scholarly journals Estimating Conditional Transfer Entropy in Time Series Using Mutual Information and Nonlinear Prediction

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1124
Author(s):  
Payam Shahsavari Baboukani ◽  
Carina Graversen ◽  
Emina Alickovic ◽  
Jan Østergaard
Keyword(s):  
Time Series ◽  
State Of The Art ◽  
Difficult Case ◽  
Nonlinear Prediction ◽  
False Detection ◽  
Uniform Embedding ◽  
Embedding Technique ◽  
New Information ◽  
Highly Correlated ◽  
Intracranial Electroencephalography

We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new information and improvement of the prediction accuracy provided by the variables. Then, using a greedy approach, the most informative subsets are selected in an iterative way. The algorithm terminates, when the highest ranked variable is not able to significantly improve the accuracy of the prediction as compared to that obtained using the existing selected subsets. In a simulation study, we compare our estimator to existing state-of-the-art methods at different data lengths and directed dependencies strengths. It is demonstrated that the proposed estimator has a significantly higher accuracy than that of existing methods, especially for the difficult case, where the data are highly correlated and coupled. Moreover, we show its false detection of directed dependencies due to instantaneous couplings effect is lower than that of existing measures. We also show applicability of the proposed estimator on real intracranial electroencephalography data.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching

2021 ◽  
Author(s):  
Lars Kegel ◽  
Claudio Hartmann ◽  
Maik Thiele ◽  
Wolfgang Lehner
Keyword(s):  
Time Series ◽  
State Of The Art ◽  
Dimensional Space ◽  
Symbolic Aggregate Approximation ◽  
Current State ◽  
Optimal Representation ◽  
Symbolic Approximation ◽  
Low Dimensional ◽  
Deterministic Behavior ◽  
Support Time

AbstractProcessing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.

scholarly journals Enhancing the performance of cancer text classification model based on cancer hallmarks

2021 ◽  
Vol 10 (2) ◽  
pp. 316
Author(s):  
Noha Ali ◽  
Ahmed H. AbuEl-Atta ◽  
Hala H. Zayed
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Classification Model ◽  
Biomedical Text ◽  
Cancer Hallmarks ◽  
Embedding Technique ◽  
Proposed Model ◽  
Biomedical Text Classification

<span id="docs-internal-guid-cb130a3a-7fff-3e11-ae3d-ad2310e265f8"><span>Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.</span></span>

scholarly journals Assessing the accuracy of remotely-sensed fire datasets across the Southwestern Mediterranean basin

2020 ◽  
Author(s):  
Luiz Felipe Galizia ◽  
Thomas Curt ◽  
Renaud Barbero ◽  
Marcos Rodrigues
Keyword(s):  
Mediterranean Basin ◽  
Quantitative Estimate ◽  
State Of The Art ◽  
Warm Season ◽  
Burned Area ◽  
Fire Size ◽  
Northern Iberian Peninsula ◽  
Fire Atlas ◽  
Highly Correlated ◽  
Size Threshold

Abstract. Recently, many remote-sensing (RS) based datasets providing features of individual fire events from gridded global burned area products have been released. Although very promising, these datasets still lack a quantitative estimate of their accuracy with respect to historical ground-based fire databases. Here, we compared three state-of-the-art RS datasets (Fire Atlas, FRY and GlobFire) with high-quality ground databases compiled by regional fire agencies (AG) across the Southwestern Mediterranean basin (2005–2015). We assessed the spatial and temporal accuracy in estimated RS burned area (BA) and number of fires (NF) aggregated at monthly and 0.25° resolutions, considering different individual fire size thresholds ranging from 1 to 500 ha. Our results show that RS datasets were highly correlated with AG in terms of monthly BA and NF but severely underestimated both (by 38 % and 96 %, respectively) when considering all fires > 1 ha. Stronger agreement was found when increasing the fire size threshold, with fires > 100 ha denoting higher correlation and much lower error (BA 10 %; NF 35%). The agreement between RS and AG was also the highest during the warm season (May to October) in particular across the regions with greater fire activity such as the Northern Iberian Peninsula. The Fire Atlas displayed a slightly better performance, with a lower relative error, although uncertainty in gridded BA product largely outpaced uncertainties across the RS datasets. Overall, our findings suggest a reasonable agreement between RS and ground-based datasets for fires larger than 100 ha, but care is needed when examining smaller fires at regional scales.

