Practical Considerations for Use of Causation Entropy in Sparsity Identification

2021 ◽  
Author(s):  
Jared Elinger ◽  
Jonathan Rogers
Keyword(s):  
Nonlinear Systems ◽  
A Priori ◽  
Estimation Accuracy ◽  
Practical Implementation ◽  
Error Sources ◽  
Entropy Estimation ◽  
Entropy Estimates ◽  
General Guidance ◽  
Selection Of ◽  
Key Questions

Abstract The selection of model structure is an important step in system identification for nonlinear systems in cases where the model form is not known a priori. This process, sometimes called covariate selection or sparsity identification, involves the selection of terms in the dynamic model and is performed prior to parameter estimation. Previous work has shown the applicability of an information theory quantity known as causation entropy in performing sparsity identification. While prior work established the overall feasibility of using causation entropy to eliminate extraneous terms in a model, key questions remained regarding practical implementation. This paper builds on previous work to explore key practical considerations of causation entropy sparsity identification. First, the effect of data size is explored through both analysis and simulation, and general guidance is provided on how much data is necessary to produce accurate causation entropy estimates. Second, the effects of measurement noise and model discretization error are investigated, showing that both cause degradation of the causation entropy estimation accuracy but in opposite ways. These practical effects and trends are illustrated on several example nonlinear systems. Overall, results show that the causation entropy approach is a practical technique for sparsity identification particularly in light of the guidelines presented here for data size selection and handling of error sources.

Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

2020 ◽  
pp. 65-72
Author(s):  
V. V. Savchenko ◽  
A. V. Savchenko
Keyword(s):  
Speech Signal ◽  
Communication Channel ◽  
Speaker Identification ◽  
A Priori ◽  
Small Samples ◽  
Practical Implementation ◽  
Identification System ◽  
New Information ◽  
Discrimination Criterion ◽  
A Priori Uncertainty

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

PERSONIFICATION OF STUDENTS INDEPENDENT WORK ON THE FORMATION OF THE ABILITY TO SOLVE COMPUTATIONAL PROBLEMS ON THE BASIS OF AUTOMATED TRAINING AND DIAGNOSTIC SYSTEM

2018 ◽  
pp. 26-32
Author(s):  
N. I. Pak ◽  
E. V. Asaulenko
Keyword(s):  
Diagnostic System ◽  
Educational Process ◽  
Box Model ◽  
Self Management ◽  
Practical Implementation ◽  
Learning Tools ◽  
Theoretical Rationale ◽  
E Learning ◽  
Independent Work ◽  
Selection Of

The relevance of the study under consideration is due to the need to increase the efficiency of students independent work in solving computational problems. A theoretical rationale is proposed and the practical implementation of an automated training and diagnostic system for the formation of skills to solve problems according to the “white box” model is described. The leading idea of the study is the construction of mental schemes for a given topic, which allow to visualize the dynamics of changes in the learner’s level of ability to solve computational problems. The methods of accounting for forgetting educational information and methods of personalized selection of tasks are substantiated. The site for self-management of user independent work is available at the link: http://msbx.ru. The materials of the article are of practical value for teachers who use e-learning tools in the educational process.

Innovative Approach to Information Search by Example of a Patent Analysis of an Important Substitution Plan

2020 ◽  
pp. 143-157
Author(s):  
Maria A. Milkova
Keyword(s):  
Information Search ◽  
Topic Modeling ◽  
Cognitive Biases ◽  
A Priori ◽  
Import Substitution ◽  
Innovative Approach ◽  
Iterative Search ◽  
Comprehensive Picture ◽  
Priori Information ◽  
Selection Of

