New explicit spike solution -- non-local component of the generalized Mixmaster attractor

By applying a standard solution-generating transformation to an arbitrary vacuum Bianchi type II solution, one generates a new solution with spikes commonly observed in numerical simulations. It is conjectured that the spike solution is part of the generalized Mixmaster attractor.Comment: Significantly revised. Colour figures simplified to accommodate non-colour printin

Coordinate Singularities in Harmonically-sliced Cosmologies

Harmonic slicing has in recent years become a standard way of prescribing the lapse function in numerical simulations of general relativity. However, as was first noticed by Alcubierre (1997), numerical solutions generated using this slicing condition can show pathological behaviour. In this paper, analytic and numerical methods are used to examine harmonic slicings of Kasner and Gowdy cosmological spacetimes. It is shown that in general the slicings are prevented from covering the whole of the spacetimes by the appearance of coordinate singularities. As well as limiting the maximum running times of numerical simulations, the coordinate singularities can lead to features being produced in numerically evolved solutions which must be distinguished from genuine physical effects.Comment: 21 pages, REVTeX, 5 figure

A randomised trial evaluating Bevacizumab as adjuvant therapy following resection of AJCC stage IIB, IIC and III cutaneous melanoma : an update

At present, there are no standard therapies for the adjuvant treatment of malignant melanoma. Patients with primary tumours with a high-Breslow thickness (stages IIB and IIC) or with resected loco-regional nodal disease (stage III) are at high risk of developing metastasis and subsequent disease-related death. Given this, it is important that novel therapies are investigated in the adjuvant melanoma setting. Since angiogenesis is essential for primary tumour growth and the development of metastasis, anti-angiogenic agents are attractive potential therapeutic candidates for clinical trials in the adjuvant setting. Therefore, we initiated a phase II trial in resected high-risk cutaneous melanoma, assessing the efficacy of bevacizumab versus observation. In the interim safety data analysis, we demonstrate that bevacizumab is a safe therapy in the adjuvant melanoma setting with no apparent increase in the surgical complication rate after either primary tumour resection and/or loco-regional lymphadenectomy

Numerical simulations of general gravitational singularities

This paper covers some of the current techniques and issues involved in performing numerical simulations of the formation of singularities.Comment: This work was part of the 2006 AEI conference on New Frontiers in Numerical Relativity and was published in an issue of Classical and Quantum Gravity on that conferenc

Initial Hypersurface Formulation: Hamilton-Jacobi Theory for Strongly Coupled Gravitational Systems

Strongly coupled gravitational systems describe Einstein gravity and matter in the limit that Newton's constant G is assumed to be very large. The nonlinear evolution of these systems may be solved analytically in the classical and semiclassical limits by employing a Green function analysis. Using functional methods in a Hamilton-Jacobi setting, one may compute the generating functional (`the phase of the wavefunctional') which satisfies both the energy constraint and the momentum constraint. Previous results are extended to encompass the imposition of an arbitrary initial hypersurface. A Lagrange multiplier in the generating functional restricts the initial fields, and also allows one to formulate the energy constraint on the initial hypersurface. Classical evolution follows as a result of minimizing the generating functional with respect to the initial fields. Examples are given describing Einstein gravity interacting with either a dust field and/or a scalar field. Green functions are explicitly determined for (1) gravity, dust, a scalar field and a cosmological constant and (2) gravity and a scalar field interacting with an exponential potential. This formalism is useful in solving problems of cosmology and of gravitational collapse.Comment: 30 pages Latex (IOP) file with 2 IOP style files, to be published in Classical and Quantum Gravity (1998

Conformational polymorphic changes in the crystal structure of the chiral antiparasitic drug praziquantel and interactions with calcium carbonate

Praziquantel is an antiparasitic drug used for decades. Currently, the praziquantel commercial preparation is a racemic mixture, in which only the levo-enantiomer possesses anthelmintic activity. The knowledge of its properties in the solid state and other chemical-physical properties is necessary for improving its efficacy and applications. Drug solid dispersions were prepared with calcium carbonate at 1:5 drug to excipient weight ratio by solvent evaporation method. Then, the modification of the crystal structure of the racemic polymorph of praziquantel in presence of calcium carbonate has been studied by means of several analytical techniques (DSC,TGA, XRD, SEM, FTIR, Raman spectroscopy and chiral liquid chromatography). This study has been completed with atomistic calculations based on empirical interatomic force fields and quantum mechanics methods applied to the crystal structure of praziquantel and of intermolecular interactions. The results evidenced that calcium carbonate provoked a conformational change in the praziquantel molecule yielding the formation of different polymorphs of praziquantel crystal. These alterations were not observed replacing calcium carbonate with colloidal silica as excipient in the solid dispersion

