Abstract

Intensity and duration are both pertinent aspects of an emotional experience, yet how they are related is unclear. Though stronger emotions usually last longer, sometimes they abate faster than the weaker ones. We present a quantitative model of affective adaptation, the process by which emotional responses to unchanging affective stimuli weaken with time, that addresses this intensity-duration problem. The model, described by three simple linear algebraic equations, assumes that the relationship between an affective stimulus and its experiencer can be broken down into three parameters. Self-relevance and explanation level combine multiplicatively to determine emotion intensity whereas the interaction of these with explanatory ease determines its duration. The model makes predictions, consistent with available empirical data, about emotion intensity, its duration, and adaptation speed for different scenarios. It predicts when the intensity-duration correlation is positive, negative or even absent, thus offering a solution to the intensity-duration problem. The model also addresses the shortcomings of past models of affective adaptation with its enhanced predictive power and by offering a more complete explanation to empirical observations that earlier models explain inadequately or fail to explain altogether. The model has potential applications in areas such as virtual reality training, games, human-computer interactions, and robotics.

Abstract

The main challenge in studying cognition & designing effective tasks for children with learning disorders is creating personalized & adaptive tasks in line with the current abilities & mood of the child. The current study confronts this challenge by testing a new paradigm to access the current state of mind and adapting the tasks based on the current mood & abilities of the child. Children were given chess puzzles with various levels of difficulty (from just identifying the pieces, legal moves and eventually even capturing pieces with depth=1). while the children were performing the tasks the pupil-metric data (for cognitive load), facial expressions and the head pose were used to gauge the current-state and adapt the puzzles accordingly. Further development of dynamic feedback and providing rewards for looking at the right squares are also underway. custom software with off the shelf web-cameras were used as the current solutions in the market are prohibitively expensive for testing on large scale.

Abstract

Affective education is an formal curriculum designed to help children better understand their feelings and respond to challenging situations,thereby, transforming themselves and the world around them.Emotions impact the learning ability at multiple levels(Attention,Memory and decision making etc.).Though they have been advancements in terms of the content(Rich multimedia-based lessons etc.) for effective learning,proportionate advancements in have not taken place in the affective learning domain-for example "How to adapt the learning based in the current situation?".Can we mitigate the adverse effects of emotions?This problem of learning is especially compounded for university students where each student has flood of information to absorb & assimilate and is constantly under under stress,furthermore,Personalized & Real time/continual mentoring by the teachers to students is not practical.We achieve this by capturing the real time gestures and facial expressions(based on universal facial expressions of 7 emotions The Task(Chess Puzzles) given to validate the effectiveness of Methods show significant Improvement on sample size of 80 students

Abstract

A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fxed SC architecture. Recent modeling attempts point to the possibility of a single difusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-difusion-kernel model (SDK) and propose a multi-scale difusion scheme. Our multiscale model is formulated as a reaction-difusion system giving rise to spatio-temporal patterns on a fxed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine difusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specifc FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specifc reorganization in the brain structure and function.

Abstract

Extreme learning machine (ELM) is a new algorithm for training single-hidden layer feedforward neural networks which provides good performance as well as fast learning speed. ELM tends to produce good generalization performance with large number of hidden neurons as the input weights and hidden neurons biases are randomly initialized and remain unchanged during the learning process, and the output weights are analytically determined. In this paper, two swarm intelligence based metaheuristic techniques, viz. Artificial Bee Colony (ABC) and Invasive Weed Optimization (IWO) are proposed for tuning the input weights and hidden biases. The proposed approaches are called ABC-ELM and IWO-ELM in which the input weights and hidden biases are selected using ABC and IWO respectively and the output weights are computed using the Moore-Penrose (MP) generalized inverse. The proposed approaches are tested on different benchmark classification data sets and simulations show that the proposed approaches obtain good generalization performance in comparison to the other techniques available in the literature.

Abstract

Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has generated data from patients that can be analysed to develop a predictive model. In this study, we have adopted a machine learning approach to identify biomarkers and build classifiers to discriminate between early and late stages of PRCC from gene expression profiles. A machine learning pipeline incorporating different feature selection algorithms and classification models is developed to analyse RNA sequencing dataset (RNASeq). Further, to get a reliable feature set, we extracted features from different partitions of the training dataset and aggregated them into feature sets for classification. We evaluated the performance of different algorithms on the basis of 10-fold cross validation and independent test dataset. 10-fold cross validation was also performed on a microarray dataset of PRCC. A random forest based feature selection (varSelRF) yielded minimum number of features (104) and a best performance with area under Precision Recall curve (PR-AUC) of 0.804, MCC (Matthews Correlation Coefficient) of 0.711 and accuracy of 88% with Shrunken Centroid classifier on a test dataset. We identified 80 genes that are consistently altered between stages by different feature selection lgorithms. The extracted features are related to cellular components - centromere, kinetochore and spindle, and biological process mitotic cell cycle. These observations reveal potential mechanisms for an increase in chromosome instability in the late stage of PRCC. Our study demonstrates that the gene expression profiles can be used to classify stages of PRCC.

