Mining Patterns and Networks from Sequence Data

Sequence data are ubiquitous in diverse domains such as bioinformatics, computational neuroscience, and user behavior analysis. As a result, many critical applications require extracting knowledge from sequences in multi-level. For example, mining frequent patterns is the central goal of motif discovery in biological sequences, while in computational neuronal science, one essential task is to infer causal networks from neural event sequences (spike trains). Given the wide application of pattern and network mining tools for sequence data, they are facing new challenges posted by modern instruments. That is, as large scale and high resolution sequence data become available, we need new methods with better efficiency and higher accuracy.In this dissertation, we propose several approaches to improve existing pattern and network mining tools to meet new challenges in terms of efficiency and accuracy. The first problem is how to scale existing motif discovery algorithms. Our work on motif discovery focuses on the challenge of discovering motifs from a large scale of short sequences that none of existing motif finding algorithms can handle. We propose an anchor based clustering algorithm that could significantly improve the scalability of all the existing motif finding algorithms without losing accuracy at all. In particular, our algorithm could reduce the running time of a very popular motif finding algorithm, MEME, from weeks to a few minutes with even better accuracy.In another work, we study the problem of how to accurately infer a functional network from neural recordings (spike trains), which is an essential task in many real world applications such as diagnosing neurodegenerative diseases. We introduce a statistical tool that could be used to accurately identify inhibitory causal relations from spike trains. While most of existing works devote their efforts on characterizing the statistics of neural spike trains, we show that it is crucial to make predictions about the response of neurons to changes. More importantly, our results are validated by real biological experiments with a novel instrument, which makes this work the first of its kind. Furthermore, while most existing methods focus on learning functional networks from purely observational data, we propose an active learning framework that could intelligently generate and utilize interventional data. We demonstrate that by intelligently adopting interventional data using the active learning models we propose, the accuracy of the inferred functional network could be substantially improved with the same amount of training data

The influence of off-diagonal disorder on resonant transmission and emergent phenomena in nanostructured carbon thin films

