Comparison of non-linear optical susceptibilities of KNbO3 and LiNbO3

From the chemical bond viewpoint, second-order non-linear optical (NLO) tenser coefficients of KNbO3 and LiNbO3 crystals have been calculated. By using the bond-valence theory of complex crystals and the modified bond-charge model, we were able to determine contributions of each type of constituent chemical bond to the total second-order NLO susceptibility. The tenser values thus calculated are in good agreement with experimental data. From the comparison of NLO tenser coefficients of these two crystals, we found that the major NLO contributors are KO12 groups and LiO6 octahedra not the distorted NbO6 octahedra. The difference between their NLO properties arises from their different structural characters, and the high coordination number of constituent elements in KNbO3 makes its valence electrons become more delocalised compared with those of LiNbO3. (C) 1997 Elsevier Science Ltd. All rights reserved.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

K-Theory of non-linear projective toric varieties

We define a category of quasi-coherent sheaves of topological spaces on projective toric varieties and prove a splitting result for its algebraic K-theory, generalising earlier results for projective spaces. The splitting is expressed in terms of the number of interior lattice points of dilations of a polytope associated to the variety. The proof uses combinatorial and geometrical results on polytopal complexes. The same methods also give an elementary explicit calculation of the cohomology groups of a projective toric variety over any commutative ring.

Poincare Analysis of Non-linear Electromagnetic Modes in Electron-Positron Plasmas

We present an investigation of coupled nonlinear electromagnetic modes in an electron-positron plasma by using the well established technique of Poincaré surface of section plots. A variety of nonlinear solutions corresponding to interesting coupled electrostatic-electromagnetic modes sustainable in electron-positron plasmas is shown on the Poincaré section. A special class of localized solitary wave solution is identified along a separatrix curve and its importance in the context of electromagnetic wave propagation in an electron-positron plasma is discussed.

Determination of Non-linear Damping Coefficients of bottom-hinged Oscillating Wave Surge Converters Using Numerical Free Decay Tests

Linear wave theory models are commonly applied to predict the performance of bottom-hinged oscillating wave surge converters (OWSC) in operational sea states. To account for non-linear effects, the additional input of coefficients not included in the model itself becomes necessary. In ocean engineering it is
common practice to obtain damping coefficients of floating structures from free decay tests. This paper presents results obtained from experimental tank tests and numerical computational fluid dynamics simulations of OWSC’s. Agreement between numerical and experimental methods is found to be very good, with CFD providing more data points at small amplitude rotations.
Analysis of the obtained data reveals that linear quadratic-damping, as commonly used in time domain models, is not able to accurately model the occurring damping over the whole regime of rotation amplitudes. The authors
conclude that a hyperbolic function is most suitable to express the instantaneous damping ratio over the rotation amplitude and would be the best choice to be used in coefficient based time domain models.

Management and Control of Water Supply Systems

The fast increase in the energy’s price has brought a growing concern about the highly expensive task of transporting water. By creating an hydraulic model of the Water Supply System’s (WSS) network and predicting its behaviour, it is possible to take advantage of the energy’s tariffs, reducing the total cost on pumping activities. This thesis was developed, in association with a technology transfer project called the E-Pumping. It focuses on finding a flexible supervision and control strategy, adaptable to any existent Water Supply System (WSS), as well as forecasting the water demand on a time period chosen by the end user, so that the pumping actions could be planned to an optimum schedule, that minimizes the total operational cost. The OPC protocol, associated to a MySQL database were used to develop a flexible tool of supervision and control, due to their adaptability to function with equipments from various manufacturers, being another integrated modular part of the E-Pumping project. Furthermore, in this thesis, through the study and performance tests of several statistical models based on time series, specifically applied to this problem, a forecasting tool adaptable to any station, and whose model parameters are automatically refreshed at runtime, was developed and added to the project as another module. Both the aforementioned modules were later integrated with an Graphical User Interface (GUI) and installed in a pilot application at the ADDP’s network. The implementation of this software on WSSs across the country will reduce the water supply companies’ running costs, improving their market competition and, ultimately, lowering the water price to the end costumer.

Root locus of fractional linear systems

In this paper an algorithm for the calculation of the root locus of fractional linear systems is presented. The proposed algorithm takes advantage of present day computational resources and processes directly the characteristic equation, avoiding the limitations revealed by standard methods. The results demonstrate the good performance for different types of expressions.

Efficient Characterisation and Optimal Control of Open Quantum Systems - Mathematical Foundations and Physical Applications

Since no physical system can ever be completely isolated from its environment, the study of open quantum systems is pivotal to reliably and accurately control complex quantum systems. In practice, reliability of the control field needs to be confirmed via certification of the target evolution while accuracy requires the derivation of high-fidelity control schemes in the presence of decoherence. In the first part of this thesis an algebraic framework is presented that allows to determine the minimal requirements on the unique characterisation of arbitrary unitary gates in open quantum systems, independent on the particular physical implementation of the employed quantum device. To this end, a set of theorems is devised that can be used to assess whether a given set of input states on a quantum channel is sufficient to judge whether a desired unitary gate is realised. This allows to determine the minimal input for such a task, which proves to be, quite remarkably, independent of system size. These results allow to elucidate the fundamental limits regarding certification and tomography of open quantum systems. The combination of these insights with state-of-the-art Monte Carlo process certification techniques permits a significant improvement of the scaling when certifying arbitrary unitary gates. This improvement is not only restricted to quantum information devices where the basic information carrier is the qubit but it also extends to systems where the fundamental informational entities can be of arbitary dimensionality, the so-called qudits. The second part of this thesis concerns the impact of these findings from the point of view of Optimal Control Theory (OCT). OCT for quantum systems utilises concepts from engineering such as feedback and optimisation to engineer constructive and destructive interferences in order to steer a physical process in a desired direction. It turns out that the aforementioned mathematical findings allow to deduce novel optimisation functionals that significantly reduce not only the required memory for numerical control algorithms but also the total CPU time required to obtain a certain fidelity for the optimised process. The thesis concludes by discussing two problems of fundamental interest in quantum information processing from the point of view of optimal control - the preparation of pure states and the implementation of unitary gates in open quantum systems. For both cases specific physical examples are considered: for the former the vibrational cooling of molecules via optical pumping and for the latter a superconducting phase qudit implementation. In particular, it is illustrated how features of the environment can be exploited to reach the desired targets.

What are the priorities for prevention and control of non-communicable diseases and injuries in sub-Saharan Africa and South East Asia?

Last year’s UN high level meeting sought to galvanise the international community into scaling up its response to the escalating global burden of non-communicable diseases. With resources tight, D Chisholm and colleagues examine which interventions should be given priority for action and investment

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer

Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite infra-red imagery: bias should be small on regional scales; sensitivity to atmospheric humidity should be small; and sensitivity of retrieved SST to surface temperature should be close to 1 K K−1. Their application is illustrated for non-linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High Resolution Radiometer (AVHRR) on Metop-A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from −0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between −0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by <0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically.