A Training Sample Sequence Planning Method for Pattern Recognition Problems

In solving pattern recognition problems, many classification methods, such as the nearest-neighbor (NN) rule, need to determine prototypes from a training set. To improve the performance of these classifiers in finding an efficient set of prototypes, this paper introduces a training sample sequence planning method. In particular, by estimating the relative nearness of the training samples to the decision boundary, the approach proposed here incrementally increases the number of prototypes until the desired classification accuracy has been reached. This approach has been tested with a NN classification method and a neural network training approach. Studies based on both artificial and real data demonstrate that higher classification accuracy can be achieved with fewer prototypes

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Predicting the class of a customer profile is a key task in marketing, which enables businesses to approach the right customer with the right product at the right time through the right channel to satisfy the customer's evolving needs. However, due to costs, privacy and/or data protection, only the business' owned transactional data is typically available for constructing customer profiles. Predicting the class of customer profiles based on such data is challenging, as the data tends to be very large, heavily sparse and highly skewed. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data from the food sales domain consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved

Digital Alchemy for Materials Design: Colloids and Beyond

Starting with the early alchemists, a holy grail of science has been to make desired materials by modifying the attributes of basic building blocks. Building blocks that show promise for assembling new complex materials can be synthesized at the nanoscale with attributes that would astonish the ancient alchemists in their versatility. However, this versatility means that making direct connection between building block attributes and bulk behavior is both necessary for rationally engineering materials, and difficult because building block attributes can be altered in many ways. Here we show how to exploit the malleability of the valence of colloidal nanoparticle "elements" to directly and quantitatively link building block attributes to bulk behavior through a statistical thermodynamic framework we term "digital alchemy". We use this framework to optimize building blocks for a given target structure, and to determine which building block attributes are most important to control for self assembly, through a set of novel thermodynamic response functions, moduli and susceptibilities. We thereby establish direct links between the attributes of colloidal building blocks and the bulk structures they form. Moreover, our results give concrete solutions to the more general conceptual challenge of optimizing emergent behaviors in nature, and can be applied to other types of matter. As examples, we apply digital alchemy to systems of truncated tetrahedra, rhombic dodecahedra, and isotropically interacting spheres that self assemble diamond, FCC, and icosahedral quasicrystal structures, respectively.Comment: 17 REVTeX pages, title fixed to match journal versio

Vacuum structure and string tension in Yang-Mills dimeron ensembles

We numerically simulate ensembles of SU(2) Yang-Mills dimeron solutions with a statistical weight determined by the classical action and perform a comprehensive analysis of their properties. In particular, we examine the extent to which these ensembles capture topological and confinement properties of the Yang-Mills vacuum. This further allows us to test the classic picture of meron-induced quark confinement as triggered by dimeron dissociation. At small bare couplings, spacial, topological-charge and color correlations among the dimerons generate a short-range order which screens topological charges. With increasing coupling this order weakens rapidly, however, in part because the dimerons gradually dissociate into their meron constituents. Monitoring confinement properties by evaluating Wilson-loop expectation values, we find the growing disorder due to these progressively liberated merons to generate a finite and (with the coupling) increasing string tension. The short-distance behavior of the static quark-antiquark potential, on the other hand, is dominated by small, "instanton-like" dimerons. String tension, action density and topological susceptibility of the dimeron ensembles in the physical coupling region turn out to be of the order of standard values. Hence the above results demonstrate without reliance on weak-coupling or low-density approximations that the dissociating dimeron component in the Yang-Mills vacuum can indeed produce a meron-populated confining phase. The density of coexisting, hardly dissociated and thus instanton-like dimerons seems to remain large enough, on the other hand, to reproduce much of the additional phenomenology successfully accounted for by non-confining instanton vacuum models. Hence dimeron ensembles should provide an efficient basis for a rather complete description of the Yang-Mills vacuum.Comment: 36 pages, 17 figure

