arXiv subjects

Suman Majumder

Publications and source records attributed to Suman Majumder.

At least 19 recordsLinked to original sources

arXiv2026

Spatially-Indexed Longitudinal Distributional Outcome Regression for Environmental Monitoring

Characterizing longitudinal changes in region-specific distribution of environmental exposures, such as total nitrate (TNO$_3$) concentrations, is critical for understanding localized ecological risks that are otherwise obscured by standard mean-level modeling. However, modeling longitudinal distributional outcomes across spatial regions presents significant methodological challenges. The random objects are spatio-temporally dependent, and mathematical constraints are inherent to distributional representations, such as, monotonicity of quantile functions. To address this, we propose a novel spatially-indexed longitudinal distributional outcome regression model. The distributional coefficients corresponding to the fixed effects of covariates are modeled using Bernstein basis polynomials, while spatio-temporal random effects are flexibly captured via tensor product expansions of splines. We develop a scalable Markov Chain Monte Carlo (MCMC) algorithm to explicitly account for spatial dependencies, and introduce a fast two-stage projected-posterior approach to preserve the monotonicity of the predicted subject-specific quantile functions. Extensive simulation studies demonstrate that the proposed framework achieves superior estimation accuracy and predictive performance compared to standard non-spatial distributional outcome regression. We apply our methodology to predict monthly, site-specific distributions of TNO$_3$ concentrations across the contiguous United States. Accounting for spatial correlation provides substantially lower uncertainty in the estimated distributional effects and improves predictive performance over the non-spatial alternative, offering a robust, interpretable tool for spatio-temporal environmental monitoring.

stat.ME↗

arXiv2026

Persistence Properties of a Phase-ordering System with Competing Dynamics

We investigate the persistence properties during phase ordering in the two-dimensional ($d=2$) Ising model evolving under competing nonconserved spin-flip and conserved spin-exchange dynamics by means of Monte Carlo simulations at zero temperature. We examine three distinct persistence probabilities: (i) the total persistence probability, defined as the probability that a lattice site has never experienced a change in the sign of the spin residing there; (ii) the spin-flip persistence probability, which exclusively measures the fraction of sites that have never undergone a spin-flip event; and (iii) the composite persistence probability, defined as the fraction of sites that have experienced neither a spin-flip nor a spin-exchange event. In the asymptotic regime, both the total and spin-flip persistence probabilities exhibit identical power-law decay, irrespective of the relative occurrence probability of the spin-flip move $p_r$. The corresponding persistence exponent $θ_i \approx 0.225$, is found to be consistent with the value reported for systems evolving purely under nonconserved dynamics. We further demonstrate that both persistence measures satisfy the scaling relation $d-d_f^i=θ_i/α_i$, where $d_f^i$ is the fractal dimension of the corresponding persistence lattice and $α_i\approx 1/2$ characterizes the asymptotic power-law growth of spatially correlated regions of non-persistent spins. In contrast, although the composite persistence probability also exhibits asymptotic power-law decay, both the corresponding persistence exponent $θ_{\rm c}$ and the fractal dimension $d_f^{\rm c}$ of the persistence lattice show strong dependence on $p_r$. Combined with the presence of a universal growth exponent $α_{\rm c}\approx 1/2$, this leads to the breakdown of the scaling relation among the characteristic exponents for the composite persistence.

cond-mat.stat-mech↗

arXiv2026

Scale-free cluster-cluster aggregation during polymer collapse

An extended polymer collapses to form a globule when subjected to a quench below the collapse transition temperature. The process begins with the formation of clusters of monomers or ``pearls''. The nascent clusters merge, resulting in growth of the average cluster size $C_s$, eventually leading to a single globule. The aggregation of the clusters are known to be analogous to droplet coalescence. This suggests a striking resemblance between such an aggregation and cluster-cluster aggregation found in many {particle systems}, like in colloidal self-assembly, typically characterized by a universal dynamic scaling behavior. Motivated by that, here, we verify the presence of such dynamic scaling during the collapse of a polymer with varying bending stiffness $κ$, using molecular dynamic simulations. We probe the dynamics via time evolution of the size distribution of clusters $N_s(t)$ and growth of $C_s(t)$. Irrespective of $κ$, we observe the power-law scalings $C_s(t)\sim t^z$ and $N_s(t)\sim t^{-w} s^{-τ}$, of which only the cluster growth is universal with {$z\approx 1.67$.} Importantly, our results indeed show that $N_s(t)$ exhibits a dynamic scaling of the form $N_s(t)\sim s^{-2}f(s/t^z)$, indicative of a scale-free cluster growth. Interestingly, for flexible and weakly stiff polymers the dynamic exponents obey the relation $w=2z$, as also found in diffusion-controlled cluster-cluster aggregation of particles. For $κ\ge 5$, the exponents show deviation from this relation, which grows continuously with $κ$. We identify the differences in local structures of the clusters formed, leading to variations in cluster-size dependence of the effective diffusion constant to be the origin of the above deviation. We also discuss potential experimental strategies to directly visualize the observed dynamic scaling in a collapsing polymer.