Plasmon excitations and 1D - 2D dimensional crossover in quantum crossbars

Spectrum of boson fields and two-point correlators are analyzed in quantum crossbars (QCBs, a superlattice formed by m crossed interacting arrays of quantum wires), with short range inter-wire capacitive interaction. Spectral and correlation properties of double (m=2) and triple (m-3) QCBs are studied. It is shown that the standard bosonization procedure is valid, and the system behaves as a sliding Luttinger liquid in the infrared limit, but the high frequency spectral and correlation characteristics have either 1D or 2D nature depending on the direction of the wave vector in the 2D elementary cell of reciprocal lattice. As a result, the crossover from 1D to 2D regime may be experimentally observed. It manifests itself as appearance of additional peaks of optical absorption, non-zero transverse space correlators and periodic energy transfer between arrays ("Rabi oscillations")

Probabilistic reframing for cost-sensitive regression

© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Knowledge Discovery from Data (TKDD), VOL. 8, ISS. 4, (October 2014) http://doi.acm.org/10.1145/2641758Common-day applications of predictive models usually involve the full use of the available contextual information. When the operating context changes, one may fine-tune the by-default (incontextual) prediction or may even abstain from predicting a value (a reject). Global reframing solutions, where the same function is applied to adapt the estimated outputs to a new cost context, are possible solutions here. An alternative approach, which has not been studied in a comprehensive way for regression in the knowledge discovery and data mining literature, is the use of a local (e.g., probabilistic) reframing approach, where decisions are made according to the estimated output and a reliability, confidence, or probability estimation. In this article, we advocate for a simple two-parameter (mean and variance) approach, working with a normal conditional probability density. Given the conditional mean produced by any regression technique, we develop lightweight “enrichment” methods that produce good estimates of the conditional variance, which are used by the probabilistic (local) reframing methods. We apply these methods to some very common families of costsensitive problems, such as optimal predictions in (auction) bids, asymmetric loss scenarios, and rejection rules.This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, and TIN 2013-45732-C4-1-P and GVA projects PROMETEO/2008/051 and PROMETEO2011/052. Finally, part of this work was motivated by the REFRAME project (http://www.reframe-d2k.org) granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA) and funded by Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).Hernández Orallo, J. (2014). Probabilistic reframing for cost-sensitive regression. ACM Transactions on Knowledge Discovery from Data. 8(4):1-55. https://doi.org/10.1145/2641758S15584G. Bansal, A. Sinha, and H. Zhao. 2008. Tuning data mining methods for cost-sensitive regression: A study in loan charge-off forecasting. Journal of Management Information System 25, 3 (Dec. 2008), 315--336.A. P. Basu and N. Ebrahimi. 1992. Bayesian approach to life testing and reliability estimation using asymmetric loss function. Journal of Statistical Planning and Inference 29, 1--2 (1992), 21--31.A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2010. Quantification via probability estimators. In Proceedings of the 2010 IEEE International Conference on Data Mining. IEEE, 737--742.A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2013. Aggregative quantification for regression. Data Mining and Knowledge Discovery (2013), 1--44.A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2009. Calibration of machine learning models. In Handbook of Research on Machine Learning Applications. IGI Global, 128--146.A. Bella, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. 2011. Using negotiable features for prescription problems. Computing 91, 2 (2011), 135--168.J. Bi and K. P. Bennett. 2003. Regression error characteristic curves. In Proceedings of the 20th International Conference on Machine Learning (ICML’03).Z. Bosnić and I. Kononenko. 2008. Comparison of approaches for estimating reliability of individual regression predictions. Data & Knowledge Engineering 67, 3 (2008), 504--516.Z. Bosnić and I. Kononenko. 2009. An overview of advances in reliability estimation of individual predictions in machine learning. Intelligent Data Analysis 13, 2 (2009), 385--401.L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth.P. F. Christoffersen and F. X. Diebold. 1996. Further results on forecasting and model selection under asymmetric loss. Journal of Applied Econometrics 11, 5 (1996), 561--571.P. F. Christoffersen and F. X. Diebold. 1997. Optimal prediction under asymmetric loss. Econometric Theory 13 (1997), 808--817.I. Cohen and M. Goldszmidt. 2004. Properties and benefits of calibrated classifiers. Knowledge Discovery in Databases: PKDD 2004 (2004), 125--136.S. Crone. 2002. Training artificial neural networks for time series prediction using asymmetric cost functions. In Proceedings of the 9th International Conference on Neural Information Processing.J. Demšar. 2006. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7 (2006), 1--30.M. Dumas, L. Aldred, G. Governatori, and A. H. M. Ter Hofstede. 2005. Probabilistic automated bidding in multiple auctions. Electronic Commerce Research 5, 1 (2005), 25--49.C. Elkan. 2001. The foundations of cost-sensitive learning. In Proceedings of the 17th International Conference on Artificial Intelligence (’01), Bernhard Nebel (Ed.). San Francisco, CA, 973--978.G. Elliott and A. Timmermann. 2004. Optimal forecast combinations under general loss functions and forecast error distributions. Journal of Econometrics 122, 1 (2004), 47--79.T. Fawcett. 2006a. An introduction to ROC analysis. Pattern Recognition Letters 27, 8 (2006), 861--874.T. Fawcett. 2006b. ROC graphs with instance-varying costs. Pattern Recognition Letters 27, 8 (2006), 882--891.C. Ferri, P. Flach, and J. Hernández-Orallo. 2002. Learning decision trees using the area under the ROC curve. In Proceedings of the International Conference on Machine Learning. 139--146.C. Ferri, P. Flach, and J. Hernández-Orallo. 