Generalized vec trick for fast learning of pairwise kernel models

Pairwise learning corresponds to the supervised learning setting where the goal is to make predictions for pairs of objects. Prominent applications include predicting drug-target or protein-protein interactions, or customer-product preferences. In this work, we present a comprehensive review of pairwise kernels, that have been proposed for incorporating prior knowledge about the relationship between the objects. Specifically, we consider the standard, symmetric and anti-symmetric Kronecker product kernels, metric-learning, Cartesian, ranking, as well as linear, polynomial and Gaussian kernels. Recently, a O(nm + nq) time generalized vec trick algorithm, where n, m, and q denote the number of pairs, drugs and targets, was introduced for training kernel methods with the Kronecker product kernel. This was a significant improvement over previous O(n(2)) training methods, since in most real-world applications m, q << n. In this work we show how all the reviewed kernels can be expressed as sums of Kronecker products, allowing the use of generalized vec trick for speeding up their computation. In the experiments, we demonstrate how the introduced approach allows scaling pairwise kernels to much larger data sets than previously feasible, and provide an extensive comparison of the kernels on a number of biological interaction prediction tasks

Treebanking Finnish

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 79-90. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891

Bayesian Inference for Predicting the Monetization Percentage in Free-to-Play Games

Free-to-play has become one of the most popular monetization models, and as a consequence game developers need to get the players to purchase in the game instead of getting players to buy the game. Game analytics and player monetization prediction are important parts in estimating the profitability of a free-to-play game. In this paper, we concentrate on predicting the fraction of monetizing players among all players. Our method is based on a survival analysis mixture cure model, and can be applied to unlabeled data collected from any free-to-play game. We formulate a statistical model and use the Expectation Maximization algorithm to solve the latent monetization percentage and the monetization rate. The original method is modified by using Bayesian inference, and the results of the versions are compared. The method can be applied as a preliminary profitability study in situations where there is no extensive historical game data available, such as game and business development scenarios that need to utilize real time analytics. Index Terms—Bayesian Inference, Free-to-play, Monetization, Survival Analysis</p

Pain process of patients with cardiac surgery — Semantic annotation of electronic patient record data

Aims and objectives: To describe and compare the pain process of the patients’ with cardiac surgery through nurses’ and physicians’ documentations in the electronic patient records.Background: Postoperative pain assessment and management should be documented regularly, to ensure optimal pain care process for patients. Despite availability of evidence‐based guidelines, pain assessment and documentation remain inadequate.Design: A retrospective patients’ record review.Methods: The original data consisted of the electronic patient records of 26,922 patients with a diagnosed heart disease. A total of 1,818 care episodes of patients with cardiac surgery were selected from the data. We used random sampling to obtain 280 care episodes for annotation. These 280 care episodes contained 2,156 physician reports and 1,327 days of nursing notes. We developed an annotation manual and schema, and then, we manually conducted semantic annotation on care episodes, using the Brat annotation tool. We analysed the annotation units using thematic analysis. Consolidated criteria for reporting qualitative research guideline was followed in reporting where appropriate in this study design.Results: We discovered expressions of six different aspects of pain process: (a) cause, (b) situation, (c) features, (d) consequences, (e) actions and (f) outcomes. We determined that five of the aspects existed chronologically. However, the features of pain were simultaneously existing. They indicated the location, quality, intensity, and temporality of the pain and they were present in every phase of the patient’s pain process. Cardiac and postoperative pain documentations differed from each other in used expressions and in the quantity and quality of descriptions.Conclusion: We could construct a comprehensive pain process of the patients with cardiac surgery from several electronic patient records. The challenge remains how to support systematic documentation in each patient.Relevance to clinical practice: The study provides knowledge and guidance of pain process aspects that can be used to achieve an effective pain assessment and more comprehensive documentation.<br /

Type 2 diabetes enhances arterial uptake of choline in atherosclerotic mice: an imaging study with positron emission tomography tracer 18F-fluoromethylcholine

Additional file 1. Supplemental data