Creating a Chemistry of Sciences with Big Data

The Data Science Institute at Imperial College London launched in April 2014, and will provide a hub for data-driven research and education. Its mission is to provide a focal point for the College's capabilities in multidisciplinary data-driven research by coordinating advanced data science research for college scientists and partners, and educating the next generation of data scientists. We surveyed the data-driven research needs at Imperial College London to gain an understanding across all disciplines offered by the College, and analysed the responses to gain insights into scientific and engineering needs for data science research. A clear message is that multidisciplinarity is essential for Big Data and data science research to enable a "chemistry of sciences": connecting all disciplines by integrating data. This paper presents our efforts to best understand datadriven research needs in a highly multidisciplinary researchintensive institution and describes our vision for the future of the Data Science Institute at Imperial College London. © Copyright 2014 ACM

Photoproduction of K∗+ΛK^{*+}\Lambda and K+Σ(1385)K^+\Sigma(1385) in the reaction \gamma \lowercase{p} \to K^+ \Lambda \pi^0 at Jefferson Lab

The search for missing nucleon resonances using coupled channel analysis has mostly been concentrated on $N\pi$ and $KY$ channels, while the contributions of $K^*Y$ and $KY^*$ channels have not been investigated thoroughly mostly due to the lack of data. With an integrated luminosity of about 75 $pb^{-1}$, the photoproduction data using a proton target recently collected by the CLAS Collaboration at Jefferson Lab with a photon energy range of 1.5-3.8 GeV provided large statistics for the study of light hyperon photoproduction through exclusive reactions. The reaction $\gamma p \to K^+ \Lambda \pi^0$ has been investigated. Preliminary results of the $K^{*+}\Lambda$ and $K^+\Sigma(1385)$ cross sections are not negligible compared with the $KY$ channels. The $\Lambda \pi^0$ invariant mass spectrum is dominated by the $\Sigma(1385)$ signal and no significant structure was found around the $\Sigma(1480)$ region.Comment: 4 pages, 3 figures, to be publised on the NSTAR05 proceeding

Spatio-temporal generalised frequency response functions

The concept of generalised frequency response functions (GFRFs), which were developed for nonlinear system identification and analysis, is extended to continuous spatio-temporal dynamical systems normally described by partial differential equations (PDEs). The paper provides the definitions and interpretation of spatio-temporal generalised frequency response functions for linear and nonlinear spatio-temporal systems based on an impulse response procedure. A new probing method is also developed to calculate the GFRFs. Both the Diffusion equation and Fisher’s equation are analysed to illustrate the new frequency domain methods

Spatio-temporal generalised frequency response functions over unbounded spatial domains

The concept of generalised frequency response functions (GFRFs), which were developed for nonlinear system identification and analysis, is extended to continuous spatio-temporal dynamical systems normally described by partial differential equations (PDEs). The paper provides the definitions and interpretation of spatio-temporal generalised frequency response functions for linear and nonlinear spatio-temporal systems, defined over unbounded spatial domains, based on an impulse response procedure. A new probing method is also developed to calculate the GFRFs. Both the Diffusion equation and Fisher’s equation are analysed to illustrate the new frequency domain methods

Consistent parameter identification of partial differential equation models from noisy observations

This paper introduces a new residual-based recursive parameter estimation algorithm for linear partial differential equations. The main idea is to replace unmeasurable noise variables by noise estimates and to compute recursively both the model parameter and noise estimates. It is proven that under some mild assumptions the estimated parameters converge to the true values with probability one. Numerical examples that demonstrate the effectiveness of the proposed approach are also provided

Multiscale identification of spatio-temporal dynamical systems using a wavelet multiresolution analysis

In this paper, a new algorithm for the multiscale identification of spatio-temporal dynamical systems is derived. It is shown that the input and output observations can be represented in a multiscale manner based on a wavelet multiresolution analysis. The system dynamics at some specific scale of interest can then be identified using an orthogonal forward leastsquares algorithm. This model can then be converted between different scales to produce predictions of the system outputs at different scales. The method can be applied to both multiscale and conventional spatio-temporal dynamical systems. For multiscale systems, the method can generate a parsimonious and effective model at a coarser scale while considering the effects from finer scales. Additionally, the proposed method can be used to improve the performance of the identification when measurements are noisy. Numerical examples are provided to demonstrate the application of the proposed new approach

Study of Multilouvered Heat Exchangers at Low Reynolds numbers

Air Conditioning and Refrigeration Project 13