Visual BFI: an Exploratory Study for Image-based Personality Test

This paper positions and explores the topic of image-based personality test. Instead of responding to text-based questions, the subjects will be provided a set of "choose-your-favorite-image" visual questions. With the image options of each question belonging to the same concept, the subjects' personality traits are estimated by observing their preferences of images under several unique concepts. The solution to design such an image-based personality test consists of concept-question identification and image-option selection. We have presented a preliminary framework to regularize these two steps in this exploratory study. A demo version of the designed image-based personality test is available at http://www.visualbfi.org/. Subjective as well as objective evaluations have demonstrated the feasibility of image-based personality test in limited questions

Score-Based Bayesian Skill Learning

We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match outcomes. TrueSkill has proven to be a very effective algorithm for matchmaking - the process of pairing competitors based on similar skill-level - in competitive online gaming. However, for the case of two teams/players, TrueSkill only learns from win, lose, or draw outcomes and cannot use additional match outcome information such as scores. To address this deficiency, we propose novel Bayesian graphical models as extensions of TrueSkill that (1) model player's offence and defence skills separately and (2) model how these offence and defence skills interact to generate score-based match outcomes. We derive efficient (approximate) Bayesian inference methods for inferring latent skills in these new models and evaluate them on three real data sets including Halo 2 XBox Live matches. Empirical evaluations demonstrate that the new score-based models (a) provide more accurate win/loss probability estimates than TrueSkill when training data is limited, (b) provide competitive and often better win/loss classification performance than TrueSkill, and (c) provide reasonable score outcome predictions with an appropriate choice of likelihood - prediction for which TrueSkill was not designed, but which can be useful in many applications. © 2012 Springer-Verlag

Should We Use Rifampicin in Periprosthetic Joint Infections Caused by Staphylococci When the Implant Has Been Exchanged? A Multicenter Observational Cohort Study

BACKGROUND: Previous studies demonstrated the efficacy of a rifampicin-based regimen in the treatment of acute staphylococcal periprosthetic joint infections (PJIs) treated with surgical debridement. However, evidence is lacking to support the use of rifampicin in cases where the implant is exchanged during revision. METHODS: We included all consecutive cases of staphylococcal PJIs treated from January 2013 to December 2018 with revision surgery in this international, retrospective, multicenter observational cohort study. PJI was defined according to the European Bone and Joint Infection Society diagnostic criteria. A relapse or reinfection during follow-up, the need for antibiotic suppressive therapy, the need for implant removal, and PJI-related death were defined as clinical failure. Cases without reimplantation or with follow-upexcluded. RESULTS: A total of 375 cases were included in the final analysis, including 124 1-stage exchanges (33.1%) and 251 2-stage exchanges (66.9%). Of those, 101 cases failed (26.9%). There was no statistically significant difference in failure of patients receiving rifampicin (22.5%, 42/187) and those not receiving rifampicin (31.4%, 59/188; CONCLUSIONS: Combination treatment with rifampicin increases treatment success in patients with chronic staphylococcal PJI treated with 2-stage exchange arthroplasty

Machine learning for pairwise data : applications for preference learning and supervised network inference

Contains fulltext : 91325.pdf (publisher's version ) (Open Access)Radboud Universiteit Nijmegen, 18 oktober 2011Promotor : Heskes, T.M.135 p

A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions

Contains fulltext : 132693pre.pdf (preprint version ) (Open Access

Regularized Output Kernel Regression for protein-protein interaction prediction: application to link transfer and transduction

Contains fulltext : 84135.pdf (postprint version ) (Open Access)Machine Learning in Computational Biology (MLCB) 2010 : A NIPS 2010 workshop (Dec. 10 or 11

Learning from multiple annotators with Gaussian processes

Contains fulltext : 91995.pdf (preprint version ) (Closed access