Blind Channel Identification Based on Noisy Observation by Stochastic Approximation Method

A stochastic approximation algorithm for estimating multichannel coefficients is proposed, and the estimate is proved to converge to the true parameters a.s. up-to a constant scaling factor. The estimate is updated after receiving each new observation, so the output data need not be collected in advance. The input signal is allowed to be dependent and the observation is allowed to be corrupted by noise, but no noise statistics are used in the estimation algorithm.     

Low Cost, High Efficiency, Gain Flattened L Band EDFA Using FBGs as C Band Seed Generators

A low cost, coolerless 980nm diode pumped, gain flattened L band EDFA with fast transient control, high pump efficiency and gain clamping effect was realized by using FBGs as C band seed generators.     

Loading aircraft for military operations

In this paper, we describe an aircraft loading problem submitted by the French military agency (DGA) as part of a more general military airlift planning problem. It can be viewed as a kind of bi-dimensional bin-packing problem, with heterogeneous bins and several additional constraints. We introduce two-phase methods for solving this NP-hard problem. The first phase consists in building good initial solutions, thanks to two fast algorithms: a list-based heuristic and a loading pattern generation method. Both algorithms call a constraint-based subroutine, able to determine quickly if the items already loaded can be reshuffled to accommodate a new object. The second phase improves these preliminary solutions using local search techniques. Results obtained on real data sets are presented.     

ON THE RATE OF WEIGHTED Lp CONVERGENCE BY THE GRUENWALD INTERPOLATORY OPERATORS

This paper gives the weighted Lp convergence rate estimations of the Gruenwald interpolatory polynomials based on the zeros of Chebyshev polymomials of the first kind,and proves that the order of the estimations is optimal for p≥1.     

Living/controlled radical copolymerization of N‐substituted maleimides with styrene in 1‐butyl‐3‐methylimidazolium hexafluorophosphate and anisole

The atom transfer radical copolymerization of N‐substituted maleimides such as N‐phenylmaleimide (PhMI), N‐cyclohexylmaleimide (ChMI), and N‐butylmaleimide (NBMI) with styrene initiated with dendritic polyarylether 2‐bromoisobutyrates in an ionic liquid, 1‐butyl‐3‐methylimidazolium hexafluorophosphate ([bmim][PF₆]), at room temperature and anisole at 110 °C was investigated. The dendritic‐linear block copolymers obtained in ionic liquid possessed well‐defined molecular weight and low polydispersity (1.05 < M_w/M_n < 1.32) and could be used as a macroinitiator for chain‐extension polymerization, suggesting the living nature of the reaction system. The ionic liquids containing catalyst could be recycled in the atom transfer radical polymerization systems without further treatment. Compared with polymerization conducted in anisole, the polymerization in ionic liquid had a stronger tendency for alternation. The tendency for alternation decreased in the order PhMI > NBMI > ChMI in [bmim][PF₆] and PhMI > ChMI > NBMI in anisole. © 2003 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 41: 2156–2165, 2003     

Reply to Nathan: How to reconstruct the causal argument

Nicholas Nathan tries to resist the current version of the causal argument for sense-data in two ways. First he suggests that, on what he considers to be the correct re-construction of the argument, it equivocates on the sense of proximate cause. Second he defends a form of disjunctivism, by claiming that there might be an extra mechanism involved in producing veridical hallucination, that is not present in perception. I argue that Nathan’s reconstruction of the argument is not the appropriate one, and that, properly interpreted, the argument does not equivocate on proximate cause. Furthermore, I claim that his postulation of a modified mechanism for hallucinations is implausibly ad hoc.     

Pattern formation on a nonlinear periodic electrical network

It is observed experimentally that a periodic array of resistively coupled LC-oscillators with ans-shaped nonlinearity exhibits stable non-uniform voltage and current distributions. The dependence of the resulting structure on the coupling resistance and the boundary conditions is investigated. The structure is rather insensitive to variations of the boundary values. The experiments are well described by a two-variable reaction-diffusion equation.     

Inhomogeneous Voter Models in One Dimension

We establish conditions for survival and extinction of types of one-dimensional voter models, and show that increasing the flip rates at a finite number of sites typically does not affect survival, unless the flipping mechanism is altered. We provide an example of a modified voter model that does not survive but can be made to survive simply by altering the flip mechanism at one site. We also show that a rather general class of such models have clustering behavior.     

The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study

The sample average approximation (SAA) method is an approach for solving stochastic optimization problems by using Monte Carlo simulation. In this technique the expected objective function of the stochastic problem is approximated by a sample average estimate derived from a random sample. The resulting sample average approximating problem is then solved by deterministic optimization techniques. The process is repeated with different samples to obtain candidate solutions along with statistical estimates of their optimality gaps.We present a detailed computational study of the application of the SAA method to solve three classes of stochastic routing problems. These stochastic problems involve an extremely large number of scenarios and first-stage integer variables. For each of the three problem classes, we use decomposition and branch-and-cut to solve the approximating problem within the SAA scheme. Our computational results indicate that the proposed method is successful in solving problems with up to 2¹⁶⁹⁴ scenarios to within an estimated 1.0% of optimality. Furthermore, a surprising observation is that the number of optimality cuts required to solve the approximating problem to optimality does not significantly increase with the size of the sample. Therefore, the observed computation times needed to find optimal solutions to the approximating problems grow only linearly with the sample size. As a result, we are able to find provably near-optimal solutions to these difficult stochastic programs using only a moderate amount of computation time.