Prediction and dimension

Given a set X of sequences over a finite alphabet, we investigate the following three quantities.

(i)

The feasible predictability of X is the highest success ratio that a polynomial-time randomized predictor can achieve on all sequences in X.

(ii)

The deterministic feasible predictability of X is the highest success ratio that a polynomial-time deterministic predictor can achieve on all sequences in X.

(iii)

The feasible dimension of X is the polynomial-time effectivization of the classical Hausdorff dimension (“fractal dimension”) of X.

Predictability is known to be stable in the sense that the feasible predictability of X∪Y is always the minimum of the feasible predictabilities of X and Y. We show that deterministic predictability also has this property if X and Y are computably presentable. We show that deterministic predictability coincides with predictability on singleton sets. Our main theorem states that the feasible dimension of X is bounded above by the maximum entropy of the predictability of X and bounded below by the segmented self-information of the predictability of X, and that these bounds are tight.     

A new version of Scilab software package for the study of dynamical systems

This work presents a new version of a software package for the study of chaotic flows, maps and fractals [1]. The codes were written using Scilab, a software package for numerical computations providing a powerful open computing environment for engineering and scientific applications. It was found that Scilab provides various functions for ordinary differential equation solving, Fast Fourier Transform, autocorrelation, and excellent 2D and 3D graphical capabilities. The chaotic behaviors of the nonlinear dynamics systems were analyzed using phase-space maps, autocorrelation functions, power spectra, Lyapunov exponents and Kolmogorov-Sinai entropy. Various well-known examples are implemented, with the capability of the users inserting their own ODE or iterative equations.

New version program summary

Program title: Chaos v2.0Catalogue identifier: AEAP_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAP_v2_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.: 1275No. of bytes in distributed program, including test data, etc.: 7135Distribution format: tar.gzProgramming language: Scilab 5.1.1. Scilab 5.1.1 should be installed before running the program. Information about the installation can be found at http://wiki.scilab.org/howto/install/windows.Computer: PC-compatible running Scilab on MS Windows or LinuxOperating system: Windows XP, LinuxRAM: below 150 MegabytesClassification: 6.2Catalogue identifier of previous version: AEAP_v1_0Journal reference of previous version: Comput. Phys. Comm. 178 (2008) 788Does the new version supersede the previous version?: YesNature of problem: Any physical model containing linear or nonlinear ordinary differential equations (ODE).Solution method:

1.

Numerical solving of ordinary differential equations for the study of chaotic flows. The chaotic behavior of the nonlinear dynamical system is analyzed using Poincare sections, phase-space maps, autocorrelation functions, power spectra, Lyapunov exponents and Kolmogorov-Sinai entropies.

2.

Numerical solving of iterative equations for the study of maps and fractals.

Reasons for new version: The program has been updated to use the new version 5.1.1 of Scilab with new graphical capabilities [2]. Moreover, new use cases have been added which make the handling of the program easier and more efficient.Summary of revisions:

1.

A new use case concerning coupled predator-prey models has been added [3].

2.

Three new use cases concerning fractals (Sierpinsky gasket, Barnsley's Fern and Tree) have been added [3].

3.

The graphical user interface (GUI) of the program has been reconstructed to include the new use cases.

4.

The program has been updated to use Scilab 5.1.1 with the new graphical capabilities.

Additional comments: The program package contains 12 subprograms.

•

interface.sce - the graphical user interface (GUI) that permits the choice of a routine as follows

•

1.sci - Lorenz dynamical system

•

2.sci - Chua dynamical system

•

3.sci - Rosler dynamical system

•

4.sci - Henon map

•

5.sci - Lyapunov exponents for Lorenz dynamical system

•

6.sci - Lyapunov exponent for the logistic map

•

7.sci - Shannon entropy for the logistic map

•

8.sci - Coupled predator-prey model

•

1f.sci - Sierpinsky gasket

•

2f.sci - Barnsley's Fern

•

3f.sci - Barnsley's Tree

Running time: 10 to 20 seconds for problems that do not involve Lyapunov exponents calculation; 60 to 1000 seconds for problems that involve high orders ODE, Lyapunov exponents calculation and fractals.References:

[1]

C.C. Bordeianu, C. Besliu, Al. Jipa, D. Felea, I. V. Grossu, Comput. Phys. Comm. 178 (2008) 788.

[2]

S. Campbell, J.P. Chancelier, R. Nikoukhah, Modeling and Simulation in Scilab/Scicos, Springer, 2006.

[3]

R.H. Landau, M.J. Paez, C.C. Bordeianu, A Survey of Computational Physics, Introductory Computational Science, Princeton University Press, 2008.

