The roles and values of personal knowledge management : an exploratory study

2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

One-parameter scaling theory for DNA extension in a nanochannel

Experiments measuring DNA extension in nanochannels are at odds with even the most basic predictions of current scaling arguments for the conformations of confined semiflexible polymers such as DNA. We show that a theory based on a weakly self-avoiding, one-dimensional "telegraph" process collapses experimental data and simulation results onto a single master curve throughout the experimentally relevant region of parameter space and explains the mechanisms at play.Comment: Revised version. 5 pages, 4 figures, revised version, supplementary informatio

Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing

Recent developments in quaternion-valued widely linear processing have established that the exploitation of complete second-order statistics requires consideration of both the standard covariance and the three complementary covariance matrices. Although such matrices have a tremendous amount of structure and their decomposition is a powerful tool in a variety of applications, the non-commutative nature of the quaternion product has been prohibitive to the development of quaternion uncorrelating transforms. To this end, we introduce novel techniques for a simultaneous decomposition of the covariance and complementary covariance matrices in the quaternion domain, whereby the quaternion version of the Takagi factorisation is explored to diagonalise symmetric quaternion-valued matrices. This gives new insights into the quaternion uncorrelating transform (QUT) and forms a basis for the proposed quaternion approximate uncorrelating transform (QAUT) which simultaneously diagonalises all four covariance matrices associated with improper quaternion signals. The effectiveness of the proposed uncorrelating transforms is validated by simulations on both synthetic and real-world quaternion-valued signals.Comment: 41 pages, single column, 10 figure

Interplay of charge and spin correlations in nickel perovskites

Analyzing the motion of low--spin $(s=1/2)$ holes in a high--spin $(S=1)$ background, we derive a sort of generalized t--J Hamiltonian for the $\rm NiO_2$ planes of Sr--doped nickelates. In addition to the rather complex carrier--spin and spin--spin couplings we take into account the coupling of the doped holes to in--plane oxygen breathing modes by a Holstein--type interaction term. Because of strong magnetic confinement effects the holes are nearly entirely prelocalized and the electron--phonon coupling becomes much more effective in forming polarons than in the isostructural cuprates. In the light of recent experiments on $\rm La_{2-x}Sr_xNiO_4$ we discuss how the variety of the observed transport and charge/spin--ordering phenomena can be qualitatively understood in terms of our model Hamiltonian.Comment: 2 pages, LTpaper.sty, Proc. XXI Int. Conf. on Low Temp. Phys. Prague 9

Generation of Differentially Private Heterogeneous Electronic Health Records

Electronic Health Records (EHRs) are commonly used by the machine learning community for research on problems specifically related to health care and medicine. EHRs have the advantages that they can be easily distributed and contain many features useful for e.g. classification problems. What makes EHR data sets different from typical machine learning data sets is that they are often very sparse, due to their high dimensionality, and often contain heterogeneous (mixed) data types. Furthermore, the data sets deal with sensitive information, which limits the distribution of any models learned using them, due to privacy concerns. For these reasons, using EHR data in practice presents a real challenge. In this work, we explore using Generative Adversarial Networks to generate synthetic, heterogeneous EHRs with the goal of using these synthetic records in place of existing data sets for downstream classification tasks. We will further explore applying differential privacy (DP) preserving optimization in order to produce DP synthetic EHR data sets, which provide rigorous privacy guarantees, and are therefore shareable and usable in the real world. The performance (measured by AUROC, AUPRC and accuracy) of our model's synthetic, heterogeneous data is very close to the original data set (within 3 - 5% of the baseline) for the non-DP model when tested in a binary classification task. Using strong $(1, 10^{-5})$ DP, our model still produces data useful for machine learning tasks, albeit incurring a roughly 17% performance penalty in our tested classification task. We additionally perform a sub-population analysis and find that our model does not introduce any bias into the synthetic EHR data compared to the baseline in either male/female populations, or the 0-18, 19-50 and 51+ age groups in terms of classification performance for either the non-DP or DP variant