cond-mat.soft↗

arXiv2026

Effect of Cylindrical Confinement on the Collapse Dynamics of a Polymer

Structure and dynamics of a polymer under confinement gets significantly altered due to the imposed geometric restrictions. Using molecular dynamics simulations, here, we explore the effect of cylindrical confinement on the kinetics of collapse of a homopolymer, when the solvent condition is abruptly changed from good to poor. The observed phenomenology for a range of the cylinder radius $R$, reveals two distinct stages of the collapse. The first stage is highlighted by the formation and growth of local connected clusters resembling a pearl necklace, eventually ending with a single sausage-like cluster. In the second stage, the sausage-like intermediate approaches a spherical globule via surface-energy minimization. These two stages are disentangled using a shape parameter of the individual pearls or clusters, allowing us to also extract the respective relaxation times, and thereby their scaling behaviors with respect to the length of the polymer. We find that the pearl-necklace relaxation time $τ_p$ is independent of $R$. On the other hand, the sausage-relaxation time $τ_s$ varies inversely up to a certain $R$, beyond which it also saturates. From the Arrhenius plots of the temperature dependence of $τ_p$ and $τ_s$, we extract the activation energies $E_{\rm a}$ of the two stages. While the estimated $E_{\rm a}$ for the pearl-necklace stage is independent of $R$, for the sausage relaxation it is significantly higher in the strongly confined case than in the weakly one. Surprisingly, at a fixed temperature, the growth of the average cluster size obeys a universal power law irrespective of $R$. However, for a fixed $R$, the behavior is rather non-universal with respect to temperature. We propose viable scenarios for experimental realization of polymer collapse inside cylindrical nanochannels.

cond-mat.soft↗

arXiv2025

Domain Growth and Aging in a Phase Separating Binary Fluid Confined Inside a Nanopore

Hydrodynamics is known to have strong effects on the kinetics of phase separation. There exist open questions on how such effects manifest in systems under confinement. Here, we have undertaken extensive studies of the kinetics of phase separation in a two-component fluid that is confined inside pores of cylindrical shape. Using a hydrodynamics-preserving thermostat, we carry out molecular dynamics simulations to obtain results for domain growth and aging for varying temperature and pore-width. We find that all systems freeze into a morphology where stripes of regions rich in one or the other component of the mixture coexist in a locked situation. Our analysis suggests that, irrespective of the temperature the growth of the average domain size, $\ell(t)$, prior to the freezing into stripped patterns, follows the power law $\ell(t)\sim t^{2/3}$, suggesting an inertial hydrodynamic growth, which typically is applicable for bulk fluids only in the asymptotic limit. Similarly, the aging dynamics, probed by the two-time order-parameter autocorrelation function, also exhibits a temperature-independent power-law scaling with an exponent $λ\simeq 2.55$, much smaller than what is observed for a bulk fluid.

cond-mat.soft↗

arXiv2025

Bayesian Variable Selection for Censored Spatial Responses with Application to PFAS Concentrations in California

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants of major public health concern due to their resistance to degradation, widespread presence, and potential health risks. Analyzing PFAS in groundwater is challenging due to left-censoring and strong spatial dependence. Although PFAS levels are influenced by sociodemographic, industrial, and environmental factors, the relative importance of these drivers remains unclear, highlighting the need for robust statistical tools to identify key predictors from a large candidate set. We present a Bayesian hierarchical framework that integrates censoring into a spatial process model via approximate Gaussian processes and employs a global-local shrinkage prior for high-dimensional variable selection. We evaluate three post-selection strategies, namely, credible interval rules, shrinkage weight thresholds, and clustering-based inclusion and compare their performance in terms of predictive accuracy, censoring robustness, and variable selection stability through cross-validation. Applied to PFOS concentrations in California groundwater, the model identifies a concise, interpretable set of predictors, including demographic composition, industrial facility counts, proximity to airports, traffic density, and environmental features such as herbaceous cover and elevation. These findings demonstrate that the proposed approach delivers stable, interpretable inference in censored, spatial, high-dimensional contexts, thereby offering actionable insights into the environmental and industrial factors affecting PFAS concentrations.

