Reports
Co-authored open-access pre-prints/reports (arXiv, bioRxiv and PeerJ pre-print)
bioRxiv (co-authored):
Automatic Identification of SARS Coronavirus using Compression-Complexity Measures, bioRxiv, March 27, 2020, doi: https://doi.org/10.1101/2020.03.24.006007
Distinguishing Different Levels Of Consciousness using a Novel Network Causal Activity Measure, bioRxiv, June 6, 2019. doi: https://doi.org/10.1101/660043.
NIAS report:
Shailaja D Sharma and Nithin Nagaraj, "On The NIAS Logo by Roddam Narasimha", Special Publication, NIAS/HUM/MHI/U/SP/08/2021, National Institute of Advanced Studies, Bengaluru, March 2021. (link)
Narendar Pani, Tarun Menon, Nithin Nagaraj, Tejal Kanitkar and Srikumar Menon, "Towards an Institutional Strategy for The Study of Sustainability", Research Report: NIAS/U/RR/09/2020, National Institute of Advanced Studies, Bengaluru, May 2020.
PeerJ Pre-print (co-authored):
Data based intervention approach for Complexity-Causality measure, PeerJ Pre-print, December, 2018.
arXiv (co-authored):
Random Heterogeneous Neurochaos Learning Architecture for Data Classification, arXiv:2410.23351 [cs.LG]
Permutation Decision Trees, arXiv:2306.02617v3 [cs.LG]
Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru, arXiv:2401.13977 [cs.LG]
Compression Spectrum: Where Shannon Meets Fourier, arXiv:2309.11640 [cs.IT]
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks, arXiv:2308.09955 [cs.LG]
Kolam Simulation using Angles at Lattice Points, arXiv:2307.02144 [cs.IT]
Granger Causality for Compressively Sensed Sparse Signals, arXiv:2210.11420 [eess.SP]
Neurochaos Feature Transformation and Classification for Imbalanced Learning, arXiv:2205.06742 [cs.NE]
Fairly Constricted Multi-Objective Particle Swarm Optimization, arXiv:2104.10040v4 [cs.NE]
Cause-Effect Preservation and Classification using Neurochaos Learning, arXiv:2201.12181v1 [cs.LG]
Learning Generalized Causal Structure in Time-series, arXiv:2112.03085v1 [cs.LG]
Problems with information theoretic approaches to causal learning, arXiv:2110.12497v1 [cs.IT]
Causal Analysis of Carnatic Music: A Preliminary Study, arXiv:2109.11782v1 [cs.SD]
When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning, arXiv:2102.01316 [q-bio.NC]
A Neurochaos Learning Architecture for Genome Classification, arXiv:2010.10995v1 [cs.NE]
Causal Discovery using Compression-Complexity Measures, arXiv:2010.09336v3 [cs.LG]
Measuring Causality: The Science of Cause and Effect, arXiv:1910.08750v1 [stat.ME]
ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification, arXiv:1910.02423v1 [cs.LG]
Causal Stability and Synchronization, arXiv:1907.00785v1 [math.DS]
Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets, arXiv:1906.01975 [astro-ph.IM]
A Novel Chaos Theory Inspired Neuronal Architecture, arXiv:1905.12601 [q-bio.NC]
A Two-Parameter Model for Ultrasonic Tissue Characterization with Harmonic Imaging, arXiv:1712.03495 [physics.med-ph]
Causality Testing: A Data Compression Framework, arXiv:1710.04538 [physics.data-an]
Simulation Study of Two Measures of Integrated Information, arXiv:1706.09570v1 [q-bio.NC]
New Empirical Evidence on Disjunction Effect and Cultural Dependence, arXiv:1703.00223 [q-bio.NC]
Three Perspectives on Complexity - Entropy, Compression, Subsymmetry, arXiv:1611.00607 [physics.data-an]
Dynamical Complexity Of Short and Noisy Time Series, arXiv:1609.01924v2 [nlin.CD]
A Compression-Complexity Measure of Integrated Information, arXiv:1608.08450 [cs.IT]
Cardiac Aging Detection Using Complexity Measures, arXiv:1603.00817 [physics.med-ph]
Neural Signal Multiplexing via Compressed Sensing, arXiv:1601.03214 [cs.IT]
Comment on 'Interpretation of the Lempel-Ziv Complexity Measure in the context of Biomedical Signal Analysis', arXiv:1308.0130 [nlin.CD]
Classification of Periodic, Chaotic and Random Sequences using NSRPS Complexity Measure, arXiv:1205.4886v1 [nlin.CD]
Lossless Data Compression with Error Detection using Cantor Set, arXiv:1308.2299v1 [cs.IT]
Lossless Compression and Complexity of Chaotic Sequences, arXiv:1101.4341v1 [nlin.CD]
Sharing Graphs, arXiv:1009.2832v1 [cs.CR]
How not to share a set of secrets, arXiv:1001.1877v2 [cs.CR]
Huffman Coding as a Non-linear Dynamical System, arXiv:0906.3575v1 [nlin.CD]
Increasing Average Period Lengths by Switching of Robust Chaos Maps in Finite Precision, arXiv:0811.1823v1 [nlin.CD]
Multiplexing of Discrete Chaotic Signals in Presence of Noise, arXiv:0810.5221v1 [nlin.CD]
One-Time Pad, Arithmetic Coding and Logic Gates: An unifying theme using Dynamical Systems, arXiv:0803.0046v1 [nlin.CD]
A Non-linear Dynamical Systems' Proof of Kraft-McMillan Inequality and its Converse, arXiv:0710.5898v1 [nlin.CD]
A non-linear dynamical systems approach to source compression for constrained sources, arXiv:0709.1545v1 [nlin.CD]
Joint Entropy Coding and Encryption using Robust Chaos, arXiv:nlin/0608051v1 [nlin.CD]
The B-Exponential Map: A Generalization of the Logistic Map, and Its Applications In Generating Pseudo-random Numbers, arXiv:cs/0607069v2 [cs.CR]
Re-visiting the One-Time Pad, arXiv:cs/0508079v1 [cs.CR]
Cryptanalysis of a Chaotic Image Encryption Algorithm, arXiv:0801.0276v2 [nlin.CD]
A Non-linear Generalization of Singular Value Decomposition and its Application to Cryptanalysis, arXiv:0711.4910v1 [nlin.CD]
I am qualified to endorse in the following arXiv categories: cs.CR, cs.IT, cs.LG, math.IT, physics.data-an, q-bio.NC (but this keeps changing, so please check arXiv for the current status)