2003. Improving the AUC of probabilistic estimation trees. In Proceedings of the 14th European Conference on Machine Learning (ECML’03). Springer, 121--132.C. Ferri and J. Hernández-Orallo. 2004. Cautious classifiers. In ROC Analysis in Artificial Intelligence, 1st International Workshop, ROCAI-2004, Valencia, Spain, August 22, 2004, J. Hernández-Orallo, C. Ferri, N. Lachiche, and P. A. Flach (Eds.). 27--36.P. Flach. 2012. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press.G. Forman. 2008. Quantifying counts and costs via classification. Data Mining and Knowledge Discovery 17, 2 (2008), 164--206.S. García and F. Herrera. 2008. An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. The Journal of Machine Learning Research 9, 2677--2694 (2008), 66.R. Ghani. 2005. Price prediction and insurance for online auctions. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD’05). ACM, New York, NY, 411--418.C. W. J. Granger. 1969. Prediction with a generalized cost of error function. Operational Research (1969), 199--207.C. W. J. Granger. 1999. Outline of forecast theory using generalized cost functions. Spanish Economic Review 1, 2 (1999), 161--173.P. Hall, J. Racine, and Q. Li. 2004. Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association 99, 468 (2004), 1015--1026.P. Hall, R. C. L. Wolff, and Q. Yao. 1999. Methods for estimating a conditional distribution function. Journal of the American Statistical Association (1999), 154--163.T. J. Hastie, R. J. Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.J. Hernández-Orallo. 2013. ROC curves for regression. Pattern Recognition 46, 12 (2013), 3395--3411.J. Hernández-Orallo, P. Flach, and C. Ferri. 2012. A unified view of performance metrics: Translating threshold choice into expected classification loss. Journal of Machine Learning Research 13 (2012), 2813--2869.J. Hernández-Orallo, P. Flach, and C. Ferri. 2013. ROC curves in cost space. Machine Learning 93, 1 (2013), 71--91.J. N. Hwang, S. R. Lay, and A. Lippman. 1994. Nonparametric multivariate density estimation: A comparative study. IEEE Transactions on Signal Processing 42, 10 (1994), 2795--2810.R. J. Hyndman, D. M. Bashtannyk, and G. K. Grunwald. 1996. Estimating and visualizing conditional densities. Journal of Computational and Graphical Statistics (1996), 315--336.N. Japkowicz and M. Shah. 2011. Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press.M. Jino, B. T. de Abreu, and others. 2010. Machine learning methods and asymmetric cost function to estimate execution effort of software testing. In Proceedings of the 2010 3rd International Conference on Software Testing, Verification and Validation (ICST’10). IEEE, 275--284.B. Kitts and B. Leblanc. 2004. Optimal bidding on keyword auctions. Electronic Markets 14, 3 (2004), 186--201.N. Lachiche and P. Flach. 2003. Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In Proceedings of the International Conference on Machine Learning, Vol. 20-1. 416.H. Papadopoulos. 2008. Inductive conformal prediction: Theory and application to neural networks. Tools in Artificial Intelligence 18 (2008), 315--330.H. Papadopoulos, K. Proedrou, V. Vovk, and A. Gammerman. 2002. Inductive confidence machines for regression. In Machine Learning: ECML 2002, Tapio Elomaa, Heikki Mannila, and Hannu Toivonen (Eds.). Lecture Notes in Computer Science, Vol. 2430. Springer, Berlin, 185--194.H. Papadopoulos, V. Vovk, and A. Gammerman. 2011. Regression conformal prediction with nearest neighbours. Journal of Artificial Intelligence Research 40, 1 (2011), 815--840.T. Pietraszek. 2007. On the use of ROC analysis for the optimization of abstaining classifiers. Machine Learning 68, 2 (2007), 137--169.J. C. Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, Boston, 61--74.F. Provost and P. Domingos. 2003. Tree induction for probability-based ranking. Machine Learning 52, 3 (2003), 199--215.R Team and others. 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.R. Ribeiro. 2011. Utility-based Regression. PhD thesis, Department of Computer Science, Faculty of Sciences, University of Porto.M. Rosenblatt. 1969. Conditional probability density and regression estimators. Multivariate Analysis II 25 (1969), 31.S. Rosset, C. Perlich, and B. Zadrozny. 2007. Ranking-based evaluation of regression models. Knowledge and Information Systems 12, 3 (2007), 331--353.R. E. Schapire, P. Stone, D. McAllester, M. L. Littman, and J. A. Csirik. 2002. Modeling auction price uncertainty using boosting-based conditional density estimation. In Proceedings of the International Conference on Machine Learning. 546--553.G. Shafer and V. Vovk. 2008. A tutorial on conformal prediction. Journal of Machine Learning Research 9 (2008), 371--421.J. A. Swets, R. M. Dawes, and J. Monahan. 2000. Better decisions through science. Scientific American 283, 4 (Oct. 2000), 82--87.R. D. Thompson and A. P. Basu. 1996. Asymmetric loss functions for estimating system reliability. In Bayesian Analysis in Statistics and Econometrics. John Wiley & Sons, 471--482.L. Torgo. 2005. Regression error characteristic surfaces. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 697--702.L. Torgo. 2010. Data Mining with R. Chapman and Hall/CRC Press.L. Torgo and R. Ribeiro. 2007. Utility-based regression. Knowledge Discovery in Databases: PKDD 2007. 597--604.L. Torgo and R. Ribeiro. 2009. Precision and recall for regression. In Discovery Science. Springer, 332--346.P. Turney. 2000. Types of cost in inductive concept learning. Canada National Research Council Publications Archive.L. Wasserman. 2006. All of Nonparametric Statistics. Springer-Verlag, New York.M. P. Wellman, D. M. Reeves, K. M. Lochner, and Y. Vorobeychik. 2004. Price prediction in a trading agent competition. Journal of Artificial Intelligence Research 21 (2004), 19--36.K. Yu and M. C. Jones. 2004. Likelihood-based local linear estimation of the conditional variance function. Journal of the American Statistical Association 99, 465 (2004), 139--144.B. Zadrozny and C. Elkan. 2002. Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 694--699.A. Zellner. 1986. Bayesian estimation and prediction using asymmetric loss functions. Journal of the American Statistical Association (1986), 446--451.H. Zhao, A. P. Sinha, and G. Bansal. 2011. An extended tuning method for cost-sensitive regression and forecasting. Decision Support Systems