scholarly journals On international migration: A review essay

2017 ◽  
Vol 44 (3-4) ◽  
pp. 200
Author(s):  
Anthony C. Masi
Keyword(s):  
Social Policy ◽  
International Migration ◽  
Review Essay ◽  
State Of The Art ◽  
Immigrant Integration ◽  
Empirical Information ◽  
New Information ◽  
Four Books ◽  
Unanticipated Consequences ◽  
Do So

International migration continues to reshape our world, sometimes in predictable ways, but often with unanticipated consequences. The four books reviewed here provide new information and important insights regarding migration and migrant adjustment. They do so either by dealing with the policy dimension of this vast topic (Freeman and Mirilovic; Phillimore) or by delving deeply into the issue of immigrant integration (Scholten et al.; Waters and Gerstein Pineau). These editors took four different approaches to their task: (1) a compilation of already published works on the topic (Phillimore); (2) original pieces on topics or countries but following a predetermined framework (Scholten, et al.); (3) chapters designed to test theories against available empirical information (Freeman and Mirilovic); and (4) a comprehensive group-written “state of the art” for a single country (Waters and Gerstein Pineau). Together, the books provide an impressive array of scholarship from a variety of disciplinary perspectives on the links between migration and social policy and on immigrant integration.

Pathological Fire-Setting 1951–1991: A Review

1994 ◽  
Vol 34 (1) ◽  
pp. 4-20 ◽  
Author(s):  
W Barnett ◽  
M Spitzer
Keyword(s):  
State Of The Art ◽  
Research Field ◽  
Theoretical Interpretation ◽  
Original Research ◽  
Medline Search ◽  
New Information ◽  
Poor Understanding ◽  
Conventional Literature ◽  
Practical Implications ◽  
Fire Setting

The purpose of this article is to review the whole literature on pathological fire-setting and related fields since 1951 in order to present a state of the art picture of our contemporary knowledge about this phenomenon. Papers were initially selected by using a Medline search. From the articles obtained in this way the references had to be pursued, because many relevant papers and books in this research field of different scientific and practical disciplines are not listed in conventional literature services. Finally, only those contributions were selected which provided new information when published, by original research, theoretical interpretation or practical implications. The last forty years have brought a growing body of data and understanding — especially concerning pathogical fire-setting by children — etiology, and therapy, which often proves successful. The situation remains unclear for arson by psychologically disturbed adults and we still have a poor understanding of arson without apparent motive. There is a conflict of opinion as to whether adult fire-setters are suitable for therapy and therapeutic efforts dealing with psychologically disturbed adults are rare. With increasing knowledge about child fire-setting and its successful treatment, etiological and therapeutic models for adult fire-setting behaviour may be developed. Research into the latter area should focus on both biographical and social conditions of development from childhood on as well as biological measures. Both will be reviewed here.

Framework for TCAD augmented machine learning on multi- I–V characteristics using convolutional neural network and multiprocessing

2021 ◽  
Vol 42 (12) ◽  
pp. 124101
Author(s):  
Thomas Hirtz ◽  
Steyn Huurman ◽  
He Tian ◽  
Yi Yang ◽  
Tian-Ling Ren
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Information Technologies ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Sufficient Data ◽  
Learning Models ◽  
Simulation Tools ◽  
New Information

Abstract In a world where data is increasingly important for making breakthroughs, microelectronics is a field where data is sparse and hard to acquire. Only a few entities have the infrastructure that is required to automate the fabrication and testing of semiconductor devices. This infrastructure is crucial for generating sufficient data for the use of new information technologies. This situation generates a cleavage between most of the researchers and the industry. To address this issue, this paper will introduce a widely applicable approach for creating custom datasets using simulation tools and parallel computing. The multi-I–V curves that we obtained were processed simultaneously using convolutional neural networks, which gave us the ability to predict a full set of device characteristics with a single inference. We prove the potential of this approach through two concrete examples of useful deep learning models that were trained using the generated data. We believe that this work can act as a bridge between the state-of-the-art of data-driven methods and more classical semiconductor research, such as device engineering, yield engineering or process monitoring. Moreover, this research gives the opportunity to anybody to start experimenting with deep neural networks and machine learning in the field of microelectronics, without the need for expensive experimentation infrastructure.

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