scholarly journals HIERARCHICAL STRUCTURE OF DENTAL DATA IN THE RANDOM EFFECTS INCLUSION APPROACH

2018 ◽  
Vol 36 (3) ◽  
pp. 700
Author(s):  
Tiago Peres da Silva SUGUIURA ◽  
Omar Cléo Neves PEREIRA ◽  
Waenya Fernandez de CARVALHO ◽  
Isolde Terezinha Santos PREVIDELLI
Keyword(s):  
Hierarchical Structure ◽  
Linear Models ◽  
Intraclass Correlation ◽  
Hierarchical Structures ◽  
Aluminium Chloride ◽  
Data Sets ◽  
Complex Structures ◽  
The Hierarchical Structure ◽  
Level Model ◽  
Multilevel Linear Models

Data sets with complex structures is increasingly common in dental research. As consequences, statistical  methods to analyze and interpret these data must be efficient and robust. Hierarchical structures is one of  the most common kind of complex structures, and a proper approach is required. The multilevel modeling used to study hierarchical structures is a powerful tool which allows the collected data to be  analyzes in several levels. This study has as objective to make a literature review on multilevel linear models and to illustrate a three level model through a matrix procedure, without the use of specific software to estimate the parameters. With this model, we analyzed the vertical gingival retraction when using the substances: Naphazoline Chloridrate, Aluminium Chloride and without any substance. The intraclass correlation coefficient on dental level within patients showed that the hierarchical structure was important to accommodate the dependence within clusters.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

Perceiving Hierarchical Structures in Nonrepresentational Paintings

1998 ◽  
Vol 16 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Tsion Avital ◽  
Gerald C. Cupchik
Keyword(s):  
Factor Analysis ◽  
Multidimensional Scaling ◽  
Hierarchical Structure ◽  
Hierarchical Structures ◽  
Exploratory Activity ◽  
Affective Responses ◽  
Hierarchical Complexity ◽  
The Hierarchical Structure ◽  
Relative Separation ◽  
Dimension 2

A series of four experiments were conducted to examine viewer perceptions of three sets of five nonrepresentational paintings. Increased complexity was embedded in the hierarchical structure of each set by carefully selecting colors and ordering them in each successive painting according to certain rules of transformation which created hierarchies. Experiment 1 supported the hypothesis that subjects would discern the hierarchical complexity underlying the sets of paintings. In Experiment 2 viewers rated the paintings on collative (complexity, disorder) and affective (pleasing, interesting, tension, and power) scales, and a factor analysis revealed that affective ratings were tied to complexity (Factor 1) but not to disorder (Factor 2). In Experiment 3, a measure of exploratory activity (free looking time) was correlated with complexity (Factor 1) but not with disorder (Factor 2). Multidimensional scaling was used in Experiment 4 to examine perceptions of the paintings seen in pairs. Dimension 1 contrasted Soft with Hard-Edged paintings, while Dimension 2 reflected the relative separation of figure from ground in these paintings. Together these results show that untrained viewers can discern hierarchical complexity in paintings and that this quality stimulates affective responses and exploratory activity.

Hierarchical Parallel Processes of Genetic Algorithms for Design Optimization of Large-Scale Products

2000 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui
Keyword(s):  
Genetic Algorithms ◽  
Hierarchical Structure ◽  
Large Scale ◽  
Hierarchical Structures ◽  
Pareto Optimum ◽  
Local Optima ◽  
Large Scale Systems ◽  
Parallel Processes ◽  
The Hierarchical Structure ◽  
Optimization Search

Abstract A large-scale machine system often has a general hierarchical structure. For hierarchical structures, optimization is difficult because many local optima almost always arise, however genetic algorithms that have a hierarchical genotype can be applied to treat such problems directly. Relations between the structural components are analyzed and this information is used to partition the hierarchical structure. Partitioning large-scale problems into sub-problems that can be solved using parallel processed GAs increases the efficiency of the optimization search. The optimization of large-scale systems then becomes possible due to information sharing of Pareto optimum solutions for the sub-problems.

