Abstract

Encoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that frst mapped musical features in auditory cortices and then decoded novel musical pieces. We then probed the infuence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We further showed that Shannon entropy is a driving factor, boosting accuracy up to 95% for music with highest information content. These fndings provide key insights for future decoding and reconstruction algorithms and open new venues for possible clinical applications.

Abstract

There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. New method: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined.

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

Bug report filing is a major part of software maintenance. Due to extensive number of bugs filed everyday in large software projects and the asynchronous nature of bug report filing ecosystem, duplicate bug reports are filed. Capturing and tagging duplicate bug reports is crucial in order to avoid assignment of the same bug to different developers. Efforts have been made in the past to detect duplicate bug reports by using topic modelling [2], discriminative methods [5], meta-attributes [6], etc. Recently, Yang et al.[8] proposed an approach to combine word embeddings, TF-IDF and meta-attributes to compute bug similarity between two bug reports.

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.