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of PhD. August 9, 2017Nano-structured carbon lms, long studied due to the promise of exceptional quantum transport properties, present a signi cant problem in condensed matter due to the disorder which inherently forms in these materials. This work addresses the role of structural disorder in low dimensional carbon systems. The in uence of structural disorder on resonant transmission is studied in diamond-like carbon superlattices. Having established a model for disorder, this model for the structural changes is then applied to interpret experimental measurements of diamond-like carbon superlattices. The role of phonons on resonant transmission under a high frequency gate potential was also studied. This model for structural disorder in heterogeneous carbon lms was then applied to disordered superconductors close to the Anderson-Mott transition using the inhomogeneous Bogoliubov-de Gennes theory. This analysis is then used in support of experimental work to understand the superconductor-insulator transition in boron doped nano-crystalline diamond lms. Coherent quantum transport e ects were demonstrated in structurally-disordered diamondlike carbon (DLC) superlattices through distinct current modulation (step-like features) with negative differential resistance in the current-voltage (I-V) measurements. A model for these structurally disordered superlattices was developed using tight-binding calculations within the Landauer-B uttiker formalism assuming a random variation of the hopping integral following a Gaussian distribution. Calculations of the I-V characteristics for different con gurations of superlattices compliment the interpretation of the measured I-V characteristics and illustrate that while these DLC superlattice structures do not behave like conventional superlattices, the present model can be used to tailor the properties of future devices. Furthermore this tandem theoretical and experimental analysis establishes the validity of the model for structural disorder. The same model for the variation of disorder was then applied to interpret the electronic transport properties of disordered graphene-like carbon thin films. The influence of disorder on the activation energy in few layer graphitic lms was modelled and compared with experimental observations through collaboration. The lms, grown by laser ablation, allowed the speci c e ects of structural disorder in the sp2 - C phase to be probed. Defects acted as effective barriers resulting in localization of charge carriers. Electron transmission spectra, calculated with a tight-binding model, accounted for the change of localization length as a result of disorder in the sp2 - C phase. This theoretical study showed that the localization length of the thin graphitic lms can be tuned with the level of disorder and was shown to be consistent with experimental studies. The in uence of nitrogen incorporation on resonant transmission in DLC superlattices was then studied theoretically. This study illuminated the speci c role of the nitrogen potential in relation to the Fermi level (EF ) in nitrogen incorporated amorphous carbon (a- CN) superlattice structures. In a-CN systems, the variation of conductivity with nitrogen percentage has been found to be strongly non-linear due to the change of disorder level. The e ect of correlated carbon and nitrogen disorder was investigated in conjunction with the nitrogen potential through analysis of transmission spectra, calculated using a tight binding model, which showed two broad peaks related to these species. It was shown that the characteristic transmission time through nitrogen centres can be controlled through a combination of the nitrogen potential and correlated disorder. In particular, by controlling the arrangement of the nitrogen sites within the sp2 - C clusters as well as their energetic position relative to EF , a crossover of the pronounced transmission peaks of nitrogen and carbon sites can be achieved. Furthermore, it was shown that nitrogen incorporated as a potential barrier can also enhance the transmission in the a-CN superlattice structures. The strong non-linear variation of resistance and the characteristic time of the structures can explain the transport features observed experimentally in a-CN fi lms. This analysis was then partnered with measurements performed on nitrogen-incorporated carbon superlattices (N-DLC QSL) by Neeraj Dwivedi (National University of Singapore). The electrical characteristics of these nitrogen incorporated superlattice devices revealed prominent negative di erential resistance (NDR) behavior. The interpretation of these measurements was supported by 1D tight binding calculations of disordered superlattice structures (chains), which included signi cant bond alternation in sp3-hybridized regions. This analysis showed improved resonant transmission, which can be ascribed to nitrogendriven structural modi cation of the N-DLC QSL structures, especially the increased sp2-C clustering that provides additional conduction paths throughout the network. In order to determine the in uence of additional factors on coherent quantum states in molecular systems as an extension to the analysis on superlattices, a theoretical study of the electron-phonon interaction in double barrier structures under the in uence of a timedependent gate potential was undertaken. The Floquet theory was employed along with expansion in a polaron eigenbasis to render a multi-dimensional single body problem. An essentially exact solution was found using the Riccati matrix technique. It was demonstrated that optimal transmission can be achieved by varying the frequency of the gate potential. In addition, it was shown that the gate potential can be used to control the energy of the resonant states very precisely while maintaining optimal transmission. Having gained a deep understanding of the structural changes induced in carbon systems through the incorporation of nitrogen, a similar structural model was then applied to study the changes induced in diamond and nanocrystalline fi lms by boron incorpora- tion. Boron doped diamond provides an interesting superconductor with ongoing debate surrounding the nature of the impurity band and the effect on the superconducting phase transition of structural changes induced by boron incorporation. The in uence of disorder, both structural (non-diagonal) and on-site (diagonal), was studied through the inhomogeneous Bogoliubov-de Gennes (BdG) theory in narrow-band disordered superconductors with a view towards understanding superconductivity in boron doped diamond (BDD) and boron-doped nanocrystalline diamond (B-NCD) lms. We employed the attractive Hubbard model within the mean eld approximation, including a short range Coulomb interaction between holes in the narrow acceptor band. We studied substitutional boron incorporation in a triangular lattice, with disorder in the form of random potential uctuations at the boron sites. The role of structural disorder was investigated through non-uniform variation of the tight-binding coupling parameter where, following experimental ndings in BDD and B-NCD lms, we incorporated the concurrent increase in structural disorder with increasing boron concentration. Stark differences between the ffects of structural and on-site disorder were demonstrated and showed that structural disorder has a much greater e ect on the density of states, mean pairing amplitude and super uid density than on-site potential disorder. We showed that structural disorder can increase the mean pairing amplitude while the spectral gap in the density of states decreases, with states eventually appearing within the spectral gap for high levels of disorder. This study illustrated how the effects of structural disorder can explain some of the features found in superconducting BDD and B-NCD lms, such as a tendency towards saturation of the critical temperature (Tc) with boron doping and deviations from the expected Bardeen-Cooper-Shrie er (BCS) theory in the temperature dependence of the pairing amplitude and spectral gap. The variation of the super uid density considering only structural disorder was markedly different from the variation with on-site disorder only and revealed that structural disorder is far more detrimental to superconductivity and accounts for the relatively low Tc of BDD and B-NCD in comparison to the Tc predicted using the conventional BCS theory. This theoretical work was then used to interpret features in the measured transport properties of B-NCD lms with di erent doping concentrations and microstructures. The temperature dependence of a distinct local maximum in eld dependent magnetoresistance measurements showed suppression of the density of states as the system breaks up into superconducting regions separated by grain boundaries. Differential resistance measurements at different temperatures and magnetic fi elds showed a transition from a local minimum at zero applied current, indicative of persisting superconducting regions, to a local maximum. A power law dependence over a certain current range in the measured I-V characteristics at di erent magnetic elds suggests a Berezinski-Kosterlitz-Thouless (BKT) transition. In addition, features in the magnetoresistance clearly indicate additional phases. Together with features in current-voltage measurements, these signatures show the coexistence of superconductivity and additional competing phases close to the Anderson-Mott transition.LG201

