Minería de datos mediante programación automática con colonias de hormigas

La presente tesis doctoral supone el primer acercamiento de la metaheur stica de programaci on autom atica mediante colonias de hormigas (Ant Programming) a tareas de miner a de datos. Esta t ecnica de aprendizaje autom atico ha demostrado ser capaz de obtener buenos resultados en problemas de optimizaci on, pero su aplicaci on a la miner a de datos no hab a sido explorada hasta el momento. Espec camente, esta tesis cubre las tareas de clasi caci on y asociaci on. Para la primera se presentan tres modelos que inducen un clasi cador basado en reglas. Dos de ellos abordan el problema de clasi caci on desde el punto de vista de evaluaci on monobjetivo y multiobjetivo, respectivamente, mientras que el tercero afronta el problema espec co de clasi caci on en conjuntos de datos no balanceados desde una perspectiva multiobjetivo. Por su parte, para la tarea de extracci on de reglas de asociaci on se han desarrollado dos algoritmos que llevan a cabo la extracci on de patrones frecuentes. El primero de ellos propone una evaluaci on de los individuos novedosa, mientras que el segundo lo hace desde un punto de vista basado en la dominancia de Pareto. Todos los algoritmos han sido evaluados en un marco experimental adecuado, utilizando numerosos conjuntos de datos y comparando su rendimiento frente a otros m etodos ya publicados de contrastada calidad. Los resultados obtenidos, que han sido veri cados mediante la aplicaci on de test estad sticos no param etricos, demuestran los bene cios de utilizar la metaheur stica de programaci on autom atica con colonias de hormigas para dichas tareas de miner a de datos.This Doctoral Thesis involves the rst approximation of the ant programming metaheuristic to data mining. This automatic programming technique has demonstrated good results in optimization problems, but its application to data mining has not been explored until the present moment. Speci cally, this Thesis deals with the classi cation and association rule mining tasks of data mining. For the former, three models for the induction of rule-based classi ers are presented. Two of them address the classi cation problem from the point of view of single-objective and multi-objective evaluation, respectively, while the third proposal tackles the particular problem of imbalanced classi cation from a multi-objective perspective. On the other hand, for the task of association rule mining two algorithms for extracting frequent patterns have been developed. The rst one evaluates the quality of individuals by using a novel tness function, while the second algorithm performs the evaluation from a Pareto dominance point of view. All the algorithms proposed in this Thesis have been evaluated in a proper experimental framework, using a large number of data sets and comparing their performance against other published methods of proved quality. The results obtained have been veri ed by applying non-parametric statistical tests, demonstrating the bene ts of using the ant programming metaheuristic to address these data mining tasks

Improving group role assignment problem by incremental assignment algorithm

The Assignment Problem is a basic combinatorial optimization problem. In a weighted bipartite graph, the Assignment Problem is to find a largest sum of weights matching. The Hungarian method is a well-known algorithm which is combinatorial optimization. Adding a new row and a new column to a weighted bipartite graph is called the Incremental Assignment Problem (IAP). The maximum weighted matching (the optimal solution) of the weighted bipartite graph has been given. The algorithm of the Incremental Assignment Problem utilizes the given optimal solution (the maximum weighted matching) and the dual variables to solve the matrix after extended bipartite graph. This thesis proposes an improvement of the Incremental Assignment Algorithm (IAA), named the Improved Incremental Assignment Algorithm. The improved algorithm will save the operation time and operation space to find the optimal solution (the maximum weighted matching) of the bipartite graph. We also present the definition of the Incremental Group Role Assignment Problem that based on the Group Role Assignment Problem (GRAP) and Incremental Assignment Problem (IAP). A solution has been designed to solve it by using the Improved Incremental Assignment Algorithm (IIAA). In this thesis, simulation results are presented. We utilize the tests to compare the algorithm of the Incremental Assignment Problem and the Improved Incremental Assignment Algorithm (IIAA) to show the advantages of IIAA.Master of Science (MSc) in Computational Science

Structural, Thermodynamic, and Electronic Properties of Mixed Ionic/Electronic Conductor Materials