Invasive Vegetation Affects Amphibian Skin Microbiota and Body Condition

Invasive plants are major drivers of habitat modification and the scale of their impact is increasing globally as anthropogenic activities facilitate their spread. In California, an invasive plant genus of great concern is Eucalyptus. Eucalyptus leaves can alter soil chemistry and negatively affect underground macro- and microbial communities. Amphibians serve as excellent models to evaluate the effect of Eucalyptus invasion on ground-dwelling species as they predate on soil arthropods and incorporate soil microbes into their microbiotas. The skin microbiota is particularly important to amphibian health, suggesting that invasive plant species could ultimately affect amphibian populations. To investigate the potential for invasive vegetation to induce changes in microbial communities, we sampled microbial communities in the soil and on the skin of local amphibians. Specifically, we compared Batrachoseps attenuatus skin microbiomes in both Eucalyptus globulus (Myrtaceae) and native Quercus agriflolia (Fagaceae) dominated forests in the San Francisco Bay Area. We determined whether changes in microbial diversity and composition in both soil and Batrachoseps attenuatus skin were associated with dominant vegetation type. To evaluate animal health across vegetation types, we compared Batrachoseps attenuatus body condition and the presence/absence of the amphibian skin pathogen Batrachochytrium dendrobatidis. We found that Eucalyptus invasion had no measurable effect on soil microbial community diversity and a relatively small effect (compared to the effect of site identity) on community structure in the microhabitats sampled. In contrast, our results show that Batrachoseps attenuatus skin microbiota diversity was greater in Quercus dominated habitats. One amplicon sequence variant identified in the family Chlamydiaceae was observed in higher relative abundance among salamanders sampled in Eucalyptus dominated habitats. We also observed that Batrachoseps attenuatus body condition was higher in Quercus dominated habitats. Incidence of Batrachochytrium dendrobatidis across all individuals was very low (only one Batrachochytrium dendrobatidis positive individual). The effect on body condition demonstrates that although Eucalyptus may not always decrease amphibian abundance or diversity, it can potentially have cryptic negative effects. Our findings prompt further work to determine the mechanisms that lead to changes in the health and microbiome of native species post-plant invasion