This document summarizes a research paper on understanding and estimating emotional expression using acoustic analysis of natural speech. The paper explores identifying seven emotional states (anger, surprise, sadness, happiness, fear, disgust, and neutral) using fifteen acoustic features extracted from the SAVEE speech database. Three models using different combinations of features were evaluated using various machine learning algorithms. The results showed that Model 2, using energy intensity, pitch, standard deviation, jitter, and shimmer, achieved the highest classification accuracy. Estimation of emotions using confidence intervals showed that most emotions could be accurately estimated using energy intensity and pitch. The paper concludes that expanding the study to include more features and databases could improve emotional state recognition.
This document discusses quantitative data analysis and presentation for a thesis or dissertation. It emphasizes considering your overall argument before including quantitative data. Common mistakes include including data just because it is available or thinking data will make the thesis more scientific. Quantitative data should support the argument, not define it. The document provides examples of how quantitative data can be used in the introduction, methods, operationalization, analysis, and discussion sections. It also discusses using charts and illustrations to improve presentation of the argument.
The+application+of+irt+using+the+rasch+model presnetation1Carlo Magno
The document discusses the application of Item Response Theory (IRT) using the Rasch model to construct cognitive measures. It provides an overview of psychometric theory, classical test theory, and IRT approaches like the Rasch model. The Rasch model assumes that the probability of a correct response depends only on the difference between a person's ability and the item difficulty. It provides sample-independent item calibrations and person measures. The document outlines the assumptions, uses, and procedures of the Rasch model for test analysis.
The document discusses research on how valuation is computed in decision making from both neuroeconomic and neurobiological perspectives. It summarizes key findings from two chapters: 1) Valuation and choice are separable processes computed in different brain regions. Valuation involves computing expected reward and risk. 2) The striatum, particularly the ventral striatum, represents anticipated and outcome values to inform choices. The ventral striatum encodes anticipated gains while the dorsal striatum encodes outcome values.
This document provides an overview of a data analysis course covering various statistical techniques including correlation, regression, hypothesis testing, clustering, and time series analysis. The course covers descriptive statistics, data exploration, probability distributions, simple and multiple linear regression analysis, logistic regression analysis, and model building for credit risk analysis. Notes are provided on correlation calculation and its properties. Assumptions and interpretations of linear regression are also summarized. The document is intended as a high-level overview of topics covered in the course rather than an in-depth treatment.
This document provides an overview of a data analysis course that covers topics such as descriptive statistics, probability distributions, correlation, regression, hypothesis testing, clustering, and time series analysis. The course introduces descriptive statistics including measures of central tendency, dispersion, frequency distributions, and histograms. Notes are provided on calculating and interpreting mean, median, mode, range, variance, standard deviation, and other descriptive statistics.
This document provides an overview of logistic regression analysis. It introduces the need for logistic regression when the dependent variable is binary. Key concepts covered include the logistic regression model, interpreting the beta coefficients, assessing goodness of fit using various tests and metrics, and an example of fitting a logistic regression line to predict burger purchasing based on a customer's age. Students are instructed to use statistical software to estimate a logistic regression model and interpret the results.
PRIORITIZING THE BANKING SERVICE QUALITY OF DIFFERENT BRANCHES USING FACTOR A...ijmvsc
In recent years, India’s service industry is developing rapidly. The objective of the study is to explore the
dimensions of customer perceived service quality in the context of the Indian banking industry. In order to
categorize the customer needs into quality dimensions, Factor analysis (FA) has been carried out on
customer responses obtained through questionnaire survey. Analytic Hierarchy Process (AHP) is employed
to determine the weights of the banking service quality dimensions. The priority structure of the quality
dimensions provides an idea for the Banking management to allocate the resources in an effective manner
to achieve more customer satisfaction. Technique for Order Preference Similarity to Ideal Solution
(TOPSIS) is used to obtain final ranking of different branches.
This document summarizes a research paper on understanding and estimating emotional expression using acoustic analysis of natural speech. The paper explores identifying seven emotional states (anger, surprise, sadness, happiness, fear, disgust, and neutral) using fifteen acoustic features extracted from the SAVEE speech database. Three models using different combinations of features were evaluated using various machine learning algorithms. The results showed that Model 2, using energy intensity, pitch, standard deviation, jitter, and shimmer, achieved the highest classification accuracy. Estimation of emotions using confidence intervals showed that most emotions could be accurately estimated using energy intensity and pitch. The paper concludes that expanding the study to include more features and databases could improve emotional state recognition.
This document discusses quantitative data analysis and presentation for a thesis or dissertation. It emphasizes considering your overall argument before including quantitative data. Common mistakes include including data just because it is available or thinking data will make the thesis more scientific. Quantitative data should support the argument, not define it. The document provides examples of how quantitative data can be used in the introduction, methods, operationalization, analysis, and discussion sections. It also discusses using charts and illustrations to improve presentation of the argument.
