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Introduction: Population aging is occurring in nearly all countries, with adults aged 80 and older representing the fastest-growing segment. Millions of older adults will stop or limit their driving after a lifetime of relying on it for independence and mobility. Driving cessation in older adults is linked to increased depressive symptoms, reduced physical functioning, and diminished social health. Anticipating driving cessation will be increasingly important as our population ages. Existing algorithms are not often operationalized in a way that enables older adults to use on their own because they require inputs from clinical tests. Methods and analysis: The objective of this study is to develop and validate algorithms with and without clinical measures to predict the risk of driving cessation in 6 years in adults aged 65 years and older. The study cohort will be derived using both the Comprehensive and Tracking cohorts in the Canadian Longitudinal Study on Aging (CLSA) among adults aged 65 years and older and driving at baseline (2010–2015; 11,762 drivers). Cases will be identified based on the incidence of driving cessation at 6 years following baseline measurement (2018–2021; 948 individuals stopped driving). Prespecified predictors include sociodemographic, self-reported health, functional and health condition variables. The base model will use self-reported information only, while the extended model will include additional clinical measures. Expected outcomes include validated multivariable models with measures of calibration and discrimination to assess predictive performance. The better performing algorithm will be used to support early identification of individuals at risk of driving cessation, enabling timely interventions and planning to maintain independence and mobility. This study protocol and the reporting of model estimation results will be guided by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statements.

Introduction: Population aging is occurring in nearly all countries, with adults aged 80 and older representing the fastest-growing segment. Millions of older adults will stop or limit their driving after a lifetime of relying on it for independence and mobility. Driving cessation in older adults is linked to increased depressive symptoms, reduced physical functioning, and diminished social health. Anticipating driving cessation will be increasingly important as our population ages. Existing algorithms are not often operationalized in a way that enables older adults to use on their own because they require inputs from clinical tests. Methods and analysis: The objective of this study is to develop and validate algorithms with and without clinical measures to predict the risk of driving cessation in 6 years in adults aged 65 years and older. The study cohort will be derived using both the Comprehensive and Tracking cohorts in the Canadian Longitudinal Study on Aging (CLSA) among adults aged 65 years and older and driving at baseline (2010–2015; 11,762 drivers). Cases will be identified based on the incidence of driving cessation at 6 years following baseline measurement (2018–2021; 948 individuals stopped driving). Prespecified predictors include sociodemographic, self-reported health, functional and health condition variables. The base model will use self-reported information only, while the extended model will include additional clinical measures. Expected outcomes include validated multivariable models with measures of calibration and discrimination to assess predictive performance. The better performing algorithm will be used to support early identification of individuals at risk of driving cessation, enabling timely interventions and planning to maintain independence and mobility. This study protocol and the reporting of model estimation results will be guided by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statements.

We present an algorithm for the asymmetric traveling salesman problem on instances which satisfy the triangle inequality. Like several existing algorithms, it achieves approximation ratio O(log n). Unlike previous algorithms, it uses randomized rounding.

I have created, for public use, a Python code allowing the simulation of light curves with any given power spectral density and any probability density function, following the algorithm described in Emmanoulopoulos et al. 2013. The simulated products have exactly the same variability and statistical properties as the observed light curves. The code and its documentation are available at: https://github.com/samconnolly/DELightcurveSimulation Note that a Mathematica code of the algorithm is given in Emmanoulopoulos et al. [arXiv:1305.0304]

Recently, M\"omke and Svensson presented a beautiful new approach for the traveling salesman problem on a graph metric (graph-TSP), which yielded a $\frac{4}{3}$-approximation guarantee on subcubic graphs as well as a substantial improvement over the $\frac{3}{2}$-approximation guarantee of Christofides' algorithm on general graphs. The crux of their approach is to compute an upper bound on the minimum cost of a circulation in a particular network, $C(G,T)$, where $G$ is the input graph and $T$ is a carefully chosen spanning tree. The cost of this circulation is directly related to the number of edges in a tour output by their algorithm. Mucha subsequently improved the analysis of the circulation cost, proving that M\"omke and Svensson's algorithm for graph-TSP has an approximation ratio of at most $\frac{13}{9}$ on general graphs. This analysis of the circulation is local, and vertices with degree four and five can contribute the most to its cost. Thus, hypothetically, there could exist a subquartic graph (a graph with degree at most four at each vertex) for which Mucha's analysis of the M\"omke-Svensson algorithm is tight. We show that this is not the case and that M\"omke and Svensson's algorithm for graph-TSP has an approximation guarantee of at most $\frac{46}{33}$ on subquartic graphs. To prove this, we present a different method to upper bound the minimum cost of a circulation on the network $C(G,T)$. Our approximation guarantee actually holds for all graphs that have an optimal solution to a standard linear programming relaxation of graph-TSP with subquartic support.

The Minimum Eccentricity Shortest Path (MESP) Problem consists in determining a shortest path (a path whose length is the distance between its extremities) of minimum eccentricity in a graph. It was introduced by Dragan and Leitert [9] who described a linear-time algorithm which is an 8-approximation of the problem. In this paper, we study deeper the double-BFS procedure used in that algorithm and extend it to obtain a linear-time 3-approximation algorithm. We moreover study the link between the MESP problem and the notion of laminarity, introduced by V{\"o}lkel et al [12], corresponding to its restriction to a diameter (i.e. a shortest path of maximum length), and show tight bounds between MESP and laminarity parameters.

Design of the R package FLSSS, Fixed Length Subset Sum Solver with error specification, and its extension to multidimensional domain and to the general-purpose Knapsack Problem.

In this paper we survey the various implementations of a new data assimilation (downscaling) algorithm based on spatial coarse mesh measurements. As a paradigm, we demonstrate the application of this algorithm to the 3D Leray-$\alpha$ subgrid scale turbulence model. Most importantly, we use this paradigm to show that it is not always necessary that one has to collect coarse mesh measurements of all the state variables, that are involved in the underlying evolutionary system, in order to recover the corresponding exact reference solution. Specifically, we show that in the case of the 3D Leray$-\alpha$ model of turbulence the solutions of the algorithm, constructed using only coarse mesh observations of any two components of the three-dimensional velocity field, and without any information of the third component, converge, at an exponential rate in time, to the corresponding exact reference solution of the 3D Leray$-\alpha$ model. This study serves as an addendum to our recent work on abridged continuous data assimilation for the 2D Navier-Stokes equations. Notably, similar results have also been recently established for the 3D viscous Planetary Geostrophic circulation model in which we show that coarse mesh measurements of the temperature alone are sufficient for recovering, through our data assimilation algorithm, the full solution; viz. the three components of velocity vector field and the temperature. Consequently, this proves the Charney conjecture for the 3D Planetary Geostrophic model; namely, that the history of the large spatial scales of temperature is sufficient for determining all the other quantities (state variables) of the model.

This paper introduces a class of Monte Carlo algorithms which are based upon simulating a Markov process whose quasi-stationary distribution coincides with the distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo in which we simulate a Markov chain whose stationary distribution is the target. We show how to approximate distributions of interest by carefully combining sequential Monte Carlo methods with methodology for the exact simulation of diffusions. Our methodology is particularly promising in that it is applicable to the same class of problems as gradient based Markov chain Monte Carlo algorithms but entirely circumvents the need to conduct Metropolis-Hastings type accept/reject steps whilst retaining exactness: we have theoretical guarantees that we recover the correct limiting target distribution. Furthermore, this methodology is highly amenable to big data problems. By employing a modification to existing naive subsampling techniques we can obtain an algorithm which is still exact but has sub-linear iterative cost as a function of data size.

Wavelets are scaleable, oscillatory functions that deviate from zero only within a limited spatial regime and have average value zero. In addition to their use as source characterizers, wavelet functions are rapidly gaining currency within the source detection field. Wavelet-based source detection involves the correlation of scaled wavelet functions with binned, two-dimensional image data. If the chosen wavelet function exhibits the property of vanishing moments, significantly non-zero correlation coefficients will be observed only where there are high-order variations in the data; e.g., they will be observed in the vicinity of sources. In this paper, we describe the mission-independent, wavelet-based source detection algorithm WAVDETECT, part of the CIAO software package. Aspects of our algorithm include: (1) the computation of local, exposure-corrected normalized (i.e. flat-fielded) background maps; (2) the correction for exposure variations within the field-of-view; (3) its applicability within the low-counts regime, as it does not require a minimum number of background counts per pixel for the accurate computation of source detection thresholds; (4) the generation of a source list in a manner that does not depend upon a detailed knowledge of the point spread function (PSF) shape; and (5) error analysis. These features make our algorithm considerably more general than previous methods developed for the analysis of X-ray image data, especially in the low count regime. We demonstrate the algorithm's robustness by applying it to various images.

We derive new equations using the mixed-frame approach for one- and two-dimensional (axisymmetric) time-dependent radiation transport and the associated couplings with matter. Our formulation is multi-group and multi-angle and includes anisotropic scattering, frequency(energy)-dependent scattering and absorption, complete velocity dependence to order v/c, rotation, and energy redistribution due to inelastic scattering. Hence, the "2D" realization is actually "6 1/2"-dimensional. The effects of radiation viscosity are automatically incorporated. Moreover, we develop Accelerated-Lambda-Iteration, Krylov subspace (GMRES), Discontinuous-Finite-Element, and Feautrier numerical methods for solving the equations and present the results of one-dimensional numerical tests of the new formalism. The virtues of the mixed-frame approach include simple velocity dependence with no velocity derivatives, straight characteristics, simple physical interpretation, and clear generalization to higher dimensions. Our treatment can be used for both photon and neutrino transport, but we focus on neutrino transport and applications to core-collapse supernova theory in the discussions and examples.

