Highly comparative feature-based time-series classification

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation

Multi-Sensor Event Detection using Shape Histograms

Vehicular sensor data consists of multiple time-series arising from a number of sensors. Using such multi-sensor data we would like to detect occurrences of specific events that vehicles encounter, e.g., corresponding to particular maneuvers that a vehicle makes or conditions that it encounters. Events are characterized by similar waveform patterns re-appearing within one or more sensors. Further such patterns can be of variable duration. In this work, we propose a method for detecting such events in time-series data using a novel feature descriptor motivated by similar ideas in image processing. We define the shape histogram: a constant dimension descriptor that nevertheless captures patterns of variable duration. We demonstrate the efficacy of using shape histograms as features to detect events in an SVM-based, multi-sensor, supervised learning scenario, i.e., multiple time-series are used to detect an event. We present results on real-life vehicular sensor data and show that our technique performs better than available pattern detection implementations on our data, and that it can also be used to combine features from multiple sensors resulting in better accuracy than using any single sensor. Since previous work on pattern detection in time-series has been in the single series context, we also present results using our technique on multiple standard time-series datasets and show that it is the most versatile in terms of how it ranks compared to other published results

Discovery of Non-Persistent Motif Mixtures using MRST (Multivariate Rhythm Sequence Technique)

In this paper we present a prototype to discover the unsupervised repeating temporary perception in a time series. The purpose of this work is to control the case of random variable and to find out the measurements caused by the phenomena of simultaneous synchronization. The proposed model has used the non-parametric Bayesian technique to trace the motifs and their occurrences in the data documents. We introduce the Multivariate Rhythm Sequence Technique (MRST) method to find the rebound and repeated motifs and their instance in every document automatically and simultaneously. This model is used in wide range of applications and concentrates on datasets from different modalities.The video footages from non-dynamic cameras and data location bounded to the motif-mining server. The high semantic internal representation of the method gives advantage in operation such as event counting or analyse the sc8BA5;. We used the sample images and videos from New York City traffic data for experiments with and the results shows better performance than the existing motif mixtures analysis in the time series

The Tannhäuser Gate. Architecture in science fiction films of the second half of the 20th and the beginning of the 21st century as a component of utopian and dystopian projections of the future.

The Tannhäuser Gate. Architecture in science fiction films of the second half of the 20th and the beginning of the 21st century as a component of utopian and dystopian projections of the future. The films of science fiction genre from the second half of the 20th and early 21st century contained many visions of the future, which were at the same time a reflection on the achievements and deficiencies of modern times. In 1960s, cinematographic works were dominated by optimism and faith in the possibility of never-ending progress. The disappearance of political divisions between the blocs of states and the joint exploration of the cosmos was foreseen. The designers undertook cooperation with scientists, which manifested itself in showing cosmic constructions far exceeding the real technical capabilities. Starting from the 1970s, pessimism and the belief that the future will bring, above all, the intensification of negative phenomena of the present began to grow in films. Fears of the future were connected with indicating various possible defects and insoluble contradictions between them. When, therefore, some dystopian visions illustrated the threat of increase in crime, others depicted the future as saturated with state control mechanisms and the prevalence of surveillance. The fears shown on the screens were also aroused by the growth of large corporations, especially by their gaining political influence or staying outside the system of democracy. The authors of the films also presented their suspicions related to the creation of new types of weapons by corporations, the use of which might breach the current legal norms. Particular objections concerned research on biological weapons and the possible spread of lethal viruses. The development of robotics and research into artificial intelligence, which must have resulted in the appearance of androids and inevitable tensions in their relations with humans, also triggered fear. Another problem for film-makers has become hybrids that are a combination of people and electronic parts. Scriptwriters and directors likewise considered the development of genetic engineering, which led to the creation of mutant human beings. A number of film dystopias contemplated the possibility of the collapse of democratic systems and the development of authoritarian regimes in their place, often based on broad public support. This kind of dystopia also includes films presenting the consequences of contemporary hedonism and consumerism. The problem is, however, that works critical of these phenomena were themselves advertisements for attractive products

Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

Translating animal art: Salin’s Style I and Anglo-Saxon cast saucer brooches

Saucer brooches are actually the most frequent bearers of Salin’s Style I in England, but have been overlooked because of perceptions of the derivative nature of their ornament. This paper seeks to rectify the inbalance by accepting that translation (in a physical and linguistic sense) is the key to understanding both the form which Style I took on saucer brooches and potentially its meanings. The study is based on 281 cast saucer brooches (almost half the total corpus of the type): half feature zoomorphic decoration on its own and half combine zoomorphic and geometric motifs. The animal art is characterised in terms of motifs, presentation and composition. While ‘coherent’ motifs, recognisable from the classic, early repertoire of Style I, are reasonably well represented, attention is mostly given to the way motifs and designs were transformed, involving both established principles of Style I design (abbreviation, addition, re-assembly and ambiguity) and adaptation to the pre-existing, geometric-based, saucer-brooch tradition. Although calibrating the pace of change (devolution?) is difficult, the process can be shown to have endured throughout the 6th century and to have been most practised in western Anglo-Saxon districts. Explaining the meaning and role of this transformed animal art is obviously hard, but it is argued that it was the result not of ignorance or carelessness, but a deliberate choice. By adopting images from Northern Germanic mythology and blending them with other (Roman and Saxon) symbols, meanings were both perpetuated and subtly altered, enabling important kindred outside Kent and the main Anglian areas to negotiate their own identity and affiliations