scholarly journals A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

2019 ◽  
Author(s):  
Ashkan Faghiri ◽  
Armin Iraji ◽  
Eswar Damaraju ◽  
Jessica Turner ◽  
Vince D. Calhoun
Keyword(s):  
Pearson Correlation ◽  
Low Frequency ◽  
Sliding Window ◽  
Group Differences ◽  
Unified Approach ◽  
Data Set ◽  
New Approach ◽  
Window Approach ◽  
Dynamic Connectivity ◽  
Activity Domain

AbstractStudying dynamic functional connectivity (dFC) has been the focus of many studies in recent years. The most commonly used estimator for dFC uses a sliding window in combination with a connectivity estimator such as Pearson correlation. Here, we propose a new approach to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. This approach which we call filter banked connectivity (FBC), implements frequency tiling directly in the connectivity domain contrary to other studies where frequency tiling is done in the activity domain. This leads to more accurate modeling, and a unified approach to capture connectivity ranging from static to highly dynamic, avoiding the need to pick a specific band as in a sliding window approach.First, we demonstrated that our proposed approach, can estimate connectivity at frequencies that sliding window approach fails. Next we evaluated the ability of the approach to identify group differences by using the FBC approach to estimate dFNC in a resting fMRI data set including schizophrenia patients (SZ, n=151) and typical controls (TC, n=163). To summarize the results, we used k-means to cluster the FBC values into different clusters. Some states showed very weak low frequency strength and as such SWPC was not well suited to capture them. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies and engaging the default mode network and its anticorrelations with other networks compared to TCs which spent more time in lower frequency states which primarily includes strong intercorrelations within the sensorimotor domains. In summary, the proposed approach offers a novel way to estimate connectivity while unifying static and dynamic connectivity analyses and can provide additional otherwise missed information about the frequency profile of connectivity patterns.

scholarly journals A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

2020 ◽  
pp. 1-27
Author(s):  
Ashkan Faghiri ◽  
Armin Iraji ◽  
Eswar Damaraju ◽  
Jessica Turner ◽  
Vince D. Calhoun
Keyword(s):  
Low Frequency ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Unified Approach ◽  
Full Spectrum ◽  
Additional Information ◽  
Multiple Frequencies ◽  
Network States ◽  
Window Approach ◽  
Dynamic Connectivity

Static and dynamic functional network connectivity (FNC) are typically studied separately, which makes us unable to see the full spectrum of connectivity in each analysis. Here, we propose an approach called filter-banked connectivity (FBC) to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. First, we demonstrate that FBC can estimate connectivity across multiple frequencies missed by a sliding-window approach. Next, we use FBC to estimate FNC in a resting-state fMRI dataset including schizophrenia patients (SZ) and typical controls (TC). The FBC results are clustered into different network states. Some states showed weak low-frequency strength and as such were not captured in the window-based approach. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies compared with TCs who spent more time in lower frequency states. Finally, we show that FBC enables us to analyze static and dynamic connectivity in a unified way. In summary, FBC offers a novel way to unify static and dynamic connectivity analyses and can provide additional information about the frequency profile of connectivity patterns.

scholarly journals Cognitive Similarity-Based Collaborative Filtering Recommendation System

2020 ◽  
Vol 10 (12) ◽  
pp. 4183 ◽  
Author(s):  
Luong Vuong Nguyen ◽  
Min-Sung Hong ◽  
Jason J. Jung ◽  
Bong-Soo Sohn
Keyword(s):  
Performance Evaluation ◽  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Cognitive Network ◽  
Data Set ◽  
New Approach ◽  
Layered Architecture ◽  
F Measure

This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.

On the Problem of Lexicographical Representation of Productive Phraseological Patterns (As Shown by Modern Greek Constructional Phrasemes)

2021 ◽  
Vol 20 (9) ◽  
pp. 34-43
Author(s):  
Elizaveta S. Onufrieva ◽  
Irina V. Tresorukova
Keyword(s):  
Low Frequency ◽  
Variable Component ◽  
Unified Approach ◽  
Common Pattern ◽  
New Approach ◽  
Modern Greek ◽  
Wide Range ◽  
Form Part ◽  
The Common ◽  
Linguistic Usage