The+application+of+irt+using+the+rasch+model presnetation1Carlo Magno
The document discusses the application of Item Response Theory (IRT) using the Rasch model to construct cognitive measures. It provides an overview of psychometric theory, classical test theory, and IRT approaches like the Rasch model. The Rasch model assumes that the probability of a correct response depends only on the difference between a person's ability and the item difficulty. It provides sample-independent item calibrations and person measures. The document outlines the assumptions, uses, and procedures of the Rasch model for test analysis.
The document discusses research on how valuation is computed in decision making from both neuroeconomic and neurobiological perspectives. It summarizes key findings from two chapters: 1) Valuation and choice are separable processes computed in different brain regions. Valuation involves computing expected reward and risk. 2) The striatum, particularly the ventral striatum, represents anticipated and outcome values to inform choices. The ventral striatum encodes anticipated gains while the dorsal striatum encodes outcome values.
This document provides an overview of a data analysis course covering various statistical techniques including correlation, regression, hypothesis testing, clustering, and time series analysis. The course covers descriptive statistics, data exploration, probability distributions, simple and multiple linear regression analysis, logistic regression analysis, and model building for credit risk analysis. Notes are provided on correlation calculation and its properties. Assumptions and interpretations of linear regression are also summarized. The document is intended as a high-level overview of topics covered in the course rather than an in-depth treatment.
This document provides an overview of a data analysis course that covers topics such as descriptive statistics, probability distributions, correlation, regression, hypothesis testing, clustering, and time series analysis. The course introduces descriptive statistics including measures of central tendency, dispersion, frequency distributions, and histograms. Notes are provided on calculating and interpreting mean, median, mode, range, variance, standard deviation, and other descriptive statistics.
This document provides an overview of logistic regression analysis. It introduces the need for logistic regression when the dependent variable is binary. Key concepts covered include the logistic regression model, interpreting the beta coefficients, assessing goodness of fit using various tests and metrics, and an example of fitting a logistic regression line to predict burger purchasing based on a customer's age. Students are instructed to use statistical software to estimate a logistic regression model and interpret the results.
PRIORITIZING THE BANKING SERVICE QUALITY OF DIFFERENT BRANCHES USING FACTOR A...ijmvsc
In recent years, India’s service industry is developing rapidly. The objective of the study is to explore the
dimensions of customer perceived service quality in the context of the Indian banking industry. In order to
categorize the customer needs into quality dimensions, Factor analysis (FA) has been carried out on
customer responses obtained through questionnaire survey. Analytic Hierarchy Process (AHP) is employed
to determine the weights of the banking service quality dimensions. The priority structure of the quality
dimensions provides an idea for the Banking management to allocate the resources in an effective manner
to achieve more customer satisfaction. Technique for Order Preference Similarity to Ideal Solution
(TOPSIS) is used to obtain final ranking of different branches.
This document discusses various statistical methods for analyzing DNA methylation data, including both global and site-specific analyses. For global analysis, it describes clustering methods like k-means and principle component analysis to identify subgroups with similar methylation profiles. For site-specific analysis, it discusses using linear models and Limma to identify differentially methylated positions and the need to control for multiple testing using methods like Bonferroni correction and false discovery rate. It also mentions checking for influential points that could impact regression results.
Conversational transfer learning for emotion recognitionTakato Hayashi
1) The document proposes an approach called TL-ERC that uses transfer learning to improve emotion recognition in conversations. TL-ERC pre-trains a hierarchical dialogue model on multi-turn conversation data and transfers its parameters to an emotion classifier.
2) Experiments show that TL-ERC improves performance and robustness over randomly initialized models, especially with limited training data. TL-ERC also reaches optimal validation performance in fewer training epochs.
3) Comparisons indicate TL-ERC outperforms previous state-of-the-art models for emotion recognition and is better able to leverage pre-trained weights than training from scratch.
Assessment of Anxiety,Depression and Stress using Machine Learning ModelsPrince Kumar
The document discusses assessing anxiety, depression, and stress using machine learning models. It aims to identify these psychological disorders at different severity levels using various machine learning algorithms and compare their accuracy. It first provides background on the disorders and related work. It then describes applying methods like KNN, naive Bayes, decision trees, random forest, and RBFN on a dataset of 39,776 instances collected through online questionnaires. Results show RBFN achieved the highest accuracy of over 96% for each disorder classification, outperforming other methods. The document concludes future work could involve analyzing larger and more diverse datasets.
