ALTDSE: An Arnoldi–Lanczos program to solve the time-dependent Schrödinger equation

We describe a general ab initio and non-perturbative method to solve the time-dependent Schrödinger equation (TDSE) for the interaction of a strong attosecond laser pulse with a general atom. While the field-free Hamiltonian and the dipole matrices may be generated using an arbitrary primitive basis, they are assumed to have been transformed to the eigenbasis of the problem before the solution of the TDSE is propagated in time using the Arnoldi–Lanczos method. Probabilities for survival of the ground state, excitation, and single ionization can be extracted from the propagated wavefunction.

Program summary

Program title: ALTDSECatalogue identifier: AEDM_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEDM_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.: 2154No. of bytes in distributed program, including test data, etc.: 30 827Distribution format: tar.gzProgramming language: Fortran 95. [A Fortran 2003 call to “flush” is used to simplify monitoring the output file during execution. If this function is not available, these statements should be commented out.].Computer: Shared-memory machinesOperating system: Linux, OpenMPHas the code been vectorized or parallelized?: YesRAM: Several Gb, depending on matrix size and number of processorsSupplementary material: To facilitate the execution of the program, Hamiltonian field-free and dipole matrix files are provided.Classification: 2.5External routines: LAPACK, BLASNature of problem: We describe a computer program for a general ab initio and non-perturbative method to solve the time-dependent Schrödinger equation (TDSE) for the interaction of a strong attosecond laser pulse with a general atom [1,2]. The probabilities for survival of the initial state, excitation of discrete states, and single ionization due to multi-photon processes can be obtained.Solution method: The solution of the TDSE is propagated in time using the Arnoldi–Lanczos method. The field-free Hamiltonian and the dipole matrices, originally generated in an arbitrary basis (e.g., the flexible B-spline R-matrix (BSR) method with non-orthogonal orbitals [3]), must be provided in the eigenbasis of the problem as input.Restrictions: The present program is restricted to a ¹S^e initial state and linearly polarized light. This is the most common situation experimentally, but a generalization is straightforward.Running time: Several hours, depending on the number of threads used.References: [1] X. Guan, O. Zatsarinny, K. Bartschat, B.I. Schneider, J. Feist, C.J. Noble, Phys. Rev. A 76 (2007) 053411. [2] X. Guan, C.J. Noble, O. Zatsarinny, K. Bartschat, B.I. Schneider, Phys. Rev. A 78 (2008) 053402. [3] O. Zatsarinny, Comput. Phys. Comm. 174 (2006) 273.     

Nearest neighbor editing aided by unlabeled data

This paper proposes a novel method for nearest neighbor editing. Nearest neighbor editing aims to increase the classifier’s generalization ability by removing noisy instances from the training set. Traditionally nearest neighbor editing edits (removes/retains) each instance by the voting of the instances in the training set (labeled instances). However, motivated by semi-supervised learning, we propose a novel editing methodology which edits each training instance by the voting of all the available instances (both labeled and unlabeled instances). We expect that the editing performance could be boosted by appropriately using unlabeled data. Our idea relies on the fact that in many applications, in addition to the training instances, many unlabeled instances are also available since they do not need human annotation effort. Three popular data editing methods, including edited nearest neighbor, repeated edited nearest neighbor and All k-NN are adopted to verify our idea. They are tested on a set of UCI data sets. Experimental results indicate that all the three editing methods can achieve improved performance with the aid of unlabeled data. Moreover, the improvement is more remarkable when the ratio of training data to unlabeled data is small.     

Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques

Automatic human face detection from video sequences is an important component of intelligent human computer interaction systems for video surveillance, face recognition, emotion recognition and face database management. This paper proposes an automatic and robust method to detect human faces from video sequences that combines feature extraction and face detection based on local normalization, Gabor wavelets transform and Adaboost algorithm. The key step and the main contribution of this work is the incorporation of a normalization technique based on local histograms with optimal adaptive correlation (OAC) technique to alleviate a common problem in conventional face detection methods: inconsistent performance due to sensitivity to variation illuminations such as local shadowing, noise and occlusion. The approach uses a cascade of classifiers to adopt a coarse-to-fine strategy for achieving higher detection rates with lower false positives. The experimental results demonstrate a significant performance improvement gains and achieved by local normalization over methods without normalizations in real video sequences with a wide range of facial variations in color, position, scale, and varying lighting conditions.     

Design and implementation of light-weight mobile multicast for fast MIPv6

Mobile multicast is a research hotspot and can provide many applications. Some mobile multicast schemes have been proposed, but most of them introduce new entities and study construction algorithms of the dynamic multicast delivery structure which is heavyweight for wireless devices. In this paper, we propose a light-weight mobile multicast (LMM) scheme for Fast Mobile IPv6 which reduces the redundant operational overhead by simplifying multicast listener discovery (MLD) proxying. The LMM scheme implements simplified MLD proxying function on home agent to reduce the complicated multicast routing protocol and modifies MLD host part function on mobile node to reduce the multicast membership messages interaction. In order to solve the tunnel convergence problem, LMM also introduces a multicast tunnel combination and reconstruction algorithm. We set up a test-bed to evaluate the performance of LMM, and compare it with other mobile multicast schemes. The experimental results show that LMM reduces the multicast disruption time at handover. Based on the experimental results, we analyze the cost of LMM, and the results show that it has lower protocol cost than other schemes.     