This paper discusses the problems of lexicographical representation of Modern Greek constructional phrasemes – productive phraseological patterns with one or more variable components (slots). The analysis of Modern Greek general and phraseological dictionaries has shown that, in Modern Greek lexicography, there is no unified approach towards the description of this type of phraseologisms. One of the significant problems associated with lexicographical treatment of Modern Greek constructional phrasemes is that some of them are registered in dictionaries as fully fixed expressions with their slot(s) filled with a specific lexeme or a specific proposition, without any indication that these expressions possess a variable component. Such lexicographical representation of productive phraseological patterns does not reflect the real linguistic usage and does not allow the reader of the dictionary to understand that the expressions described in the dictionary as fully fixed show considerable variation and possess one or two slots that can be filled with a wide range of words or word combinations. The corpus analysis of the constructional phraseme Ούτε να Ρ (literally, ‘neither if’), which is registered in Modern Greek dictionaries in five different, all fully lexically specified forms, has shown that the specific realizations of this productive phraseological pattern included in the dictionaries either have relatively low frequency of occurrence in the corpus, or are not encountered in the corpus at all. Other realizations of this phraseological pattern account for over 92 % of all the cases of its use in the corpus, but the common pattern behind them can hardly be identified with the help of the existing lexicographical descriptions, as it is registered in the dictionaries under the lemmas of five different lexemes that do not form part of its fixed component. Based on the findings of this study, the paper raises the issue of developing a new approach towards the description of productive phraseological patterns that currently pose a significant challenge for adequate lexicographical representation.

A New Approach for the Analysis of Mixture Toxicity Data

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2345-2348 ◽  
Author(s):  
C. N. Haas
Keyword(s):  
Quantitative Analysis ◽  
Mixture Toxicity ◽  
New Method ◽  
Metal Exposure ◽  
Positive Interactions ◽  
Toxicity Data ◽  
Data Set ◽  
New Approach ◽  
Negative Interactions

A new method for the quantitative analysis of multiple toxicity data is described and illustrated using a data set on metal exposure to copepods. Positive interactions are observed for Ni-Pb and Pb-Cr, with weak negative interactions observed for Ni-Cr.

An approach for document retrieval using cluster-based inverted indexing

2021 ◽  
pp. 016555152110184
Author(s):  
Gunjan Chandwani ◽  
Anil Ahlawat ◽  
Gaurav Dubey
Keyword(s):  
High Performance ◽  
Clustering Algorithm ◽  
Pearson Correlation ◽  
Relevant Information ◽  
Document Retrieval ◽  
Bhattacharyya Distance ◽  
Data Set ◽  
Query Matching ◽  
Inverted Indexing ◽  
Query Optimisation

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

scholarly journals Impact of Working Capital Management on Corporate Profitability- Empirical Evidence from Pharmaceutical Industry of Bangladesh

2018 ◽  
Vol 10 (9) ◽  
pp. 136
Author(s):  
Rakibul Islam ◽  
Mohammad Emdad Hossain ◽  
Mohammad Nazmul Hoq ◽  
Md. Morshedul Alam
Keyword(s):  
Regression Analysis ◽  
Correlation Matrix ◽  
Negative Impact ◽  
Pearson Correlation ◽  
Capital Management ◽  
Working Capital ◽  
Data Set ◽  
Working Capital Management ◽  
Pharmaceutical Firms ◽  
The Impact

Working capital management plays centric role in enhancing operational efficiency and their ultimate profitability. Globally financial managers have been searching the proper way on how to utilize working capital components which prolong profitability. The purpose of this study is to assess the impact of working capital components on profitability indicators of selected pharmaceutical firms in Bangladesh. The paper used financial data of 9 pharmaceutical firms listed in Dhaka stock exchange (DSE) covered 2011-2015. Two methods were used in this study for analysis data set. Firstly, to measure the relationship between selected variables Pearson Correlation matrix was used. Secondly, multiple regression analysis was used to investigate the impact working capital components on profitability of selected pharmaceutical firms. The study also conducted Durbin Watson test to assess autocorrelation of selected variables. In this study the correlation matrix identified a negative correlation between working capital components and profitability, whereas regression analysis found number of days account receivable (AR) had significant positive and current ratio (CR) and debt ratio (DR) had appeared a significant negative impact on profitability.

Poststack impedance inversion considering the diffractive component of the wavefield

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. R11-R28 ◽  
Author(s):  
Kun Xiang ◽  
Evgeny Landa
Keyword(s):  
Specular Reflection ◽  
Low Frequency ◽  
Velocity Model ◽  
Forward Modeling ◽  
Small Scale ◽  
Fracture Detection ◽  
Frequency Model ◽  
New Approach ◽  
Impedance Inversion ◽  
Diffraction Imaging

