SPECTRAL MEASUREMENT AND CLASSIFICATION IN THE ERA OF BIG DATA

2021 ◽  
Author(s):  
F.S. Webler ◽  
M. Andersen
Keyword(s):  
Data Compression ◽  
Feature Space ◽  
Natural World ◽  
Natural Scenes ◽  
Scientific Disciplines ◽  
Multichannel Filter ◽  
Filter Array ◽  
Low Dimensional ◽  
Imaging Sensors

The measurement and classification of light is essential across many scientific disciplines. Devices used to measure light range from the highly precise scanning spectroradiometers to the more practical compact multichannel filter-array type imaging sensors and the ubiquitous RGB pixel. While there have been numerous successful efforts to reconstruct spectrum from RGB, RGB-to-spectrum reconstruction has historically been limited to natural scenes and other edge cases under strict constraints. However, information theory and recent advances in deep learning have shed new light on the vast amount of redundancy contained within data collected in the natural world, including light. In this paper, we will investigate how analytic methods can help map high dimensional spectra data to a low-dimensional feature space with minimal inductive bias. Through a better understanding of the intrinsic dimension of the data, we can use the features expressed in this representation to exploit regularities and make tasks like data compression, measurement and classification more efficient. The aim of this analysis is to help inform how and when low-dimensional representation of spectra is useful in practice for designing compact sensors as well as for lossy data compression and robust classification.

A Primer on Mapping Class Groups (PMS-49)

2017 ◽  
Author(s):  
Benson Farb ◽  
Dan Margalit
Keyword(s):  
Graduate Students ◽  
Moduli Space ◽  
Riemann Surfaces ◽  
Class Group ◽  
Mapping Class ◽  
Torelli Group ◽  
Surface Bundles ◽  
Surface Homeomorphisms ◽  
Low Dimensional

The study of the mapping class group Mod(S) is a classical topic that is experiencing a renaissance. It lies at the juncture of geometry, topology, and group theory. This book explains as many important theorems, examples, and techniques as possible, quickly and directly, while at the same time giving full details and keeping the text nearly self-contained. The book is suitable for graduate students. It begins by explaining the main group-theoretical properties of Mod(S), from finite generation by Dehn twists and low-dimensional homology to the Dehn–Nielsen–Baer–theorem. Along the way, central objects and tools are introduced, such as the Birman exact sequence, the complex of curves, the braid group, the symplectic representation, and the Torelli group. The book then introduces Teichmüller space and its geometry, and uses the action of Mod(S) on it to prove the Nielsen-Thurston classification of surface homeomorphisms. Topics include the topology of the moduli space of Riemann surfaces, the connection with surface bundles, pseudo-Anosov theory, and Thurston's approach to the classification.

THE ISSUE OF CONTEXT AND SOCIOLOGICAL POETICS OF THE "BAKHTIN'S CIRCLE"

2020 ◽  
pp. 10-18
Author(s):  
Grigorii I. Nesmeyanov ◽  
Keyword(s):  
Discourse Analysis ◽  
Intellectual History ◽  
Literary Theory ◽  
Philosophical Hermeneutics ◽  
Research Fields ◽  
Interdisciplinary Field ◽  
Scientific Disciplines ◽  
Methodological Approaches ◽  
A Current

The article formulates main questions related to the concept of context. The issue of context is considered as a current-day interdisciplinary field of research. There are many definitions of context in dictionaries and in various humanities (including scientific disciplines). In connection with that issue various methodological approaches arise in the humanities, which can be designated by the umbrella term “contextual”. By the example of one of such approaches to the sociological poetics of the “Bakhtin’s circle”, the author substantiates the possibility of creating an interdisciplinary classification of contextual approaches. That classification may include scientific developments of different years and research fields, including: philosophical hermeneutics, a number of approaches to the Russian and foreign literary theory (M.M. Bakhtin, Yu.M. Lotman, B.M. Eichenbaum, F. Moretti, A. Compagnon, etc.), intellectual history, discourse analysis, etc.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

Automated classification of human lung sound signals using phase space representation of intrinsic mode function