Personality Recognition" includes automatic classification of authors' personality traits, that can be compared against gold standard annotation obtained by means of the big5 personality test
Evaluation of multilabel multi class classificationSridhar Nomula
This document discusses multi-label and multi-class classification as well as evaluation metrics for multi-label classifiers. It explains that multi-label classification allows instances to belong to more than one class, while multi-class classification assigns each instance to only one class. The document outlines example-based and label-based evaluation metrics for multi-label classifiers, including precision, recall, F1 score, hamming loss, and average precision. It provides examples of calculating these metrics and discusses the benefits of different averaging approaches.
This document discusses various techniques for interpreting and explaining deep neural network models. It begins by motivating the need for interpretability and distinguishing between interpretation and explanation. It then covers several techniques for interpreting models, including activation maximization, sensitivity analysis, and simple Taylor decomposition. For explaining individual predictions, it discusses gradient-based and decomposition-based approaches like layer-wise relevance propagation and guided backpropagation. It also evaluates explanation quality based on continuity and selectivity. Finally, it discusses applications like model validation and analyzing scientific data domains.
The document discusses various steps involved in analyzing and interpreting data, including developing an analysis plan, collecting and cleaning data, analyzing the data using appropriate techniques, interpreting the results by drawing conclusions and recommendations while also considering limitations. It provides examples of different analysis techniques like descriptive statistics, inferential statistics, and qualitative data analysis and emphasizes the importance of interpreting data in the context of the research questions.
This document provides an overview of a machine learning workshop. It begins with introducing the presenter and their background. It then outlines the topics that will be covered, including machine learning applications, different machine learning algorithms like decision trees and neural networks, and the necessary math foundations. It discusses the differences between supervised, unsupervised, and reinforcement learning. It also covers evaluating models and challenges like overfitting. The goal is to demystify machine learning concepts and algorithms.
Analyzing Road Side Breath Test Data with WEKAYogesh Shinde
The document discusses analyzing a roadside breath test dataset containing approximately 300,000 records to classify intoxication. It explores using attributes like reason for test, time, age, and gender for classification. Three algorithms - J48 decision trees, JRip rule-based classifier, and logistic regression - are applied and evaluated. Regression performed best with an accuracy of 88.34%. The models can help understand factors predicting intoxication and their impact when drivers are stopped. Further testing is recommended to improve the models.
This document provides an overview of basic probability and statistics concepts. It covers variables, descriptive statistics like mean and standard deviation, frequency distributions through histograms, the normal distribution, linear regression, and includes a practice test in the appendices. Key topics are qualitative and quantitative data, parameters versus statistics, measures of central tendency and dispersion, and generating frequency tables and histograms from data sets.
This document provides an overview of basic probability and statistics concepts. It covers variables, descriptive statistics like mean and standard deviation, frequency distributions through histograms, the normal distribution, linear regression, and includes a practice test in the appendices. Key topics are qualitative and quantitative data, parameters versus statistics, measures of central tendency and dispersion, and generating frequency tables and histograms from data sets.
This document discusses pre-calibration models and frameworks for automatically generating assessment items. It proposes a conceptual frame that defines cognitive task models, item forms, form-level characteristics, item models, primary content, item families, and secondary content. It then proposes a pre-calibration model that represents the generative process at different levels. As an illustration, it analyzes data from a summer math program that administered automatically generated math items to students. The analysis found good correlation with a calibration model and provided estimates of properties at different generative levels. The discussion notes that variation among generated instances is different than residual unmodeled variation, and evaluating generative properties supports item banking and refinement.
Dive into the world of sentiment analysis applied to movie reviews. Explore how data science techniques can uncover the true sentiments behind the words, providing valuable insights for filmmakers and critics alike. Join us as we analyze the highs and lows of movie emotions. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights
Multiple discriminant analysis (MDA) is used to classify cases into groups when there are more than two categories. MDA derives multiple discriminant functions to discriminate between groups, with the first function accounting for the most variation between groups. The number of functions derived is usually equal to the number of groups minus one or the number of predictor variables, whichever is smaller. MDA outputs include standardized discriminant function coefficients, structure correlations, group centroids, and a classification matrix assessing prediction accuracy.
SemEval - Aspect Based Sentiment AnalysisAditya Joshi
SemEval is an ongoing series of evaluations of computational semantic analysis systems that evolved from word sense evaluation. SemEval 2014 included several tasks, including aspect based sentiment analysis (Task 4) which had four subtasks: (1) aspect term extraction, (2) aspect term polarity classification, (3) aspect category detection, and (4) aspect category polarity classification. The top performing system for this task used a semi-Markov tagger for aspect term extraction and SVMs trained on lexical, syntactic, and semantic features for the other subtasks.
Hierarchical Transformer for Early Detection of Alzheimer’s DiseaseJinho Choi
This document summarizes a hierarchical transformer model for early detection of Alzheimer's disease from speech transcripts. The model uses BERT, RoBERTa, and ALBERT transformers trained on individual speech tasks and then combined in a pipeline and joint learning manner. Performance is evaluated on the B-SHARP dataset using accuracy, sensitivity, and specificity metrics. Attention analysis is conducted to understand what language features the models focus on. An ensemble of the transformers achieves the best performance for early Alzheimer's detection.