Combined MCDM techniques for exploring stock selection based on Gordon model

Basing on the Gordon model perspective and applying multiple criteria decision making (MCDM), this research explores the influential factors and relative weight of dividend, discount rate, and dividend growth rate. The purpose is to establish an investment decision model and provides investors with a reference/selection of stocks most suitable for investing effects to achieve the greatest returns. Taking full consideration of the interrelation effect among variables of the decision model, this paper introduced analytical network process (ANP) and examined leading electronics companies spanning the hottest sectors of lens, solar, and handset by experts. Empirical findings indicated that dividend was affected by industry outlook, earnings, operating cash flow, and dividend payout rate; discount rate was affected by market β and risk-free rate; and dividend growth rate was affected by earnings growth rate and dividend payout growth rate. Also, according to literatures, discount rate possessed a self-effect relationship. Among the eight evaluation criteria, market β was the most important factor influencing investment decisions, followed by dividend growth rate and risk-free rate. In stock evaluations, leadership companies in the solar industry outperformed those in handset and lens, becoming investors’ favorite stock group at the time that this research was conducted.     

冷床区钢坯自动跟踪系统关键技术的研发

安钢中板厂的炉号跟踪系统中,冷金属检测器CMD用来检测冷床区钢坯的运动,但是CMD只能检测出辊道上是否有钢坯经过,而不能判断钢坯在冷床区具体上到哪个冷床,因此,目前冷床部分的钢坯跟踪是靠人工手动完成的.针对该问题,提出一种新的运用数字图像处理技术完成冷床区钢坯自动跟踪的方法.该方法利用运动目标检测方法检测出辊道上有钢坯运动,然后经过最佳阈值分割、改进的区域生长算法、质心标记、数据库程序等步骤完成钢坯运动轨迹的标记,实现自动跟踪.     

基于群决策的多Agent资源管理模型

在大部分系统中,资源都是有限供给的.因此,为了提高系统的性能,保证资源使用的效率与公平,必须对资源进行有效的管理.而合理分配资源和解决资源使用冲突是系统资源管理的核心问题.基于群决策方法提出了一个多Agent资源管理模型,以动态优先级的方式灵活地解决了系统资源分配与使用冲突问题.     

Controllability of heterogeneous multi-agent systems under directed and weighted topology

This paper considers the controllability problem for both continuous- and discrete-time linear heterogeneous multi-agent systems with directed and weighted communication topology. First, two kinds of neighbour-based control protocols based on the distributed protocol of first-order and second-order multi-agent systems are proposed, under which it is shown that a heterogeneous multi-agent system is controllable if the underlying communication topology is controllable. Then, under special leader selection, the result shows that the controllability of a heterogeneous multi-agent system is solely decided by its communication topology graph. Furthermore, some necessary and/or sufficient conditions are derived for controllability of communication topology from algebraic and graphical perspectives. Finally, simulation examples are presented to demonstrate the effectiveness of the theoretical results.     

Optimal replacement policy for a two-dissimilar-component cold standby system with different repair actions

In this paper, a cold standby repairable system consisting of two dissimilar components and one repairman is studied. When failures occur, the repair of both component 1 and component 2 are not ‘as good as new’. The consecutive operating times of component 1 after repair constitute a decreasing geometric process, while the repair times of component 1 are independent and identically distributed. For component 2, its failure is rectified by minimal repair, and the repair time is negligible. Component 1 has priority in use when both components are good. The replacement policy N is based on the failure number of component 1. Under policy N, we derive the explicit expression of the long-run average cost rate C(N) as well as the average number of repairs of component 2 before the system replaced. The optimal replacement policy N*, which minimises the long-run average cost rate C(N), is obtained theoretically. If the failure rate r(t) of component 2 is increasing, the existence and uniqueness of the optimal policy N* is also proved. Finally, a numerical example is given to validate the developed theoretical model. Some sensitivity analyses are provided to show the influence of some parameters, such as the costs for replacement and repair, and the parameters of the lifetime and repair time distributions of both components, to the optimal replacement policy N* and corresponding average cost rate C(N*).     

Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer

As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems to be solved, such as the modeling problem. This paper adopts support vector regression (SVR) to construct the prediction model of blast furnace silicon content. To ensure a good generalization performance for the given datasets, it is important to select proper parameters for SVR. In view of this problem, a new particle swarm optimizer called DMS-PSO-CLS is presented to optimize the parameters of SVR. In DMS-PSO-CLS, a new cooperative learning strategy is hybridized with DMS-PSO, which makes particle information be used more effectively for generating better-quality solutions. DMS-PSO-CLS takes merits of the DMS-PSO and the cooperative learning strategy so that both the convergence speed and the convergence precision can be improved. Experimental results show that DMS-PSO-CLS can find the optimal parameters of SVR with high speed and the SVR model optimized by DMS-PSO-CLS can achieve a good regression precision on the predictive problem of blast furnace.