GRACE/SUSY: Automatic generation of tree amplitudes in the minimal supersymmetric standard model

GRACE/SUSY is a program package for generating the tree-level amplitude and evaluating the corresponding cross section of processes of the minimal supersymmetric extension of the standard model (MSSM). The Higgs potential adopted in the system, however, is assumed to have a more general form indicated by the two-Higgs-doublet model. This system is an extension of GRACE for the standard model (SM) of the electroweak and strong interactions. For a given MSSM process the Feynman graphs and amplitudes at tree-level are automatically created. The Monte Carlo phase space integration by means of BASES gives the total and differential cross sections. When combined with SPRING, an event generator, the program package provides us with the simulation of the SUSY particle productions.     

An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range

In this paper, we present an automatic C-code generation strategy for real-time nonlinear model predictive control (NMPC), which is designed for applications with kilohertz sample rates. The corresponding code export module has been implemented within the software package ACADO Toolkit. It is capable of exporting fixed step-size integrators together with their sensitivities as well as a real-time Gauss–Newton method. Here, we employ the symbolic representation of optimal control problems in ACADO in order to auto-generate plain C-code which is optimized for final production. The exported code has been tested for model predictive control scenarios comprising constrained nonlinear dynamic systems with four states and a control horizon of ten samples. The numerical simulations show a promising performance of the exported code being able to provide feedback in much less than a millisecond.     

An analysis of the parametrization by data driven local coordinates for multivariable linear systems

In this paper, we study a novel parametrization for state-space systems, namely data driven local coordinates (DDLC) which have recently been introduced and applied. Even though DDLC has meanwhile become the default parametrization used in the system identification toolbox of the software package MATLAB, an analysis of properties of DDLC, which are relevant to identification, has not been performed up to now. In this paper, we provide insights into the geometry and topology of the DDLC construction and show a number of results which are important for actual identification such as maximum likelihood-type estimation.     

Extended rate, more GFUN

We present a software package that guesses formulae for sequences of, for example, rational numbers or rational functions, given the first few terms. We implement an algorithm due to Bernhard Beckermann and George Labahn, together with some enhancements to render our package efficient. Thus we extend and complement Christian Krattenthaler’s program Rate.m, the parts concerned with guessing of Bruno Salvy and Paul Zimmermann’s GFUN, the univariate case of Manuel Kauers’ Guess.m and Manuel Kauers’ and Christoph Koutschan’s qGeneratingFunctions.m.     

A new tool for sharing and querying of clinical documents modeled using HL7 Version 3 standard

We present a new software tool called CDN (Collaborative Data Network) for sharing and querying of clinical documents modeled using HL7 v3 standard (e.g., Clinical Document Architecture (CDA), Continuity of Care Document (CCD)). Similar to the caBIG initiative, CDN aims to foster innovations in cancer treatment and diagnosis through large-scale, sharing of clinical data. We focus on cancer because it is the second leading cause of deaths in the US. CDN is based on the synergistic combination of peer-to-peer technology and the extensible markup language XML and XQuery. Using CDN, a user can pose both structured queries and keyword queries on the HL7 v3 documents hosted by data providers. CDN is unique in its design – it supports location oblivious queries in a large-scale, network wherein a user does not explicitly provide the location of the data for a query. A location service in CDN discovers data of interest in the network at query time. CDN uses standard cryptographic techniques to provide security to data providers and protect the privacy of patients. Using CDN, a user can pose clinical queries pertaining to cancer containing aggregations and joins across data hosted by multiple data providers. CDN is implemented with open-source software for web application development and XML query processing. We ran CDN in a distributed environment using Amazon EC2 as a testbed. We report its performance on real and synthetic datasets of discharge summaries. We show that CDN can achieve good performance in a setup with large number of data providers and documents.     

MCgrid: Projecting cross section calculations on grids

MCgrid  is a software package that provides access to the APPLgrid  interpolation tool for Monte Carlo event generator codes, allowing for fast and flexible variations of scales, coupling parameters and PDFs in cutting edge leading- and next-to-leading-order QCD calculations. This is achieved by providing additional tools to the Rivet  analysis system for the construction of MCgrid  enhanced Rivet  analyses. The interface is based around a one-to-one correspondence between a Rivet  histogram class and a wrapper for an APPLgrid  interpolation grid. The Rivet  system provides all of the analysis tools required to project a Monte Carlo weight upon an observable bin, and the MCgrid  package provides the correct conversion of the event weight to an APPLgrid  fill call. MCgrid  has been tested and designed for use with the SHERPA  event generator, however as with Rivet  the package is suitable for use with any code which can produce events in the HepMC event record format.     