Natural Language Understanding in HealthcareDavid Talby
The ability of software to reason, answer questions and intelligently converse about clinical notes, patient stories or biomedical papers has risen dramatically in the past few years.
This talk covers state of the art natural language processing, deep learning, and machine learning libraries in this space. We'll share benchmarks from industry & research projects on use cases such as clinical data abstraction, patient risk prediction, named entity recognition & resolution, and negation scope detection.
This document provides an overview of a project to build a machine learning model to predict Parkinson's disease. It discusses the process of data cleaning, feature engineering, model building and evaluation using different classification techniques. Random forest was found to perform best with an accuracy of 97.2% at predicting Parkinson's disease status based on speech attributes. Key features identified were Delta3, MFCC3, MFCC9, MFCC8 and HNR05. Further improvements could include additional data and techniques like XGBoost.
This document proposes a two-stage approach to automatically identify emotions induced by Carnatic music using machine learning techniques. In the first classification stage, music samples are classified into emotional dimensions like devotion, pathos, and calmness. In the second clustering stage, music samples are grouped based on their emotional similarity as identified in the first stage. The results show some improvement over the baseline for classification but evaluations indicate that expanding the dataset with more labeled samples could further improve the accuracy of automatically identifying emotions in Carnatic music.
This document discusses various statistical methods for analyzing DNA methylation data, including both global and site-specific analyses. For global analysis, it describes clustering methods like k-means and principle component analysis to identify subgroups with similar methylation profiles. For site-specific analysis, it discusses using linear models and Limma to identify differentially methylated positions and the need to control for multiple testing using methods like Bonferroni correction and false discovery rate. It also mentions checking for influential points that could impact regression results.
Conversational transfer learning for emotion recognitionTakato Hayashi
1) The document proposes an approach called TL-ERC that uses transfer learning to improve emotion recognition in conversations. TL-ERC pre-trains a hierarchical dialogue model on multi-turn conversation data and transfers its parameters to an emotion classifier.
2) Experiments show that TL-ERC improves performance and robustness over randomly initialized models, especially with limited training data. TL-ERC also reaches optimal validation performance in fewer training epochs.
3) Comparisons indicate TL-ERC outperforms previous state-of-the-art models for emotion recognition and is better able to leverage pre-trained weights than training from scratch.
Assessment of Anxiety,Depression and Stress using Machine Learning ModelsPrince Kumar
The document discusses assessing anxiety, depression, and stress using machine learning models. It aims to identify these psychological disorders at different severity levels using various machine learning algorithms and compare their accuracy. It first provides background on the disorders and related work. It then describes applying methods like KNN, naive Bayes, decision trees, random forest, and RBFN on a dataset of 39,776 instances collected through online questionnaires. Results show RBFN achieved the highest accuracy of over 96% for each disorder classification, outperforming other methods. The document concludes future work could involve analyzing larger and more diverse datasets.
Personality Recognition" includes automatic classification of authors' personality traits, that can be compared against gold standard annotation obtained by means of the big5 personality test
Evaluation of multilabel multi class classificationSridhar Nomula
This document discusses multi-label and multi-class classification as well as evaluation metrics for multi-label classifiers. It explains that multi-label classification allows instances to belong to more than one class, while multi-class classification assigns each instance to only one class. The document outlines example-based and label-based evaluation metrics for multi-label classifiers, including precision, recall, F1 score, hamming loss, and average precision. It provides examples of calculating these metrics and discusses the benefits of different averaging approaches.
This document discusses various techniques for interpreting and explaining deep neural network models. It begins by motivating the need for interpretability and distinguishing between interpretation and explanation. It then covers several techniques for interpreting models, including activation maximization, sensitivity analysis, and simple Taylor decomposition. For explaining individual predictions, it discusses gradient-based and decomposition-based approaches like layer-wise relevance propagation and guided backpropagation. It also evaluates explanation quality based on continuity and selectivity. Finally, it discusses applications like model validation and analyzing scientific data domains.
The document discusses various steps involved in analyzing and interpreting data, including developing an analysis plan, collecting and cleaning data, analyzing the data using appropriate techniques, interpreting the results by drawing conclusions and recommendations while also considering limitations. It provides examples of different analysis techniques like descriptive statistics, inferential statistics, and qualitative data analysis and emphasizes the importance of interpreting data in the context of the research questions.
This document provides an overview of a machine learning workshop. It begins with introducing the presenter and their background. It then outlines the topics that will be covered, including machine learning applications, different machine learning algorithms like decision trees and neural networks, and the necessary math foundations. It discusses the differences between supervised, unsupervised, and reinforcement learning. It also covers evaluating models and challenges like overfitting. The goal is to demystify machine learning concepts and algorithms.