stat.AP↗

arXiv2025

Influence of Thermostats on the Dynamics of the Helix-Coil Transition

We present results from all-atom molecular dynamics simulations for the nonequilibrium dynamics of the collapse and helix-coil transition in polyalanine. In particular, we compare the influence of three different thermostats, viz., the Langevin, Andersen, and Nosé-Hoover thermostats. For that purpose, we investigate the nonequilibrium pathways of the transition from the high-temperature random-coil state to the low-temperature helical state. Additionally, we analyze the time evolution of the potential energy and temperature. Our results show only small differences in the observed phenomenology, albeit quantitatively the dynamics appear to be different for the three thermostats.

cond-mat.soft↗

arXiv2025

Nonequilibrium Dynamics of the Helix-Coil Transition in Polyalanine

In this work, the nonequilibrium pathways of the collapse of the helix-forming biopolymer polyalanine are investigated. To this end, the full time evolution of the helix-coil transition is simulated using molecular dynamics simulations. At the start of the transition short $3_{10}$-helices form, seemingly leading to the molecule becoming more aspherical midway through the collapse. After the completed collapse, the formation of $α$-helices seems to become the prevalent ordering mechanism leading to helical bundles, a structure representative for the equilibrium behavior of longer chains. The dynamics of this transition is explored in terms of the power-law scaling of two associated relaxation times as a function of the chain length.

cond-mat.soft↗

arXiv2025

A Bayesian Multivariate Spatial Point Pattern Model: Application to Oral Microbiome FISH Image Data

Advances in cellular imaging technologies, especially those based on fluorescence in situ hybridization (FISH) now allow detailed visualization of the spatial organization of human or bacterial cells. Quantifying this spatial organization is crucial for understanding the function of multicellular tissues or biofilms, with implications for human health and disease. To address the need for better methods to achieve such quantification, we propose a flexible multivariate point process model that characterizes and estimates complex spatial interactions among multiple cell types. The proposed Bayesian framework is appealing due to its unified estimation process and the ability to directly quantify uncertainty in key estimates of interest, such as those of inter-type correlation and the proportion of variance due to inter-type relationships. To ensure stable and interpretable estimation, we consider shrinkage priors for coefficients associated with latent processes. Model selection and comparison are conducted by using a deviance information criterion designed for models with latent variables, effectively balancing the risk of overfitting with that of oversimplifying key quantities. Furthermore, we develop a hierarchical modeling approach to integrate multiple image-specific estimates from a given subject, allowing inference at both the global and subject-specific levels. We apply the proposed method to microbial biofilm image data from the human tongue dorsum and find that specific taxon pairs, such as Streptococcus mitis-Streptococcus salivarius and Streptococcus mitis-Veillonella, exhibit strong positive spatial correlations, while others, such as Actinomyces-Rothia, show slight negative correlations. For most of the taxa, a substantial portion of spatial variance can be attributed to inter-taxon relationships.

stat.ME↗

arXiv2025

Finite-Size Effects in Aging can be Interpreted as Sub-Aging

Systems brought out of equilibrium through a rapid quench from a disordered initial state into an ordered phase undergo physical aging in the form of phase-ordering kinetics, with characteristic dynamical scaling. In many systems, notably glasses, dynamical scaling is often described through sub-aging, where a phenomenological sub-aging exponent $0<μ< 1$ is empirically chosen to achieve the best possible data collapse. Here it is shown that finite-size effects modify the dynamical scaling behavior, away from simple aging with $μ=1$ towards $μ<1$, such that phenomenologically it would appear as sub-aging. This is exemplified for the exactly solved dynamical spherical model in dimensions $2<d<4$ and numerical simulations of the two-dimensional Ising model, with short-ranged and long-ranged interactions.

cond-mat.stat-mech↗

arXiv2024

Multivariate cluster point process to quantify and explore multi-entity configurations: Application to biofilm image data

