Šiais laikais vis daugiau domimasi daugiamačių duomenų aproksimavimo teorija. Daugiamatėje erdvėje aproksimavimo teorija palčiai kur taikoma, pavyzdžiui, skaitinių metodų analizėje, bangų analizėje, signalų apdorojime, įvairiose informacinių technologijų sistemose, kompiuterių grafikoje, astronomijoje, naftos klodų tyrinėjime. Ši sritis viliojanti, nes didelė dalis klasikinės matematikos sunkiai pritaikoma daugiamačiams uždaviniams analizuoti. Taigi senoms problemoms spręsti reikalingi nauji įrankiai. Funkcijų aproksimavimo uždavinių gausu įvairiose matematikos, fizikos ir technikos srityse. Gausu ir jų sprendimų būdų bei metodų. Nesunkiai šie uždaviniai sprendžiami, kai funkcija priklauso nuo vieno ar dviejų kintamųjų. Tačiau realiame gyvenime naudojamos funkcijos turi daug daugiau nežinomųjų. Didėjant kintamųjų skaičiui uždavinio sudėtingumas taip pat auga. Pavyzdžiui, kai funkcija priklauso nuo vieno kintamojo, ją galima pavaizduoti plokštumoje kaip kreivę. Dviejų kintamųjų funkciją atitinka paviršius, nubrėžtas trimatėje erdvėje. Funkcijų, kurios priklauso nuo trijų ir daugiau kintamųjų, vaizdavimas jau sukelia problemų, nes žmogus nebegali suvokti didesnio matumo erdvės. Kadangi trimatę erdvę galima pavaizduoti plokštumoje, manoma, kad panašiu principu keturmatę erdvę galima pavaizduoti trimatėje, o šią vėl plokštumoje. Jei pavyktų sugalvoti tokį metodą, erdvės matumas nebesukeltų problemų. Visgi trijų kintamųjų funkciją bandoma vaizduoti dviem būdais: 1. pateikti... [toliau žr. visą tekstą] / This Master‘s work covers a mathematical analysis system which can visualize multivariate data layers, approximate multi-dimensional functions by polynomials, estimate approximation accuracy and present few the most effective aproximation models. Multivariate approximation theory is an increasingly active research area today. It encompasses a wide range of tools for multivariate approximation such as multi-dimensional splines and finite elements, shift-invariant spaces and radial-basis functions. Approximation theory in the multivariate setting has many applications including numerical analysis, wavelet analysis, signal processing, geographic information systems, computer aided geometric design and computer graphics. The field is fascinating since much of the mathematics of the classical univariate theory does not straightforwardly generalize to the multivariate setting, so new tools are required. Graphs of one variable functions are frequantly displayed as curves, bivariate functions - as contour plots. In generally it is very hard to display or realize function in the multivariate setting. However, some efforts have been made to render functions of precisely three variables. Two obvious approaches suggest themselves: 1. Display a number of cross sections where one of the variables is held constant, or, 2. display contour surfaces where the value of function equals some constant. We will use the first method modification in this Master‘s work. All function variables except... [to full text]

Indiana University-Purdue University Indianapolis (IUPUI) / The purpose of this work is to prove that the visualization is at least as powerful as the best automatic feature selection algorithms. This is achieved by applying our visualization technique to the online review classification into fake and genuine reviews. Our technique uses radial chart and color overlaps to explore the best feature selection through visualization for classification. Every review is treated as a radial translucent red or blue membrane with its dimensions determining the shape of the membrane. This work also shows how the dimension ordering and combination is relevant in the feature selection process. In brief, the whole idea is about giving a structure to each text review based on certain attributes, comparing how different or how similar the structure of the different or same categories are and highlighting the key features that contribute to the classification the most. Colors and saturations aid in the feature selection process. Our visualization technique helps the user get insights into the high dimensional data by providing means to eliminate the worst features right away, pick some best features without statistical aids, understand the behavior of the dimensions in different combinations.

No description available.

