Paracloud: bringing application insight into cloud operations Nadgowda et al., HotCloud'17 We'll be looking at a selection of papers from HotCloud'17 this week. The HotCloud workshop focuses on new and emerging trends in cloud computing, and the CfP particularly encourages position papers that describe novel research directions and work that is in its formative stages. … Continue reading Paracloud: bringing application insight into cloud operations
Author: adriancolyer
Venture Partner with Accel in London, author of 'The Morning Paper'
Deep photo style transfer
Deep photo style transfer Luan et al., arXiv 2017 Here's something a little fun for Friday: a collaboration between researchers at Cornell and Adobe, on photographic style transfer. Will we see something like this in a Photoshop of the future? In 2015 in the Neural Style Transfer paper ('A neural algorithm of artistic style'), Gatys … Continue reading Deep photo style transfer
On the design of distributed programming models
On the design of distributed programming models Meiklejohn, arXiv 2017. Today's choice is a lovely thought piece by Christopher Meiklejohn, making the case for distributed programming models. We've witnessed a progression in data structures from sequential (non-thread safe) to concurrent, to distributed (think CRDTs). Will the same thing happen with our programming models? And if … Continue reading On the design of distributed programming models
Interactions of individual and pair programmers with an intelligent tutoring system for computer science
Interactions of individual and pair programmers with an intelligent tutoring system for computer science Harsley et al., SIGCSE'17 A short and easy paper for today, which examines the difference between pair programming and working individually in an educational context. The study involves 116 students participating in a series of seven linked-list programming tasks designed to … Continue reading Interactions of individual and pair programmers with an intelligent tutoring system for computer science
Writing parsers like it is 2017
Writing parsers like it is 2017 Chifflier & Couprie, SPW'17 With thanks to Glyn Normington for pointing this paper out to me. Earlier this year we looked at 'System programming in Rust: beyond safety' which made the case for switching from C to Rust as the default language of choice for system-level programming. Today's paper … Continue reading Writing parsers like it is 2017
Cardiologist-level arrhythmia detection with convolutional neural networks
Cardiologist-level arrythmia detection with convolutional neural networks Rajpurkar, Hannun, et al., arXiv 2017 See also https://stanfordmlgroup.github.io/projects/ecg. This is a story very much of our times: development and deployment of better devices/sensors (in this case an iRhythm Zio) leads to collection of much larger data sets than have been available previously. Apply state of the art … Continue reading Cardiologist-level arrhythmia detection with convolutional neural networks
Automatic database management system tuning through large-scale machine learning
Automatic database management system tuning through large-scale machine learning Aken et al. , SIGMOD'17 Achieving good performance in DBMSs is non-trivial as they are complex systems with many tunable options that control nearly all aspects of their runtime operation. OtterTune uses machine learning informed by data gathered from previous tuning sessions to tune new DBMS … Continue reading Automatic database management system tuning through large-scale machine learning
Enabling signal processing over data streams
Enabling signal processing over data streams Nikolic et al., SIGMOD '17 If you're processing data coming from networks of sensors and devices, then it's not uncommon to use a mix of relational and signal processing operations. Data analysts use relational operators, for example, to group signals by different data sources or join signals with historical … Continue reading Enabling signal processing over data streams
Complete event trend detection in high-rate data streams
Complete Event Trend detection in high-rate event streams Poppe et al., SIGMOD'17 Today's paper choice looks at the tricky problem of detecting Complete Event Trends (CET) in high-rate event streams. CET detection is useful in fraud detection, health care analytics, stock trend analytics and other similar scenarios looking for complex patterns in event streams. Detecting … Continue reading Complete event trend detection in high-rate data streams
A general purpose counting filter: making every bit count
A general purpose counting filter: making every bit count Pandey et al., SIGMOD'17 It's been a while since we looked at a full on algorithms and data structures paper, but this one was certainly worth waiting for. We're in the world of Approximate Membership Query (AMQ) data structures, of which probably the best known example … Continue reading A general purpose counting filter: making every bit count
the morning paper 