Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

School of Artificial Intelligence and Data Science (AIDE)

We assess change in metastability to characterize age-effects on the dynamic repertoire of the functional networks at rest. Resting state fMRI signals from each subject (N=48) have been used and metastability is evaluated as the standard deviation of mean phase synchrony of BOLD signals across whole-brain as well as across known resting state networks. The results suggest that significant whole-brain metastability changes occur between middle to old age. We also demonstrate that static time-averaged FC largely undermines age-effects on the interaction between functional networks. Discriminant Function Analysis reveals existence of two different patterns of change in metastability, which maximally discriminates between two different processes of maturation and ageing. © 2017 IEEE.

World University root node

Proceedings of the International Joint Conference on Neural Networks

Metastability of cortical BOLD signals in maturation and senescence

Here, we argue systematically about the promises and pitfalls of relating Human Connectome to cognitive enhancement. We also highlight three key areas where further resolution is required before the generalization of the white-matter-related cause of cognitive enhancement across a variety of cognitive modalities is made. These key areas are: (a) inherent limitations in estimating of diffusion-weighted anisotropy index near volumes with high abundance of crossing fibers; (b) species differences in cell types and only a putative link between brain rhythms and modulation of activity in precursor cells in rodents; (c) sparse evidence of finding white matter structural connectivity change between two remote brain networks causing cognitive enhancement. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

Cognitive Neuroscience

Promises and pitfalls of relating alteration of white matter pathways causing improvement in cognitive performance

Experimental data show that perceptual cues can either exacerbate or ameliorate freezing of gait (FOG) in Parkinson’s Disease (PD). For example, simple visual stimuli like stripes on the floor can alleviate freezing whereas complex stimuli like narrow doorways can trigger it. We present a computational model of the cognitive and motor cortico-basal ganglia loops that explains the effects of sensory and cognitive processes on FOG. The model simulates strong causative factors of FOG including decision conflict (a disagreement of various sensory stimuli in their association with a response) and cognitive load (complexity of coupling a stimulus with downstream mechanisms that control gait execution). Specifically, the model simulates gait of PD patients (freezers and non-freezers) as they navigate a series of doorways while simultaneously responding to several Stroop word cues in a virtual reality setup. The model is based on an actor-critic architecture of Reinforcement Learning involving Utility-based decision making, where Utility is a weighted sum of Value and Risk functions. The model accounts for the following experimental data: (a) the increased foot-step latency seen in relation to high conflict cues, (b) the high number of motor arrests seen in PD freezers when faced with a complex cue compared to the simple cue, and (c) the effect of dopamine medication on these motor arrests. The freezing behavior arises as a result of addition of task parameters (doorways and cues) and not due to inherent differences in the subject group. The model predicts a differential role of risk sensitivity in PD freezers and non-freezers in the cognitive and motor loops. Additionally this first-of-its-kind model provides a plausible framework for understanding the influence of cognition on automatic motor actions in controls and Parkinson’s Disease. © 2017 Muralidharan, Balasubramani, Chakravarthy, Gilat, Lewis and Moustafa.

Fulltext

Frontiers in Human Neuroscience

A neurocomputational model of the effect of cognitive load on freezing of gait in parkinson’s disease

Evolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decisionmaker (DM) is confounded with the primary task of selecting the best solution amongst all the equally good solutions on the Pareto-front. In this paper, we complement the popular NSGA-2 EMOA by posteriori identifying a DM’s best solution among the candidate solutions on the Pareto-front, generated through NSGA-2. To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects the compromises the DM makes against a set of conflicting objectives. The solution that is closest to this reference-point is then predicted as the DM’s best solution. The pairwise comparisons of the candidate solutions provides the training information for our learning model. The experimental results on ZDT1 dataset shows that the proposed approach is not only intuitive, but also easy to apply, and robust to inconsistencies in the DM’s preference statements. © Springer International Publishing AG 2017.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Preferences-based choice prediction in evolutionary multi-objective optimization

The decision making in the real world is inevitably characterized with vagueness, and imprecision due to incomplete knowledge. To this end, we combine the information set with the rough set theory to represent both the vagueness and imprecision at the same time. We term the proposed structure as rough information set that has information sets based on fuzzy equivalence relations as its building blocks. The usefulness of the proposed structure is demonstrated through a case study in credit scoring analysis, and a biometrics application on knuckle-based recognition. © 1993-2012 IEEE.