Due to the mainstream CMOS technology facing a rapid approach to the fundamental downscaling limit, beyond CMOS technologies are under active investigation and development with the intention of revolutionizing and sustaining a wide range of applications including sensors, cryptography, neuromorphic and quantum computing, memory, and logic, among others. Resistive switching electronics, for example, are devices that can change their electrical resistance with an applied external field. Despite their simple metal-insulator-metal structure, resistive switching devices exhibit an intricate set of IV characteristics based on the chemical composition of the solid electrolyte that ranges from non-volatile bipolar and non-polar switching to volatile threshold switching (abrupt but reversible change in resistance). This rich variety of electrical responses offer new alternatives to traditional CMOS applications in the areas of RF-signal switching, relaxation oscillators, over-voltage protection, and notably, memory cells and two-terminal non-linear selector devices. With the aim of unraveling the physics behind two of such conduction mechanisms, filamentary and threshold, in electrochemical cells consisting solid mixed ionic-electronic conductor electrolytes, this work focused on using first-principles calculations to characterize the structural, thermodynamic, and electronic properties of copper-doped amorphous silicon dioxide and copper-doped germanium-based glassy chalcogenides. The Cu/a-SiO2 system is a promising candidate for resistive switching memory applications. The conduction mechanism in the low-resistance state is known to be filamentary, that is, a physical metallic filament bridges between the metallic electrodes through the amorphous silica. However, many fundamental materials processes that would aid the design and optimization of this devices, such the shape and size of stable metallic filaments, remain unknown. In the first part of this work, the morphology and diffusion of small copper clusters embedded in amorphous silicon dioxide were characterized by density functional theory calculations. The average formation energy of a single copper ion in the amorphous matrix is found to be 2.4 eV, about 50% lower than in the case of silicon dioxide in its cristobalite or quartz phases. The theoretical predictions show that copper clusters with an equiaxed morphology are always energetically favorable relative to the dissolved copper ions in a-SiO2; hence, stable clusters do not exhibit a critical size. The stochasticity in the atomistic structure of the amorphous silicon dioxide leads to a broad distribution activation energies for diffusion of copper in the matrix, ranging from 0.4 to 1.1 eV. Since ab initio molecular dynamics are prohibitively expensive to simulate the switching process in Cu/a-SiO2 electrochemical metallization cells, a multi-scale simulation approach based on electrochemical dynamics with implicit degrees of freedom and density functional theory was developed to study the electronic evolution during metallic filament formation cells. These simulations suggest that the electronic transport in the low-resistance configuration is attributed to copper derived states belonging to the filament bridging between electrodes. Interestingly, the participation of states derived from intrinsic defects in the amorphous SiO2 around the Fermi energy are negligible and do not contribute to conduction. Unlike the Cu/a-SiO2 system which only exhibits filamentary switching, copper-doped germanium-based glassy chalcogenides show filamentary or threshold type of conduction depending on the chemical composition of the glass and copper concentration. Ab initio molecular dynamics based on density functional theory is used to understand the atomistic origin of the electronic transport in these materials. The theoretical predictions show that glasses containing tellurium tend to favor the formation of copper clusters; hence, these materials exhibit filamentary conduction. Threshold conduction is predicted to be the transport mechanism for glassy sulfides and selenides due to the ability of copper to remain dissolved in the amorphous matrix even at high metal concentration. Furthermore, the charge carrier transport in sulfur and selenium glasses was found to be assisted by defective states derived from chalcogen atoms whose bonds exhibit a polar character. Finally, taking advantage of the van der Waals gap as intercalation sites and crystal order in molybdenum disulfide, a novel mixed ionic-electronic conductor material based on copper and silver intercalation of MoS2 is proposed. The theoretical predictions show that on average, the intercalation energy of copper into MoS2 is 1 eV, while intercalation of silver shows a strong dependence on concentration ranging from 2.2 to 0.75 eV for low and high concentrations, respectively. The activation energy for diffusion of copper and silver intercalated within the van der Waals gap of MoS2 is predicted to be 0.32 and 0.38 eV, respectively, comparable to other superionic conductors. Upon Cu and Ag intercalation, MoS2 undergoes a semiconductor-to-metal transition, where the in-plane and out-of-plane conductances are comparable and exhibit a linear dependence with metal concentration

Categories and Units in Language and Linguistics

The papers in this volume concern different linguistic categories from a variety of perspectives. The first and second papers by Janusz Badio deal with evaluation in EFL written stories and the linguistic coding of events in agreement with their variable salience in conceptualisation. The chapter by Alan Cienki analyses gesture units to draw conclusions about language in general. Kamila Ciepiela looks at the category of identity as a product and process. Craig Callender presents a chapter on the perception and the nature of the phoneme followed by a chapter by Henryk Kardela, who discusses how meanings and forms are fused into single morphological units. Krzysztof Kosecki turns the readers’ attention on the prototype of narrative and emotion in E. Hemingway’s story Cat in the Rain and on the analysis of Thomas Mann’s The Magic Mountain. Przemysław Krakowian’s interesting article explains the role of Multi Facet Rash Analysis in tests of oral production. Ourania Papadima expertly overviews the very important categories of second language pedagogy, ESP and EAP. Jana Richterova provides an analysis of resource books for advanced learners of English. Nezrin Samedova-Hajiyeva’s chapter centres on the grammatical category of aspect. Last, Jacek Waliński describes the categorization of directional verbs in English