Nowadays the process of information accumulation is so rapid that the concept of the usual iterative search requires revision. Being in the world of oversaturated information in order to comprehensively cover and analyze the problem under study, it is necessary to make high demands on the search methods. An innovative approach to search should flexibly take into account the large amount of already accumulated knowledge and a priori requirements for results. The results, in turn, should immediately provide a roadmap of the direction being studied with the possibility of as much detail as possible. The approach to search based on topic modeling, the so-called topic search, allows you to take into account all these requirements and thereby streamline the nature of working with information, increase the efficiency of knowledge production, avoid cognitive biases in the perception of information, which is important both on micro and macro level. In order to demonstrate an example of applying topic search, the article considers the task of analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1,500 products and technologies for the proposed import substitution. The use of patent search based on topic modeling allows to search immediately by the blocks of a priori information – terms of industrial plans for import substitution and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to visually obtain more detailed information about which groups of products and technologies have been patented.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals Microbial Electrochemical Systems: Principles, Construction and Biosensing Applications

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1279
Author(s):  
Rabeay Y.A. Hassan ◽  
Ferdinando Febbraio ◽  
Silvana Andreescu
Keyword(s):  
Electrode Materials ◽  
Electrical Power ◽  
Chemical Energy ◽  
Practical Implementation ◽  
Development Status ◽  
Electrochemical Systems ◽  
Recent Developments ◽  
Energy Environment ◽  
Microbial Electrochemical Systems ◽  
Selection Of

Microbial electrochemical systems are a fast emerging technology that use microorganisms to harvest the chemical energy from bioorganic materials to produce electrical power. Due to their flexibility and the wide variety of materials that can be used as a source, these devices show promise for applications in many fields including energy, environment and sensing. Microbial electrochemical systems rely on the integration of microbial cells, bioelectrochemistry, material science and electrochemical technologies to achieve effective conversion of the chemical energy stored in organic materials into electrical power. Therefore, the interaction between microorganisms and electrodes and their operation at physiological important potentials are critical for their development. This article provides an overview of the principles and applications of microbial electrochemical systems, their development status and potential for implementation in the biosensing field. It also provides a discussion of the recent developments in the selection of electrode materials to improve electron transfer using nanomaterials along with challenges for achieving practical implementation, and examples of applications in the biosensing field.

scholarly journals Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

2018 ◽  
Vol 10 (8) ◽  
pp. 1285 ◽  
Author(s):  
Reza Attarzadeh ◽  
Jalal Amini ◽  
Claudia Notarnicola ◽  
Felix Greifeneder
Keyword(s):  
Soil Moisture ◽  
Optical Data ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Practical Applications ◽  
Initial Stage ◽  
Object Based ◽  
Single Polarization ◽  
Selection Of

This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

WTLS-Bayes Filter-based Relative Pose Estimation for Space Non-cooperative Unknown Target Without a Priori Knowledge

2021 ◽  
Author(s):  
CHENGGUANG ZHU ◽  
zhongpai Gao ◽  
Jiankang Zhao ◽  
Haihui Long ◽  
Chuanqi Liu
Keyword(s):  
Pose Estimation ◽  
A Priori ◽  
Convergence Time ◽  
Estimation Accuracy ◽  
A Priori Knowledge ◽  
One Step ◽  
Relative Pose ◽  
The One ◽  
Negative Factors ◽  
Priori Knowledge

Abstract The relative pose estimation of a space noncooperative target is an attractive yet challenging task due to the complexity of the target background and illumination, and the lack of a priori knowledge. Unfortunately, these negative factors have a grave impact on the estimation accuracy and the robustness of filter algorithms. In response, this paper proposes a novel filter algorithm to estimate the relative pose to improve the robustness based on a stereovision system. First, to obtain a coarse relative pose, the weighted total least squares (WTLS) algorithm is adopted to estimate the relative pose based on several feature points. The resulting relative pose is fed into the subsequent filter scheme as observation quantities. Second, the classic Bayes filter is exploited to estimate the relative state except for moment-of-inertia ratios. Additionally, the one-step prediction results are used as feedback for WTLS initialization. The proposed algorithm successfully eliminates the dependency on continuous tracking of several fixed points. Finally, comparison experiments demonstrate that the proposed algorithm presents a better performance in terms of robustness and convergence time.

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