The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition

Machine learning competitions such as those organized by Kaggle or KDD represent a useful benchmark for data science research. In this work, we present our winning solution to the Game Data Mining competition hosted at the 2017 IEEE Conference on Computational Intelligence and Games (CIG 2017). The contest consisted of two tracks, and participants (more than 250, belonging to both industry and academia) were to predict which players would stop playing the game, as well as their remaining lifetime. The data were provided by a major worldwide video game company, NCSoft, and came from their successful massively multiplayer online game Blade and Soul. Here, we describe the long short-term memory approach and conditional inference survival ensemble model that made us win both tracks of the contest, as well as the validation procedure that we followed in order to prevent overfitting. In particular, choosing a survival method able to deal with censored data was crucial to accurately predict the moment in which each player would leave the game, as censoring is inherent in churn. The selected models proved to be robust against evolving conditions---since there was a change in the business model of the game (from subscription-based to free-to-play) between the two sample datasets provided---and efficient in terms of time cost. Thanks to these features and also to their a ability to scale to large datasets, our models could be readily implemented in real business settings

STiCMAC: A MAC Protocol for Robust Space-Time Coding in Cooperative Wireless LANs

Relay-assisted cooperative wireless communication has been shown to have significant performance gains over the legacy direct transmission scheme. Compared with single relay based cooperation schemes, utilizing multiple relays further improves the reliability and rate of transmissions. Distributed space-time coding (DSTC), as one of the schemes to utilize multiple relays, requires tight coordination between relays and does not perform well in a distributed environment with mobility. In this paper, a cooperative medium access control (MAC) layer protocol, called \emph{STiCMAC}, is designed to allow multiple relays to transmit at the same time in an IEEE 802.11 network. The transmission is based on a novel DSTC scheme called \emph{randomized distributed space-time coding} (\emph{R-DSTC}), which requires minimum coordination. Unlike conventional cooperation schemes that pick nodes with good links, \emph{STiCMAC} picks a \emph{transmission mode} that could most improve the end-to-end data rate. Any station that correctly receives from the source can act as a relay and participate in forwarding. The MAC protocol is implemented in a fully decentralized manner and is able to opportunistically recruit relays on the fly, thus making it \emph{robust} to channel variations and user mobility. Simulation results show that the network capacity and delay performance are greatly improved, especially in a mobile environment.Comment: This paper is a revised version of a paper with the same name submitted to IEEE Transaction on Wireless Communications. STiCMAC protocol with RTS/CTS turned off is presented in the appendix of this draf

Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models

Nowadays, video game developers record every virtual action performed by their players. As each player can remain in the game for years, this results in an exceptionally rich dataset that can be used to understand and predict player behavior. In particular, this information may serve to identify the most valuable players and foresee the amount of money they will spend in in-app purchases during their lifetime. This is crucial in free-to-play games, where up to 50% of the revenue is generated by just around 2% of the players, the so-called whales. To address this challenge, we explore how deep neural networks can be used to predict customer lifetime value in video games, and compare their performance to parametric models such as Pareto/NBD. Our results suggest that convolutional neural network structures are the most efficient in predicting the economic value of individual players. They not only perform better in terms of accuracy, but also scale to big data and significantly reduce computational time, as they can work directly with raw sequential data and thus do not require any feature engineering process. This becomes important when datasets are very large, as is often the case with video game logs. Moreover, convolutional neural networks are particularly well suited to identify potential whales. Such an early identification is of paramount importance for business purposes, as it would allow developers to implement in-game actions aimed at retaining big spenders and maximizing their lifetime, which would ultimately translate into increased revenue

Procedure for improving wildfire simulations using observations

This report suggests a variational update method for improving wildfire simulations using observations as feedback to update information. We first assume a onedimensional fire model for simplicity and present numerical simulations obtained in this case. As possible alternative approaches, we also discuss two other update methods: a particle filter method and an optimal control method

Activity modulation and allosteric control of a scaffolded DNAzyme using a dynamic DNA nanostructure.

Recognition of the fundamental importance of allosteric regulation in biology dates back to not long after its discovery in the 1960s. Our ability to rationally engineer this potentially useful property into normally non-allosteric catalysts, however, remains limited. In response we report a DNA nanotechnology-enabled approach for introducing allostery into catalytic nucleic acids. Specifically, we have grafted one or two copies of a peroxidase-like DNAzyme, hemin-bound G-quadruplex (hemin-G), onto a DNA tetrahedral nanostructure in such a manner as to cause them to interact, modulating their catalytic activity. We achieve allosteric regulation of these catalysts by incorporating dynamically responsive oligonucleotides that respond to specific "effector" molecules (complementary oligonucleotides or small molecules), altering the spacing between the catalytic sites and thus regulating their activity. This designable approach thus enables subtle allosteric modulation in DNAzymes that is potentially of use for nanomedicine and nanomachines

Concurrent semantic priming and lexical interference for close semantic relations in blocked-cyclic picture naming:Electrophysiological signatures

In the present study, we employed event-related brain potentials to investigate the effects of semantic similarity on different planning stages during language production. We manipulated semantic similarity by controlling feature overlap within taxonomical hierarchies. In a blocked-cyclic naming task, participants named pictures in repeated cycles, blocked in semantically close, distant, or unrelated conditions. Only closely related items, but not distantly related items, induced semantic blocking effects. In the first presentation cycle, naming was facilitated, and amplitude modulations in the N1 component around 140–180 ms post-stimulus onset predicted this behavioral facilitation. In contrast, in later cycles, naming was delayed, and a negative-going posterior amplitude modulation around 250–350 ms post-stimulus onset predicted this interference. These findings indicate easier object recognition or identification underlying initial facilitation and increased difficulties during lexical selection. The N1 modulation was reduced but persisted in later cycles in which interference dominated, and the posterior negativity was also present in cycle 1 in which facilitation dominated, demonstrating concurrent effects of conceptual priming and lexical interference in all naming cycles. Our assumptions about the functional role these two opposing forces play in producing semantic context effects are further supported by the finding that the joint modulation of these two ERPs on naming latency exclusively emerged when naming closely related, but not unrelated items. The current findings demonstrate that close relations, but not distant taxonomic relations, induce stronger semantic blocking effects, and that temporally overlapping electrophysiological signatures reflect a trade-off between facilitatory priming and interfering lexical competition.Peer Reviewe

Which factors affect willingness-to-pay for automated vehicle services? Evidence from public road deployment in Stockholm, Sweden

"jats:title"Introduction"/jats:title" "jats:p"Travel demand and travel satisfaction of a transport service are affected by user perceptions of the service quality attributes, and such perceptions should be included in studying user willingness-to-pay (WTP) for automated vehicle (AV) services. This study applied structural equation modelling with service quality attribute perceptions as latent variables affecting WTP."/jats:p" "/jats:sec""jats:sec" "jats:title"Objectives"/jats:title" "jats:p"We investigated how WTP AV services are affected by socio-demographic characteristics, knowledge and experiences with AV, existing travel modes and particularly, perceptions of the associated service quality attributes. The AV services are: 1) "jats:italic"on-demand personalised AV (PAV) service"/jats:italic", 2) "jats:italic"demand responsive shared AV (SAV) service"/jats:italic", and 3) "jats:italic"first−/last-mile automated bus (AB) service"/jats:italic"."/jats:p" "/jats:sec""jats:sec" "jats:title"Methods"/jats:title" "jats:p"The data were collected from 584 potential users of a first−/last-mile AB service trial operated in Kista, Stockholm."/jats:p" "/jats:sec""jats:sec" "jats:title"Results"/jats:title" "jats:p"Results show people hold different expectations towards each type of AV service. These expectations act as the minimum requirements for people to pay for the AV services. Respondents are found to be willing to pay more for PAV service if it is safe, provides good ride comfort, and is competitively priced relative to the price travelling by metro and train over a same distance. Other than service quality attribute perceptions, income level, existing travel modes for daily trips, familiarity with automated driving technology and AB ride experience are important factors affecting WTP for the AV services."/jats:p" "/jats:sec""jats:sec" "jats:title"Conclusion"/jats:title" "jats:p"The developed model can be applied to understand expectations of potential users towards a new AV service, and to identify user groups who are willing to pay the service. New AV services can thus be designed sensibly according to users’ actual needs."/jats:p" "/jats:sec Document type: Articl