Transgenic approaches for the investigation of putative airway stem cells as potential targets for gene correction therapy

Since the discovery of the CFTR gene over a decade ago, Cystic Fibrosis (CF) has been regarded as amenable to intervention by gene therapy. The ultimate aim of gene therapy must be the correction, within cells capable of repopulating the tissue, of the genetic defect in its chromosomal context. Towards that end, a mouse model designed to evaluate the efficiency of gene correction was created, and a transgenic approach was taken to the investigation of a putative progenitor cell population in the adult murine respiratory tract. Before gene correction systems can be considered as valid therapeutic agents, their utility in the cells and tissues of living animals must be demonstrated. Thus, an in vivo system permitting the simple quantification of correction frequency in a wide range of tissues would be a valuable resource for the gene correction community. The generation and analysis of a transgenic mouse carrying an inactivated, but potentially correctable, reporter transgene is described. The full potential of a gene correction strategy to provide a single-dose, permanent solution to a genetically-diseased tissue will only be realised once the therapy is able to target resident stem cells. For CF lung disease, this will require the prior identification of stem cells in the respiratory epithelium. Previous work has indicated that potential stem cells are spatially coincident with small groups of cells expressing high levels of keratin 5 (K5) protein in the proximal murine trachea. In order to investigate lineage arising from this putative stem cell niche, transgenic mice have been generated which express an inducible form of Cre recombinase from the K5 promoter. Preliminary experiments demonstrate recombination of a conditional reporter gene after induction of Cre activity in K5-expressing tissue. Comparison of the inducible system with a constitutive K5 promoter-driven Cre line validated the choice of the former, as the clarity of data obtained from the conventional system was undermined as a result of K5 promoter activity causing reporter gene activation prior to the onset ofthe experiment. In the course of these studies it became evident that the conventional, constitutive Cre line gave rise to segregating patterns of reporter gene activation. While some mice displayed the expected K5-derived expression profile, other animals demonstrated ubiquitous expression. Universal activation of the conditional reporter was detected only in animals derived from females carrying the Cre transgene, and was found to be the result of unanticipated production of Cre protein in the maternal germline. This transgenic line is unusual and valuable in offering a choice of tissuespecific and generalised recombination of floxed alleles

Quantum Chemistry in Nanoscale Environments: Insights on Surface-Enhanced Raman Scattering and Organic Photovoltaics

The understanding of molecular effects in nanoscale environments is becoming increasingly relevant for various emerging fields. These include spectroscopy for molecular identification as well as in finding molecules for energy harvesting. Theoretical quantum chemistry has been increasingly useful to address these phenomena to yield an understanding of these effects. In the first part of this dissertation, we study the chemical effect of surface-enhanced Raman scattering (SERS). We use quantum chemistry simulations to study the metal-molecule interactions present in these systems. We find that the excitations that provide a chemical enhancement contain a mixed contribution from the metal and the molecule. Moreover, using atomistic studies we propose an additional source of enhancement, where a transition metal dopant surface could provide an additional enhancement. We also develop methods to study the electrostatic effects of molecules in metallic environments. We study the importance of image-charge effects, as well as field-bias to molecules interacting with perfect conductors. The atomistic modeling and the electrostatic approximation enable us to study the effects of the metal interacting with the molecule in a complementary fashion, which provides a better understanding of the complex effects present in SERS. In the second part of this dissertation, we present the Harvard Clean Energy project, a high-throughput approach for a large-scale computational screening and design of organic photovoltaic materials. We create molecular libraries to search for candidates structures and use quantum chemistry, machine learning and cheminformatics methods to characterize these systems and find structure-property relations. The scale of this study requires an equally large computational resource. We rely on distributed volunteer computing to obtain these properties. In the third part of this dissertation we present our work related to the acceleration of electronic structure methods using graphics processing units. This hardware represents a change of paradigm with respect to the typical CPU device architectures. We accelerate the resolution-of-the-identity Moller-Plesset second-order perturbation theory algorithm using graphics cards. We also provide detailed tools to address memory and single-precision issues that these cards often present