     

micrOMEGAs 2.0.7: a program to calculate the relic density of dark matter in a generic model

micrOMEGAs2.0.7 is a code which calculates the relic density of a stable massive particle in an arbitrary model. The underlying assumption is that there is a conservation law like R-parity in supersymmetry which guarantees the stability of the lightest odd particle. The new physics model must be incorporated in the notation of CalcHEP, a package for the automatic generation of squared matrix elements. Once this is done, all annihilation and coannihilation channels are included automatically in any model. Cross-sections at v=0, relevant for indirect detection of dark matter, are also computed automatically. The package includes three sample models: the minimal supersymmetric standard model (MSSM), the MSSM with complex phases and the NMSSM. Extension to other models, including non supersymmetric models, is described.

Program summary

Title of program:micrOMEGAs2.0.7Catalogue identifier:ADQR_v2_1Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADQR_v2_1.htmlProgram obtainable from: CPC Program Library, Queen's University of 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.:216 529No. of bytes in distributed program, including test data, etc.:1 848 816Distribution format:tar.gzProgramming language used:C and FortranComputer:PC, Alpha, Mac, SunOperating system:UNIX (Linux, OSF1, SunOS, Darwin, Cygwin)RAM:17 MB depending on the number of processes requiredClassification:1.9, 11.6Catalogue identifier of previous version:ADQR_v2_0Journal version of previous version:Comput. Phys. Comm. 176 (2007) 367Does the new version supersede the previous version?:YesNature of problem:Calculation of the relic density of the lightest stable particle in a generic new model of particle physics.Solution method:In numerically solving the evolution equation for the density of dark matter, relativistic formulae for the thermal average are used. All tree-level processes for annihilation and coannihilation of new particles in the model are included. The cross-sections for all processes are calculated exactly with CalcHEP after definition of a model file. Higher-order QCD corrections to Higgs couplings to quark pairs are included.Reasons for new version:The main changes in this new version consist, on the one hand, in improvements of the user interface and treatment of error codes when using spectrum calculators in the MSSM and, on the other hand, on a completely revised code for the calculation of the relic density in the NMSSM based on the code NMSSMTools1.0.2 for the computation of the spectrum.Summary of revisions:

•

The version of CalcHEP was updated to CalcHEP 2.4.

•

The procedure for shared library generation has been improved. Now the libraries are recalculated each time the model is modified.

•

The default value for the top quark mass has been set to 171.4 GeV.

Changes specific to the MSSM model.

•

The deltaMb correction is now included in the B,t,H-vertex and is always included for other Higgs vertices.

•

In case of a fatal error in an RGE program, micrOMEGAs now continues operation while issuing a warning that the given point is not valid. This is important when running scans over parameter space. However this means that the standard ˆC command that could be used to cancel a job now only cancels the RGE program. To cancel a job, use “kill -9 -N” where N is the micrOMEGAs process id, all child processes launched by micrOMEGAs will be killed at once.

•

Following the last SLHA2 release, we use key=26 item of EXTPAR block for the pole mass of the CP-odd Higgs so that micrOMEGAs can now use SoftSUSY for spectrum calculation with EWSB input. The Isajet interface was corrected too, so the user has to recompile the isajet_slha executable. For SuSpect we still support an old “wrong” interface where key=24 is used for the mass of the CP-odd Higgs.

•

In the non-universal SUGRA model, we set the value of M₀ (M1/2,A₀) to the value of the largest subset of equal parameters among scalar masses (gaugino masses, trilinear couplings). In the previous version these parameters were set arbitrarily to be equal to MH2, MG2 and At respectively. The spectrum calculators need an input value for M₀,M1/2 and A₀ for initialisation purposes.

•

We have removed bugs in micrOMEGAs-Isajet interface in case of non-universal SUGRA.

•

$(FFLAGS) is added to compilation instruction of suspect.exe. It was omitted in version 2.0.

•

The treatment of errors in reading of the LesHouches accord file is improved. Now, if the SPINFO block is absent in the SLHA output it is considered as a fatal error.

•

Instructions for calculation of Δρ, μ(g−2), Br(b→sγ) and Br(Bs→μ⁺μ⁻) constraints are included in EWSB sample main programs omg.c/omg.cpp/omg.F.

•

We have corrected the name of the library for neutralino-neutralino annihilation in our sample files MSSM/cs br.*.

Changes specific to the NMSSM model.

•

The NMSSM has been completely revised. Now it is based on NMSSMTools_1.0.2.

•

The deltaMb corrections in the NMSSM are included in the Higgs potential.