Performance analysis often considers numerous factors contributing to performance, and the relative importance of these factors is evolving based on dynamic conditions and requirements. Investigating large numbers of factors and understanding individual factors' predictability within the ultimate performance are challenging tasks. A visual analytics approach that integrates interactive analysis, novel visual representations, and predictive machine learning models can provide new capabilities to examine performance effectively and thoroughly. Currently, only limited research has been done on the possible applications of visual analytics for performance evaluation. In this dissertation, two specific types of performance analysis are presented: (1) organizational employee performance evaluation and (2) performance improvement of machine learning models with interactive feature selection. Both application scenarios leverage the human-in-the-loop approach to assist the identification of influential factors. For organizational employee performance evaluation, a novel visual analytics system, MetricsVis, is developed to support exploratory organizational performance analysis. MetricsVis incorporates hybrid evaluation metrics that integrate quantitative measurements of observed employee achievements and subjective feedback on the relative importance of these achievements to demonstrate employee performance at and between multiple levels regarding the organizational hierarchy. MetricsVis II extends the original system by including actual supervisor ratings and user-guided rankings to capture preferences from users through derived weights. Comparing user preferences with objective employee workload data enables users to relate user evaluation to historical observations and even discover potential bias. For interactive feature selection and model evaluation, a visual analytics system, FeatureExplorer, allows users to refine and diagnose a model iteratively by selecting features based on their domain knowledge, interchangeable features, feature importance, and the resulting model performance. FeatureExplorer enables users to identify stable, trustable, and credible predictive features that contribute significantly to a prediction model.

Systematic Literature Reviews (SLR) are a powerful research tool to identify and select literature to answer a certain question. However, an approach to extract inherent analytical data in Systematic Literature Reviews’ multi-dimensional datasets was lacking. Previous Systematic Literature Review tools do not incorporate the capability of providing said analytical insight. Therefore, this thesis aims to provide a useful approach comprehending various algorithms and data treatment techniques to provide the user with analytical insight on their data that is not evident in the bare execution of a Systematic Literature Review. For this goal, a literature review has been conducted to find the most relevant techniques to extract data from multi-dimensional data sets and the aforementioned approach has been tested on a survey regarding Self-Adaptive Systems (SAS) using a web-application. As a result, we find out what are the most adequate techniques to incorporate into the approach this thesis will provide.

No description available.

<p>Glutathione transferases (GSTs) are detoxication enzymes involved in the cellular protection against a wide range of reactive substances. The role of GSTs is to catalyze the conjugation of glutathione with electrophilic compounds, which generally results in less toxic products. </p><p>The ability to catalyze the denitrosation of the anticancer drug 1,3-bis(2-chloroethyl)- 1-nitrosourea (BCNU) was measured in twelve different GSTs. Only three of the enzymes showed any measurable activity with BCNU, of which human GST T1-1 was the most efficient. This is of special interest, since human GST T1-1 is a polymorphic protein and its expression in different patients may be crucial for the response to BCNU.</p><p>DNA shuffling was used to create a mutant library by recombination of cDNA coding for two different Theta-class GSTs. In total, 94 randomly picked mutants were characterized with respect to their catalytic activity with six different substrates, expression level and sequence. A clone with only one point mutation compared to wild-type rat GST T2-2 had a significantly different substrate-activity pattern. A high expressing mutant of human GST T1-1 was also identified, which is important, since the yield of the wild-type GST T1-1 is generally low. </p><p>Characterization of the Theta library demonstrated divergence of GST variants both in structure and function. The properties of every mutant were treated as a point in a six-dimensional substrate-activity space. Groups of mutants were formed based on euclidian distances and K-means cluster analyses. Both methods resulted in a set of five mutants with high alkyltransferase activities toward dichloromethane and 4-nitrophenethyl bromide (NPB). </p><p>The five selected mutants were used as parental genes in a new DNA shuffling. Addition of cDNA coding for mouse and rat GST T1-1 improved the genetic diversity of the library. The evolution of GST variants was directed towards increased alkyltransferase activity including activity with the anticancer drug BCNU. NPB was used as a surrogate substrate in order to facilitate the screening process. A mutant from the second generation displayed a 65-fold increased catalytic activity with NPB as substrate compared to wild-type human GST T1-1. The BCNU activity with the same mutant had increased 175-fold, suggesting that NPB is a suitable model substrate for the anticancer drug. Further evolution presented a mutant in the fifth generation of the library with 110 times higher NPB activity than wild-type human GST T1-1.</p>