Analyzing Road Side Breath Test Data with WEKAYogesh Shinde
The document discusses analyzing a roadside breath test dataset containing approximately 300,000 records to classify intoxication. It explores using attributes like reason for test, time, age, and gender for classification. Three algorithms - J48 decision trees, JRip rule-based classifier, and logistic regression - are applied and evaluated. Regression performed best with an accuracy of 88.34%. The models can help understand factors predicting intoxication and their impact when drivers are stopped. Further testing is recommended to improve the models.
This document provides an overview of basic probability and statistics concepts. It covers variables, descriptive statistics like mean and standard deviation, frequency distributions through histograms, the normal distribution, linear regression, and includes a practice test in the appendices. Key topics are qualitative and quantitative data, parameters versus statistics, measures of central tendency and dispersion, and generating frequency tables and histograms from data sets.
This document provides an overview of basic probability and statistics concepts. It covers variables, descriptive statistics like mean and standard deviation, frequency distributions through histograms, the normal distribution, linear regression, and includes a practice test in the appendices. Key topics are qualitative and quantitative data, parameters versus statistics, measures of central tendency and dispersion, and generating frequency tables and histograms from data sets.
This document discusses pre-calibration models and frameworks for automatically generating assessment items. It proposes a conceptual frame that defines cognitive task models, item forms, form-level characteristics, item models, primary content, item families, and secondary content. It then proposes a pre-calibration model that represents the generative process at different levels. As an illustration, it analyzes data from a summer math program that administered automatically generated math items to students. The analysis found good correlation with a calibration model and provided estimates of properties at different generative levels. The discussion notes that variation among generated instances is different than residual unmodeled variation, and evaluating generative properties supports item banking and refinement.
Dive into the world of sentiment analysis applied to movie reviews. Explore how data science techniques can uncover the true sentiments behind the words, providing valuable insights for filmmakers and critics alike. Join us as we analyze the highs and lows of movie emotions. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights
Multiple discriminant analysis (MDA) is used to classify cases into groups when there are more than two categories. MDA derives multiple discriminant functions to discriminate between groups, with the first function accounting for the most variation between groups. The number of functions derived is usually equal to the number of groups minus one or the number of predictor variables, whichever is smaller. MDA outputs include standardized discriminant function coefficients, structure correlations, group centroids, and a classification matrix assessing prediction accuracy.
SemEval - Aspect Based Sentiment AnalysisAditya Joshi
SemEval is an ongoing series of evaluations of computational semantic analysis systems that evolved from word sense evaluation. SemEval 2014 included several tasks, including aspect based sentiment analysis (Task 4) which had four subtasks: (1) aspect term extraction, (2) aspect term polarity classification, (3) aspect category detection, and (4) aspect category polarity classification. The top performing system for this task used a semi-Markov tagger for aspect term extraction and SVMs trained on lexical, syntactic, and semantic features for the other subtasks.
Hierarchical Transformer for Early Detection of Alzheimer’s DiseaseJinho Choi
This document summarizes a hierarchical transformer model for early detection of Alzheimer's disease from speech transcripts. The model uses BERT, RoBERTa, and ALBERT transformers trained on individual speech tasks and then combined in a pipeline and joint learning manner. Performance is evaluated on the B-SHARP dataset using accuracy, sensitivity, and specificity metrics. Attention analysis is conducted to understand what language features the models focus on. An ensemble of the transformers achieves the best performance for early Alzheimer's detection.
Natural Language Understanding in HealthcareDavid Talby
The ability of software to reason, answer questions and intelligently converse about clinical notes, patient stories or biomedical papers has risen dramatically in the past few years.
This talk covers state of the art natural language processing, deep learning, and machine learning libraries in this space. We'll share benchmarks from industry & research projects on use cases such as clinical data abstraction, patient risk prediction, named entity recognition & resolution, and negation scope detection.
This document provides an overview of a project to build a machine learning model to predict Parkinson's disease. It discusses the process of data cleaning, feature engineering, model building and evaluation using different classification techniques. Random forest was found to perform best with an accuracy of 97.2% at predicting Parkinson's disease status based on speech attributes. Key features identified were Delta3, MFCC3, MFCC9, MFCC8 and HNR05. Further improvements could include additional data and techniques like XGBoost.
