arXiv subjects

Athokpam Langlen Chanu

Publications and source records attributed to Athokpam Langlen Chanu.

10 recordsLinked to original sources

arXiv2026

Learning a quantitative criterion for distinguishing chaos from noise

Distinguishing chaos from noise using time-series data is fundamentally challenging because both exhibit irregular fluctuations and share many statistical and dynamical characteristics. Existing methods face two key limitations: temporally correlated noise can yield spurious signatures of chaos, and analyses of scalar time series often require explicit choices of embedding parameters. Here, we propose a purely data-driven method for distinguishing chaos and noise based on a reservoir-computing framework with a cross-prediction scheme. In the proposed approach, the model is trained to predict the future change of a variable from its current value, thereby combining a short-term predictability test with a test of the smoothness of deterministic flows. The recurrent structure of reservoir computing enables effective prediction of high-dimensional chaotic dynamics even from scalar time series without explicit delay-coordinate reconstruction, while the cross-prediction framework strongly suppresses spurious predictive correlations arising from noise. We apply the proposed method to diverse synthetic and empirical time series. Chaotic systems consistently yield strong correlations between the true and predicted future changes, whereas noise processes remain clearly separated in a low-correlation regime. The method also exhibits substantial robustness to practical limitations in empirical data, including measurement noise, limited data length, and increasing prediction lag. These results demonstrate that the squared Pearson correlation coefficient provides a simple quantitative criterion for distinguishing chaos from noise directly from observed time-series data.

nlin.CD↗

arXiv2026

Human brain state classification via permutation entropy of EEG phase dynamics across consciousness levels and inattentive-type ADHD

We analyze electroencephalography (EEG) signals using the ordinal pattern framework to investigate whether different human brain states can be distinguished based on the disorder of EEG dynamics. Rather than analyzing raw EEG signals, we focus on the principal mode of EEG phase dynamics, reflecting anterior-posterior information flow, and quantify disorder using permutation entropy. We apply this to two datasets: (i) EEG recordings from a general anesthesia protocol, and (ii) EEG recordings acquired in the resting state from healthy control subjects and individuals with inattentive-type attention deficit hyperactivity disorder (ADHD), including eyes-open and eyes-closed conditions. We find that the permutation entropy distributions exhibit a clear dependence on brain state. In particular, conscious, inattentive-type ADHD, and eyes closed conditions show lower mean values and larger standard deviations of permutation entropy. To evaluate the discriminative power of permutation entropy, we train classification models using permutation entropy as an input feature. The results show that the distinction between conscious and unconscious states can be reliably captured in the general-anesthesia dataset. In the resting-state dataset, eyes-open and eyes-closed conditions are distinguishable, whereas classification between control and inattentive-type ADHD groups does not show clear separability. This indicates that information not captured in ordinal patterns, such as the original time-series values, may play a more crucial role in detecting inattentive-type ADHD. Our findings demonstrate that permutation entropy derived from EEG phase dynamics provides an effective indicator of brain states, particularly in relation to consciousness, while also highlighting its limitations for identifying individuals with inattentive-type ADHD.

q-bio.NC↗

arXiv2026

Morphological complexity of NGC 628 - a multiwavelength multiscale analysis using the ordinal pattern framework

As statistical systems, galaxies exhibit a rich interplay between organized structure and stochastic fluctuations across a broad range of spatial scales. This duality motivates the need for quantitative frameworks capable of capturing their morphological complexity. The ordinal patterns framework, along with its associated statistical measures: permutation entropy ($H$), disequilibrium ($D_E$), statistical complexity ($C$), and ordinal network node entropy, has recently emerged as a powerful tool for analyzing such complexity in physical systems. We apply this framework in a multiwavelength, multiscale analysis of the galaxy NGC 628, utilizing observations in the near-ultraviolet, near-infrared, mid-infrared, and millimeter bands. Our results reveal a characteristic spatial scale of approximately 200 parsecs, marking the transition from small-scale structures influenced by star formation and stellar feedback to larger-scale morphology governed by the galaxy's dynamics. Furthermore, we find that the $C$ vs. $H$ trajectories for all wavelengths converge toward a common attractor curve, consistent with the behavior of isotropic Gaussian random fields. This convergence suggests a universal statistical behavior in galactic structure at large scales, despite the differing physical processes traced by each wavelength.