Seismic diffraction waveform energy contains important information about small-scale subsurface elements, and it is complementary to specular reflection information about subsurface properties. Diffraction imaging has been used for fault, pinchout, and fracture detection. Very little research, however, has been carried out taking diffraction into account in the impedance inversion. Usually, in the standard inversion scheme, the input is the migrated data and the assumption is taken that the diffraction energy is optimally focused. This assumption is true only for a perfectly known velocity model and accurate true amplitude migration algorithm, which are rare in practice. We have developed a new approach for impedance inversion, which takes into account diffractive components of the total wavefield and uses the unmigrated input data. Forward modeling, designed for impedance inversion, includes the classical specular reflection plus asymptotic diffraction modeling schemes. The output model is composed of impedance perturbation and the low-frequency model. The impedance perturbation is estimated using the Bayesian approach and remapped to the migrated domain by the kinematic ray tracing. Our method is demonstrated using synthetic and field data in comparison with the standard inversion. Results indicate that inversion with taking into account diffraction can improve the acoustic impedance prediction in the vicinity of local reflector discontinuities.

scholarly journals The Sum and Difference of Two Lognormal Random Variables

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
C. F. Lo
Keyword(s):  
Stochastic Process ◽  
Probability Distributions ◽  
Operator Splitting ◽  
Splitting Method ◽  
Unified Approach ◽  
New Approach ◽  
Numerical Examples ◽  
Operator Splitting Method ◽  
Approximate Probability ◽  
Analytical Series

We have presented a new unified approach to model the dynamics of both the sum and difference of two correlated lognormal stochastic variables. By the Lie-Trotter operator splitting method, both the sum and difference are shown to follow a shifted lognormal stochastic process, and approximate probability distributions are determined in closed form. Illustrative numerical examples are presented to demonstrate the validity and accuracy of these approximate distributions. In terms of the approximate probability distributions, we have also obtained an analytical series expansion of the exact solutions, which can allow us to improve the approximation in a systematic manner. Moreover, we believe that this new approach can be extended to study both (1) the algebraic sum ofNlognormals, and (2) the sum and difference of other correlated stochastic processes, for example, two correlated CEV processes, two correlated CIR processes, and two correlated lognormal processes with mean-reversion.

scholarly journals Assessment of the Relationship between Cerebrospinal Meningitis and Climate Variables in Kaduna State, Nigeria

2021 ◽  
pp. 33-41
Author(s):  
Joyce Imara Nchom ◽  
A. S. Abubakar ◽  
F. O. Arimoro ◽  
B. Y. Mohammed
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Linear Trend ◽  
Pearson Correlation ◽  
Maximum Temperature ◽  
Data Set ◽  
International Research Institute ◽  
Wind Speed Maximum ◽  
Weather Parameters ◽  
The Relationship

This study examines the relationship between Meningitis and weather parameters (air temperature, maximum temperature, relative humidity, and rainfall) in Kaduna state, Nigeria on a weekly basis from 2007–2019. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), Bureau of Statistics and weather parameters were sourced from daily satellite data set National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). The daily data were aggregated weekly to suit the study. The data were analysed using linear trend and Pearson correlation for relationship. The linear trend results revealed a weekly decline in Cerebro Spinal Meningitis (CSM), wind speed, maximum and air temperature and an increase in relative humidity and rainfall. Generally, results reveal that the most important explanatory weather variables influencing CSM amongst the five (5) are the weekly maximum temperature and air temperature with a positive correlation of 0.768 and 0.773. This study recommends that keen interest be placed on temperature as they play an essential role in the transmission of this disease and most times aggravate the patients' condition.

scholarly journals Low Frequency Oscillations drive EEG's complexity changes during wakefulness and sleep

2021 ◽  
Author(s):  
Joaquin Gonzalez ◽  
Diego M. Mateos ◽  
Matias Cavelli ◽  
Alejandra Mondino ◽  
Claudia Pascovich ◽  
...  
Keyword(s):  
Slow Wave Sleep ◽  
Low Frequency ◽  
Neuronal Population ◽  
Complexity Measures ◽  
Low Frequencies ◽  
New Approach ◽  
Frequency Bands ◽  
Cortical Oscillations ◽  
Brain Signals ◽  
Low Frequency Oscillations

Recently, the sleep-wake states have been analysed using novel complexity measures, complementing the classical analysis of EEGs by frequency bands. This new approach consistently shows a decrease in EEG's complexity during slow-wave sleep, yet it is unclear how cortical oscillations shape these complexity variations. In this work, we analyse how the frequency content of brain signals affects the complexity estimates in freely moving rats. We find that the low-frequency spectrum - including the Delta, Theta, and Sigma frequency bands - drives the complexity changes during the sleep-wake states. This happens because low-frequency oscillations emerge from neuronal population patterns, as we show by recovering the complexity variations during the sleep-wake cycle from micro, meso, and macroscopic recordings. Moreover, we find that the lower frequencies reveal synchronisation patterns across the neocortex, such as a sensory-motor decoupling that happens during REM sleep. Overall, our works shows that EEG's low frequencies are critical in shaping the sleep-wake states' complexity across cortical scales.

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