Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Geometrical Insights for Implicit Generative Modeling

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differences. In particular, we can establish surprising approximate global convergence guarantees for the $1$-Wasserstein distance,even when the parametric generator has a nonconvex parametrization.Comment: this version fixes a typo in a definitio

Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-

Advanced tools and techniques to add value to soil stabilization practice

The aim of this paper is to demonstrate the advanced tools and techniques used for adding value to the soil stabilization practice. The tools presented involve advanced laboratory tests and modeling using codes and soft computing to evaluate the mechanical behavior of stabilized soils with cement, ranging from short-term to long-term behavior. More precisely, these tools are able to: 1. Predict the mechanical behavior of the stabilized soils over time from data obtained in the early ages saving time in laboratory tests; 2. Predict the mechanical behavior of the stabilized soils over time based on basic parameters of soil type and binder using historical accurate data, avoiding mechanical laboratory tests. 3. Incorporate the serviceability limit state concept in a novel proposal to estimate the design modulus in function of the uniaxial compressive strength and the strain level, making more economic and sustainable geotechnical solutions.This work was supported by FCT—‘‘Fundação para a Ciência e a Tecnologia’’, within ISISE, project UID/ECI/04029/2013 and through the post doctoral Grant fellowship with reference SFRH/BPD/94792/2013. This work was also partly financed by FEDER funds through the Competitivity Factors Operational Programme—COMPETE and by national funds through FCT within the scope of the project POCI-01-0145-FEDER-007633.info:eu-repo/semantics/publishedVersio

Are groups more rational than individuals? A review of interactive decision making in groups

Many decisions are interactive; the outcome of one party depends not only on its decisions or on acts of nature but also on the decisions of others. In the present article, we review the literature on decision making made by groups of the past 25 years. Researchers have compared the strategic behavior of groups and individuals in many games: prisoner's dilemma, dictator, ultimatum, trust, centipede and principal-agent games, among others. Our review suggests that results are quite consistent in revealing that groups behave closer to the game-theoretical assumption of rationality and selfishness than individuals. We conclude by discussing future research avenues in this area

Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene

BACKGROUND: There is a need to develop robust and clinically applicable gene expression signatures. Hypoxia is a key factor promoting solid tumour progression and resistance to therapy; a hypoxia signature has the potential to be not only prognostic but also to predict benefit from particular interventions. METHODS: An approach for deriving signatures that combine knowledge of gene function and analysis of in vivo co-expression patterns was used to define a common hypoxia signature from three head and neck and five breast cancer studies. Previously validated hypoxia-regulated genes (seeds) were used to generate hypoxia co-expression cancer networks. RESULTS: A common hypoxia signature, or metagene, was derived by selecting genes that were consistently co-expressed with the hypoxia seeds in multiple cancers. This was highly enriched for hypoxia-regulated pathways, and prognostic in multivariate analyses. Genes with the highest connectivity were also the most prognostic, and a reduced metagene consisting of a small number of top-ranked genes, including VEGFA, SLC2A1 and PGAM1, outperformed both a larger signature and reported signatures in independent data sets of head and neck, breast and lung cancers. CONCLUSION: Combined knowledge of multiple genes' function from in vitro experiments together with meta-analysis of multiple cancers can deliver compact and robust signatures suitable for clinical application

Environmental variables, habitat discontinuity and life history shaping the genetic structure of Pomatoschistus marmoratus

Coastal lagoons are semi-isolated ecosystems exposed to wide fluctuations of environmental conditions and showing habitat fragmentation. These features may play an important role in separating species into different populations, even at small spatial scales. In this study, we evaluate the concordance between mitochondrial (previous published data) and nuclear data analyzing the genetic variability of Pomatoschistus marmoratus in five localities, inside and outside the Mar Menor coastal lagoon (SE Spain) using eight microsatellites. High genetic diversity and similar levels of allele richness were observed across all loci and localities, although significant genic and genotypic differentiation was found between populations inside and outside the lagoon. In contrast to the FST values obtained from previous mitochondrial DNA analyses (control region), the microsatellite data exhibited significant differentiation among samples inside the Mar Menor and between lagoonal and marine samples. This pattern was corroborated using Cavalli-Sforza genetic distances. The habitat fragmentation inside the coastal lagoon and among lagoon and marine localities could be acting as a barrier to gene flow and contributing to the observed genetic structure. Our results from generalized additive models point a significant link between extreme lagoonal environmental conditions (mainly maximum salinity) and P. marmoratus genetic composition. Thereby, these environmental features could be also acting on genetic structure of coastal lagoon populations of P. marmoratus favoring their genetic divergence. The mating strategy of P. marmoratus could be also influencing our results obtained from mitochondrial and nuclear DNA. Therefore, a special consideration must be done in the selection of the DNA markers depending on the reproductive strategy of the species

Non-allergic rhinitis: a case report and review

Rhinitis is characterized by rhinorrhea, sneezing, nasal congestion, nasal itch and/or postnasal drip. Often the first step in arriving at a diagnosis is to exclude or diagnose sensitivity to inhalant allergens. Non-allergic rhinitis (NAR) comprises multiple distinct conditions that may even co-exist with allergic rhinitis (AR). They may differ in their presentation and treatment. As well, the pathogenesis of NAR is not clearly elucidated and likely varied. There are many conditions that can have similar presentations to NAR or AR, including nasal polyps, anatomical/mechanical factors, autoimmune diseases, metabolic conditions, genetic conditions and immunodeficiency. Here we present a case of a rare condition initially diagnosed and treated as typical allergic rhinitis vs. vasomotor rhinitis, but found to be something much more serious. This case illustrates the importance of maintaining an appropriate differential diagnosis for a complaint routinely seen as mundane. The case presentation is followed by a review of the potential causes and pathogenesis of NAR