astro-ph.GA↗

arXiv2026

A hybrid dynamical-stochastic model of maximum temperature time series of Imphal, Northeast India incorporating nonlinear feedback and noise diagnostics

Climate variability is a complex phenomenon resulting from numerous interacting components of a climate system across a wide range of temporal and spatial scales. Although significant advances have been made in understanding global climate variability, there are relatively less studies on regional climate modeling, particularly in developing countries. In this work, we propose a framework of data driven hybrid dynamical stochastic modeling to investigate the variability of maximum temperature recorded for the capital city of Imphal in the state of Manipur, located in the Northeast India. In light of increasing concerns over global warming, studying maximum temperature variability over varying time scales is an important area of research. Analysis using publicly available climate data over the course of 73 years, our approach yields key insights into the temperature dynamics, such as a positive increase in temperature in the region during the period investigated. Our hybrid model, combining spectral analysis and Fourier decomposition methods with stochastic noise terms and nonlinear feedback mechanisms, is found to effectively reproduce the observed dynamics of maximum temperature variability with high accuracy. Our results are validated by robust statistical and qualitative tests. We further derive Langevin and Fokker-Planck equations for the maximum temperature dynamics, offering the theoretical ground and analytical interpretation of the model that links the temperature dynamics with underlying physical principles.

nlin.CD↗

arXiv2024

Exploring the Interplay of Intrinsic Fluctuation and Complexity in Intracellular Calcium Dynamics

The concentration of intracellular calcium ion (Ca$^{2+}$) exhibits complex oscillations, including bursting and chaos, as observed experimentally. These dynamics are influenced by inherent fluctuations within cells, which serve as crucial determinants in cellular decision-making processes and fate determination. In this study, we systematically explore the interplay between intrinsic fluctuation and the complexity of intracellular cytosolic Ca$^{2+}$ dynamics using complexity measures such as permutation entropy (PE) and statistical complexity (SC). Using the chemical Langevin equation, we simulate the stochastic dynamics of cytosolic Ca$^{2+}$. Our findings reveal that PE and SC effectively characterize the diverse, dynamic states of cytosolic Ca$^{2+}$ and illustrate their interactions with intrinsic fluctuation. PE analysis elucidates that the chaotic state is more sensitive to intrinsic fluctuation than the other periodic states. Furthermore, we identify distinct states of cytosolic Ca$^{2+}$ occupying specific locations within the theoretical bounds of the complexity-entropy causality plane. These locations indicate varying complexity and information content as intrinsic fluctuation varies. When adjusting the permutation order, the SC for the different states exhibits peaks in an intermediate range of intrinsic fluctuation values. Additionally, we identify scale-free or self-similar patterns in this intermediate range, which are further corroborated by multifractal detrended fluctuation analysis. These high-complexity states likely correspond to optimal Ca$^{2+}$ dynamics with biological significance, revealing rich and complex dynamics shaped by the interplay of intrinsic fluctuation and complexity. Our investigation enhances our understanding of how intrinsic fluctuation modulates the complexity of intracellular Ca$^{2+}$ dynamics that play crucial roles in biological cells.

nlin.AO↗

arXiv2024

Quantifying dissipation in stochastic complex oscillations

Fluctuations-driven complex oscillations are experimentally observed in cellular systems such as hepatocytes, cardiac cells, neuronal cells, etc. These systems are generally operating in regimes far from thermodynamic equilibrium. To study nonequilibrium thermodynamic properties such as energy dissipation in stochastic complex oscillations, we consider stochastic modeling of two nonlinear biological oscillators, namely, the intracellular calcium (Ca$^{2+}$) oscillation model and the Hindmarsh-Rose model for neuronal dynamics. These models exhibit various types of complex oscillations like bursting and quasi-periodic oscillations for various system parameter values. In this work, we formulate open chemical reaction schemes for the two model systems driving the systems far from thermodynamic equilibrium. We then analyze the steady-state total entropy production rate (EPR) in the various types of stochastic complex oscillations. Our results show higher values of steady-state total EPR in stochastic complex oscillations than simple periodic oscillations. Moreover, in the Hindmarsh-Rose neuronal model, we observe an order-to-disorder transition from periodic (organized) bursts of spikes to chaotic (unorganized) oscillations with distinct behaviors of steady-state total EPR. Our results reveal that stochastic complex oscillations are produced at the cost of higher energy consumption and that it requires a higher thermodynamic cost to maintain the periodic bursts than chaotic oscillations. Our findings indicate that complex cellular regulatory or signaling processes by Ca$^{2+}$ that help perform complex tasks of the nervous system or rich information coding by neurons involve a higher thermodynamic cost. The results deepen our understanding of energy dissipation in nonlinear, nonequilibrium biological systems with stochastic complex oscillatory dynamics.

