Feature modeling: An extended perspective to design IT infrastructure

Feature modeling is a widely used method in systems engineering. Feature models help to communicate the requirements and architecture of IT systems. Applying feature models to IT infrastructure is a rare explored field in system development. We advocate using feature modeling for designing IT infrastructure to give an additional perspective on systems architecture and implement functions and properties of IT infrastructure. In this research-inprogress paper we present the idea of combining feature modeling and IT infrastructures and propose an exemplified feature model in context of IT infrastructure and its corresponding research questions. The discussion leads to functional and non-functional-requirements, feature-oriented description of architecture, and scope of IT infrastructure

Nonparametric modeling and forecasting electricity demand: an empirical study

This paper uses half-hourly electricity demand data in South Australia as an empirical study of nonparametric modeling and forecasting methods for prediction from half-hour ahead to one year ahead. A notable feature of the univariate time series of electricity demand is the presence of both intraweek and intraday seasonalities. An intraday seasonal cycle is apparent from the similarity of the demand from one day to the next, and an intraweek seasonal cycle is evident from comparing the demand on the corresponding day of adjacent weeks. There is a strong appeal in using forecasting methods that are able to capture both seasonalities. In this paper, the forecasting methods slice a seasonal univariate time series into a time series of curves. The forecasting methods reduce the dimensionality by applying functional principal component analysis to the observed data, and then utilize an univariate time series forecasting method and functional principal component regression techniques. When data points in the most recent curve are sequentially observed, updating methods can improve the point and interval forecast accuracy. We also revisit a nonparametric approach to construct prediction intervals of updated forecasts, and evaluate the interval forecast accuracy.Functional principal component analysis; functional time series; multivariate time series, ordinary least squares, penalized least squares; ridge regression; seasonal time series

Functional Mixed Membership Models

Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this paper, we propose a Bayesian mixed membership model for functional data. By using the multivariate Karhunen-Lo\`eve theorem, we are able to derive a scalable representation of Gaussian processes that maintains data-driven learning of the covariance structure. Within this framework, we establish conditional posterior consistency given a known feature allocation matrix. Compared to previous work on mixed membership models, our proposal allows for increased modeling flexibility, with the benefit of a directly interpretable mean and covariance structure. Our work is motivated by studies in functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). In this context, our work formalizes the clinical notion of "spectrum" in terms of feature membership proportions.Comment: 77 pages, 16 figure

Validity of choosing the modeling method in researching the proces of multicultural competence of future teachers development

У статті представлено узагальнене визначення моделювання як методу науковго пізнання й визначено специфічні особливості моделювання. Дано універсальне визначення моделі, яка є відмінну рису моделювання за Марвіном Мінським. Проаналізовано етапи моделювання. Охарактеризовано класифікацію моделей за Б.О. Глінським. Обгурнтовано  логіку  вибору  нами  структурно‐функціональної  моделі  для  дослідження  розвитку  полікультурної компетентності.As a method of scientific knowledge in the study of the development of multicultural competence of future teachers we have chosen the modeling method. This method of scientific knowledge is widely distributed in educational science. The task of the article – to justify the choice of modeling method for studying the process of development of multicultural competence of future teachers. The process of the developing of the multicultural competence is the holistic and systemic, and providing the presence of interrelated elements. That holistic and system due to the fact that the elements that make up the process associated with each specific pattern. That logicality elements connection is an objective basis for modeling capabilities. The main feature of modeling as a method of scientific knowledge is the model using. Using the logic, we have chosen the structural and functional model in the way of the development of multicultural competence, from the diversity of represented types of models. The simulation process requires the following components: a subject (researcher), the object of study (the development of multicultural competence) and model. The simulation process requires compliance with certain phasing or structure. So we will use structural and functional model. The model consists of four components: a conceptual component, content component, component technology and procedural. Each component includes interrelated elements. With this model, we can answer the question: is our development effective in the achieving

Support vector classification analysis of resting state functional connectivity fMRI

