Self-Consistent Effects on the Starting Current of Gyrotron Oscillators

Self-consistent effects on the starting current of gyrotron oscillators are examined. Field profiles in the open cavity are shown to be sensitive to the interaction dynamics. This can either significantly raise or lower the oscillation threshold, particularly for the low-Q modes. The transition from resonant-mode oscillations at the low magnetic field to backward-wave oscillations at the high magnetic field is demonstrated.     

Dynamic Bending Strains in Planar Trusses with Pinned or Rigid Joints

The method of reverberation ray matrix is applied to analyze the dynamic behavior of structural members in trusses with pinned joints subjected to suddenly applied force. The results are compared with those in planar trusses with rigid joints. Detailed calculations are made with two types of trusses, a small laboratory model made of slender aluminum bars, and a hypothetical real size Pratt truss made of structural steel. It is found that the maximum dynamic bending strains in the members of both types of trusses are very large, comparable with the dynamic axial strains in the same member. The magnitudes of bending strains in the pinned truss differs little from those in the truss with rigid joints for both types of trusses. Such a large dynamic bending strain in a structure member, which is contradictory to the static behavior of the same truss, could be caused by the inertial force of the mass in structural members of the truss.     

Stochastic choice of basis functions in adaptive functionapproximation and the functional-link net

A theoretical justification for the random vector version of the functional-link (RVFL) net is presented in this paper, based on a general approach to adaptive function approximation. The approach consists of formulating a limit-integral representation of the function to be approximated and subsequently evaluating that integral with the Monte-Carlo method. Two main results are: (1) the RVFL is a universal approximator for continuous functions on bounded finite dimensional sets, and (2) the RVFL is an efficient universal approximator with the rate of approximation error convergence to zero of order O(C/ radicaln), where n is number of basis functions and with C independent of n. Similar results are also obtained for neural nets with hidden nodes implemented as products of univariate functions or radial basis functions. Some possible ways of enhancing the accuracy of multivariate function approximations are discussed.     

FHUQI-Miner: Fast high utility quantitative itemset mining

Applied Intelligence - High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task...     

PREACO: A fast ant colony optimization for codebook generation

This paper presents an effective and efficient method for speeding up ant colony optimization (ACO) in solving the codebook generation problem. The proposed method is inspired by the fact that many computations during the convergence process of ant-based algorithms are essentially redundant and thus can be eliminated to boost their convergence speed, especially for large and complex problems. To evaluate the performance of the proposed method, we compare it with several state-of-the-art metaheuristic algorithms. Our simulation results indicate that the proposed method can significantly reduce the computation time of ACO-based algorithms evaluated in this paper while at the same time providing results that match or outperform those ACO by itself can provide.     

Fault tolerant authenticated quantum direct communication immune to collective noises

This study proposes two new coding functions for GHZ states and GHZ-like states, respectively. Based on these coding functions, two fault tolerant authenticated quantum direct communication (AQDC) protocols are proposed. Each of which is robust under one kind of collective noises: collective-dephasing noise and collective-rotation noise, respectively. Moreover, the proposed AQDC protocols enable a sender to send a secure as well as authenticated message to a receiver within only one step quantum transmission without using the classical channels.     

Quantum dialogue protocols immune to collective noise

This work proposes two quantum dialogue protocols, each of which is robust against one of the following two kinds of collective noise: collective-dephasing noise and collective-rotation noise. Both quantum dialogue protocols are constructed from four-qubit DF states that consist of two Bell states. The receiver simply performs two Bell state measurements to obtain the secret message. Moreover, the proposed protocols are free from information leakage because some shared private quantum states are established in the new protocols to allow the legitimate users to exchange their secret messages securely.     

Isotope effect and tTc suppression in d-wave superconductors

We have studied the isotope effect on the superconducting transition temperatureT _c ofd-wave pairing instabilities due to antiferromagnetic spin fluctuation exchange. We found that models with conventional coupling toharmonic phonon degrees of freedom and reasonable limits on coupling strength and phonon spectra, give values for the isotope exponent which are at least one order of magnitude smaller than typical observed values of in the cuprates. Models with coupling to non-linear tunneling modes in a stronglyanharmonic multi-well lattice potential and realistic coupling strengths have effectively larger isotopic mass dependences andmay therefore account for the observed magnitudes of.This work was supported by the National Science Foundation (Grant DMR-8913878 and DMR-9215123) and by the University of Georgia Office of the Vice President for Research. Computing support from UCNS, University of Georgia, and NCSA, University of Illinois, is gratefully acknowledged.     

Maintenance of fast updated frequent pattern trees for record deletion

The Frequent-Pattern-tree (FP tree) is an efficient data structure for association-rule mining without generation of candidate itemsets. It was used to represent a database into a tree structure which stored only frequent items. It, however, needed to process all transactions in a batch way. In the past, Hong et al. thus proposed an efficient incremental mining algorithm for handling newly inserted transactions. In addition to record insertion, record deletion from databases is also commonly seen in real-applications. In this paper, we thus attempt to modify the FP-tree construction algorithm for efficiently handling deletion of records. A fast updated FP-tree (FUFP-tree) structure is used, which makes the tree update process become easier. An FUFP-tree maintenance algorithm for the deletion of records is also proposed for reducing the execution time in reconstructing the tree when records are deleted. Experimental results also show that the proposed FUFP-tree maintenance algorithm for deletion of records runs faster than the batch FP-tree construction algorithm for handling deleted records and generates nearly the same tree structure as the FP-tree algorithm. The proposed approach can thus achieve a good trade-off between execution time and tree complexity.     

A time-efficient pattern reduction algorithm for k-means clustering

This paper presents an efficient algorithm, called pattern reduction (PR), for reducing the computation time of k-means and k-means-based clustering algorithms. The proposed algorithm works by compressing and removing at each iteration patterns that are unlikely to change their membership thereafter. Not only is the proposed algorithm simple and easy to implement, but it can also be applied to many other iterative clustering algorithms such as kernel-based and population-based clustering algorithms. Our experiments—from 2 to 1000 dimensions and 150 to 10,000,000 patterns—indicate that with a small loss of quality, the proposed algorithm can significantly reduce the computation time of all state-of-the-art clustering algorithms evaluated in this paper, especially for large and high-dimensional data sets.