CP violation model.

•

We have included in our package the MSSM with CP violation. Our implementation was described in Phys. Rev. D 73 (2006) 115007. It is based on the CPSUPERH package published in Comput. Phys. Comm. 156 (2004) 283.

Unusual features:Depending on the parameters of the model, the program generates additional new code, compiles it and loads it dynamically.Running time:0.2 seconds     

Code OK3 - An upgraded version of OK2 with beam wobbling function

For computer simulations on heavy ion beam (HIB) irradiation onto a target with an arbitrary shape and structure in heavy ion fusion (HIF), the code OK2 was developed and presented in Computer Physics Communications 161 (2004). Code OK3 is an upgrade of OK2 including an important capability of wobbling beam illumination. The wobbling beam introduces a unique possibility for a smooth mechanism of inertial fusion target implosion, so that sufficient fusion energy is released to construct a fusion reactor in future.

New version program summary

Program title: OK3Catalogue identifier: ADST_v3_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADST_v3_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.: 221 517No. of bytes in distributed program, including test data, etc.: 2 471 015Distribution format: tar.gzProgramming language: C++Computer: PC (Pentium 4, 1 GHz or more recommended)Operating system: Windows or UNIXRAM: 2048 MBytesClassification: 19.7Catalogue identifier of previous version: ADST_v2_0Journal reference of previous version: Comput. Phys. Comm. 161 (2004) 143Does the new version supersede the previous version?: YesNature of problem: In heavy ion fusion (HIF), ion cancer therapy, material processing, etc., a precise beam energy deposition is essentially important [1]. Codes OK1 and OK2 have been developed to simulate the heavy ion beam energy deposition in three-dimensional arbitrary shaped targets [2, 3]. Wobbling beam illumination is important to smooth the beam energy deposition nonuniformity in HIF, so that a uniform target implosion is realized and a sufficient fusion output energy is released.Solution method: OK3 code works on the base of OK1 and OK2 [2, 3]. The code simulates a multi-beam illumination on a target with arbitrary shape and structure, including beam wobbling function.Reasons for new version: The code OK3 is based on OK2 [3] and uses the same algorithm with some improvements, the most important one is the beam wobbling function.Summary of revisions:

1.

In the code OK3, beams are subdivided on many bunches. The displacement of each bunch center from the initial beam direction is calculated.

2.

Code OK3 allows the beamlet number to vary from bunch to bunch. That reduces the calculation error especially in case of very complicated mesh structure with big internal holes.

3.

The target temperature rises during the time of energy deposition.

4.

Some procedures are improved to perform faster.

5.

The energy conservation is checked up on each step of calculation process and corrected if necessary.

New procedures included in OK3

1.

Procedure BeamCenterRot( ) rotates the beam axis around the impinging direction of each beam.

2.

Procedure BeamletRot( ) rotates the beamlet axes that belong to each beam.

3.

Procedure Rotation( ) sets the coordinates of rotated beams and beamlets in chamber and pellet systems.

4.

Procedure BeamletOut( ) calculates the lost energy of ions that have not impinged on the target.

5.

Procedure TargetT( ) sets the temperature of the target layer of energy deposition during the irradiation process.

6.

Procedure ECL( ) checks up the energy conservation law at each step of the energy deposition process.

7.

Procedure ECLt( ) performs the final check up of the energy conservation law at the end of deposition process.

Modified procedures in OK3

1.

Procedure InitBeam( ): This procedure initializes the beam radius and coefficients A1, A2, A3, A4 and A5 for Gauss distributed beams [2]. It is enlarged in OK3 and can set beams with radii from 1 to 20 mm.

2.

Procedure kBunch( ) is modified to allow beamlet number variation from bunch to bunch during the deposition.

3.

Procedure ijkSp( ) and procedure Hole( ) are modified to perform faster.

4.

Procedure Espl( ) and procedure ChechE( ) are modified to increase the calculation accuracy.

5.

Procedure SD( ) calculates the total relative root-mean-square (RMS) deviation and the total relative peak-to-valley (PTV) deviation in energy deposition non-uniformity. This procedure is not included in code OK2 because of its limited applications (for spherical targets only). It is taken from code OK1 and modified to perform with code OK3.

Running time: The execution time depends on the pellet mesh number and the number of beams in the simulated illumination as well as on the beam characteristics (beam radius on the pellet surface, beam subdivision, projectile particle energy and so on). In almost all of the practical running tests performed, the typical running time for one beam deposition is about 30 s on a PC with a CPU of Pentium 4, 2.4 GHz.References:

[1]

A.I. Ogoyski, et al., Heavy ion beam irradiation non-uniformity in inertial fusion, Phys. Lett. A 315 (2003) 372-377.

