scholarly journals Learning of Hierarchical Temporal Structures for Guided Improvisation

2020 ◽  
Vol 43 (2-3) ◽  
pp. 109-124
Author(s):  
Ken Déguernel ◽  
Emmanuel Vincent ◽  
Jérôme Nika ◽  
Gérard Assayag ◽  
Kamel Smaïli
Keyword(s):  
Mutual Information ◽  
Hierarchical Structures ◽  
Phrase Structure Grammar ◽  
Professional Musicians ◽  
Multilevel Structure ◽  
Sequence Selection ◽  
The Hierarchical Structure ◽  
Temporal Structures ◽  
Anticipatory Behavior ◽  
Different Time Scales

This article focuses on learning the hierarchical structure of what we call a “temporal scenario” (for instance, a chord progression) to perform automatic improvisation consistently over several different time scales. We first present a way to represent hierarchical structures with a phrase structure grammar. Such a grammar enables us to analyze a scenario at several levels of organization, creating a “multilevel scenario.” We then develop a method to automatically induce this grammar from a corpus, based on sequence selection with mutual information. We applied this method to a corpus of transcribed improvisations based on the chord sequence, also with chord substitutions, from George Gershwin's “I Got Rhythm.” From these we obtained multilevel scenarios similar to the analyses performed by professional musicians. We then present a novel heuristic approach, exploiting the multilevel structure of a scenario to guide the improvisation with anticipatory behavior in an improvisation paradigm driven by a factor oracle. This method ensures consistency of the improvisation with regard to the global form, and it opens up possibilities when playing on chords that do not exist in memory. This system was evaluated by professional improvisers during listening sessions and received excellent feedback.

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.

scholarly journals Examining dependencies among different time scales in episodic memory - An experience sampling study

2019 ◽  
Author(s):  
Hyungwook Yim ◽  
Paul Garrett ◽  
Megan Baker ◽  
Vishnu Sreekumar ◽  
Simon Dennis
Keyword(s):  
Retention Interval ◽  
Mutual Information ◽  
Time Scales ◽  
Memory Retrieval ◽  
Daily Life ◽  
Ecological Validity ◽  
Experience Sampling ◽  
Recurrence Plots ◽  
Collection Phase ◽  
Different Time Scales

We re-examined whether memories of different time scales such as week, day of week, and hour of day are used independently during memory retrieval as has been previously argued (i.e., independence of scales). To overcome the limitations of previous studies, we used experience sampling technology to obtain test stimuli that have higher ecological validity and used pointwise mutual information to directly measure the degree of dependency in a formal way. Participants wore a smartphone around their neck for two weeks, which was equipped with an app that automatically collected time, images, GPS, audio and accelerometry. After a one-week retention interval, participants were presented with an image that was captured during their data collection phase, and were tested on their memory of when the event happened (i.e., week, day of week, and hour). We find that, in contrast to previous arguments, memories of different time scales were not retrieved independently. Moreover, through rendering recurrence plots of the images that the participants collected, we provide evidence the dependency may have originated from the repetitive events that the participants encountered in their daily life.

Smart Optimization of Machine Systems Using Hierarchical Genotype Representations

1999 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui
Keyword(s):  
Structural Design ◽  
Hierarchical Structures ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Problems ◽  
Design Variables ◽  
The Hierarchical Structure ◽  
Machine Systems ◽  
System Designs ◽  
Crossover And Mutation

Abstract Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, it is difficult to concurrently optimize all design variables of portions within the hierarchical structure. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design to demonstrate its effectiveness.

Hierarchical Clustering Using Evolutionary Algorithms

2011 ◽  
pp. 146-156
Author(s):  
Monica Chis
Keyword(s):  
Evolutionary Algorithm ◽  
Hierarchical Clustering ◽  
Cluster Structure ◽  
Hierarchical Structures ◽  
Linear Representation ◽  
Large Area ◽  
Data Set ◽  
The Hierarchical Structure ◽  
Data Points ◽  
Variation Operators

Clustering is an important technique used in discovering some inherent structure present in data. The purpose of cluster analysis is to partition a given data set into a number of groups such that data in a particular cluster are more similar to each other than objects in different clusters. Hierarchical clustering refers to the formation of a recursive clustering of the data points: a partition into many clusters, each of which is itself hierarchically clustered. Hierarchical structures solve many problems in a large area of interests. In this paper a new evolutionary algorithm for detecting the hierarchical structure of an input data set is proposed. Problem could be very useful in economy, market segmentation, management, biology taxonomy and other domains. A new linear representation of the cluster structure within the data set is proposed. An evolutionary algorithm evolves a population of clustering hierarchies. Proposed algorithm uses mutation and crossover as (search) variation operators. The final goal is to present a data clustering representation to find fast a hierarchical clustering structure.

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.

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.

Mining Text Documents for Thematic Hierarchies Using Self-Organizing Maps

Data Mining ◽  
2011 ◽  
pp. 199-219 ◽  
Author(s):  
Hsin-Chang Yang ◽  
Chung-Hong Lee
Keyword(s):  
Text Categorization ◽  
Hierarchical Structures ◽  
Self Organizing Map ◽  
Feature Maps ◽  
Text Documents ◽  
Self Organizing Maps ◽  
Test Corpus ◽  
The Hierarchical Structure ◽  
Document Collection ◽  
Self Organizing

Recently, many approaches have been devised for mining various kinds of knowledge from texts. One important application of text mining is to identify themes and the semantic relations among these themes for text categorization. Traditionally, these themes were arranged in a hierarchical manner to achieve effective searching and indexing as well as easy comprehension for human beings. The determination of category themes and their hierarchical structures was mostly done by human experts. In this work, we developed an approach to automatically generate category themes and reveal the hierarchical structure among them. We also used the generated structure to categorize text documents. The document collection was trained by a self-organizing map to form two feature maps. We then analyzed these maps and obtained the category themes and their structure. Although the test corpus contains documents written in Chinese, the proposed approach can be applied to documents written in any language, and such documents can be transformed into a list of separated terms.

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.

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