Beamtrees: Compact Visualization of Large Hierarchies

2003 ◽  
Vol 2 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Frank van Ham ◽  
Jarke J. van Wijk
Keyword(s):  
Hierarchical Structure ◽  
User Study ◽  
Three Dimensional ◽  
New Method ◽  
Data Sets ◽  
Two Dimensional ◽  
Hierarchical Data ◽  
Organization Structures ◽  
The Hierarchical Structure

Beamtrees are a new method for the visualization of large hierarchical data sets, such as directory structures and organization structures. Nodes are shown as stacked circular beams such that both the hierarchical structure as well as the size of nodes are depicted. The dimensions of beams are calculated using a variation of the treemap algorithm. Both a two-dimensional and a three-dimensional variant are presented. A small user study indicated that beamtrees are significantly more effective than nested treemaps and cushion treemaps for the extraction of global hierarchical information.

scholarly journals TreeSummarizedExperiment: a S4 class for data with hierarchical structure

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1246
Author(s):  
Ruizhu Huang ◽  
Charlotte Soneson ◽  
Felix G.M. Ernst ◽  
Kevin C. Rue-Albrecht ◽  
Guangchuang Yu ◽  
...  
Keyword(s):  
Hierarchical Structure ◽  
Hierarchical Structures ◽  
Cell Types ◽  
Profile Data ◽  
Biological Profile ◽  
Data Manipulation ◽  
The Hierarchical Structure ◽  
R Packages ◽  
Popular Classes ◽  
Tree Representations

Data organized into hierarchical structures (e.g., phylogenies or cell types) arises in several biological fields. It is therefore of interest to have data containers that store the hierarchical structure together with the biological profile data, and provide functions to easily access or manipulate data at different resolutions. Here, we present TreeSummarizedExperiment, a R/S4 class that extends the commonly used SingleCellExperiment class by incorporating tree representations of rows and/or columns (represented by objects of the phylo class). It follows the convention of the SummarizedExperiment class, while providing links between the assays and the nodes of a tree to allow data manipulation at arbitrary levels of the tree. The package is designed to be extensible, allowing new functions on the tree (phylo) to be contributed. As the work is based on the SingleCellExperiment class and the phylo class, both of which are popular classes used in many R packages, it is expected to be able to interact seamlessly with many other tools.

Embedding Hierarchical Structures for Venue Category Representation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-29
Author(s):  
Meng Chen ◽  
Lei Zhu ◽  
Ronghui Xu ◽  
Yang Liu ◽  
Xiaohui Yu ◽  
...  
Keyword(s):  
Hierarchical Structure ◽  
Hierarchical Structures ◽  
Category Representation ◽  
Semantic Relationships ◽  
The Hierarchical Structure ◽  
Sequential Information ◽  
Location Based Social Networks ◽  
Low Dimensional ◽  
The Given ◽  
Embedding Methods

Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic relationships between those categories. To understand the venue semantics, existing methods usually embed venue categories into low-dimensional spaces by modeling the linear context (i.e., the positional neighbors of the given category) in check-in sequences. However, the hierarchical structure of venue categories, which inherently encodes the relationships between categories, is largely untapped. In this article, we propose a venue C ategory E mbedding M odel named Hier-CEM , which generates a latent representation for each venue category by embedding the Hier archical structure of categories and utilizing multiple types of context. Specifically, we investigate two kinds of hierarchical context based on any given venue category hierarchy and show how to model them together with the linear context collaboratively. We apply Hier-CEM to three tasks on two real check-in datasets collected from Foursquare. Experimental results show that Hier-CEM is better at capturing both semantic and sequential information inherent in venues than state-of-the-art embedding methods.

Hierarchical Parallel Processes of Genetic Algorithms for Design Optimization of Large-Scale Products

2004 ◽  
Vol 126 (2) ◽  
pp. 217-224 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui
Keyword(s):  
Genetic Algorithms ◽  
Hierarchical Structure ◽  
Large Scale ◽  
Hierarchical Structures ◽  
Pareto Optimum ◽  
Local Optima ◽  
Large Scale Systems ◽  
Parallel Processes ◽  
The Hierarchical Structure ◽  
Optimization Search

A large-scale machine system often has a general hierarchical structure. For hierarchical structures, optimization is difficult because many local optima almost always arise, however genetic algorithms that have a hierarchical genotype can be applied to treat such problems directly. Relations between the structural components are analyzed and this information is used to partition the hierarchical structure. Partitioning large-scale problems into sub-problems that can be solved using parallel processed GAs increases the efficiency of the optimization search. The optimization of large-scale systems then becomes possible due to information sharing of Pareto optimum solutions for the sub-problems.