This document proposes a two-stage approach to automatically identify emotions induced by Carnatic music using machine learning techniques. In the first classification stage, music samples are classified into emotional dimensions like devotion, pathos, and calmness. In the second clustering stage, music samples are grouped based on their emotional similarity as identified in the first stage. The results show some improvement over the baseline for classification but evaluations indicate that expanding the dataset with more labeled samples could further improve the accuracy of automatically identifying emotions in Carnatic music.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
1. Automating annotations of the cognitive
neuroimaging literature using ATHENA
Riedel MC, Salo T, Hays J, Turner MD, Sutherland MT, Turner
JA, & Laird AR
Neuroinformatics and
Brain Connectivity Lab
2. Neuroimaging Research
• Increasing in volume and scope
• Embedded in this literature is knowledge capturing a system-level probing of
functional brain organization
• The challenge for cognitive neuroscience is harnessing this knowledge and
translating it into improved neurocognitive models
0
2000
4000
6000
8000
10000
12000
14000
2000 2002 2004 2006 2008 2010 2012 2014
Published Neuroimaging Studies
3. Cognitive Paradigm Ontology
• Knowledge modeling effort to study the relationship between
brain structure and function
• Seeks to represent stimuli, responses, and instructions that
define conditions of an fMRI experiment in a standardized
format
• System of labels for annotating neuroimaging articles
4. Cognitive Paradigm Ontology
Behavioral Domain
Paradigm Class
Diagnosis
Instruction
Context
Stimulus Modality
Stimulus Type
Response Modality
Response Type
Action
Cognition
Emotion
Interoception
Perception
Anger
Fear
Happiness
Sadness
n-back
Face Monitor/Discrimination
Classical conditioning
Delay discounting
Film viewing
Go/No-Go
Autism Spectrum Disorders
Bipolar Disorders
Depression
Normal
Schizophrenia
Attend
Count
Detect
Discriminate
Recall
Disease Effects
Drug Effects
Normal Mapping
Auditory
Tactile
Visual
Digits
Faces
Letters
Pictures
Shapes
Hand
None
Oral/Facial
Button Press
None
Speech
5. Goals
• Develop framework for automated annotations of neuroimaging articles
• Evaluate classifier performance across variable parameters:
• corpus
• feature space
• classification algorithm
• Characterize relationships between labels by assessing similar vocabularies used
for classification
Problem
• Manual annotation is time-limiting, field is too large
• Bias/human error
6. Classification Features
• Property or characteristic of something being measured
• Related to explanator variables in linear regression
• Examples:
• Speech recognition: noise ratios, length of sounds, relative power, filter
matches
• Spam detection: email headers, email structure, language, term frequency
• Character recognition: histogram counts of black pixels in horizontal and
vertical direction, number of internal holes, stroke detection
7. Background-Studies incorporating direct
comparisons across all phases of bipolar
(BP) disorder are needed to elucidate the
pathophysiology of bipolar disorder.
However functional, neuroimaging studies
that differentiate bipolar mood states from
each other and from healthy subjects are
few and have yielded inconsistent
findings.
Feature Spaces
bag-of-words
Cognitive Atlas
bipolar
bipolar disorder
disorder
bipolar
bipolar mood
bipolar mood states
mood states
mood
states
bipolar disorder
mood
8. Classification Procedure
neuroimaging
article
n = 2,633
Behavioral Domain
Context
Diagnosis
Instruction
Paradigm Class
Response Modality
Response Type
Stimulus Modality
Stimulus Type
abstracts-only
full-text
CogPO Labels
corpora
text extraction
bag-of-words
Cognitive Atlas
feature spaces
training/test
dataset splits
k = 5
80%/20%
feature
vectorization
and reduction
f = 1,754
parameter
tuning
k = 2
classification
Bernoulli naïve Bayes
k-nearest neighbors
logistic regression
support vector classifier
cross-validation
100 iterations
9. Assessing Classifier Performance
• Classifier performance evaluated using F1-score
• 𝐹1 = 2 ×
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛×𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙
, 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑡𝑝
𝑡𝑝+𝑓𝑝
, 𝑟𝑒𝑐𝑎𝑙𝑙 =
𝑡𝑝
𝑡𝑝+𝑓𝑛
• Ranges from 0 to 1
• F1-scores averaged across labels for overall performance
11. Representation of Classification Features
• bag-of-words features used to classify each label for Behavioral
Domain and Paradigm Class
• Used distributions of feature representation to calculate correlation
matrix
• Regressed co-occurrence of labels from correlation coefficients
• Performed hierarchical clustering on resulting matrix to assess
similarity of classification features between labels
14. Conclusions and Future Works
• full-text, bag-of-words performed best
• Cognitive Atlas features outperform bag-of-words when only using text from abstracts
• Anatomical terms dominate features for classification when using bag-of-words
• Test on independent dataset
• Validate by replicating existing meta-analyses
• Specify Cognitive Atlas
• Integrate with existing frameworks
15. Acknowledgements
External Collaborators
Dr. Angela Laird
Dr. Matthew Sutherland
Dr. Michael Tobia
Dr. Veronica Del Prete
Jessica Bartley
Katherine Bottenhorn
Jessica Flannery
Ranjita Poudel
Taylor Salo
Lauren Hill
Chelsea Greaves
Rosario Pintos Lobo
Laura Ucros
Diamela Arencibia
Jennifer Foreman
Ariel Gonzalez
Neuroinformatics and Brain Connectivity Lab
Jessica Turner
Matthew Turner
Neuroinformatics and
Brain Connectivity Lab
NSF 1631325
NSF REAL DRL1420627
NSF CNS 1532061
NIH R01 DA041353
NIH U01 DA041156
NIH K01 DA037819
NIH U54 MD012393
16. Classifiers
• Bernoulli naïve Bayes
• Trains on binary word occurrence vectors instead of word counts
• logistic regression
• Linear model for classification
• k-nearest neighbors
• Identifies nearest k articles in distance and uses majority vote to
determine if its about a label
• support vector machine
• Creates high-dimensional decision hyper-plane
Editor's Notes
Thank you for the opportunity to speak here today. Im going to present some work we have been doing using classification techniques to automatically annotate the neuroimaging literature with labels from a cognitive ontology.