[2]

A.I. Ogoyski, et al., Code OK1 - Simulation of multi-beam irradiation on a spherical target in heavy ion fusion, Comput. Phys. Comm. 157 (2004) 160-172.

[3]

A.I. Ogoyski, et al., Code OK2 - A simulation code of ion-beam illumination on an arbitrary shape and structure target, Comput. Phys. Comm. 161 (2004) 143-150.

     

Partition arguments in multiparty communication complexity

Consider the “Number in Hand” multiparty communication complexity model, where k players holding inputs x₁,…,x_k∈{0,1}ⁿ communicate to compute the value f(x₁,…,x_k) of a function f known to all of them. The main lower bound technique for the communication complexity of such problems is that of partition arguments: partition the k players into two disjoint sets of players and find a lower bound for the induced two-party communication complexity problem.In this paper, we study the power of partition arguments. Our two main results are very different in nature:

(i)

For randomized communication complexity, we show that partition arguments may yield bounds that are exponentially far from the true communication complexity. Specifically, we prove that there exists a 3-argument function f whose communication complexity is Ω(n), while partition arguments can only yield an Ω(logn) lower bound. The same holds for nondeterministiccommunication complexity.

(ii)

For deterministic communication complexity, we prove that finding significant gaps between the true communication complexity and the best lower bound that can be obtained via partition arguments, would imply progress on a generalized version of the “log-rank conjecture” in communication complexity. We also observe that, in the case of computing relations (search problems), very large gaps do exist.

We conclude with two results on the multiparty “fooling set technique”, another method for obtaining communication complexity lower bounds.     

LCG MCDB—a knowledgebase of Monte-Carlo simulated events

In this paper we report on LCG Monte-Carlo Data Base (MCDB) and software which has been developed to operate MCDB. The main purpose of the LCG MCDB project is to provide a storage and documentation system for sophisticated event samples simulated for the LHC Collaborations by experts. In many cases, the modern Monte-Carlo simulation of physical processes requires expert knowledge in Monte-Carlo generators or significant amount of CPU time to produce the events. MCDB is a knowledgebase mainly dedicated to accumulate simulated events of this type. The main motivation behind LCG MCDB is to make the sophisticated MC event samples available for various physical groups. All the data from MCDB is accessible in several convenient ways. LCG MCDB is being developed within the CERN LCG Application Area Simulation project.

Program summary

Program title: LCG Monte-Carlo Data BaseCatalogue identifier: ADZX_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADZX_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: GNU General Public LicenceNo. of lines in distributed program, including test data, etc.: 30 129No. of bytes in distributed program, including test data, etc.: 216 943Distribution format: tar.gzProgramming language: PerlComputer: CPU: Intel Pentium 4, RAM: 1 Gb, HDD: 100 GbOperating system: Scientific Linux CERN 3/4RAM: 1 073 741 824 bytes (1 Gb)Classification: 9External routines:

•

perl >= 5.8.5;

•

Perl modules

○

DBD-mysql >= 2.9004,

○

File::Basename,

○

GD::SecurityImage,

○

GD::SecurityImage::AC,

○

Linux::Statistics,

○

XML::LibXML > 1.6,

○

XML::SAX,

○

XML::NamespaceSupport;

•

Apache HTTP Server >= 2.0.59;

•

mod auth external >= 2.2.9;

•

edg-utils-system RPM package;

•

gd >= 2.0.28;

•

rpm package CASTOR-client >= 2.1.2-4;

•

arc-server (optional)

Nature of problem: Often, different groups of experimentalists prepare similar samples of particle collision events or turn to the same group of authors of Monte-Carlo (MC) generators to prepare the events. For example, the same MC samples of Standard Model (SM) processes can be employed for the investigations either in the SM analyses (as a signal) or in searches for new phenomena in Beyond Standard Model analyses (as a background). If the samples are made available publicly and equipped with corresponding and comprehensive documentation, it can speed up cross checks of the samples themselves and physical models applied. Some event samples require a lot of computing resources for preparation. So, a central storage of the samples prevents possible waste of researcher time and computing resources, which can be used to prepare the same events many times.Solution method: Creation of a special knowledgebase (MCDB) designed to keep event samples for the LHC experimental and phenomenological community. The knowledgebase is realized as a separate web-server (http://mcdb.cern.ch). All event samples are kept on types at CERN. Documentation describing the events is the main contents of MCDB. Users can browse the knowledgebase, read and comment articles (documentation), and download event samples. Authors can upload new event samples, create new articles, and edit own articles.Restrictions: The software is adopted to solve the problems, described in the article and there are no any additional restrictions.Unusual features: The software provides a framework to store and document large files with flexible authentication and authorization system. Different external storages with large capacity can be used to keep the files. The WEB Content Management System provides all of the necessary interfaces for the authors of the files, end-users and administrators.Running time: Real time operations.References:[1] The main LCG MCDB server, http://mcdb.cern.ch/.[2] P. Bartalini, L. Dudko, A. Kryukov, I.V. Selyuzhenkov, A. Sherstnev, A. Vologdin, LCG Monte-Carlo data base, hep-ph/0404241.[3] J.P. Baud, B. Couturier, C. Curran, J.D. Durand, E. Knezo, S. Occhetti, O. Barring, CASTOR: status and evolution, cs.oh/0305047.     