IEEE Transactions on Fuzzy Systems

Rough Information Set and Its Applications in Decision Making

In this article, we would like to trace the history of the growth of diesel traction of Indian railways without completely sacrificing technical details. It starts from the very early times and discusses at length the coming of the ALCO locomotives on the Indian Railways (IR) and the reappearance of EMD locos on IR. We have tried to mix the historical facts with technical facts therefore calling the study presented in this paper as technical history. © Springer Nature Singapore Pte Ltd. 2017. All rights reserved.

Locomotives and Rail Road Transportation: Technology, Challenges and Prospects

Diesel locomotives of indian railways: A technical history

In this article, our main aim is to develop gap functions and error bounds for a (non-smooth) convex vector optimization problem. We show that by focusing on convexity we are able to quite efficiently compute the gap functions and try to gain insight about the structure of set of weak Pareto minimizers by viewing its graph. We will discuss several properties of gap functions and develop error bounds when the data are strongly convex. We also compare our results with some recent results on weak vector variational inequalities with set-valued maps, and also argue as to why we focus on the convex case. © 2017 Informa UK Limited, trading as Taylor & Francis Group.

Optimization

Gap functions and error bounds for nonsmooth convex vector optimization problem

Generalized attitudinal Choquet integral (GACI) is a recent aggregation operator that subsumes a multitude of aggregation operators, including both linear as well as non-linear and exponential integrals. In this study, against the background of preference learning, we use the GACI operator to represent the utility function of a decision-maker (DM), and learn its parameters. The exemplary preference information in the form of pair-wise comparisons of alternatives constitutes the training information. More specifically, given the exemplary pairwise choices of a DM, we present an approach to infer the unique preference model of the DM, in terms of the parameter values of GACI operator. We test our approach on standard datasets, and the prediction performance is compared with state-of-the-art methods. © 2016 Elsevier B.V.

Knowledge-Based Systems

Learning of aggregation models in multi criteria decision making

Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300-600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus, our study indicates that the temporal integration underlying multisensory speech perception requires to be understood in the framework of large-scale functional brain network mechanisms in addition to the established cortical loci of multisensory speech perception. © 2016 Kumar, Halder, Jaiswal, Mukherjee, Roy and Banerjee.

Frontiers in Psychology

Large scale functional brain networks underlying temporal integration of audio-visual speech perception: An eeg study

Stroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The poststroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS) techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEG) and functional-near-infrared spectroscopy (fNIRS) can be leveraged for Brain State Dependent Electrotherapy (BSDE). In this hypothesis and theory article, we propose a computational approach based on excitation-inhibition (E-I) balance hypothesis to objectively quantify the poststroke individual brain state using online fNIRS-EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local E-I (that is the ratio of Glutamate/GABA), which may be targeted with NIBS using a computational pipeline that includes individual "forward models" to predict current flow patterns through the lesioned brain or brain target region. The current flow will polarize the neurons, which can be captured with E-I-based brain models. Furthermore, E-I balance hypothesis can be used to find the consequences of cellular polarization on neuronal information processing, which can then be implicated in changes in function. We first review the evidence that shows how this local imbalance between E-I leading to functional dysfunction can be restored in targeted sites with NIBS (motor cortex and somatosensory cortex) resulting in large-scale plastic reorganization over the cortex, and probably facilitating recovery of functions. Second, we show evidence how BSDE based on E-I balance hypothesis may target a specific brain site or network as an adjuvant treatment. Hence, computational neural mass model-based integration of neurostimulation with online neuroimaging systems may provide less ambiguous, robust optimization of NIBS, and its application in neurological conditions and disorders across individual patients. © 2016 Dagar, Chowdhury, Bapi, Dutta and Roy.

Frontiers in Neurology

Near-infrared spectroscopy - electroencephalography-based brain-state-dependent electrotherapy: A computational approach based on excitation-inhibition balance hypothesis