Glutathione transferases (GSTs) are detoxication enzymes involved in the cellular protection against a wide range of reactive substances. The role of GSTs is to catalyze the conjugation of glutathione with electrophilic compounds, which generally results in less toxic products. The ability to catalyze the denitrosation of the anticancer drug 1,3-bis(2-chloroethyl)- 1-nitrosourea (BCNU) was measured in twelve different GSTs. Only three of the enzymes showed any measurable activity with BCNU, of which human GST T1-1 was the most efficient. This is of special interest, since human GST T1-1 is a polymorphic protein and its expression in different patients may be crucial for the response to BCNU. DNA shuffling was used to create a mutant library by recombination of cDNA coding for two different Theta-class GSTs. In total, 94 randomly picked mutants were characterized with respect to their catalytic activity with six different substrates, expression level and sequence. A clone with only one point mutation compared to wild-type rat GST T2-2 had a significantly different substrate-activity pattern. A high expressing mutant of human GST T1-1 was also identified, which is important, since the yield of the wild-type GST T1-1 is generally low. Characterization of the Theta library demonstrated divergence of GST variants both in structure and function. The properties of every mutant were treated as a point in a six-dimensional substrate-activity space. Groups of mutants were formed based on euclidian distances and K-means cluster analyses. Both methods resulted in a set of five mutants with high alkyltransferase activities toward dichloromethane and 4-nitrophenethyl bromide (NPB). The five selected mutants were used as parental genes in a new DNA shuffling. Addition of cDNA coding for mouse and rat GST T1-1 improved the genetic diversity of the library. The evolution of GST variants was directed towards increased alkyltransferase activity including activity with the anticancer drug BCNU. NPB was used as a surrogate substrate in order to facilitate the screening process. A mutant from the second generation displayed a 65-fold increased catalytic activity with NPB as substrate compared to wild-type human GST T1-1. The BCNU activity with the same mutant had increased 175-fold, suggesting that NPB is a suitable model substrate for the anticancer drug. Further evolution presented a mutant in the fifth generation of the library with 110 times higher NPB activity than wild-type human GST T1-1.

Visual analytics of large amounts of spatio-temporal data is challenging due to the overlap and clutter from movements of multiple objects. A common approach for analyzing such data is to consider how groups of items cluster and move together in space and time. However, most methods for showing Spatio-temporal Cluster (STC) properties, concentrate on a few dimensions of the cluster (e.g. the cluster movement direction or cluster density) and many other properties are not represented. Furthermore, while representing multiple attributes of clusters in a single view existing methods fail to preserve the original shape of the cluster or distort the actual spatial covering of the dataset. In this thesis, I propose a simple yet effective visualization, FlockViz, for showing multiple STC data dimensions in a single view by preserving the original cluster shape. To evaluate this method I develop a framework for categorizing the wide range of tasks involved in analyzing STCs. I conclude this work through a controlled user study comparing the performance of FlockViz with alternative visualization techniques that aid with cluster-based analytic tasks. Finally the exploration capability of FlockViz is demonstrated in some real life data sets such as fish movement, caribou movement, eagle migration, and hurricane movement. The results of the user studies and use cases confirm the advantage and novelty of the novel FlockViz design for visual analytic tasks.

Problémy jako je jemnozrnná kategorizace či výpočty s využitím lidských zdrojů se v posledních letech v komunitě stávají stále populárnějšími, což dosvědčuje i značné množství publikací na tato témata. Zatímco většina těchto prací využívá "klasických'' obrazových příznaků extrahovaných počítačem, tato se zaměřuje především na percepční vlastnosti, které nemohou být snadno zachyceny počítači a vyžadují zapojení lidí do procesu sběru dat. Práce zkoumá možnosti levného a efektivního získávání percepčních podobností od uživatelů rovněž ve vztahu ke škálovatelnosti. Dále vyhodnocuje několik relevantních experimentů a představuje metody zlepšující efektivitu sběru dat. Jsou zde také shrnuty a porovnány metody učení multidimenzionálního indexování a prohledávání tohoto prostoru. Získané výsledky jsou následně užity v komplexním experimentu vyhodnoceném na datasetu obrázků jídel. Procedura začíná získáváním podobností od uživatelů, pokračuje vytvořením multidimenzionálního prostoru jídel a končí prohledáváním tohoto prostoru.