ODPEVP: A program for computing eigenvalues and eigenfunctions and their first derivatives with respect to the parameter of the parametric self-adjoined Sturm-Liouville problem

A FORTRAN 77 program is presented for calculating with the given accuracy eigenvalues, eigenfunctions and their first derivatives with respect to the parameter of the parametric self-adjoined Sturm-Liouville problem with the parametric third type boundary conditions on the finite interval. The program calculates also potential matrix elements - integrals of the eigenfunctions multiplied by their first derivatives with respect to the parameter. Eigenvalues and matrix elements computed by the ODPEVP program can be used for solving the bound state and multi-channel scattering problems for a system of the coupled second-order ordinary differential equations with the help of the KANTBP programs [O. Chuluunbaatar, A.A. Gusev, A.G. Abrashkevich, A. Amaya-Tapia, M.S. Kaschiev, S.Y. Larsen, S.I. Vinitsky, Comput. Phys. Commun. 177 (2007) 649-675; O. Chuluunbaatar, A.A. Gusev, S.I. Vinitsky, A.G. Abrashkevich, Comput. Phys. Commun. 179 (2008) 685-693]. As a test desk, the program is applied to the calculation of the potential matrix elements for an integrable 2D-model of three identical particles on a line with pair zero-range potentials, a 3D-model of a hydrogen atom in a homogeneous magnetic field and a hydrogen atom on a three-dimensional sphere.

Program summary

Program title: ODPEVPCatalogue identifier: AEDV_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEDV_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC license, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 3001No. of bytes in distributed program, including test data, etc.: 24 195Distribution format: tar.gzProgramming language: FORTRAN 77Computer: Intel Xeon EM64T, Alpha 21264A, AMD Athlon MP, Pentium IV Xeon, Opteron 248, Intel Pentium IVOperating system: OC Linux, Unix AIX 5.3, SunOS 5.8, Solaris, Windows XPRAM: depends on

1.

the number and order of finite elements;

2.

the number of points; and

3.

the number of eigenfunctions required.

Test run requires 4 MBClassification: 2.1, 2.4External routines: GAULEG [3]Nature of problem: The three-dimensional boundary problem for the elliptic partial differential equation with an axial symmetry similar to the Schrödinger equation with the Coulomb and transverse oscillator potentials is reduced to the two-dimensional one. The latter finds wide applications in modeling of photoionization and recombination of oppositively charged particles (positrons, antiprotons) in the magnet-optical trap [4], optical absorption in quantum wells [5], and channeling of likely charged particles in thin doped films [6,7] or neutral atoms and molecules in artificial waveguides or surfaces [8,9]. In the adiabatic approach [10] known in mathematics as Kantorovich method [11] the solution of the two-dimensional elliptic partial differential equation is expanded over basis functions with respect to the fast variable (for example, angular variable) and depended on the slow variable (for example, radial coordinate ) as a parameter. An averaging of the problem by such a basis leads to a system of the second-order ordinary differential equations which contain potential matrix elements and the first-derivative coupling terms (see, e.g., [12,13,14]). The purpose of this paper is to present the finite element method procedure based on the use of high-order accuracy approximations for calculating eigenvalues, eigenfunctions and their first derivatives with respect to the parameter of the parametric self-adjoined Sturm-Liouville problem with the parametric third type boundary conditions on the finite interval. The program developed calculates potential matrix elements - integrals of the eigenfunctions multiplied by their derivatives with respect to the parameter. These matrix elements can be used for solving the bound state and multi-channel scattering problems for a system of the coupled second-order ordinary differential equations with the help of the KANTBP programs [1,2].Solution method: The parametric self-adjoined Sturm-Liouville problem with the parametric third type boundary conditions is solved by the finite element method using high-order accuracy approximations [15]. The generalized algebraic eigenvalue problem AF=EBF with respect to a pair of unknown (E,F) arising after the replacement of the differential problem by the finite-element approximation is solved by the subspace iteration method using the SSPACE program [16]. First derivatives of the eigenfunctions with respect to the parameter which contained in potential matrix elements of the coupled system equations are obtained by solving the inhomogeneous algebraic equations. As a test desk, the program is applied to the calculation of the potential matrix elements for an integrable 2D-model of three identical particles on a line with pair zero-range potentials described in [1,17,18], a 3D-model of a hydrogen atom in a homogeneous magnetic field described in [14,19] and a hydrogen atom on a three-dimensional sphere [20].Restrictions: The computer memory requirements depend on:

1.

the number and order of finite elements;

2.

the number of points; and

3.

the number of eigenfunctions required.

Restrictions due to dimension sizes may be easily alleviated by altering PARAMETER statements (see sections below and listing for details). The user must also supply DOUBLE PRECISION functions POTCCL and POTCC1 for evaluating potential function U(ρ,z) of Eq. (1) and its first derivative with respect to parameter ρ. The user should supply DOUBLE PRECISION functions F1FUNC and F2FUNC that evaluate functions f₁(z) and f₂(z) of Eq. (1). The user must also supply subroutine BOUNCF for evaluating the parametric third type boundary conditions.Running time: The running time depends critically upon:

1.

the number and order of finite elements;

2.

the number of points on interval [zmin,zmax]; and

3.

the number of eigenfunctions required.

The test run which accompanies this paper took 2 s with calculation of matrix potentials on the Intel Pentium IV 2.4 GHz.References:

[1]

O. Chuluunbaatar, A.A. Gusev, A.G. Abrashkevich, A. Amaya-Tapia, M.S. Kaschiev, S.Y. Larsen, S.I. Vinitsky, Comput. Phys. Comm. 177 (2007) 649-675

[2]

O. Chuluunbaatar, A.A. Gusev, S.I. Vinitsky, A.G. Abrashkevich, Comput. Phys. Comm. 179 (2008) 685-693.

[3]

W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes: The Art of Scientific Computing, Cambridge University Press, Cambridge, 1986.

[4]

O. Chuluunbaatar, A.A. Gusev, S.I. Vinitsky, V.L. Derbov, L.A. Melnikov, V.V. Serov, Phys. Rev. A 77 (2008) 034702-1-4.

[5]

E.M. Kazaryan, A.A. Kostanyan, H.A. Sarkisyan, Physica E 28 (2005) 423-430.

[6]

Yu.N. Demkov, J.D. Meyer, Eur. Phys. J. B 42 (2004) 361-365.

[7]

P.M. Krassovitskiy, N.Zh. Takibaev, Bull. Russian Acad. Sci. Phys. 70 (2006) 815-818.

[8]

V.S. Melezhik, J.I. Kim, P. Schmelcher, Phys. Rev. A 76 (2007) 053611-1-15.

[9]

F.M. Pen'kov, Phys. Rev. A 62 (2000) 044701-1-4.

[10]

M. Born, X. Huang, Dynamical Theory of Crystal Lattices, The Clarendon Press, Oxford, England, 1954.

[11]

L.V. Kantorovich, V.I. Krylov, Approximate Methods of Higher Analysis, Wiley, New York, 1964.

[12]

U. Fano, Colloq. Int. C.N.R.S. 273 (1977) 127;

A.F. Starace, G.L. Webster, Phys. Rev. A 19 (1979) 1629-1640.

[13]

C.V. Clark, K.T. Lu, A.F. Starace, in: H.G. Beyer, H. Kleinpoppen (eds.), Progress in Atomic Spectroscopy, Part C, Plenum, New York, 1984, pp. 247-320.

[14]

O. Chuluunbaatar, A.A. Gusev, V.L. Derbov, M.S. Kaschiev, L.A. Melnikov, V.V. Serov, S.I. Vinitsky, J. Phys. A 40 (2007) 11485-11524.

[15]

A.G. Abrashkevich, D.G. Abrashkevich, M.S. Kaschiev, I.V. Puzynin, Comput. Phys. Comm. 85 (1995) 40-64.

[16]

K.J. Bathe, Finite Element Procedures in Engineering Analysis, Englewood Cliffs, Prentice-Hall, New York, 1982.

[17]

O. Chuluunbaatar, A.A. Gusev, M.S. Kaschiev, V.A. Kaschieva, A. Amaya-Tapia, S.Y. Larsen, S.I. Vinitsky, J. Phys. B 39 (2006) 243-269.