physics.chem-ph↗

arXiv2024

Analysis of the structural complexity of Crab Nebula observed at radio frequency using a multifractal approach

The Crab Nebula is an astrophysical system that exhibits complex morphological patterns at different observing frequencies. We carry out a systematic investigation of the structural complexity of the nebula using publicly available imaging data at radio frequency. For the analysis, we use the well-known multifractal detrended fluctuation analysis in two dimensions. We find that radio data exhibit long-range correlations, as expected from the underlying physics of the supernova explosion and evolution. The correlations follow a power-law scaling with length scales. The structural complexity is found to be multifractal in nature, as evidenced by the dependence of the generalized Hurst exponent on the order of the moments of the detrended fluctuation function. By repeating the analysis on shuffled data, we further probe the origin of the multifractality in the radio imaging data. For the radio data, we find that the probability density function is close to a Gaussian form. Hence, the multifractal behavior is due to the differing nature of long-range correlations of the large and small detrended fluctuation field values. We investigate the multifractal parameters across different partitions of the radio image and find that the structures across the image are highly heterogeneous, making the Crab Nebula a structurally complex astrophysical system. Our analysis thus provides a fresh perspective on the morphology of the Crab Nebula from a complexity science viewpoint.

nlin.AO↗

arXiv2022

Thermodynamic Uncertainty Relation in the interlinked cascade of RabGTPases

We model the well-known interlinked cascade of Rab GTPases found in eukaryotic cells by using a network of Markov states to investigate the universal Thermodynamic Uncertainty Relation for the non-equilibrium system. First, we prove numerically the TUR in both single Rab species model and the interlinked two Rab species model. Our results show that when two Rab GTPase proteins are interlinked at far from equilibrium, the thermodynamic cost and hence precision is greatly enhanced as compared to single species switching. This implies that at far from equilibrium, the proteins tries to optimise the precision of the performance of their biomolecular processes by forming interlinks in the cascade. Again,our results imply that the interlinked cascade (or oscillator) can achieve a range of tunable rate constants (or frequencies) which suggests a means of maintaining its robustness. Lastly, we highlight a close relation between thermodynamic cost-precision, triangular motifs and disease dynamics.

physics.bio-ph↗

arXiv2021

Non-Markovian process with variable memory functions

We present a treatment of non-Markovian character of memory by incorporating different forms of Mittag-Leffler (ML) function, which generally arises in the solution of fractional master equation, as different memory functions in the Generalized Kolmogorov-Feller Equation (GKFE). The cross-over from the short time (stretched exponential) to long time (inverse power law) approximations of the ML function incorporated in the GKFE is proven. We have found that the GKFE solutions are the same for negative exponential and for upto frst order expansion of stretched exponential function for very small $τ\rightarrow 0$. A generalized integro-differential equation form of the GKFE along with an asymptotic case is provided.

cond-mat.stat-mech↗

arXiv2020

Stochastic approach to study control strategies of Covid-19 pandemic in India

India is one of the worst affected countries by the Covid-19 pandemic at present. We studied publicly available data of the Covid-19 patients in India and analyzed possible impacts of quarantine and social distancing within the stochastic framework of the SEQIR model to illustrate the controlling strategy of the pandemic. Our simulation results clearly show that proper quarantine and social distancing should be maintained from an early time just at the start of the pandemic and should be continued till its end to effectively control the pandemic. This calls for a more socially disciplined lifestyle in this perspective in future. The demographic stochasticity, which is quite visible in the system dynamics, has a critical role in regulating and controlling the pandemic.

q-bio.PE↗