scholarly journals Observability of Complex Systems by Means of Relative Distances Between Homological Groups

2020 ◽  
Vol 8 ◽  
Author(s):  
Juan G. Diaz Ochoa
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Quantitative Analysis ◽  
Complex Systems ◽  
Large Datasets ◽  
Mobile Sensors ◽  
Good Strategy ◽  
Data Intensive ◽  
Persistent Topology ◽  
Definition Of

It is common to consider using a data-intensive strategy as a way to develop systemic and quantitative analysis of complex systems so that data collection, sampling, standardization, visualization, and interpretation can determine how causal relationships are identified and incorporated into mathematical models. Collecting enough large datasets seems to be a good strategy in reducing bias of the collected data; but persistent and dynamic anomalies in the data structure, generated from variations in intrinsic mechanisms, can actually induce persistent entropy thus affecting the overall validity of quantitative models. In this research, we are introducing a method based on the definition of homological groups that aims at evaluating this persistent entropy as a complexity measure to estimate the observability of the systems. This method identifies patterns with persistent topology, extracted from the combination of different time series and clustering them to identify persistent bias in the data. We tested this method on accumulated data from patients using mobile sensors to measure the response of physical exercise in real-world conditions outside the lab. With this method, we aim to better stratify time series and customize models in complex biological systems.

scholarly journals Relative distances between homology groups to assess persistent defects in time series

2019 ◽  
Author(s):  
Juan G. Diaz Ochoa
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Large Datasets ◽  
Mobile Sensors ◽  
Patient Stratification ◽  
Good Strategy ◽  
Homology Groups ◽  
Data Intensive ◽  
Persistent Anomalies ◽  
Definition Of

AbstractIt is common to consider a data-intensive strategy to be an appropriate way to develop systemic analyses in biology and physiology. Therefore, options for data collection, sampling, standardization, visualization, and interpretation determine how causes are identified in time series to build mathematical models. However, there are often biases in the collected data that can affect the validity of the model: while collecting enough large datasets seems to be a good strategy for reducing the bias of the collected data, persistent and dynamical anomalies in the data structure can affect the overall validity of the model. In this work we present a methodology based on the definition of homological groups to evaluate persistent anomalies in the structure of the sampled time series. In this evaluation relevant patterns in the combination of different time series are clustered and grouped to customize the identification of causal relationships between parameters. We test this methodology on data collected from patients using mobile sensors to test the response to physical exercise in real-world conditions and outside the lab. With this methodology we plan to obtain a patient stratification of the time series to customize models in medicine.

scholarly journals Влияние шума наблюдений на эффект ложной связи между осцилляторами при оценивании по временным рядам

2019 ◽  
Vol 45 (16) ◽  
pp. 6
Author(s):  
С.Н. Крылов ◽  
Д.А. Смирнов ◽  
Б.П. Безручко
Keyword(s):  
Time Series ◽  
Quantitative Analysis ◽  
Complex Systems ◽  
Granger Causality ◽  
Mutual Coupling ◽  
Noise Levels ◽  
Observational Noise

The practice of identifying structure of couplings between elements of complex systems from experimental recordings of their oscillations (time series) using the Wiener – Granger causality method has revealed a number of problems that prevent one from obtaining reliable results. In particular, the presence of observational noise can lead to the “effect of spurious coupling”, i.e. to inference of mutual coupling between two elements that are actually coupled in a unidirectional way. A quantitative analysis of this phenomenon is carried out and recommendations allowing one to reduce its probability are presented. It is shown that the effect typically takes place only for large noise levels comparable to the level of observed oscillations. However, we have also singled out less typical situations where the effect occurs at much weaker noise.

scholarly journals AnoA: A Framework for Analyzing Anonymous Communication Protocols

2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Michael Backes ◽  
Aniket Kate ◽  
Praveen Manoharan ◽  
Sebastian Meiser ◽  
Esfandiar Mohammadi
Keyword(s):  
Quantitative Analysis ◽  
Complex Systems ◽  
Differential Privacy ◽  
The Internet ◽  
Anonymous Communication ◽  
Universal Composability ◽  
Multiplicative Factor ◽  
Generic Framework ◽  
The Subject ◽  
Definition Of

Anonymous communication (AC) protocols such as the widely used Tor network have been designed to provide anonymity over the Internet to their participating users. While AC protocols have been the subject of several security and anonymity analyses in the last years, there still does not exist a framework for analyzing these complex systems and their different anonymity properties in a unified manner.   In this work we present AnoA: a generic framework for defining, analyzing, and quantifying anonymity properties for AC protocols. In addition to quantifying the (additive) advantage of an adversary in an indistinguishability-based definition, AnoA uses a multiplicative factor, inspired from differential privacy. AnoA enables a unified quantitative analysis of well-established anonymity properties, such as sender anonymity, sender unlinkability, and relationship anonymity. AnoA modularly specifies adversarial capabilities by a simple wrapper-construction, called adversary classes. We examine the structure of these adversary classes and identify conditions under which it suffices to establish anonymity guarantees for single messages in order to derive guarantees for arbitrarily many messages. This then leads us to the definition of Plug’n’Play adversary classes (PAC), which are easy-to-use, expressive, and satisfy this condition. We prove that our framework is compatible with the universal composability (UC) framework and show how to apply AnoA to a simplified version of Tor against passive adversaries, leveraging a recent realization proof in the UC framework.