[18]

Yu.A. Kuperin, P.B. Kurasov, Yu.B. Melnikov, S.P. Merkuriev, Ann. Phys. 205 (1991) 330-361.

[19]

O. Chuluunbaatar, A.A. Gusev, V.P. Gerdt, V.A. Rostovtsev, S.I. Vinitsky, A.G. Abrashkevich, M.S. Kaschiev, V.V. Serov, Comput. Phys. Comm. 178 (2008) 301-330.

[20]

A.G. Abrashkevich, M.S. Kaschiev, S.I. Vinitsky, J. Comp. Phys. 163 (2000) 328-348.

     

A new version of Visual tool for estimating the fractal dimension of images

This work presents a new version of a Visual Basic 6.0 application for estimating the fractal dimension of images (Grossu et al., 2009 [1]). The earlier version was limited to bi-dimensional sets of points, stored in bitmap files. The application was extended for working also with comma separated values files and three-dimensional images.

New version program summary

Program title: Fractal Analysis v02Catalogue identifier: AEEG_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEG_v2_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.: 9999No. of bytes in distributed program, including test data, etc.: 4 366 783Distribution format: tar.gzProgramming language: MS Visual Basic 6.0Computer: PCOperating system: MS Windows 98 or laterRAM: 30 MClassification: 14Catalogue identifier of previous version: AEEG_v1_0Journal reference of previous version: Comput. Phys. Comm. 180 (2009) 1999Does the new version supersede the previous version?: YesNature of problem: Estimating the fractal dimension of 2D and 3D images.Solution method: Optimized implementation of the box-counting algorithm.Reasons for new version:

1.

The previous version was limited to bitmap image files. The new application was extended in order to work with objects stored in comma separated values (csv) files. The main advantages are:

a)

Easier integration with other applications (csv is a widely used, simple text file format);

b)

Less resources consumed and improved performance (only the information of interest, the “black points”, are stored);

c)

Higher resolution (the points coordinates are loaded into Visual Basic double variables [2]);

d)

Possibility of storing three-dimensional objects (e.g. the 3D Sierpinski gasket).

2.

In this version the optimized box-counting algorithm [1] was extended to the three-dimensional case.

Summary of revisions:

1.

The application interface was changed from SDI (single document interface) to MDI (multi-document interface).

2.

One form was added in order to provide a graphical user interface for the new functionalities (fractal analysis of 2D and 3D images stored in csv files).

Additional comments: User friendly graphical interface; Easy deployment mechanism.Running time: In the first approximation, the algorithm is linear.References:

[1] I.V. Grossu, C. Besliu, M.V. Rusu, Al. Jipa, C.C. Bordeianu, D. Felea, Comput. Phys. Comm. 180 (2009)  1999-2001.

[2] F. Balena, Programming Microsoft Visual Basic 6.0, Microsoft Press, US, 1999.

     

Tight bounds for the cover time of multiple random walks

We study the cover time of multiple random walks on undirected graphs G=(V,E). We consider k parallel, independent random walks that start from the same vertex. The speed-up is defined as the ratio of the cover time of a single random walk to the cover time of these k random walks. Recently, Alon et al. (2008) [5] derived several upper bounds on the cover time, which imply a speed-up of Ω(k) for several graphs; however, for many of them, k has to be bounded by O(logn). They also conjectured that, for any 1?k?n, the speed-up is at most O(k) on any graph. We prove the following main results:

•

We present a new lower bound on the speed-up that depends on the mixing time. It gives a speed-up of Ω(k) on many graphs, even if k is as large as n.

•

We prove that the speed-up is O(klogn) on any graph. For a large class of graphs we can also improve this bound to O(k), matching the conjecture of Alon et al.

•

We determine the order of the speed-up for any value of 1?k?n on hypercubes, random graphs and degree restricted expanders. For d-dimensional tori with d>2, our bounds are tight up to logarithmic factors.

•

Our findings also reveal a surprisingly sharp threshold behaviour for certain graphs, e.g., the d-dimensional torus with d>2 and hypercubes: there is a value T such that the speed-up is approximately min{T,k} for any 1?k?n.

     

A spectral lower bound for the treewidth of a graph and its consequences

We give a lower bound for the treewidth of a graph in terms of the second smallest eigenvalue of its Laplacian matrix. We use this lower bound to show that the treewidth of a d-dimensional hypercube is at least ⌊3·2^d/(2(d+4))⌋−1. The currently known upper bound is . We generalize this result to Hamming graphs. We also observe that every graph G on n vertices, with maximum degree Δ

(1)

contains an induced cycle (chordless cycle) of length at least 1+log_Δ(μn/8) (provided G is not acyclic),

(2)

has a clique minor K_h for some ,

where μ is the second smallest eigenvalue of the Laplacian matrix of G.     