Abstract

Brain connectivity analysis has provided crucial insights to pinpoint the differences between autistic and typically developing (TD) children during development. The aim of this study is to investigate the functional connectomics of autism spectrum disorder (ASD) versus TD and underpin the effects of development, disease, and their interactions on the observed atypical brain connectivity patterns. Resting-state functional magnetic resonance imaging (rs-fMRI) from the Autism Brain Imaging Data Exchange (ABIDE) data set, which is stratified into two cohorts: children (9–12 years) and adolescents (13–16 years), is used for the analysis. Differences in various graph theoretical network measures are calculated between ASD and TD in each group. Furthermore, two-factor analysis of variance test is used to study the effect of age, disease, and their interaction on the network measures and the network edges. Furthermore, the differences in connection strength between TD and ASD subjects are assessed using network-based statistics. The results showed that ASD exhibits increased functional integration at the expense of decreased functional segregation. In ASD adolescents, there is a significant decrease in odularity suggesting a less robust modular organization, and an increase in participation coefficient suggesting more random integration and widely distributed connection edges. Furthermore, there is significant hypoconnectivity observed in the adolescent group especially in the default mode network, while the children group shows both hyper- and hypoconnectivity. This study lends support to a model of global atypical connections and further identifies functional networks and areas that are independently affected by age, disease, and their interaction.

Abstract

In this study, we investigated the effects of variable Response-to-Stimulus interval (RSI) on sequence learning using both empirical and computational methods. In the empirical study, the serial reaction time task (SRT) was conducted which was followed by free generation and recognition tasks. Results showed that learning becomes explicit with increase in RSI despite its varying temporal nature. We constructed a computational model based on modified Elman network architecture to obtain a functional account of the empirical findings. The model illustrates how explicit learning could emerge due to a longer temporal window between stimuli which could potentially give insights into the mechanisms of sequence learning in variable RSI conditions.

Abstract

Encoding models of functional magnetic resonance imaging (fMRI) data attempt to learn a forward mapping that relates stimuli to the corresponding brain activation. Computational tractability usually forces current encoding as well as decoding solutions to typically consider only a small subset of voxels from the actual 3D volume of activation. Further, while brain decoding has received wider attention, there have been only a few attempts at constructing encoding solutions in the extant neuroimaging literature. In this paper, we present a deep autoencoder consisting of convolutional neural networks in tandem with long short-term memory (CNNLSTM) model. The model is trained on fMRI slice sequences and predicts the entire brain volume rather than a small subset of voxels from the information in stimuli (text and image). We argue that the resulting solution avoids the problem of devising encoding models based on a rule-based selection of informative voxels and the concomitant issue of wide spatial variability of such voxels across participants. The perturbation experiments indicate that the proposed deep encoder indeed learns to predict brain activations with high spatial accuracy. On the challenging universal decoder imaging datasets (Pereira et al., 2018), our model yielded encouraging results.

Abstract

The dispute of how the human brain represents conceptual knowledge has been argued in many scientific fields. Brain imaging studies have shown that the spatial patterns of neural activation in the brain are correlated with thinking about different semantic categories of words (for example, tools, animals, and buildings) or when viewing the related pictures. In this paper, we present a computational model that learns to predict the neural activation captured in functional magnetic resonance imaging (fMRI) data of test words. Unlike the models with hand-crafted features that have been used in the literature, in this paper we propose a novel approach wherein decoding models are built with features extracted from popular linguistic encodings of Word2Vec, GloVe, Meta-Embeddings in conjunction with the empirical fMRI data associated with viewing several dozen concrete nouns. We compared these models with several other models that use word features extracted from FastText, Randomly-generated features, Mitchell’s 25 features [1]. The experimental results show that the predicted fMRI images using Meta-Embeddings meet the state-of-the-art performance. Although models with features from GloVe and Word2Vec predict fMRI images similar to the state-of-the-art model, model with features from Meta-Embeddings predicts significantly better. The proposed scheme that uses popular linguistic encoding offers a simple and easy approach for semantic decoding from fMRI experiments.