Atomic scale modeling of ordering phenomena

Ordering phenomena in materials often have a crucial impact on materials properties.They are governed by the competition between entropy and energy.Accordingly simulating these aspects requires the construction of models that enable a computationally efficient exploration of the relevant configuration space.The alloy cluster expansion technique is particular well suited for this task as they can be trained to reach high accuracy while being computationally suitable for rapid sampling via Monte Carlo simulations. In paper I we present the icet software for the construction and sampling of alloy cluster expansions.In this thesis the alloy cluster expansion method is applied to study several different materials. The first group of materials studied are inorganic clathrates.In paper II and III we studied the ordering behavior and related properties as a function of composition and temperature for the clathrates Ba8AlxSi46-x, Ba8AlxGe46-x, Ba8GaxGe46-x, and Ba8GaxSi46-x.We achieved very good agreement with the available experimental data for the site occupancy factors (SOFs). In paper IV and V we constructed the phase diagram for the W-Ti and W-C system respectively.A cluster expansion for each system was constructed and the configurational free energy was calculated.By also including other contributions to the free energy, most notably the vibrational free energy, the phase diagrams for these systems could be constructed. In paper VI we studied the SSZ-13 zeolite and showed both that the L\uf6wenstein rule is not respected with hydrogen as counterion and provided a rationale for this behavior

Charge-Lattice-Spin Interactions in Molecule-Based Magnets

Many of the most attractive properties of multifunctional materials can be traced to the competition between charge, structure, and magnetism. The discovery that these interactions can be tuned with various physical stimuli has accelerated interest in their behavior under extreme conditions. In this dissertation I present a spectroscopic investigation of several model molecule-based magnets under external stimuli of magnetic field and temperature. The compounds of interest include MII[N(CN)2]2 (M=Mn, Co) and [Ru2(O2CMe)4]3[Cr(CN)6]. These materials are attractive for their subtle interplay between electronic, magnetic and structural degrees of freedom offering both physical property tunability and models with which to carry out fundamental studies of coupling phenomena. The vibrational properties of Mn[N(CN)2]2 reveal the magnetoelastic coupling through the quantum critical transition at 30.4 T that drives the system from the canted antiferromagnetic to the fully polarized state. The local lattice distortions, manifested in systematic phonon frequency shifts, suggest a combined MnN6 octahedra distortion + counter-rotation mechanism that reduces antiferromagnetic interactions and accommodates the developing field-induced state. Work on Co[N(CN)2]2 combines high field Raman and infrared spectroscopies to explore the effect of the chemical tuning on lattice dynamics and coupling processes in a ferromagnet. In addition to a large anisotropy, our studies uncover electron-phonon coupling as a field-driven avoided crossings of the low-lying Co2+ electronic excitation with the ligand phonons and a magnetoelastic effect that signals a flexible local CoN6 environment under external field. Finally, we employ vibrational spectroscopies to probe spin-lattice interactions in the [Ru2(O2CMe)4]3[Cr(CN)6] metamagnet. In applied field, correlation between the vibrational response, the displacement patterns, and local lattice distortions reveals magnetoelastically-active [Cr(CN)6]3− octahedral units and rigid [Ru2(O2CMe)4]+ paddle wheel dimers as the system is driven away from the antiferromagnetic ground state. At the same time, variable temperature studies show pronounced changes in modes connected with the [Cr(CN)6]3− octahedra, demonstrating the overall softness of this moiety and its readiness to adapt to a new physical environment. These findings deepen our understanding of coupling in multifunctional materials and demonstrate the tunability of competing interactions under extreme conditions

Intrusion detection by machine learning = Behatolás detektálás gépi tanulás által