Neuroimaging research has seen an explosion of growth over the past 20 years, indicated here by the number of published neuroimaging studies per year. As we have continued to explore the relationship between brain structure and function, the research has also become increasingly complex.
With such a wealth of information embedded in this literature about brain organization, the challenge for us as neuroscientists is to harness this knowledge in a digestible manner in a way that enhances our understanding of neurocognitive models.
The Cognitive Paradigm Ontology or CogPO is a step in this direction, providing a discrete set of terms meant to help study the relationship between brain structure and function.
These terms are meant to characterize elements of experimental design like stimuli, responses, instructions, and can be used to annotate neuroimaging articles to provide concise references about the research in that that article.
Briefly, CogPO consists of 9 dimensions. One of which is behavioral domain, which describes a mental construct and contains labels such as Action, Cognition, Emotion, Interoception and Perception. And these labels may be even more descriptive such as Anger, Fear, or Happiness for Emotion.
Then Paradigm Class describes different tasks, such as n-back, delay-discounting and go/no-go. Then there are other types of labels such as Diagnosis, Instruction, Context, and Response and Stimulus Modality and Type.
Thus, CogPO informs cognitive models by being able to synthesize articles with similar labels to perform meta-analyses.
The problem we are currently facing is that with such a large amount of research available, manual annotation of the literature is time-limiting and nearly impossible to keep up with. Plus add in bias and human error associated with manual annotations.
Therefore, we sought to develop a framework for annotating neuroimaging articles in an automated manner using the CogPO labels.
To do this, we wanted to evaluate classification performance by varying three parameters: corpus, features, and different classification algorithms.
Then we wanted to characterize the relationships between labels by assessing the most frequently used features in the classification process. That is, can we use data-driven approaches to determine if neuroimagers are using similar vocabularies to describe certain cognitive paradigms.
First, when I talk about classification features, they are a measurable property or characteristic that can be used for classification.
They are similar to the explanatory variables in a linear regression.
Some examples of features in classification are noise ratios and lengths of sound in speech recognition, and email text or headers in spam detection.
We wanted to evaluate the performance of two types of features: terms extracted from the text, which is the bag-of-words approach, and representation of terms defined by the Cognitive Atlas. A description of the Cognitive Atlas deserves more time here, but briefly, it is a vocabulary of about 1700 terms describing concepts, tasks, disorders, and theories in cognitive science. What also makes the Cognitive Atlas unique is the relationships between terms, such as working memory is a KIND OF memory, and a PART OF decision making.
Here is a short example of how the bag of words and Cognitive Atlas approaches for defining features differs. Consider this small text.
The bag-of-words approach would take the words “bipolar disorder” and break it into “bipolar” “bipolar disorder” and “disorder”, and “bipolar mood states” can then be broken into all combinations of three or less terms shown here. I should mention that all terms in this text could be used for classification, Im just focusing on these two example for illustrative purposes.
Then, in the Cognitive Atlas approach, only terms defined by the Cognitive Atlas are used, so the only terms that would be used for classification would be “bipolar disorder” and “mood”, and all other terms in this text are ignored.
Now I’ll walk you through how we defined our classifiers for each CogPO label.
We utilized a dataset of 2,633 neuroimaging articles that were manually annotated with CogPO labels.
As I mentioned before, we evaluate extracting features from either just the abstracts or the full-text.
Once text was extracted according to the bag-of-words or Cognitive Atlas feature space, we performed 100 iterations of a repeated 5-fold cross-validation procedure. In this procedure, in each iteration, the dataset was split into 5 folds, where each fold was divided into a training dataset, which consisted of 80% of the articles, and a test dataset, which consisted of 20% of the articles.