Radiation hydrodynamical transport of ionization fronts in the next generation of cosmological reionization simulations holds the promise of predicting UV escape fractions from first principles as well as investigating the role of photoionization in feedback processes and structure formation. We present a multistep integration scheme for radiative transfer and hydrodynamics for accurate propagation of I-fronts and ionized flows from a point source in cosmological simulations. The algorithm is a photon-conserving method which correctly tracks the position of I-fronts at much lower resolutions than non-conservative techniques. The method applies direct hierarchical updates to the ionic species, bypassing the need for the costly matrix solutions required by implicit methods while retaining sufficient accuracy to capture the true evolution of the fronts. We review the physics of ionization fronts in power-law density gradients, whose analytical solutions provide excellent validation tests for radiation coupling schemes. The advantages and potential drawbacks of direct and implicit schemes are also considered, with particular focus on problem timestepping which if not properly implemented can lead to morphologically plausible I-front behavior that nonetheless departs from theory. We also examine the effect of radiation pressure from very luminous central sources on the evolution of I-fronts and flows.

This paper provides full \Matlab-code and informal correctness proofs for the lexicographic reverse search algorithm for convex hull calculations. The implementation was tested on a 1993 486-PC for various small and some larger, partially highly degenerate combinatorial polytopes, one of which (a certain 13-dimensional 24 vertex polyhedron) occurs naturally in the study of a well known problem posed by Professor Graciano de Oliveira: see end of section 1.

We consider in this paper the problem of sampling a high-dimensional probability distribution $\pi$ having a density with respect to the Lebesgue measure on $\mathbb{R}^d$, known up to a normalisation factor $\mathrm{e}^{-U(x)}/\int_{\mathbb{R}^d} \mathrm{e}^{-U(y)} \mathrm{d} y$. Such problem naturally occurs for example in Bayesian inference and machine learning. Under the assumption that $U$ is continuously differentiable, $\nabla U$ is globally Lipschitz and $U$ is strongly convex, we obtain non-asymptotic bounds for the convergence to stationarity in Wasserstein distance of order $2$ and total variation distance of the sampling method based on the Euler discretization of the Langevin stochastic differential equation, for both constant and decreasing step sizes. The dependence on the dimension of the state space of the obtained bounds is studied to demonstrate the applicability of this method. The convergence of an appropriately weighted empirical measure is also investigated and bounds for the mean square error and exponential deviation inequality are reported for functions which are either Lipchitz continuous or measurable and bounded. An illustration to a Bayesian inference for binary regression is presented.

Hybrid of Adaptive Response Surface methodology and Genetic Algorithm Monte Carlo Optimization method and Its Application in the Design of Moderator-Collimator for Accelerator based Thermal Neutron Radiography 作者： Yang, Mr. Chenxiao 1,2 Chen, Dr. Size 1,2 Zhang, Dr. Lianxin 2 Peng, Mr. Chuan 2,4 Xiao, Mr. Dan 3 作者单位： 1. Anhui University 2. Hefei Institutes of Physical Science Chinese Academy of Sciences 3. Hefei Institutes of Physical Science Chinese Academy of Sciences 4. University of Science and Technology of China 通讯作者： Chen, Dr. Size Email:[email protected] 提交时间： 2025-03-09 14:47:29 摘要: A new Monte Carlo (MC) neutronics optimization method named Hybrid of Adaptive Response Surface methodology and Genetic Algorithm Monte Carlo Optimization (HRG-MCO) is proposed to address the strong empirical dependence and low efficiency of global multi-parameter optimization in traditional neutronics design. HRG-MCO integrates the advantages of Response Surface Methodology (RSM) and Genetic Algorithm (GA). Specifically, neutron MC simulation results are iteratively utilized to adaptively construct an RSM model, ensuring the required accuracy. Subsequently, GA is employed to perform multi-parameter optimization based on the constructed RSM model, enabling the rapid determination of optimal design parameters. These optimized parameters are then fed back into the MC simulation model to derive the final design values. Comparative analysis with the traditional enumeration method and GA alone demonstrates the superior optimization efficiency of the proposed approach. To further validate its effectiveness, the method is applied to the optimization of a moderator-collimator system for a thermal neutron radiography system based on an accelerator. Two optimization tasks are performed: (1) determining the optimal efficiency under different source neutron energies and (2) optimization of thermal neutron photon yield ratio. The results highlight the efficiency and applicability of HRG-MCO in neutronics optimization design. Neutronics Design Optimization Monte Carlo simulation Response Surface Methodology Genetic Algorithm Thermal Neutron Radiography 来自： Chen, Dr. Size 分类： 物理学 >> 核物理学 备注: 已向《Nuclear Science and Techniques》投稿 引用： ChinaXiv:202503.00074 (或此版本 ChinaXiv:202503.00074V1 ) DOI:10.12074/202503.00074 CSTR:32003.36.ChinaXiv.202503.00074 推荐引用方式： Yang, Mr. Chenxiao,Chen, Dr. Size,Zhang, Dr. Lianxin,Peng, Mr. Chuan,Xiao, Mr. Dan.Hybrid of Adaptive Response Surface methodology and Genetic Algorithm Monte Carlo Optimization method and Its Application in the Design of Moderator-Collimator for Accelerator based Thermal Neutron Radiography.中国科学院科技论文预发布平台.[DOI:10.12074/202503.00074] 版本历史 [V1] 2025-03-09 14:47:29 ChinaXiv:202503.00074V1 下载全文