Since its discovery in 1995 resting state functional connectivity derived from functional MRI data has become a popular neuroimaging method for study psychiatric disorders. Current methods for analyzing resting state functional connectivity in disease involve thousands of univariate tests, and the specification of regions of interests to employ in the analysis. There are several drawbacks to these methods. First the mass univariate tests employed are insensitive to the information present in distributed networks of functional connectivity. Second, the null hypothesis testing employed to select functional connectivity dierences between groups does not evaluate the predictive power of identified functional connectivities. Third, the specification of regions of interests is confounded by experimentor bias in terms of which regions should be modeled and experimental error in terms of the size and location of these regions of interests. The objective of this dissertation is to improve the methods for functional connectivity analysis using multivariate predictive modeling, feature selection, and whole brain parcellation. A method of applying Support vector classification (SVC) to resting state functional connectivity data was developed in the context of a neuroimaging study of depression. The interpretability of the obtained classifier was optimized using feature selection techniques that incorporate reliability information. The problem of selecting regions of interests for whole brain functional connectivity analysis was addressed by clustering whole brain functional connectivity data to parcellate the brain into contiguous functionally homogenous regions. This newly developed famework was applied to derive a classifier capable of correctly seperating the functional connectivity patterns of patients with depression from those of healthy controls 90% of the time. The features most relevant to the obtain classifier match those previously identified in previous studies, but also include several regions not previously implicated in the functional networks underlying depression.Ph.D.Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthon

Functional modeling of high-dimensional data: a Manifold Learning approach

This article belongs to the Special Issue Methodological and Applied Contributions on Stochastic Modelling and ForecastingThis paper introduces stringing via Manifold Learning (ML-stringing), an alternative to the original stringing based on Unidimensional Scaling (UDS). Our proposal is framed within a wider class of methods that map high-dimensional observations to the infinite space of functions,allowing the use of Functional Data Analysis (FDA). Stringing handles general high-dimensional data as scrambled realizations of an unknown stochastic process. Therefore, the essential feature of the method is a rearrangement of the observed values. Motivated by the linear nature of UDS and the increasing number of applications to biosciences (e.g., functional modeling of gene expression arrays and single nucleotide polymorphisms, or the classification of neuroimages) we aim to recover more complex relations between predictors through ML. In simulation studies, it is shown that MLstringing achieves higher-quality orderings and that, in general, this leads to improvements in the functional representation and modeling of the data. The versatility of our method is also illustrated with an application to a colon cancer study that deals with high-dimensional gene expression arrays.This paper shows that ML-stringing is a feasible alternative to the UDS-based version. Also, it opens a window to new contributions to the field of FDA and the study of high-dimensional data.This research was funded in part by Ministerio de Ciencia, Innovación y Universidades grant numbers PID2019-104901RB-I00 and MTM2017-88708-P

Supporting the automated generation of modular product line safety cases

Abstract The effective reuse of design assets in safety-critical Software Product Lines (SPL) would require the reuse of safety analyses of those assets in the variant contexts of certification of products derived from the SPL. This in turn requires the traceability of SPL variation across design, including variation in safety analysis and safety cases. In this paper, we propose a method and tool to support the automatic generation of modular SPL safety case architectures from the information provided by SPL feature modeling and model-based safety analysis. The Goal Structuring Notation (GSN) safety case modeling notation and its modular extensions supported by the D-Case Editor were used to implement the method in an automated tool support. The tool was used to generate a modular safety case for an automotive Hybrid Braking System SPL

In Situ Lithiation–Delithiation of Mechanically Robust Cu–Si Core–Shell Nanolattices in a Scanning Electron Microscope

Nanoarchitected Cu–Si core–shell lattices were fabricated via two-photon lithography and tested as mechanically robust Li-ion battery electrodes which accommodate ∼250% Si volume expansion during lithiation. The superior mechanical performance of the nanolattice electrodes is directly observed using an in situ scanning electron microscope, which allows volume expansion and morphological changes to be imaged at multiple length scales, from single lattice beam to the architecture level, during electrochemical testing. Finite element modeling of lithiation-induced volume expansion in a core–shell structure reveals that geometry and plasticity mechanisms play a critical role in preventing damage in the nanolattice electrodes. The two-photon lithography-based fabrication method combined with computational modeling and in situ characterization capabilities would potentially enable the rational design and fast discovery of mechanically robust and kinetically agile electrode materials that independently optimize geometry, feature size, porosity, surface area, and chemical composition, as well as other functional devices in which mechanical and transport phenomena are important