Top-Emitting OLEDs: Improvement of the Light Extraction Efficiency and Optimization of Microcavity Effects for White Emission

In the last decades, investigations of organic light-emitting diodes (OLEDs) have tackled several key challenges of this lighting technology and have brought the electron to photon conversion efficiency close to unity. However, currently only 20% to 30% of the photons can typically be extracted from OLED structures, as total internal reflection traps the major amount of the generated light inside the devices. This work focuses on the optimization of the optical properties of top-emitting OLEDs, in which the emission is directed away from the substrate. In this case, opaque materials, e.g. a metal foil or a display backplane can be used as substrate as well. Even though top-emitting OLEDs are often preferred for applications such as displays, two main challenges remain: the application of light extraction structures and the deposition of highly transparent materials as top electrode, without harming the organic layers below. Both issues are addressed in this work. First, top-emitting OLEDs are deposited on top of periodically corrugated light outcoupling structures, in order to extract internally trapped light modes by Bragg scattering and to investigate the basic scattering mechanisms in these devices. It is shown for the first time that the electrical performance is maintained in corrugated top-emitting OLEDs deposited on top of light extraction structures. Furthermore, as no adverse effects to the internal quantum efficiency have been observed, the additional emission from previously trapped light modes directly increases the device efficiency. It has been proven that the spectral emission of corrugated OLEDs is determined by the interference of all light modes inside the air light-cone, including the observation of destructive interference and anti-crossing phenomena. The formation of a coherently coupled mode pair of the initial radiative cavity mode and a Bragg scattered mode has been first observed, when grating structures with an aspect ratio > 0.2 are applied. There, the radiative cavity mode partially vanishes. The observation and analysis of such new emission phenomena in corrugated top-emitting OLEDs has been essential in obtaining a detailed insight on fundamental scattering processes as well as for the optimization and control of the spectral emission by light extraction structures. Second, the adverse impact of using only moderately transparent silver electrodes in white top-emitting OLEDs has been compensated improving the metal film morphology, as the organic materials often prevent a replacement by state-of-the-art electrodes, like Indium-tin-oxide (ITO). A high surface energy Au wetting layer, also in combination with MoO3, deposited underneath the Ag leads to smooth, homogeneous, and closed films. This allows to decrease the silver thickness from the state-of-the-art 15 nm to 3 nm, which has the advantage of increasing the transmittance significantly while maintaining a high conductivity. Thereby, a transmittance comparable to the ITO benchmark has been reached in the wavelength regime of the emitters. White top-emitting OLEDs using the wetting layer electrodes outperform state-of-the art top-emitting devices with neat Ag top electrodes, by improving the angular colorstability, the color rendering, and the device efficiency, further reaching sightly improved characteristics compared to references with ITO bottom electrode. The enormous potential of wetting layer metal electrodes in improving the performance of OLEDs has been further validated in inverted top-emitting devices, which are preferred for display applications, as well as transparent OLEDs, in which the brittle ITO electrode is replaced by a wetting layer electrode. Combining both concepts, wetting layer electrodes and light extraction structures, allows for the optimization of the grating-OLED system. The impact of destructive mode interference has been reduced and thus the efficiency increased by a decrease of the top electrode thickness, which would have not been achieved without a wetting layer. The optimization of corrugated white top-emitting OLEDs with a top electrode of only 2 nm gold and 7 nm silver on top of a grating with depth of 150 nm and period of 0.8 µm have yielded a reliable device performance and increased efficiency by a factor of 1.85 compared to a planar reference (5.0% to 9.1% EQE at 1000 cd/m2). This enhancement is comparable to common light extraction structures, such as half-sphere lenses or microlens foils, which are typically restricted to bottom-emitting devices. Overall, the deposition of top-emitting OLEDs on top of light extraction structures finally allow for an efficient extraction of internally trapped light modes from these devices, while maintaining a high device yield. Finally, the investigations have resulted in a significant efficiency improvement of top-emitting OLEDs and the compensation of drawbacks (optimization of the white light emission and the extraction of internal light modes) in comparison to the bottom-emitting devices. The investigated concepts are beneficial for OLEDs in general, since the replacement of the brittle ITO electrodes and the fabrication of roll-to-roll processing compatible light extraction structures are also desirable for bottom-emitting, or transparent OLEDs

Underlying physics and effects of silicon APD aging in automotive LiDAR applications