Clusters of similar or dissimilar objects are encountered in many fields. Frequently used approaches treat the central object of each cluster as latent. Yet, often objects of one or more types cluster around objects of another type. Such arrangements are common in biomedical images of cells, in which nearby cell types likely interact. Quantifying spatial relationships may elucidate biological mechanisms. Parent-offspring statistical frameworks can be usefully applied even when central objects (parents) differ from peripheral ones (offspring). We propose the novel multivariate cluster point process (MCPP) to quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used approaches, the MCPP exploits locations of the central parent object in clusters. It accounts for possibly multilayered, multivariate clustering. The model formulation requires specification of which object types function as cluster centers and which reside peripherally. If such information is unknown, the relative roles of object types may be explored by comparing fit of different models via the deviance information criterion (DIC). In simulated data, we compared DIC of a series of models; the MCPP correctly identified simulated relationships. It also produced more accurate and precise parameter estimates than the classical univariate Neyman-Scott process model. We also used the MCPP to quantify proposed configurations and explore new ones in human dental plaque biofilm image data. MCPP models quantified simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae around Streptococcus and successfully captured hypothesized structures for all taxa. Further exploration suggested the presence of clustering between Fusobacterium and Leptotrichia, a previously unreported relationship.

stat.AP↗

arXiv2024

Anomalous Conformations and Dynamics of Active Block Copolymers

Heterogeneous distribution of passive and active domains in the chromosome plays a crucial role for its dynamic organization within the cell nucleus. Motivated by that here we investigate the steady-state conformation and dynamics of a model active-block copolymer using numerical simulations. Our results show that depending on the relative arrangements of the active and passive blocks, the polymer shows an unusual swelling, even larger than the corresponding fully active polymer. On the one hand, the dynamics of the full polymer show usual enhanced diffusion and Rouse-like scaling behavior. On the other hand, individual passive and active blocks show anomalous transient super- and sub-diffusive dynamics. We characterize this anomalous dynamics in terms of the dependence of a generalized diffusion constant with the polymer length and activity strength.

cond-mat.soft↗

arXiv2024

BlockChain I/O: Enabling Cross-Chain Commerce

Blockchain technology enables secure tokens transfers in digital marketplaces, and recent advances in this field provide other desirable properties such as efficiency, privacy, and price stability. However, these properties do not always generalize to a setting across multiple independent blockchains. Despite the growing number of existing blockchain platforms, there is a lack of an overarching framework whose components provide all of the necessary properties for practical cross-chain commerce. We present BlockChain I/O to provide such a framework. BlockChain I/O introduces entities called cross-chain services to relay information between different blockchains. The proposed design ensures that cross-chain services cannot violate transaction safety, and they are furthermore disincentivized from other types of misbehavior through an audit system. BlockChain I/O uses native stablecoins to mitigate price fluctuations, and a decentralized ID system to allow users to prove aspects of their identity without violating privacy. After presenting the core architecture of BlockChain I/O, we demonstrate how to use it to implement a cross-chain marketplace and discuss how its desirable properties continue to hold in the end-to-end system. Finally, we use experimental evaluations to demonstrate BlockChain I/O's practical performance.

cs.CR↗

arXiv2024

Temperature and Solvent Viscosity Tune the Intermediates During the Collapse of a Polymer

Dynamics of a polymer chain in solution gets significantly affected by the temperature and the frictional forces arising due to solvent viscosity. Here, using an explicit solvent framework for polymer simulation with the liberty to tune the solvent viscosity, we study the nonequilibrium dynamics of a flexible homopolymer when it is suddenly quenched from an extended coil state in good solvent to poor solvent conditions. Results from our extensive simulations reveal that depending on the temperature $T$ and solvent viscosity, one encounters long-lived sausage-like intermediates following the usual pearl-necklace intermediates. Use of shape factors of polymers allows us to disentangle these two distinct stages of the overall collapse process, and the corresponding relaxation times. The relaxation time $τ_s$ of the sausage stage, which is the rate-limiting stage of the overall collapse process, follows an anti-Arrhenius behavior in the high-$T$ limit, and the Arrhenius behavior in the low-$T$ limit. Furthermore, the variation of $τ_s$ with the solvent viscosity provides evidence of internal friction of the polymer, that modulates the overall collapse significantly, analogous to what is observed for relaxation rates of proteins during their folding. This suggests that the origin of internal friction in proteins is plausibly intrinsic to its polymeric backbone rather than other specifications.

