Cascades of Dynamical Transitions in an Adaptive Population

In an adaptive population which models financial markets and distributed control, we consider how the dynamics depends on the diversity of the agents' initial preferences of strategies. When the diversity decreases, more agents tend to adapt their strategies together. This change in the environment results in dynamical transitions from vanishing to non-vanishing step sizes. When the diversity decreases further, we find a cascade of dynamical transitions for the different signal dimensions, supported by good agreement between simulations and theory. Besides, the signal of the largest step size at the steady state is likely to be the initial signal.Comment: 4 pages, 8 figure

The temperature dependence of the local tunnelling conductance in cuprate superconductors with competing AF order

Based on the $t-t'-U-V$ model with proper chosen parameters for describing the cuprate superconductors, it is found that near the optimal doping at low temperature ($T$), only the pure d-wave superconductivity ($d$SC) prevails and the antiferromagnetic (AF) order is completely suppressed. At higher $T$, the AF order with stripe modulation and the accompanying charge order may emerge, and they could exist above the $d$SC transition temperature. We calculate the local differential tunnelling conductance (LDTC) from the local density of states (LDOS) and show that their energy variations are rather different from each other as $T$ increases. Although the calculated modulation periodicity in the LDTC/LDOS and bias energy dependence of the Fourier amplitude of LDTC in the "pseudogap" region are in good agreement with the recent STM experiment [Vershinin $et al.$, Science {\bf 303}, 1995 (2004)], we point out that some of the energy dependent features in the LDTC do not represent the intrinsic characteristics of the sample

Efficient serial and parallel implementations of the cutting angle global optimisation technique

We examine efficient computer implementation of one method of deterministic global optimisation, the cutting angle method. In this method the objective function is approximated from values below the function with a piecewise linear auxiliary function. The global minimum of the objective function is approximated from the sequence of minima of this auxiliary function. Computing the minima of the auxiliary function is a combinatorial problem, and we show that it can be effectively parallelised. We discuss the improvements made to the serial implementation of the cutting angle method, and ways of distributing computations across multiple processors on parallel and cluster computers.<br /

Finite-Time Disentanglement via Spontaneous Emission

We show that under the influence of pure vacuum noise two entangled qubits become completely disentangled in a finite time, and in a specific example we find the time to be given by $\ln \Big(\frac{2 +\sqrt 2}{2}\Big)$ times the usual spontaneous lifetime.Comment: revtex, 4 pages, 2 figure

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

The mechanism of hole carrier generation and the nature of pseudogap- and 60K-phases in YBCO

In the framework of the model assuming the formation of NUC on the pairs of Cu ions in CuO$_{2}$ plane the mechanism of hole carrier generation is considered and the interpretation of pseudogap and 60 K-phases in $YBa_{2}Cu_{3}O_{6+\delta}$. is offered. The calculated dependences of hole concentration in $YBa_{2}Cu_{3}O_{6+\delta}$ on doping $\delta$ and temperature are found to be in a perfect quantitative agreement with experimental data. As follows from the model the pseudogap has superconducting nature and arises at temperature $T^{*}>T_{c\infty}>T_{c}$ in small clusters uniting a number of NUC's due to large fluctuations of NUC occupation. Here $T_{c\infty}$ and $T_{c}$ are the superconducting transition temperatures of infinite and finite clusters of NUC's, correspondingly. The calculated $T^{*}(\delta)$ and $T_{n}(\delta)$ dependences are in accordance with experiment. The area between $T^{*}(\delta)$ and $T_{n}(\delta)$ corresponds to the area of fluctuations where small clusters fluctuate between superconducting and normal states owing to fluctuations of NUC occupation. The results may serve as important arguments in favor of the proposed model of HTSC.Comment: 12 pages, 7 figure

The Importance of High-Frequency, Small-Eddy Turbulence in Spark Ignited, Premixed Engine Combustion

The different roles played by small and large eddies in engine combustion were studied. Experiments compared natural gas combustion in a converted, single cylinder Volvo TD 102 engine and in a 125 mm cubical cell. Turbulence is used to enhance flame growth, ideally giving better efficiency and reduced cyclic variation. Both engine and test cell results showed that flame growth rate correlated best with the level of high frequency, small eddy turbulence. The more effective, small eddy turbulence also tended to lower cyclic variations. Large scales and bulk flows convected the flame relative to cool surfaces and were most important to the initial flame kernel