structure of the world
Continuous online sequence learning with an unsupervised neural network model
Continuous online sequence learning with an unsupervised neural network model Cui et al., Neural Computation, 2016 Yesterday we looked at the biological inspirations for the Hierarchical Temporal Memory (HTM) neural network model. Today’s paper demonstrates more of the inner workings, and shows how well HTM networks perform on online sequence learning tasks as compared to … Continue reading Continuous online sequence learning with an unsupervised neural network model
Why neurons have thousands of synapses, a theory of sequence memory in neocortex
Why neurons have thousands of synapses, a theory of sequence memory in neocortex Hawkins & Ahmad, Front. Neural Circuits 2016 It all began with a fascinating lunchtime conversation with Martin Thompson (@mjpt777), who mentioned to me a thought-provoking video he’d seen online from Jeff Hawkins regarding models of behaviour in the brain. A few days … Continue reading Why neurons have thousands of synapses, a theory of sequence memory in neocortex
the morning paper 