Particle and energy transport in strongly driven one-dimensional quantum systems

This Dissertation concerns the transport properties of a strongly–correlated one–dimensional system of spinless fermions, driven by an external electric field which induces the flow of charges and energy through the system. Since the system does not exchange information with the environment, the evolution can be accurately followed to arbitrarily long times by solving numerically the time–dependent Schrödinger equation, going beyond Kubo’s linear response theory. The thermoelectric response of the system is here characterized, using the ratio of the induced energy and particle currents, in the nonequilibrium state under the steady applied electric field. Even though the equilibrium response can be reached for vanishingly small driving, strong fields produce quantum–mechanical Bloch oscillations in the currents, which disrupt the proportionality of the currents. The effects of the driving on the local state of the ring are analyzed via the reduced density matrix of small subsystems. The local entropy density can be defined and shown to be consistent with the laws of thermodynamics for quasistationary evolution. Even integrable systems are shown to thermalize under driving, with heat being produced via the Joule effect by the flow of currents. The spectrum of the reduced density matrix is shown to be distributed according the Gaussian unitary ensemble predicted by random–matrix theory, both during driving and a subsequent relaxation. The first fully–quantum model of a thermoelectric couple is realized by connecting two correlated quantum wires. The field is shown to produce heating and cooling at the junctions according to the Peltier effect, by mapping the changes in the local entropy density. In the quasiequilibrium regime, a local temperature can be defined, at the same time verifying that the subsystems are in a Gibbs thermal state. The gradient of temperatures, established by the external field, is shown to counterbalance the flow of energy in the system, terminating the operation of the thermocouple. Strong applied fields lead to new nonequilibrium phenomena. At the junctions, observable Bloch oscillations of the density of charge and energy develop at the junctions. Moreover, in a thermocouple built out of Mott insulators, a sufficiently strong field leads to a dynamical transition reversing the sign of the charge carriers and the Peltier effect

Tree diversity and species identity effects on soil fungi, protists and animals are context dependent

Plant species richness and the presence of certain influential species (sampling effect) drive the stability and functionality of ecosystems as well as primary production and biomass of consumers. However, little is known about these floristic effects on richness and community composition of soil biota in forest habitats owing to methodological constraints. We developed a DNA metabarcoding approach to identify the major eukaryote groups directly from soil with roughly species-level resolution. Using this method, we examined the effects of tree diversity and individual tree species on soil microbial biomass and taxonomic richness of soil biota in two experimental study systems in Finland and Estonia and accounted for edaphic variables and spatial autocorrelation. Our analyses revealed that the effects of tree diversity and individual species on soil biota are largely context dependent. Multiple regression and structural equation modelling suggested that biomass, soil pH, nutrients and tree species directly affect richness of different taxonomic groups. The community composition of most soil organisms was strongly correlated due to similar response to environmental predictors rather than causal relationships. On a local scale, soil resources and tree species have stronger effect on diversity of soil biota than tree species richness per se