T-Cube: A Data Structure for Fast Extraction of Time Series from Large Datasets

2007 ◽  
Author(s):  
Maheshkumar Sabhnani ◽  
Andrew W. Moore ◽  
Artur W. Dubrawski
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Large Datasets ◽  
Fast Extraction

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

scholarly journals Characterization of Spent Ni-MH Batteries

2012 ◽  
Vol 730-732 ◽  
pp. 569-574
Author(s):  
Marta Cabral ◽  
Fernanda Margarido ◽  
Carlos A. Nogueira
Keyword(s):  
Quantitative Analysis ◽  
Electrode Materials ◽  
Phase Identification ◽  
Recycling Process ◽  
X Ray ◽  
Leaching Experiments ◽  
Plastic Components ◽  
Definition Of ◽  
Physical And Chemical

Spent Ni-MH batteries are not considered too dangerous for the environment, but they have a considerable economical value due to the chemical composition of electrodes which are highly concentrated in metals. The present work aimed at the physical and chemical characterisation of spent cylindrical and thin prismatic Ni-MH batteries, contributing for a better definition of the recycling process of these spent products. The electrode materials correspond to more than 50% of the batteries weight and contain essentially nickel and rare earths (RE), and other secondary elements (Co, Mn, Al). The remaining components are the steel parts from the external case and supporting grids (near 30%) containing Fe and Ni, and the plastic components (<10%). Elemental quantitative analysis showed that the electrodes are highly concentrated in metals. Phase identification by X-ray powder diffraction combined with chemical analysis and leaching experiments allowed advancing the electrode materials composition. The cathode is essentially constituted by 6% metallic Ni, 66% Ni(OH)2, 4.3% Co(OH)2 and the anode consists mainly in 62% RENi5 and 17% of substitutes and/or additives such as Co, Mn and Al.

IDENTIFYING DISTINCT STOCHASTIC DYNAMICS FROM CHAOS: A STUDY ON MULTIMODAL NEURAL FIRING PATTERNS

2009 ◽  
Vol 19 (02) ◽  
pp. 453-485 ◽  
Author(s):  
MINGHAO YANG ◽  
ZHIQIANG LIU ◽  
LI LI ◽  
YULIN XU ◽  
HONGJV LIU ◽  
...  
Keyword(s):  
Time Series ◽  
Limit Cycle ◽  
Stochastic Dynamics ◽  
Nonlinear Time Series ◽  
Special Importance ◽  
Neuronal System ◽  
Neural Firing ◽  
Firing Patterns ◽  
Theoretical Simulation ◽  
Definition Of

Some chaotic and a series of stochastic neural firings are multimodal. Stochastic multimodal firing patterns are of special importance because they indicate a possible utility of noise. A number of previous studies confused the dynamics of chaotic and stochastic multimodal firing patterns. The confusion resulted partly from inappropriate interpretations of estimations of nonlinear time series measures. With deliberately chosen examples the present paper introduces strategies and methods of identification of stochastic firing patterns from chaotic ones. Aided by theoretical simulation we show that the stochastic multimodal firing patterns result from the effects of noise on neuronal systems near to a bifurcation between two simpler attractors, such as a point attractor and a limit cycle attractor or two limit cycle attractors. In contrast, the multimodal chaotic firing trains are generated by the dynamics of a specific strange attractor. Three systems were carefully chosen to elucidate these two mechanisms. An experimental neural pacemaker model and the Chay mathematical model were used to show the stochastic dynamics, while the deterministic Wang model was used to show the deterministic dynamics. The usage and interpretation of nonlinear time series measures were systematically tested by applying them to firing trains generated by the three systems. We successfully identified the distinct differences between stochastic and chaotic multimodal firing patterns and showed the dynamics underlying two categories of stochastic firing patterns. The first category results from the effects of noise on the neuronal system near a Hopf bifurcation. The second category results from the effects of noise on the period-adding bifurcation between two limit cycles. Although direct application of nonlinear measures to interspike interval series of these firing trains misleadingly implies chaotic properties, definition of eigen events based on more appropriate judgments of the underlying dynamics leads to accurate identifications of the stochastic properties.

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

Distributed data structure templates for data-intensive remote sensing applications

2012 ◽  
Vol 25 (12) ◽  
pp. 1784-1797 ◽  
Author(s):  
Yan Ma ◽  
Lizhe Wang ◽  
Dingsheng Liu ◽  
Tao Yuan ◽  
Peng Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Structure ◽  
Distributed Data ◽  
Data Intensive ◽  
Sensing Applications ◽  
Remote Sensing Applications
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