The RFI Fast Mitigation Algorithm Based on Block LMS Filter 作者： Han Wu, Hai-Long Zhang, Ya-Zhou Zhang, Jie Wang, Xu Du, Ting Zhang and Xin-Chen Ye 1 作者单位： 1. Han Wu, Hai-Long Zhang, Ya-Zhou Zhang, Jie Wang, Xu Du, Ting Zhang and Xin-Chen Ye 提交时间： 2024-02-01 14:50:09 摘要: The radio telescope possesses high sensitivity and strong signal collection capabilities. While receiving celestial radiation signals, it also captures Radio Frequency Interferences (RFIs) introduced by human activities. RFI, as signals originating from sources other than the astronomical targets, significantly impacts the quality of astronomical data. This paper presents an RFI fast mitigation algorithm based on block Least Mean Square (LMS) algorithm. It enhances the traditional adaptive LMS filter by grouping L adjacent time-sampled points into one block and applying the same filter coefficients for filtering within each block. This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem. The algorithm is tested using baseband data from the Parkes 64 m radio telescope's pulsar observations and simulated data. The results confirm the algorithm's effectiveness, as the pulsar profile after RFI mitigation closely matches the original pulsar profile. 期刊： Research in Astronomy and Astrophysics 分类： 物理学 >> 地球物理学、天文学和天体物理学 投稿状态： 已在期刊出版 引用： ChinaXiv:202402.00044 (或此版本 ChinaXiv:202402.00044V1 ) DOI:https://doi.org/10.1088/1674-4527/ad05e9 CSTR:32003.36.ChinaXiv.202402.00044.V1 推荐引用方式： Han Wu, Hai-Long Zhang, Ya-Zhou Zhang, Jie Wang, Xu Du, Ting Zhang and Xin-Chen Ye.(2024).The RFI Fast Mitigation Algorithm Based on Block LMS Filter.Research in Astronomy and Astrophysics.doi:https://doi.org/10.1088/1674-4527/ad05e9 版本历史 [V1] 2024-02-01 14:50:09 ChinaXiv:202402.00044V1 下载全文

We establish rigourously the scaling properties of the Lanczos process applied to an arbitrary extensive Many-Body System which is carried to convergence n to infinity and the thermodynamic limit N to infinity taken. In this limit the solution for the limiting Lanczos coefficients are found exactly and generally through two equivalent sets of equations, given initial knowledge of the exact cumulant generating function. The measure and the Orthogonal Polynomial System associated with the Lanczos process in this regime are also given explicitly. Some important representations of these Lanczos functions are given, including Taylor series expansions, and theorems controlling their general properties are proven.

One of the most interesting questions about a group is if its word problem can be solved and how. The word problem in the braid group is of particular interest to topologists, algebraists and geometers, and is the target of intensive current research. We look at the braid group from a topological point of view (rather than a geometrical one). The braid group is defined by the action of diffeomorphisms on the fundamental group of a punctured disk. We exploit the topological definition of the braid group in order to give a new approach for solving its word problem. Our algorithm is faster, in comparison with known algorithms, for short braid words with respect to the number of generators combining the braid, and it is almost independent of the number of strings in the braids. Moreover, the algorithm is based on a new computer presentation of the elements of the fundamental group of a punctured disk. This presentation can be used also for other algorithms.

We show using the Beylkin-Coifman-Rokhlin algorithm in the Haar basis that any singular integral operator can be written as the sum of a bounded operator on $L^p$, $1

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Basing on a modification of the "Dichotomy Algorithm" (Terekhov, 2010), we propose a parallel procedure for solving tridiagonal systems of equations with Toeplitz matrices. Taking the structure of the Toeplitz matrices, we may substantially reduce the number of the "preliminary calculations" of the Dichotomy Algorithm, which makes it possible to effectively solve a series as well as a single system of equations. On the example of solving of elliptic equations by the Separation Variable Method, we show that the computation accuracy is comparable with the sequential version of the Thomas method, and the dependence of the speedup on the number of processors is almost linear. The proposed modification is aimed at parallel realization of a broad class of numerical methods including the inversion of Toeplitz and quasi-Toeplitz tridiagonal matrices.

