Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!Comment: 8 page

Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November 11-16, 2018, Dallas, TX, US

Obvious: a meta-toolkit to encapsulate information visualization toolkits. One toolkit to bind them all

This article describes “Obvious”: a meta-toolkit that abstracts and encapsulates information visualization toolkits implemented in the Java language. It intends to unify their use and postpone the choice of which concrete toolkit(s) to use later-on in the development of visual analytics applications. We also report on the lessons we have learned when wrapping popular toolkits with Obvious, namely Prefuse, the InfoVis Toolkit, partly Improvise, JUNG and other data management libraries. We show several examples on the uses of Obvious, how the different toolkits can be combined, for instance sharing their data models. We also show how Weka and RapidMiner, two popular machine-learning toolkits, have been wrapped with Obvious and can be used directly with all the other wrapped toolkits. We expect Obvious to start a co-evolution process: Obvious is meant to evolve when more components of Information Visualization systems will become consensual. It is also designed to help information visualization systems adhere to the best practices to provide a higher level of interoperability and leverage the domain of visual analytics

Statistical Arbitrage Mining for Display Advertising

We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015

Biokinetics Of microbial consortia using biogenic sulfur as a novel electron donor for sustainable denitrification

In this study, the biokinetics of autotrophic denitrification with biogenic S0 (ADBIOS) for the treatment of nitrogen pollution in wastewaters were investigated. The used biogenic S0, a by-product of gas desulfurization, was an elemental microcrystalline orthorhombic sulfur with a median size of 4.69 µm and a specific surface area of 3.38 m2/g, which made S0 particularly reactive and bioavailable. During denitritation, the biomass enriched on nitrite (NO2–) was capable of degrading up to 240 mg/l NO2–-N with a denitritation activity of 339.5 mg NO2–-N/g VSS·d. The use of biogenic S0 induced a low NO2–-N accumulation, hindering the NO2–-N negative impact on the denitrifying consortia and resulting in a specific denitrification activity of 223.0 mg NO3–-N/g VSS·d. Besides Thiobacillus being the most abundant genus, Moheibacter and Thermomonas were predominantly selected for denitrification and denitritation, respectively

Benchtop flow-NMR for rapid online monitoring of RAFT and free radical polymerisation in batch and continuous reactors

A “Benchtop” NMR spectrometer is used for detailed monitoring of controlled and free radical polymerisations performed in batch and continuous reactors both offline and in real-time. This allows detailed kinetic analysis with unprecedented temporal resolution for reactions which reach near completion in under five minutes

Proceedings of the First Karlsruhe Service Summit Workshop - Advances in Service Research, Karlsruhe, Germany, February 2015 (KIT Scientific Reports ; 7692)

Since April 2008 KSRI fosters interdisciplinary research in order to support and advance the progress in the service domain. KSRI brings together academia and industry while serving as a European research hub with respect to service science. For KSS2015 Research Workshop, we invited submissions of theoretical and empirical research dealing with the relevant topics in the context of services including energy, mobility, health care, social collaboration, and web technologies

Cosmic cookery : making a stereoscopic 3D animated movie.

This paper describes our experience making a short stereoscopic movie visualizing the development of structure in the universe during the 13.7 billion years from the Big Bang to the present day. Aimed at a general audience for the Royal Society's 2005 Summer Science Exhibition, the movie illustrates how the latest cosmological theories based on dark matter and dark energy are capable of producing structures as complex as spiral galaxies and allows the viewer to directly compare observations from the real universe with theoretical results. 3D is an inherent feature of the cosmology data sets and stereoscopic visualization provides a natural way to present the images to the viewer, in addition to allowing researchers to visualize these vast, complex data sets. The presentation of the movie used passive, linearly polarized projection onto a 2m wide screen but it was also required to playback on a Sharp RD3D display and in anaglyph projection at venues without dedicated stereoscopic display equipment. Additionally lenticular prints were made from key images in the movie. We discuss the following technical challenges during the stereoscopic production process; 1) Controlling the depth presentation, 2) Editing the stereoscopic sequences, 3) Generating compressed movies in display speci¯c formats. We conclude that the generation of high quality stereoscopic movie content using desktop tools and equipment is feasible. This does require careful quality control and manual intervention but we believe these overheads are worthwhile when presenting inherently 3D data as the result is signi¯cantly increased impact and better understanding of complex 3D scenes