We then vectorized the features based on frequency of appearance in an article and incorporated the frequency of that feature across all articles. Because the Cognitive Atlas only consisted of 1,754 terms, we reduced the bag-of-words terms using a chi-square test that removes all but the top 1,754 features for a particular label.
Then, in preparation for classification, we performed a 2-fold cross-validation procedure to tune the hyperparameters for classification. Depending on the classification algorithm, this step basically optimizes the cost function and smoothing kernel.
Finally, we used 4 different classification algorithms to generate a classifier for each CogPO label, logistic regression, Bernoulli naïve bayes, k-nearest neighbors, and support vector machine.
To assess classifier performance, we used F1-scores, which are dependent on precision and recall.
The F1-score can range from 0 to 1, where 1 represents perfect classification
We calculated the F1-score for each label and averaged across all 100 iterations. Then, to determine which combination of corpus, feature space, and classification algorithm performed best, we averaged across all CogPO labels.
This graph represents overall performance, separated by classification algorithm on the bottom. Abstracts are in blue, full-text is in orange, bag-of-words are represented by circles and Cognitive Atlas by X’s.
Here, the top performer used full-text, bag-of-words, and the logistic regression algorithm.
Its also worth noting that when using the support vector machine algorithm, the cognitive Atlas approach did not differ that greatly from the bag-of-words approach when using full-text.
And perhaps more interesting, the Cognitive Atlas feature space approach actually outperformed the bag-of-words approach when only using article abstracts. This could be particularly useful for two reasons: 1) Cognitive Atlas provides a platform for classifying based on an ontology specifically designed for the cognitive sciences, and 2) currently abstract-text are more accessible than full-article text, and may provide a means for annotating a larger proportion of the literature.
Since the bag-of-words approach performed the best, we wanted to determine which CogPO labels in Behavioral Domain and Paradigm Class used the same features for classification across iterations. This provides insight into vocabularies used to discuss similar constructs.
We used the distribution of feature representations to calculate a correlation matrix, and corrected for the fact that some labels tend to be assigned together a lot, such as Emotion and Emotion.Fear.
Then we performed hierarchical clustering on the resulting matrix to provide a visual representation of similar labels.
Here is the dendrogram, and just based on visual inspection of the dendrogram, we isolated four clusters of labels. You can see that each cluster contains labels from both Paradigm Class and Behavioral Domains.
The green cluster contains labels related to cognition, perception and language. We created a word cloud of the top 10% of features within the labels associated with this cluster and see dominant terms such as temporal gyrus, anterior cingulate. This cluster seems to be dominated by anatomical terms.
The blue cluster seems primarily related to inhibition, and the resulting word cloud exhibits cingulate cortex, anterior cingulate, “event related”. Now we can see some terms related to task design involved in the classification process.
The purple cluster is pretty large, containing terms related to emotion and memory. The resulting word cloud exhibits terms like working memory, prefrontal cortex, facial expression, and major depressive disorder. Here disorders frequently studied within a domain become prominent in addition to more information about task design.
Finally, the red cluster contains terms related to pain and action. The resulting word cloud contains terms such as reaction time, working memory. This may be a little less informative which isn’t that surprising given the diversity of the labels assigned to the cluster.
We can also see tight groupings of labels related to specific constructs, such as language, emotion, memory, and pain.
While these topics are somewhat subjectively chosen, we generated word clouds for each one.
Within the language topic we can see terms such as superior temporal being dominant, and amygdala, emotion, and fusiform dominant for emotion. The memory topic contains terms like working memory, prefrontal cortex, dorsolateral, and cingulate. Finally the pain topic contains terms such as anterior cingulate and insula.
Again, we can see here that related to specific constructs, anatomical terms really seemed to dominate the features used for classification. But it does demonstrate that within constructs, neuroimagers are discussing similar brain structures!
To wrap everything up, we evaluated classifier performance for CogPO labels using text from either abstracts or the full article, bag-of-words features or Cognitive Atlas terms, and different classification algorithms. We found that the combination of full-text, bag-of-words, and logistic regression performed the best.
The Cognitive Atlas features outperformed the bag-of-words features when only using text from the abstracts.
Anatomical terms dominated the features used for classification when using bag-of-words.
Our future works include testing on an independent dataset, and validating these classifiers by replicating existing meta-analyses of manually annotated articles.
We would additionally like to assist in the process of fully specifying the relationships between terms in the Cognitive Atlas and seek to integrate these classifiers in existing frameworks.
I would like to thank everyone in the Neuroinformatics and Brain Connectivity Lab for their contributions to this project, especially Taylor Salo. Id also like to thank our collaborators at Georgia State for project and analysis development. And of course thank you again for inviting me to present our work here today.
I’ll take any questions you have at this time.
Logistic regression – probabilities describing the possible outcomes of a single trial are modeled using the S-shaped logistic function