We propose a full-wave pseudo-analytical numerical electromagnetic (EM) algorithm to model subsurface induction sensors, traversing planar-layered geological formations of arbitrary EM material anisotropy and loss, which are used, for example, in the exploration of hydrocarbon reserves. Unlike past pseudo-analytical planar-layered modeling algorithms that impose parallelism between the formation's bed junctions however, our method involves judicious employment of Transformation Optics techniques to address challenges related to modeling relative slope (i.e., tilting) between said junctions (including arbitrary azimuth orientation of each junction). The algorithm exhibits this flexibility, both with respect to loss and anisotropy in the formation layers as well as junction tilting, via employing special planar slabs that coat each "flattened" (i.e., originally tilted) planar interface, locally redirecting the incident wave within the coating slabs to cause wave fronts to interact with the flattened interfaces as if they were still tilted with a specific, user-defined orientation. Moreover, since the coating layers are homogeneous rather than exhibiting continuous material variation, a minimal number of these layers must be inserted and hence reduces added simulation time and computational expense. As said coating layers are not reflectionless however, they do induce artificial field scattering that corrupts legitimate field signatures due to the (effective) interface tilting. Numerical results, for two half-spaces separated by a tilted interface, quantify error trends versus material and sensor characteristics. We finally exhibit responses of sensors traversing three-layered media, where we vary the anisotropy, loss, and relative tilting of the formations and explore the sensitivity of the sensor's complex-valued measurements.

We present improved convergence results for the Boosting Algorithm (BA), and demonstrate that an existing formulation of the Heterogeneous Multiscale Methods (HMM) is accurate to first order only in the macro time step, regardless of the order of the numerical solvers employed. These results are obtained by considering the BA and two other formulations of HMM as special cases of a general formulation of HMM applied to dissipative stiff ordinary differential equations.

In this paper we show how to solve the \emph{Maximum Weight Stable Set Problem} in a claw-free graph $G(V, E)$ with $\alpha(G) \le 3$ in time ${\cal O}(|E|\log|V|)$. More precisely, in time ${\cal O}(|E|)$ we check whether $\alpha(G) \le 3$ or produce a stable set with cardinality at least $4$; moreover, if $\alpha(G) \le 3$ we produce in time ${\cal O}(|E|\log|V|)$ a maximum stable set of $G$. This improves the bound of ${\cal O}(|E||V|)$ due to Faenza et al.

In this paper we study the asymptotic behavior of the Random-Walk Metropolis algorithm on probability densities with two different `scales', where most of the probability mass is distributed along certain key directions with the `orthogonal' directions containing relatively less mass. Such class of probability measures arise in various applied contexts including Bayesian inverse problems where the posterior measure concentrates on a sub-manifold when the noise variance goes to zero. When the target measure concentrates on a linear sub-manifold, we derive analytically a diffusion limit for the Random-Walk Metropolis Markov chain as the scale parameter goes to zero. In contrast to the existing works on scaling limits, our limiting Stochastic Differential Equation does not in general have a constant diffusion coefficient. Our results show that in some cases, the usual practice of adapting the step-size to control the acceptance probability might be sub-optimal as the optimal acceptance probability is zero (in the limit).

Lifting operators play an important role in starting a kinetic Boltzmann model from given macroscopic information. The macroscopic variables need to be mapped to the distribution functions, mesoscopic variables of the Boltzmann model. A well-known numerical method for the initialization of Boltzmann models is the Constrained Runs algorithm. This algorithm is used in literature for the initialization of lattice Boltzmann models, special discretizations of the Boltzmann equation. It is based on the attraction of the dynamics toward the slow manifold and uses lattice Boltzmann steps to converge to the desired dynamics on the slow manifold. We focus on applying the Constrained Runs algorithm to map density, average flow velocity, and temperature, the macroscopic variables, to distribution functions. Furthermore, we do not consider only lattice Boltzmann models. We want to perform the algorithm for different discretizations of the Boltzmann equation and consider a standard finite volume discretization.

The orbit problem is at the heart of symmetry reduction methods for model checking concurrent systems. It asks whether two given configurations in a concurrent system (represented as finite strings over some finite alphabet) are in the same orbit with respect to a given finite permutation group (represented by their generators) acting on this set of configurations by permuting indices. It is known that the problem is in general as hard as the graph isomorphism problem, whose precise complexity (whether it is solvable in polynomial-time) is a long-standing open problem. In this paper, we consider the restriction of the orbit problem when the permutation group is cyclic (i.e. generated by a single permutation), an important restriction of the problem. It is known that this subproblem is solvable in polynomial-time. Our main result is a linear-time algorithm for this subproblem.