2021 ◽  
Author(s):  
Sibghatullah I. Khan ◽  
Vikram Palodiya ◽  
Lavanya Poluboyina
Keyword(s):  
Phase Space ◽  
Human Lung ◽  
Feature Space ◽  
Normal Lung ◽  
Space Representation ◽  
Support Vector ◽  
Lung Sounds ◽  
Lung Sound ◽  
Phase Space Representation

Abstract Bronchiectasis and chronic obstructive pulmonary disease (COPD) are common human lung diseases. In general, the expert pulmonologistcarries preliminary screening and detection of these lung abnormalities by listening to the adventitious lung sounds. The present paper is an attempt towards the automatic detection of adventitious lung sounds ofBronchiectasis,COPD from normal lung sounds of healthy subjects. For classification of the lung sounds into a normaland adventitious category, we obtain features from phase space representation (PSR). At first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). The IMFs are then further processed to construct two dimensional (2D) and three dimensional (3D) PSR. The feature space includes the 95% confidence ellipse area and interquartile range (IQR) of Euclidian distances computed from 2D and 3D PSRs, respectively. The process is carried out for the first four IMFs correspondings to normal and adventitious lung sound signals. The computed features depicta significant ability to discriminate the two categories of lung sound signals.To perform classification, we use the least square support vector machine with two kernels, namely, polynomial and radial basis function (RBF).Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying normal and adventitious lung sound signals. LS-SVM is employing RBF kernel provides the highest classification accuracy of 97.67 % over feature space constituted by first, second, and fourth IMF.

scholarly journals A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms

2019 ◽  
Vol 29 (07) ◽  
pp. 1850058 ◽  
Author(s):  
Juan M. Górriz ◽  
Javier Ramírez ◽  
F. Segovia ◽  
Francisco J. Martínez ◽  
Meng-Chuan Lai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Structure ◽  
Feature Space ◽  
Classification Problem ◽  
Small Sample ◽  
Biological Sex ◽  
Machine Learning Approach ◽  
Learning Machine ◽  
Small Sample Sizes ◽  
Low Dimensional

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.

scholarly journals Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

2021 ◽  
Author(s):  
Rogini Runghen ◽  
Daniel B Stouffer ◽  
Giulio Valentino Dalla Riva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Link Prediction ◽  
Graph Embedding ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Large Networks ◽  
Data Set ◽  
Learning Techniques ◽  
Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

scholarly journals A Novel Density-based Technique for Outlier Detection of High Dimensional Data Utilizing Full Feature Space

2021 ◽  
Vol 50 (1) ◽  
pp. 138-152
Author(s):  
Mujeeb Ur Rehman ◽  
Dost Muhammad Khan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
High Dimensional Data ◽  
Research Work ◽  
Feature Space ◽  
High Dimensional ◽  
Data Set ◽  
Data Points ◽  
Low Dimensional ◽  
Intrinsic Feature

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

scholarly journals The impact of convergent technologies on changing labor markets

KANT ◽  
2020 ◽  
Vol 37 (4) ◽  
pp. 240-245
Author(s):  
Tatiana Vorontsova
Keyword(s):  
Labor Market ◽  
Social Relations ◽  
Technological Development ◽  
Natural World ◽  
Social Rights ◽  
Physical Strength ◽  
Humanistic Approach ◽  
The One ◽  
The Impact

The article analyzes the problems of the development of convergent technologies, which, on the one hand, make it possible to overcome the natural limitations of man and expand his capabilities, on the other hand, threaten humanity. The author identifies various research positions in assessing the prospects for NBIC convergence - from overtly alarmist to overly enthusiastic. A classification of possible results of technological innovations is proposed, in which changes in the natural world, the technical environment and the transformation of social relations and spiritual and moral values are highlighted. Trends in the labor market are noted such as job cuts due to automation, the polarization of the labor market for highly paid intellectual workers and cheap physical strength, the emergence of new professions that require special education in several areas, changes in the organization of labor by the type of network interaction, the emergence of new forms of employment - temporary, deprived of guarantees and infringing on social rights. The future labor market is characterized as fragmented and isolated. The conclusion is drawn about the need for a humanistic approach in assessing the prospects of technological development.

Sign in / Sign up

Export Citation Format

Share Document