Over 90% of traffic accidents are caused by human error. Therefore, the realization of autonomous driving could save countless lives and drastically reduce the associated financial expenses. Moreover, the collective behavior of self-driving cars would avoid traffic jams and thus reduce fuel consumption and greenhouse gas emissions. The majority of concepts is based on Light Detection And Ranging (LiDAR), which is the most precise method to measure distances. Matched to the 95% of commercial LiDAR systems based on laser wavelengths of mostly 905nm, siliconbased photo sensors are used. Avalanche photo diodes (APD) are the only sensor solution in mass production [6]. Due to an internal multiplication mechanism based on impact ionization, high signal-noise-ratios (SNR) are achieved and provide the required resolution of low signals from more than 100m distant targets. Currently none of the LiDAR technologies meet the reliability requirements of the automotive industry concerning the aging of installed components. Consequently, autonomous driving cannot yet be realized for public use. Very little is known about the aging of APDs in general and nothing at all in the field of automotive LiDAR. In order to provide novel insights into APD aging that help designers to achieve more robust sensors and thus to enable a step closer to the realization of autonomous driving, it was the aim of this thesis prepared in the industrial environment to reveal the underlying physical aging mechanisms and their effects on the function of APDs in automotive LiDAR application. At first, a novel APD degradation model was developed encompassing a wide range of processes, treating numerous fundamental aspects of negative oxide charge generation and Si:SiO2 interface trap generation. So far, no model is known covering the kinetics of APD degradation comprehensively in such deep detail. Due to the feedback between degradation phenomena and sensor internal fields and currents, a coupled problem arose. It was tackled by a sophisticated numerical iteration approach which was tailor-made and solved this problem self-consistently in a tandem procedure combining the simulation of sensor degradation and the Silvaco Atlas device simulator. This led to novel insights into the APD degradation behavior. The generation of negative oxide charges was identified to cause a drift of the impact ionization rate in the sensor edge. The generation of interface traps promotes the accumulation of negative oxide charges by their supply of thermally generated dark current. In this way, degradation is about 14% faster. In order to reflect not only the causal relations of APD degradation, the model was calibrated on experimental degradation data. With the calibrated degradation model and its self-consistent simulation approach an elaborated powerful tool was available. Stress experiments have been performed on test sensors under a variation of operation conditions and on APDs. APDs of the studied design are currently tested and installed in automotive LiDAR modules. The entire set of experimental results found its complete physical interpretation in conjunction with the degradation model which achieved an excellent agreement. Thereby, numerous novel insights were revealed: The extent of degradation is induced by the properties of the sensors oxide layer. The degradation pace increases with temperature, voltage and intensity of illumination whereas the impact of temperature is particularly strong due to the significant participation of the dark current during degradation. The oxygen vacancy was proven to be the dominant trap in the oxide layer of the studied sensors. An empirical distribution of individual sensor properties was achieved. In some cases, the impact ionization rate in the sensor edge increased which indicates a major problem, as noise increases when the generation- recombination processes in the sensor become more pronounced during degradation. In order to estimate the impact of the degradation induced increase of noise on the LiDAR application, the empirical distribution of individual sensor properties was extrapolated to the tail where sensors are very prone to degradation. Furthermore, the available noise models were extended to cover the effect of degradation. Application of the calibrated APD degradation model revealed, that the APD noise is highly effected and even triples during aging. The origin was exclusively assigned to the edge contribution. There, the avalanche breakdown of the edge dark current caused by degradation is the main initiator. Consequently, for the first time ever, the signal-noise-ratio (SNR) degradation mode of APDs in LiDAR application was identified. During degradation, the SNR of small signals from 100m distant objects degrades to a value below 1, where even theoretically a resolution is impossible. Finally, the picture of APD degradation was completed by the estimation of lifetime. In the case of the most severe conditions in LiDAR operation, it amounts to only 1000 h, which falls much below the requirements of the automotive industry of several decades

The uphill battle of ant programming vs. genetic programming

Ant programming has been proposed as an alternative to Genetic Programming (GP) for the automated production of computer programs. Generalized Ant Programming (GAP) - an automated programming technique derived from principles of swarm intelligence - has shown promise in solving symbolic regression and other hard problems. Enhanced Generalized Ant Programming (EGAP) has improved upon the performance of GAP; however, a comparison with GP has not been performed. This paper compares EGAP and GP on 3 well-known tasks: Quartic symbolic regression, multiplexer and an ant trail problem. When comparing EGAP and GP, GP is found to be statistically superior to EGAP. An analysis of the evolving program populations shows that EGAP suffers from premature diversity loss