2835 research outputs found
Sort by
Is Backwardness Immune to State Intervention? (NIAS/SSc/IHD/U/WP/16/2023)
Get PDFThis paper uses the experience of a village in the Kalyana Karnataka region to argue that state investment does not always remove backwardness, the impact of state investment is constrained by the fact that secondary investments prompted by the original spending is not limited within the region
Spatio-temporal variability and possible source identification of criteria pollutants from Ahmedabad-a megacity of Western India
No full textThis study addresses the spatio-temporal variability and plausible sources of criteria air pollutants in the Western Indian city-Ahmedabad. The air pollutants PM10, PM2.5, O3, NO2, SO2, and CO have been analyzed at ten locations in Ahmedabad from 2017 to 2019. The seasonal variability indicates that the air pollutant concentration is highest during winter, followed by pre-monsoon, post-monsoon, and monsoon seasons. The concentration of PM2.5 (59.52 ± 16.68–89.72 ± 20.68) and PM10 (107.25 ± 30.43–176.04 ± 38.34) crosses the National Ambient Air Quality Standards (NAAQS) in all seasons. However, the seasonal difference from winter to pre-monsoon is not highly significant (p > 0.05), indicating that the pollution remains fairly similar during these two seasons. The spatial variability of air pollutants over Ahmedabad indicates that the concentration is highest in the south and central region of Ahmedabad and lowest at the east location. The Ventilation Coefficient (VC) has been used to understand the dispersion of air pollutants. The K-means clustering was performed to assess the locations within Ahmedabad with similar air pollutants sources followed by source identification using Principal Component Analysis-Multiple Linear Regression method (PCA-MLR) of 5 clusters. The different locations identified were industrial, residential, and traffic which mainly contribute to the air pollutants in Ahmedabad city. The health risk assessment indicates PMs are the leading pollutant and causing excess risk (ER > 1) at all the locations. With the help of the different statistical techniques, it helps in ascertaining the hotspots of air pollution in a region which will be beneficial in studying health exposure and for policymakers to adopt mitigation strategies
Analysis of logistic map based neurons in neurochaos learning architectures for data classification
No full textArtificial neurons used in Artificial Neural Networks and Deep Learning architectures do not mimic the chaotic behavior of biological neurons found in the brain. Recently, a chaos based learning algorithm namely Neurochaos Learning (NL) for classification has been proposed that gives comparable performance with state-of-the-art Machine Learning methods and sometimes exceeding them, especially in the low training sample regime. NL uses 1D chaotic maps namely Generalized Lüroth Series (GLS) as neurons and the success of NL owes to the rich properties of these chaotic GLS maps. In this study, we propose for the first time, two extensions to NL: (a) 1D Logistic map neurons instead of GLS neurons, and (b) Heterogeneous Neurochaos Learning (HNL) architecture which incorporates both GLS and Logistic map neurons which is inspired by the presence of heterogeneous types of neurons in the human brain and central nervous system. We evaluate the performance of these proposed schemes on classification tasks on well known publicly available datasets such as Iris, Ionosphere, Wine, Bank Note Authentication, Haberman’s Survival, Breast Cancer Wisconsin, Statlog (Heart) and Seeds. We extract chaos-based features (ChaosFEX) from these NL architectures and feed them to SVM to further boost classification performance. Results indicate the superior performance of these proposed architecture over homogeneous NL (GLS neurons) architecture in most cases. We also investigate the relationship between the degree of chaos as measured by the Lyapunov exponent and the classification accuracies obtained by NL using logistic map (with different ‘
’ values) and heterogeneous neurons
Granger causality for compressively sensed sparse signals
No full textCompressed sensing is a scheme that allows for sparse signals to be acquired, transmitted, and stored using far fewer measurements than done by conventional means employing the Nyquist sampling theorem. Since many naturally occurring signals are sparse (in some domain), compressed sensing has rapidly seen popularity in a number of applied physics and engineering applications, particularly in designing signal and image acquisition strategies, e.g., magnetic resonance imaging, quantum state tomography, scanning tunneling microscopy, and analog to digital conversion technologies. Contemporaneously, causal inference has become an important tool for the analysis and understanding of processes and their interactions in many disciplines of science, especially those dealing with complex systems. Direct causal