scholarly journals Zn-P Co-Modified Hierarchical ZSM-5 Zeolites Directly Synthesized via Dry Gel Conversion for Enhanced Methanol to Aromatics Reaction

Catalysts ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1388
Author(s):  
Youhe Wang ◽  
Zhihong Li ◽  
Chang Dai ◽  
Ningning Du ◽  
Tingting Li ◽  
...  
Keyword(s):  
Hierarchical Structure ◽  
Lewis Acid ◽  
Hierarchical Structures ◽  
Acid Sites ◽  
Impregnation Method ◽  
Lewis Acid Sites ◽  
Dry Gel Conversion ◽  
The Hierarchical Structure ◽  
Unique Method ◽  
Benzene Toluene

A unique method to prepare Zn-P co-modified hierarchical ZSM-5 zeolites was developed. The ZSM-5 zeolite was directly synthesized by a dry gel conversion without adding any templates or seeds. Afterwards, the hierarchical structure was endowed to the ZSM-5 zeolite by the sequential desilication-dealumination. Zn and P species were then introduced into the hierarchical ZSM-5 zeolites by the impregnation method and their activity in methanol to aromatics process was investigated. It was found that the Zn-P co-modified hierarchical ZSM-5 zeolites possessed more Zn-related Lewis acid sites, and the ratio of Zn(OH)+/ZnO was increased. The catalytic evaluation results revealed that the benzene, toluene and xylene (BTX) and aromatics selectivity were significantly improved from 20.59% and 29.41% of pristine ZSM-5 zeolite to 28.12% and 41.88% of Zn-P co-modified hierarchical counterpart (1.5Zn0.3P/HZSM-5), respectively. Owing to the introduced highly stable Zn-P co-modified hierarchical structures, the lifetime (conversion not less than 99%) of ZSM-5 zeolite during methanol to aromatics reaction was increased from 6 h to 18 h.

The Oxford Handbook of Soft Condensed Matter

2015 ◽  
Keyword(s):  
Hierarchical Structure ◽  
Condensed Matter ◽  
Granular Matter ◽  
Complex Structures ◽  
Soft Condensed Matter ◽  
Fundamental Physics ◽  
Polymer Systems ◽  
Colloid Science ◽  
Solvent Dynamics ◽  
The Hierarchical Structure

This Handbook serves both as an introduction and an overview of the field of soft condensed matter. The discussion covers topics ranging from the fundamentals of colloid science to the principles and action of surfactants, modern directions of research in liquid crystals, and the key properties of foams. The book also explores the fundamental physics that controls the structure and mechanics of granular matter; how the unusual and often dramatic mechanical properties of concentrated polymer systems are determined by the physics of entanglements; the complex structures formed by block copolymers and the methods of structure analysis; rubber elasticity and new emerging classes of rubber-elastic materials; the physics of polyelectrolytes; the solvent dynamics in polymer gels, in equilibrium and under mechanical stress; the hierarchical structure and characteristics of an extracellular matrix; and the hierarchical structure and resulting physical properties of the cell cytoskeleton. The book concludes with an analysis of the properties of interfaces and membranes.

scholarly journals Three-dimensional numerical investigation of dendritic self-organizational structure growth on a nanopost surface

2017 ◽  
Vol 9 (2) ◽  
pp. 168781401668335 ◽  
Author(s):  
Hua-Yi Hsu ◽  
Bo-Ting Lin ◽  
You-Ren Hsu
Keyword(s):  
Hierarchical Structure ◽  
Three Dimensional ◽  
Phase Field Model ◽  
Hierarchical Structures ◽  
Material Surface ◽  
Passive Component ◽  
Self Organized ◽  
The Hierarchical Structure ◽  
Reflection Gratings ◽  
Hierarchical Architectures

Nanopost arrays are generally used in applications of reflection gratings and in changing material surface wettability. Nanopost arrays can be used as a passive component to induce dendritic self-organized hierarchical architectures. In this study, through the use of a phase-field model, we performed a three-dimensional numerical simulation to demonstrate that nanopost structures affect the expanding speed of the surface of a dendritic self-organized structure in the growing path of a hierarchical structure. Additionally, we demonstrated that the nanopost array arrangement on the surface changed the hierarchical structure branching. Finally, introducing an externally applied force to the system enabled the use of a nanopost as an active component. Nanopost surroundings were determined to significantly affect the final distribution of dendritic structures and induce hierarchical structures after an external force was introduced to the system.

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