Since the early days of information technology, there have been many stakeholders who used the technological capabilities for their own benefit, be it legal operations, or illegal access to computational assets and sensitive information. Every year, businesses invest large amounts of effort into upgrading their IT infrastructure, yet, even today, they are unprepared to protect their most valuable assets: data and knowledge. This lack of protection was the main reason for the creation of this dissertation. During this study, intrusion detection, a field of information security, is evaluated through the use of several machine learning models performing signature and hybrid detection. This is a challenging field, mainly due to the high velocity and imbalanced nature of network traffic. To construct machine learning models capable of intrusion detection, the applied methodologies were the CRISP-DM process model designed to help data scientists with the planning, creation and integration of machine learning models into a business information infrastructure, and design science research interested in answering research questions with information technology artefacts. The two methodologies have a lot in common, which is further elaborated in the study. The goals of this dissertation were two-fold: first, to create an intrusion detector that could provide a high level of intrusion detection performance measured using accuracy and recall and second, to identify potential techniques that can increase intrusion detection performance. Out of the designed models, a hybrid autoencoder + stacking neural network model managed to achieve detection performance comparable to the best models that appeared in the related literature, with good detections on minority classes. To achieve this result, the techniques identified were synthetic sampling, advanced hyperparameter optimization, model ensembles and autoencoder networks. In addition, the dissertation set up a soft hierarchy among the different detection techniques in terms of performance and provides a brief outlook on potential future practical applications of network intrusion detection models as well

Charge Transport in Manganites: Hopping Conduction, the Anomalous Hall Effect and Universal Scaling

The low-temperature Hall resistivity \rho_{xy} of La_{2/3}A_{1/3}MnO_3 single crystals (where A stands for Ca, Pb and Ca, or Sr) can be separated into Ordinary and Anomalous contributions, giving rise to Ordinary and Anomalous Hall effects, respectively. However, no such decomposition is possible near the Curie temperature which, in these systems, is close to metal-to-insulator transition. Rather, for all of these compounds and to a good approximation, the \rho_{xy} data at various temperatures and magnetic fields collapse (up to an overall scale), on to a single function of the reduced magnetization m=M/M_{sat}, the extremum of this function lying at m~0.4. A new mechanism for the Anomalous Hall Effect in the inelastic hopping regime, which reproduces these scaling curves, is identified. This mechanism, which is an extension of Holstein's model for the Ordinary Hall effect in the hopping regime, arises from the combined effects of the double-exchange-induced quantal phase in triads of Mn ions and spin-orbit interactions. We identify processes that lead to the Anomalous Hall Effect for localized carriers and, along the way, analyze issues of quantum interference in the presence of phonon-assisted hopping. Our results suggest that, near the ferromagnet-to-paramagnet transition, it is appropriate to describe transport in manganites in terms of carrier hopping between states that are localized due to combined effect of magnetic and non-magnetic disorder. We attribute the qualitative variations in resistivity characteristics across manganite compounds to the differing strengths of their carrier self-trapping, and conclude that both disorder-induced localization and self-trapping effects are important for transport.Comment: 29 pages, 20 figure

Dealing with Incomplete Household Panel Data in Inequality Research

Population surveys around the world face the problem of declining cooperation and participation rates of respondents. Not only can item nonresponse and unit nonresponse impair important outcome measures for inequality research such as total household disposable income; there is also a further case of missingness confronting household panel surveys that potentially biases results. The approach commonly used in such surveys of interviewing all adult household members and aggregating their individual incomes to yield a final outcome measure for welfare analyses often suffers from partial unit non-response (PUNR), i.e., the non-response of at least one unit, or member, of an otherwise participating household. In these cases, the aggregate income of all household members lacks at least one individual's income. These processes are typically not random and require appropriate correction. Using data from more than twenty waves of the German Socio-Economic Panel (SOEP) we evaluate four different strategies to deal with this phenomenon: (a) Ignorance, i.e., assuming the missing individual's income to be zero. (b) Adjustment of the equivalence scale to account for differences in household size and composition. (c) Elimination of all households observed to suffer PUNR, and re-weighting of households observed to be at risk of but not affected by PUNR. (d) Longitudinal imputation of the missing income components. The aim of this paper is to show how the choice of technique affects substantive results in the inequality research. We find indications of substantial bias on income inequality and poverty as well as on income mobility. These findings are obviously even more important in cross-national comparative analyses if the data providers in the individual countries deal differently with PUNR in the underlying data.Household Panel Surveys, Partial Unit Non-Response, Inequality, Mobility, Imputation, SOEP