The central problem of concern to Serrano, Boguna and Vespignani ("Extracting the multiscale backbone of complex weighted networks", Proc Natl Acad Sci 106:6483-6488 [2009]) can be effectively and elegantly addressed using a well-established two-stage algorithm that has been applied to internal migration flows for numerous nations and several other forms of "transaction flow data".

We adapt the Deutsch-Josza algorithm to the context of formal language theory. Specifically, we use the algorithm to distinguish between trivial and nontrivial words in groups given by finite presentations, under the promise that a word is of a certain type. This is done by extending the original algorithm to functions of arbitrary length binary output, with the introduction of a more general concept of parity. We provide examples in which properties of the algorithm allow to reduce the number of oracle queries with respect to the deterministic classical case. This has some consequences for the word problem in groups with a particular kind of presentation.

The k_t and Cambridge/Aachen inclusive jet finding algorithms for hadron-hadron collisions can be seen as belonging to a broader class of sequential recombination jet algorithms, parametrised by the power of the energy scale in the distance measure. We examine some properties of a new member of this class, for which the power is negative. This ``anti-k_t'' algorithm essentially behaves like an idealised cone algorithm, in that jets with only soft fragmentation are conical, active and passive areas are equal, the area anomalous dimensions are zero, the non-global logarithms are those of a rigid boundary and the Milan factor is universal. None of these properties hold for existing sequential recombination algorithms, nor for cone algorithms with split--merge steps, such as SISCone. They are however the identifying characteristics of the collinear unsafe plain ``iterative cone'' algorithm, for which the anti-k_t algorithm provides a natural, fast, infrared and collinear safe replacement.

A polymer chain containing $N$ monomers confined in a finite cylindrical tube of diameter $D$ grafted at a distance $L$ from the open end of the tube may undergo a rather abrupt transition, where part of the chain escapes from the tube to form a "crown-like" coil outside of the tube. When this problem is studied by Monte Carlo simulation of self-avoiding walks on the simple cubic lattice applying a cylindrical confinement and using the standard pruned-enriched Rosenbluth method (PERM), one obtains spurious results, however: with increasing chain length the transition gets weaker and weaker, due to insufficient sampling of the "escaped" states, as a detailed analysis shows. In order to solve this problem, a new variant of a biased sequential sampling algorithm with re-sampling is proposed, force-biased PERM: the difficulty of sampling both phases in the region of the first order transition with the correct weights is treated by applying a force at the free end pulling it out of the tube. Different strengths of this force need to be used and reweighting techniques are applied. Using rather long chains (up to N=18000) and wide tubes (up to D=29 lattice spacings), the free energy of the chain, its end-to-end distance, the number of "imprisoned" monomers can be estimated, as well as the order parameter and its distribution. It is suggested that this new algorithm should be useful for other problems involving state changes of polymers, where the different states belong to rather disjunct "valleys" in the phase space of the system.

The results on Vandermonde-like matrices were introduced as a generalization of polynomial Vandermonde matrices, and the displacement structure of these matrices was used to derive an inversion formula. In this paper we first present a fast Gaussian elimination algorithm for the polynomial Vandermonde-like matrices. Later we use the said algorithm to derive fast inversion algorithms for quasiseparable, semiseparable and well-free Vandermonde-like matrices having $\mathcal{O}(n^2)$ complexity. To do so we identify structures of displacement operators in terms of generators and the recurrence relations(2-term and 3-term) between the columns of the basis transformation matrices for quasiseparable, semiseparable and well-free polynomials. Finally we present an $\mathcal{O}(n^2)$ algorithm to compute the inversion of quasiseparable Vandermonde-like matrices.

We put forth a new string matching algorithm which matches the pattern from neither the left nor the right end, instead a special position. Comparing with the Knuth-Morris-Pratt algorithm and the Boyer-Moore algorithm, the new algorithm is more flexible to pick the position for starting comparisons. The option really brings it a saving in cost.

We describe an efficient parallel implementation of the selected inversion algorithm for distributed memory computer systems, which we call \texttt{PSelInv}. The \texttt{PSelInv} method computes selected elements of a general sparse matrix $A$ that can be decomposed as $A = LU$, where $L$ is lower triangular and $U$ is upper triangular. The implementation described in this paper focuses on the case of sparse symmetric matrices. It contains an interface that is compatible with the distributed memory parallel sparse direct factorization \texttt{SuperLU\_DIST}. However, the underlying data structure and design of \texttt{PSelInv} allows it to be easily combined with other factorization routines such as \texttt{PARDISO}. We discuss general parallelization strategies such as data and task distribution schemes. In particular, we describe how to exploit the concurrency exposed by the elimination tree associated with the $LU$ factorization of $A$. We demonstrate the efficiency and accuracy of \texttt{PSelInv} by presenting a number of numerical experiments. In particular, we show that \texttt{PSelInv} can run efficiently on more than $4,000$ cores for a modestly sized matrix. We also demonstrate how \texttt{PSelInv} can be used to accelerate large-scale electronic structure calculations.