analysis for compressively sensed data is required to avoid the task of reconstructing the compressed data. Also, for some sparse signals, such as for sparse temporal data, it may be difficult to discover causal relations directly using available data-driven or model-free causality estimation techniques. In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger causality (GC). We then verify this theorem on a number of bivariate and multivariate coupled sparse signal simulations which are compressed using these matrices. We also demonstrate a real world application of network causal connectivity estimation from sparse neural spike train recordings from rat prefrontal cortex. In addition to demonstrating the effectiveness of structured matrices for GC estimation from sparse signals, we also show a computational time advantage of the proposed strategy for causal inference from compressed signals of both sparse and regular autoregressive processes as compared to standard GC estimation from original signals
Paradigm shift to professional psychological practices towards creating an inclusive Society
No full textPerspectives on Capital Punishment: In Observation of the 75th Year of the Assassination of Mahatma Gandhi (Panel Discussion)
No full textPanelists
Prof. Narendar Pani (NIAS)
Dr. Aparna Chandra (NLSIU)
Ms. Preeti Pratishruti Dash (NLSIU)
Moderator
Prof. Sarasu Esther Thomas (NISUL
Compression-Complexity Measures for Analysis and Classification of Coronaviruses
No full textFinding a vaccine or specific antiviral treatment for a global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In this study, we investigate the use of compression-complexity (Effort-to-Compress or ETC and Lempel-Ziv or LZ complexity) based distance measures for analyzing genomic sequences. The proposed distance measure is used to successfully reproduce the phylogenetic trees for a mammalian dataset consisting of eight species clusters, a set of coronaviruses belonging to group I, group II, group III, and SARS-CoV-1 coronaviruses, and a set of coronaviruses causing COVID-19 (SARS-CoV-2), and those not causing COVID-19. Having demonstrated the usefulness of these compression complexity measures, we employ them for the automatic classification of COVID-19-causing genome sequences using machine learning techniques. Two flavors of SVM (linear and quadratic) along with linear discriminant and fine K Nearest Neighbors classifer are used for classification. Using a data set comprising 1001 coronavirus sequences (causing COVID-19 and those not causing COVID-19), a classification accuracy of 98% is achieved with a sensitivity of 95% and a specificity of 99.8%. This work could be extended further to enable medical practitioners to automatically identify and characterize coronavirus strains and their rapidly growing mutants in a fast and efficient fashion
National Air Quality Resource Framework of India (NARFI), (NIAS/NSE/EECP/U/WR/01/2023)
No full textAir pollution is serious and most talked about issue in India as impacts are fast realized in a shorter time, unlike climate change. North India including the Indian capital experience extreme levels of pollution during winters due to extreme weather compounded by biomass burning episodes. On the other hand, Western India often witnesses pollution extremes during summer due to intercontinental dust flows whereas the peninsular and east India relatively remain better, but the frequency of weather extremes is increasing, and climate change is impacting the delicate balance. It is resulting in deteriorating health and food security. The problem is well recognized, but information and actions are fragmented warranting an integrated approach to build a resource framework for air quality in a mission mode. Network of specialists working on various aspects of air pollution and knowledge sharing for optimal and broad-scale results that benefit all. To address this gap, the National Institute of Advanced Study (NIAS, IISc Campus, Bengaluru) organized the brainstorming workshop for stakeholders along with the office of Principal Scientific Advisor to Govt. of India to help develop pathways for dialogue and knowledge sharing for air pollution information, alerts, capacity building, effective mitigation, and policy at the national level. The current strategic document is an outcome of this effort. This NARFI aims to build an integrated framework in air quality for a sustainable future by bringing together researchers, practitioners, and experts from diverse sectors and disciplines – air quality monitoring, health, and social inclusion, livelihoods, knowledge management, and communication, as well as those dealing with open burning, transport sectors, etc. Also, regional pollution issues like stubble burning, smog, and fog –to share their knowledge and experience on air pollution. The framework will support the public-industry partnership in air quality management that has the potential to influence decision-makers