Improved fixed parameter tractable algorithms for two “edge” problems: MAXCUT and MAXDAG

We give improved parameterized algorithms for two “edge” problems MAXCUT and MAXDAG, where the solution sought is a subset of edges. MAXCUT of a graph is a maximum set of edges forming a bipartite subgraph of the given graph. On the other hand, MAXDAG of a directed graph is a set of arcs of maximum size such that the graph induced on these arcs is acyclic. Our algorithms are obtained through new kernelization and efficient exact algorithms for the optimization versions of the problems. More precisely our results include:

(i)

a kernel with at most αk vertices and βk edges for MAXCUT. Here 0<α?1 and 1<β?2. Values of α and β depends on the number of vertices and the edges in the graph;

(ii)

a kernel with at most 4k/3 vertices and 2k edges for MAXDAG;

(iii)

an O^∗(k^1.2418) parameterized algorithm for MAXCUT in undirected graphs. This improves the O^∗(k^1.4143)¹ algorithm presented in [E. Prieto, The method of extremal structure on the k-maximum cut problem, in: The Proceedings of Computing: The Australasian Theory Symposium (CATS), 2005, pp. 119-126];

(iv)

an O^∗(n²) algorithm for optimization version of MAXDAG in directed graphs. This is the first such algorithm to the best of our knowledge;

(v)

an O^∗(k²) parameterized algorithm for MAXDAG in directed graphs. This improves the previous best of O^∗(k⁴) presented in [V. Raman, S. Saurabh, Parameterized algorithms for feedback set problems and their duals in tournaments, Theoretical Computer Science 351 (3) (2006) 446-458];

(vi)

an O^∗(k¹⁶) parameterized algorithm to determine whether an oriented graph having m arcs has an acyclic subgraph with at least m/2+k arcs. This improves the O^∗(k²) algorithm given in [V. Raman, S. Saurabh, Parameterized algorithms for feedback set problems and their duals in tournaments, Theoretical Computer Science 351 (3) (2006) 446-458].

In addition, we show that if a directed graph has minimum out degree at least f(n) (some function of n) then Directed Feedback Arc Set problem is fixed parameter tractable. The parameterized complexity of Directed Feedback Arc Set is a well-known open problem.     

Super-connected and super-arc-connected Cartesian product of digraphs

We study the super-connected, hyper-connected and super-arc-connected Cartesian product of digraphs. The following two main results will be obtained:

(i)

If δ⁺(Di)=δ⁻(Di)=δ(Di)=κ(Di) for i=1,2, then D₁×D₂ is super-κ if and only if ,

(ii)

If δ⁺(Di)=δ⁻(Di)=δ(Di)=λ(Di) for i=1,2, then D₁×D₂ is super-λ if and only if ,

where λ(D)=δ(D)=1, denotes the complete digraph of order n and n?2.     

Ten commandments for good default expression simplification

This article provides goals for the design and improvement of default computer algebra expression simplification. These goals can also help users recognize and partially circumvent some limitations of their current computer algebra systems. Although motivated by computer algebra, many of the goals are also applicable to manual simplification, indicating what transformations are necessary and sufficient for good simplification when no particular canonical result form is required.After motivating the ten goals, the article then explains how the Altran partially factored form for rational expressions was extended for Derive and for the computer algebra in Texas Instruments products to help fulfill these goals. In contrast to the distributed Altran representation, this recursive partially factored semi-fraction form:

•

does not unnecessarily force common denominators,

•

discovers and preserves significantly more factors,

•

can represent general expressions, and

•

can produce an entire spectrum from fully factored over a common denominator through complete multivariate partial fractions, including a dense subset of all intermediate forms.

     

Analysis of architecture evaluation data

The output of 18 software architecture evaluations is analyzed. The goal of the analysis is to find patterns in the important quality attributes and risk themes identified in the evaluations. The major results are

•

A categorization of risk themes.

•

The observation that twice as many risk themes are risks of “omission” as are risks of “commission”.

•

A failure to find a relationship between the business and mission goals of a system and the risk themes from an evaluation of that system.

•

A failure to find a correlation between the domain of a system being evaluated and the important quality attributes for that system.

•

A wide diversity of names used for various quality attributes.

The results of this investigation have application to practitioners by suggesting activities on which developers should put greater focus. They also have application to researchers by suggesting further areas of investigation.