cond-mat.soft↗

arXiv2024

Computationally Scalable Bayesian SPDE Modeling for Censored Spatial Responses

Observations of groundwater pollutants, such as arsenic or Perfluorooctane sulfonate (PFOS), are riddled with left censoring. These measurements have impact on the health and lifestyle of the populace. Left censoring of these spatially correlated observations are usually addressed by applying Gaussian processes (GPs), which have theoretical advantages. However, this comes with a challenging computational complexity of $\mathcal{O}(n^3)$, which is impractical for large datasets. Additionally, a sizable proportion of the data being left-censored creates further bottlenecks, since the likelihood computation now involves an intractable high-dimensional integral of the multivariate Gaussian density. In this article, we tackle these two problems simultaneously by approximating the GP with a Gaussian Markov random field (GMRF) approach that exploits an explicit link between a GP with Matérn correlation function and a GMRF using stochastic partial differential equations (SPDEs). We introduce a GMRF-based measurement error into the model, which alleviates the likelihood computation for the censored data, drastically improving the speed of the model while maintaining admirable accuracy. Our approach demonstrates robustness and substantial computational scalability, compared to state-of-the-art methods for censored spatial responses across various simulation settings. Finally, the fit of this fully Bayesian model to the concentration of PFOS in groundwater available at 24,959 sites across California, where 46.62\% responses are censored, produces prediction surface and uncertainty quantification in real time, thereby substantiating the applicability and scalability of the proposed method. Code for implementation is made available via GitHub.

stat.ME↗

arXiv2024

Thermodynamically Stable Knots in Semiflexible Polymers

Semiflexible polymers are widely used as a paradigm for understanding structural phases in biomolecules including folding of proteins. Here, we compare bead-spring and bead-stick variants of coarse-grained semiflexible polymer models that cover the whole range from flexible to stiff by conducting extensive replica-exchange Monte Carlo computer simulations. In the data analysis we focus on knotted conformations whose stability is shown to depend on the ratio $r_b/r_{\rm min}$ with $r_b$ denoting the equilibrium bond length and $r_{\rm min}$ the distance of the strongest nonbonded interactions. For both models, our results provide evidence that at low temperatures for $r_b/r_{\rm min}$ outside a small range around unity one always encounters knots as generic stable phases along with the usual frozen and bent-like structures. By varying the bending stiffness, we observe rather strong first-order-like structural transitions between the coexisting phases characterized by these geometrically different motifs. Through analyses of the energy distributions close to the transition point, we present exploratory estimates of the free-energy barriers between the coexisting phases.

cond-mat.soft↗

arXiv2024

Spontaneous Micro Flocking of Active Inertial Particles without Alignment Interaction

Observing spontaneous velocity ordering or flocking during motility induced phase separation (MIPS) in a system of spherical active Brownian particles without alignment interaction is challenging. We take up this problem by performing simulations of spherical active inertial particles with purely repulsive potential in presence of thermal noise and absence of any explicit alignment interaction. Our results not only show the presence of MIPS, but also reveal a micro-flocking transition. We characterize this transition in terms of a velocity order parameter as well as a characteristic length scale derived from the spatial correlation of the velocities.

cond-mat.soft↗

arXiv2023

Interplay of phase segregation and chemical reaction: Crossover and effect on growth laws

By combining the nonconserved spin-flip dynamics driving ferromagnetic ordering with the conserved Kawasaki-exchange dynamics driving phase segregation, we perform Monte Carlo simulations of the nearest neighbor Ising model. Such a set up mimics a system consisting of a binary mixture of \emph{isomers} which is simultaneously undergoing a segregation and an \emph{interconversion} reaction among themselves . Here, we study such a system following a quench from the high-temperature homogeneous phase to a temperature below the demixing transition. We monitor the growth of domains of both the \emph{winner}, the \emph{isomer} which survives as the majority and the \emph{loser}, the \emph{isomer} that perishes. Our results show a strong interplay of the two dynamics at early times leading to a growth of the average domain size of both the \emph{winner} and \emph{loser} as $\sim t^{1/7}$, slower than a purely phase-segregating system. At later times, eventually the dynamics becomes reaction dominated, and the \emph{winner} exhibits a $\sim t^{1/2}$ growth, expected for a system with purely nonconserved dynamics. On the other hand, the \emph{loser} at first show a faster growth, albeit, slower than the \emph{winner}, and then starts to decay before it almost vanishes. Further, we estimate the time $τ_s$ marking the crossover from the early-time slow growth to the late-time reaction dominated faster growth. As a function of the reaction probability $p_r$, we observe a power-law scaling $τ_s \sim p_r^{-x}$, where $x\approx 1.05$, irrespective of temperature. For a fixed value of $p_r$ too, $τ_s$ appears to be independent of temperature.

cond-mat.stat-mech↗