We present a new algorithm, CAMIRA, to identify clusters of galaxies in wide-field imaging survey data. We base our algorithm on the stellar population synthesis model to predict colours of red-sequence galaxies at a given redshift for an arbitrary set of bandpass filters, with additional calibration using a sample of spectroscopic galaxies to improve the accuracy of the model prediction. We run the algorithm on ~11960 deg^2 of imaging data from the Sloan Digital Sky Survey (SDSS) Data Release 8 to construct a catalogue of 71743 clusters in the redshift range 0.1

This paper describes an algorithm for fitting finite mixtures of unrestricted Multivariate Skew t (FM-uMST) distributions. The package EMMIX-uskew implements a closed-form expectation-maximization (EM) algorithm for computing the maximum likelihood (ML) estimates of the parameters for the (unrestricted) FM-MST model in R. EMMIX-uskew also supports visualization of fitted contours in two and three dimensions, and random sample generation from a specified FM-uMST distribution. Finite mixtures of skew t-distributions have proven to be useful in modelling heterogeneous data with asymmetric and heavy tail behaviour, for example, datasets from flow cytometry. In recent years, various versions of mixtures with multivariate skew t (MST) distributions have been proposed. However, these models adopted some restricted characterizations of the component MST distributions so that the E-step of the EM algorithm can be evaluated in closed form. This paper focuses on mixtures with unrestricted MST components, and describes an iterative algorithm for the computation of the ML estimates of its model parameters. The usefulness of the proposed algorithm is demonstrated in three applications to real data sets. The first example illustrates the use of the main function fmmst in the package by fitting a MST distribution to a bivariate unimodal flow cytometric sample. The second example fits a mixture of MST distributions to the Australian Institute of Sport (AIS) data, and demonstrate that EMMIX-uskew can provide better clustering results than mixtures with restricted MST components. In the third example, EMMIX-uskew is applied to classify cells in a trivariate flow cytometric dataset. Comparisons with other available methods suggests that the EMMIX-uskew result achieved a lower misclassification rate with respect to the labels given by benchmark gating analysis.

We propose a recursive quantum algorithm to find the lowest eigenstate of a general Hamiltonian. It yields the lowest eigenstate directly from an arbitrary chosen initial state without diagonalizing the Hamiltonian matrix. Remarkably, the effectiveness of the algorithm does not depend on any specific Hamiltonian structure as far as the initial state is not orthogonal to the lowest eigenstate. Further, our algorithm does not get trapped in the local-minima of the Hamiltonian. The number of recursions required for high accuracy $\simeq 1-\epsilon$ ($\epsilon \ll 1$) are bounded by the order of ${\cal O}((D\epsilon)^{-1})$, where $D$ is the difference between the two lowest eigenvalues.

An improved weight-coded evolutionary algorithm (IWCEA) is proposed for solving multidimensional knapsack problems. This IWCEA uses a new decoding method and incorporates a heuristic method in initialization. Computational results show that the IWCEA runs faster and performs better than a weight-coded evolutionary algorithm proposed by Raidl (1999) and to some existing benchmarks, it can yield better results than the ones reported in the OR-library.

We establish the pointwise convergence of the iterative Lloyd algorithm, also known as $k$-means algorithm, when the quadratic quantization error of the starting grid (with size $N\ge 2$) is lower than the minimal quantization error with respect to the input distribution is lower at level $N-1$. Such a protocol is known as the splitting method and allows for convergence even when the input distribution has an unbounded support. We also show under very light assumption that the resulting limiting grid still has full size $N$. These results are obtained without continuity assumption on the input distribution. A variant of the procedure taking advantage of the asymptotic of the optimal quantizer radius is proposed which always guarantees the boundedness of the iterated grids.

The Gaver-Stehfest algorithm for numerical inversion of Laplace transform was developed in the late 1960s. Due to its simplicity and good performance it is becoming increasingly more popular in such diverse areas as Geophysics, Operations Research and Economics, Financial and Actuarial Mathematics, Computational Physics and Chemistry. Despite the large number of applications and numerical studies, this method has never been rigorously investigated. In particular, it is not known whether the Gaver-Stehfest approximations converge and what is the rate of convergence. In this paper we answer the first of these two questions: We prove that the Gaver-Stehfest approximations converge for functions of bounded variation and functions satisfying an analogue of Dini criterion.