Designing group sequential randomized clinical trials with time to event end points using a R function

A major and difficult task is the design of clinical trials with a time to event endpoint. In fact, it is necessary to compute the number of events and in a second step the required number of patients. Several commercial software packages are available for computing sample size in clinical trials with sequential designs and time to event endpoints, but there are a few R functions implemented. The purpose of this paper is to describe features and use of the R function. plansurvct.func, which is an add-on function to the package gsDesign which permits in one run of the program to calculate the number of events, and required sample size but also boundaries and corresponding p-values for a group sequential design. The use of the function plansurvct.func is illustrated by several examples and validated using East software.     

An R implementation for generalized Birnbaum-Saunders distributions

The Birnbaum-Saunders (BS) model is a positively skewed statistical distribution that has received great attention in recent decades. A generalized version of this model was derived based on symmetrical distributions in the real line named the generalized BS (GBS) distribution. The R package named gbs was developed to analyze data from GBS models. This package contains probabilistic and reliability indicators and random number generators from GBS distributions. Parameter estimates for censored and uncensored data can also be obtained by means of likelihood methods from the gbs package. Goodness-of-fit and diagnostic methods were also implemented in this package in order to check the suitability of the GBS models. In this article, the capabilities and features of the gbs package are illustrated by using simulated and real data sets. Shape and reliability analyses for GBS models are presented. A simulation study for evaluating the quality and sensitivity of the estimation method developed in the package is provided and discussed.     

Open-Source Model Checking

We present GMC², a software model checker for GCC, the open-source compiler from the Free Software Foundation (FSF). GMC², which is part of the GMC static-analysis and model-checking tool suite for GCC under development at SUNY Stony Brook, can be seen as an extension of Monte Carlo model checking to the setting of concurrent, procedural programming languages. Monte Carlo model checking is a newly developed technique that utilizes the theory of geometric random variables, statistical hypothesis testing, and random sampling of lassos in Büchi automata to realize a one- sided error, randomized algorithm for LTL model checking. To handle the function call/return mechanisms inherent in procedural languages such as C/C++, the version of Monte Carlo model checking implemented in GMC² is optimized for pushdown-automaton models. Our experimental results demonstrate that this approach yields an efficient and scalable software model checker for GCC.     

Vscape V1.1.0. An interactive tool for metastable vacua

Vscape is an interactive tool for studying the one-loop effective potential of an ungauged supersymmetric model of chiral multiplets. The program allows the user to define a supersymmetric model by specifying the superpotential. The F-terms and the scalar and fermionic mass matrices are calculated symbolically. The program then allows you to search numerically for (meta)stable minima of the one-loop effective potential. Additional commands enable you to further study specific minima, by, e.g., computing the mass spectrum for those vacua. Vscape combines the flexibility of symbolic software, with the speed of a numerical package.

Program summary

Program title:Vscape 1.1.1Catalogue identifier: ADZW_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADZW_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 80 507No. of bytes in distributed program, including test data, etc.: 6 708 938Distribution format: tar.gzProgramming language: C++Computer: Pentium 4 PC Computers: need (GNU) C++ compiler, Linux standard GNU installation (./configure; make; make install). A precompiled Windows XP version is included in the distribution packageOperating system: Linux, Windows XP using cygwinRAM: 10 MBWord size: 32 bitsClassification: 11.6External routines: GSL (http://www.gnu.org/software/gsl/), CLN (http://www.ginac.de/CLN/), GiNaC (http://directory.fsf.org/GiNaC.html)Nature of problem:Vscape is an interactive tool for studying the one-loop effective potential of an ungauged supersymmetric model of chiral multiplets. The program allows the user to define a supersymmetric model by specifying the superpotential. The F-terms and the scalar and fermionic mass matrices are calculated symbolically. The program then allows you to search numerically for (meta)stable minima of the one-loop effective potential. Additional commands enable you to further study specific minima, by, e.g., computing the mass spectrum for those vacua. Vscape combines the flexibility of symbolic software with the speed of a numerical package.Solution method: Coleman-Weinberg potential is computed using numerical matrix diagonalization. Minima of the one-loop effective potential are found using the Nelder and Mead simplex algorithm. The one-loop effective potential can be studied using numerical differentiation. Symbolic users interface implemented using flex and bison.Restrictions:N=1 supersymmetric chiral models onlyUnusual features: GiNaC (+CLN), GSL, ReadLib (not essential)Running time: Interactive users interface. Most commands execute in a few ms. Computationally intensive commands execute in order of minutes, depending on the complexity of the user defined model.     

hsmm — An R package for analyzing hidden semi-Markov models

Hidden semi-Markov models are a generalization of the well-known hidden Markov model. They allow for a greater flexibility of sojourn time distributions, which implicitly follow a geometric distribution in the case of a hidden Markov chain. The aim of this paper is to describe hsmm, a new software package for the statistical computing environment R. This package allows for the simulation and maximum likelihood estimation of hidden semi-Markov models. The implemented Expectation Maximization algorithm assumes that the time spent in the last visited state is subject to right-censoring. It is therefore not subject to the common limitation that the last visited state terminates at the last observation. Additionally, hsmm permits the user to make inferences about the underlying state sequence via the Viterbi algorithm and smoothing probabilities.     

Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design

Logistic regression models are frequently used in epidemiological studies for estimating associations that demographic, behavioral, and risk factor variables have on a dichotomous outcome, such as disease being present versus absent. After the coefficients in a logistic regression model have been estimated, goodness-of-fit of the resulting model should be examined, particularly if the purpose of the model is to estimate probabilities of event occurrences. While various goodness-of-fit tests have been proposed, the properties of these tests have been studied under the assumption that observations selected were independent and identically distributed. Increasingly, epidemiologists are using large-scale sample survey data when fitting logistic regression models, such as the National Health Interview Survey or the National Health and Nutrition Examination Survey. Unfortunately, for such situations no goodness-of-fit testing procedures have been developed or implemented in available software. To address this problem, goodness-of-fit tests for logistic regression models when data are collected using complex sampling designs are proposed. Properties of the proposed tests were examined using extensive simulation studies and results were compared to traditional goodness-of-fit tests. A Stata ado function svylogitgof for estimating the F-adjusted mean residual test after svylogit fit is available at the author's website http://www.people.vcu.edu/~kjarcher/Research/Data.htm.     

Cobra: A package for co-breaking analysis

Cobra, an econometric software package designed for CO-BReaking Analysis, is introduced. Cobra is programmed in Ox, the object-oriented statistical system, and consists of three modules: Firstly, CobraDgp is derived from the Database class and enables the user to generate a multivariate time series that is subject to multiple breaks in its intercept as well as linear trend. Secondly, the Cobra module is derived from the Modelbase class and implements two algorithms for the estimation of co-breaking relationships. Taking advantage of the capabilities provided by Modelbase, Cobra may be loaded into OxPack and used with the GUI GiveWin as front end. Finally, CobraSim is derived from the Simulation class and wrapped around CobraDgp and Cobra to allow a straightforward implementation of Monte Carlo experiments. A brief introduction of the concept of co-breaking is given before Cobra is illustrated by means of an empirical example as well as a simple Monte Carlo. Excerpts of Ox code as well as screenshots are provided.     

Generalized linear models with clustered data: Fixed and random effects models

The statistical analysis of mixed effects models for binary and count data is investigated. In the statistical computing environment R, there are a few packages that estimate models of this kind. The package lme4 is a de facto standard for mixed effects models. The package glmmML allows non-normal distributions in the specification of random intercepts. It also allows for the estimation of a fixed effects model, assuming that all cluster intercepts are distinct fixed parameters; moreover, a bootstrapping technique is implemented to replace asymptotic analysis. The random intercepts model is fitted using a maximum likelihood estimator with adaptive Gauss-Hermite and Laplace quadrature approximations of the likelihood function. The fixed effects model is fitted through a profiling approach, which is necessary when the number of clusters is large. In a simulation study, the two approaches are compared. The fixed effects model has severe bias when the mixed effects variance is positive and the number of clusters is large.     

SARAH 4: A tool for (not only SUSY) model builders

We present the new version of the Mathematica package SARAH which provides the same features for a non-supersymmetric model as previous versions for supersymmetric models. This includes an easy and straightforward definition of the model, the calculation of all vertices, mass matrices, tadpole equations, and self-energies. Also the two-loop renormalization group equations for a general gauge theory are now included and have been validated with the independent Python code PyR@TE. Model files for FeynArts, CalcHep/CompHep, WHIZARD and in the UFO format can be written, and source code for SPheno for the calculation of the mass spectrum, a set of precision observables, and the decay widths and branching ratios of all states can be generated. Furthermore, the new version includes routines to output model files for Vevacious for both, supersymmetric and non-supersymmetric, models. Global symmetries are also supported with this version and by linking Susyno the handling of Lie groups has been improved and extended.     

Numerical object oriented quantum field theory calculations

The qft++ package is a library of C++ classes that facilitate numerical (not algebraic) quantum field theory calculations. Mathematical objects such as matrices, tensors, Dirac spinors, polarization and orbital angular momentum tensors, etc. are represented as C++ objects in qft++. The package permits construction of code which closely resembles quantum field theory expressions, allowing for quick and reliable calculations.     

Clustering of discretely observed diffusion processes

A new distance to classify time series is proposed. The underlying generating process is assumed to be a diffusion process solution to stochastic differential equations and observed at discrete times. The mesh of observations is not required to shrink to zero. The new dissimilarity measure is based on the L¹ distance between the Markov operators estimated on two observed paths. Simulation experiments are used to analyze the performance of the proposed distance under several conditions including perturbation and misspecification. As an example, real financial data from NYSE/NASDAQ stocks are analyzed and evidence is provided that the new distance seems capable to catch differences in both the drift and diffusion coefficients better than other commonly used non-parametric distances. Corresponding software is available in the add-on package sde for the R statistical environment.