In 1979 J.J. Kohn gave an indirect argument via the Diederich-Forn\ae ss Theorem showing that finite D'Angelo type implies termination of the Kohn algorithm for a pseudoconvex domain with real-analytic boundary. We give here a direct argument for this same implication using the stratification coming from Catlin's notion of a boundary system as well as algebraic geometry on the ring of real-analytic functions. We also indicate how this argument could be used in order to compute an effective lower bound for the subelliptic gain in the $\bar\partial$-Neumann problem in terms of the D'Angelo type, the dimension of the space, and the level of forms provided that an effective \L ojasiewicz inequality can be proven in the real-analytic case and slightly more information obtained about the behavior of the sheaves of multipliers in the Kohn algorithm.

This brief paper: (1) Discusses strategies to generate random test cases that can be used to extensively test any Linear Distance Program (LDP) software. (2) Gives three numerical examples of input cases generated by this strategy that cause problems in the Lawson and Hanson LDP module. (3) Proposes, as a standard matter of acceptable implementation procedures, that (unless it is done internally in the software itself, but, in general, this seems to be much rarer than one would expect) all users should test the returned output from any LDP module for self-consistency since it incurs only a small amount of added computational overhead and it is not hard to do.

This brief paper: (1) Discusses strategies to generate random test cases that can be used to extensively test any Linear Distance Program (LDP) software. (2) Gives three numerical examples of input cases generated by this strategy that cause problems in the Lawson and Hanson LDP module. (3) Proposes, as a standard matter of acceptable implementation procedures, that (unless it is done internally in the software itself, but, in general, this seems to be much rarer than one would expect) all users should test the returned output from any LDP module for self-consistency since it incurs only a small amount of added computational overhead and it is not hard to do.

This brief paper: (1) Discusses strategies to generate random test cases that can be used to extensively test any Linear Distance Program (LDP) software. (2) Gives three numerical examples of input cases generated by this strategy that cause problems in the Lawson and Hanson LDP module. (3) Proposes, as a standard matter of acceptable implementation procedures, that (unless it is done internally in the software itself, but, in general, this seems to be much rarer than one would expect) all users should test the returned output from any LDP module for self-consistency since it incurs only a small amount of added computational overhead and it is not hard to do.

We analyze the effect of interference on the convergence rate of average consensus algorithms, which iteratively compute the measurement average by message passing among nodes. It is usually assumed that these algorithms converge faster with a greater exchange of information (i.e., by increased network connectivity) in every iteration. However, when interference is taken into account, it is no longer clear if the rate of convergence increases with network connectivity. We study this problem for randomly-placed consensus-seeking nodes connected through an interference-limited network. We investigate the following questions: (a) How does the rate of convergence vary with increasing communication range of each node? and (b) How does this result change when each node is allowed to communicate with a few selected far-off nodes? When nodes schedule their transmissions to avoid interference, we show that the convergence speed scales with $r^{2-d}$, where $r$ is the communication range and $d$ is the number of dimensions. This scaling is the result of two competing effects when increasing $r$: Increased schedule length for interference-free transmission vs. the speed gain due to improved connectivity. Hence, although one-dimensional networks can converge faster from a greater communication range despite increased interference, the two effects exactly offset one another in two-dimensions. In higher dimensions, increasing the communication range can actually degrade the rate of convergence. Our results thus underline the importance of factoring in the effect of interference in the design of distributed estimation algorithms.

We present an algorithmic approach to the conjugacy problems in monoids, using rewriting systems. We extend the classical theory of rewriting developed by Knuth and Bendix to a rewriting that takes into account the cyclic conjugates.

In the study of open quantum systems, one typically obtains the decoherence dynamics by solving a master equation. The master equation is derived using knowledge of some basic properties of the system, the environment and their interaction: one basically needs to know the operators through which the system couples to the environment and the spectral density of the environment. For a large system, it could become prohibitively difficult to even write down the appropriate master equation, let alone solve it on a classical computer. In this paper, we present a quantum algorithm for simulating the dynamics of an open quantum system. On a quantum computer, the environment can be simulated using ancilla qubits with properly chosen single-qubit frequencies and with properly designed coupling to the system qubits. The parameters used in the simulation are easily derived from the parameters of the system+environment Hamiltonian. The algorithm is designed to simulate Markovian dynamics, but it can also be used to simulate non-Markovian dynamics provided that this dynamics can be obtained by embedding the system of interest into a larger system that obeys Markovian dynamics. We estimate the resource requirements for the algorithm. In particular, we show that for sufficiently slow decoherence a single ancilla qubit could be sufficient to represent the entire environment, in principle.

We present an analysis of the performance of an elitist Evolutionary algorithm using a recombination operator known as 1-Bit-Swap on the Royal Roads test function based on a population. We derive complete, approximate and asymptotic convergence rates for the algorithm. The complete model shows the benefit of the size of the population and re- combination pool.

We analyse the resilience of the quantum search algorithm in the presence of quantum noise modelled as trace preserving completely positive maps. We study the influence of noise on computational complexity of the quantum search algorithm. We show that only for small amounts of noise the quantum search algorithm is still more efficient than any classical algorithm.