arXiv subjects

Suchismita Das

Publications and source records attributed to Suchismita Das.

10 recordsLinked to original sources

arXiv2026

Compounded Linear Failure Rate Distribution: Properties, Simulation and Analysis

This paper proposes a new extension of the linear failure rate (LFR) model to better capture real-world lifetime data. The model incorporates an additional shape parameter to increase flexibility. It helps model the minimum survival time from a set of LFR distributed variables. We define the model, derive certain statistical properties such as the mean residual life, the mean inactivity time, moments, quantile, order statistics and also discuss the results on stochastic orders of the proposed distribution. The proposed model has increasing, bathtub shaped and inverse bathtub shaped hazard rate function. We use the method of maximum likelihood estimation to estimate the unknown parameters. We conduct simulation studies to examine the behavior of the estimators. We also use three real datasets to evaluate the model, which turns out superior compared to classical alternatives.

stat.ME↗

arXiv2025

Active wetting transitions induced by rotational noise at solid interfaces

We investigate the wetting transitions displayed by the collection of active Brownian particles (ABPs) confined within rigid, impenetrable, flat walls. In our computational study using Brownian dynamics simulations, the wall-particle interactions are implemented with a short-range repulsive potential. An enhanced rotational diffusion at the walls is used as a control parameter for wetting transitions. Increasing the wall rotational diffusion destabilizes complete wetting, and the aggregate shows morphological transitions. We observe a sequence of morphological transitions with an increase in wall rotational diffusion: symmetric complete wetting (SCW), asymmetric complete wetting (ACW), partial wetting (PW) with droplet formation, and drying. In the PW state, we compute the contact angle as a function of activity and rotational noise. Our analysis indicates that these transitions are linked to enhanced kinetic energy fluctuations of particles and bubble formations in the dense state. We further characterize the nature of these transitions by systematically analyzing an order parameter. Our work shows that modifying local reorientation rates alone is sufficient to induce wetting transitions in active systems.

cond-mat.soft↗

arXiv2025

A Compounded Burr Probability Distribution for Fitting Heavy-Tailed Data with Applications to Biological Networks

Complex biological networks, encompassing metabolic pathways, gene regulatory systems, and protein-protein interaction networks, often exhibit scale-free structures characterized by heavy-tailed degree distributions. However, empirical studies reveal significant deviations from ideal power law behavior, underscoring the need for more flexible and accurate probabilistic models. In this work, we propose the Compounded Burr (CBurr) distribution, a novel four parameter family derived by compounding the Burr distribution with a discrete mixing process. This model is specifically designed to capture both the body and tail behavior of real-world network degree distributions with applications to biological networks. We rigorously derive its statistical properties, including moments, hazard and risk functions, and tail behavior, and develop an efficient maximum likelihood estimation framework. The CBurr model demonstrates broad applicability to networks with complex connectivity patterns, particularly in biological, social, and technological domains. Extensive experiments on large-scale biological network datasets show that CBurr consistently outperforms classical power-law, log-normal, and other heavy-tailed models across the full degree spectrum. By providing a statistically grounded and interpretable framework, the CBurr model enhances our ability to characterize the structural heterogeneity of biological networks.

stat.AP↗

arXiv2024

Flocking by Turning Away

Flocking, as paradigmatically exemplified by birds, is the coherent collective motion of active agents. As originally conceived, flocking emerges through alignment interactions between the agents. Here, we report that flocking can also emerge through interactions that turn agents away from each other. Combining simulations, kinetic theory, and experiments, we demonstrate this mechanism of flocking in self-propelled Janus colloids with stronger repulsion on the front than on the rear. The polar state is stable because particles achieve a compromise between turning away from left and right neighbors. Unlike for alignment interactions, the emergence of polar order from turn-away interactions requires particle repulsion. At high concentration, repulsion produces flocking Wigner crystals. Whereas repulsion often leads to motility-induced phase separation of active particles, here it combines with turn-away torques to produce flocking. Therefore, our findings bridge the classes of aligning and non-aligning active matter. Our results could help to reconcile the observations that cells can flock despite turning away from each other via contact inhibition of locomotion. Overall, our work shows that flocking is a very robust phenomenon that arises even when the orientational interactions would seem to prevent it.

cond-mat.soft↗

arXiv2023

Feature selection simultaneously preserving both class and cluster structures

When a data set has significant differences in its class and cluster structure, selecting features aiming only at the discrimination of classes would lead to poor clustering performance, and similarly, feature selection aiming only at preserving cluster structures would lead to poor classification performance. To the best of our knowledge, a feature selection method that simultaneously considers class discrimination and cluster structure preservation is not available in the literature. In this paper, we have tried to bridge this gap by proposing a neural network-based feature selection method that focuses both on class discrimination and structure preservation in an integrated manner. In addition to assessing typical classification problems, we have investigated its effectiveness on band selection in hyperspectral images. Based on the results of the experiments, we may claim that the proposed feature/band selection can select a subset of features that is good for both classification and clustering.

cs.LG↗

arXiv2022

Understanding the classes better with class-specific and rule-specific feature selection, and redundancy control in a fuzzy rule based framework

Recently, several studies have claimed that using class-specific feature subsets provides certain advantages over using a single feature subset for representing the data for a classification problem. Unlike traditional feature selection methods, the class-specific feature selection methods select an optimal feature subset for each class. Typically class-specific feature selection (CSFS) methods use one-versus-all split of the data set that leads to issues such as class imbalance, decision aggregation, and high computational overhead. We propose a class-specific feature selection method embedded in a fuzzy rule-based classifier, which is free from the drawbacks associated with most existing class-specific methods. Additionally, our method can be adapted to control the level of redundancy in the class-specific feature subsets by adding a suitable regularizer to the learning objective. Our method results in class-specific rules involving class-specific subsets. We also propose an extension where different rules of a particular class are defined by different feature subsets to model different substructures within the class. The effectiveness of the proposed method has been validated through experiments on three synthetic data sets.

cs.LG↗

arXiv2020

Morphological transitions of active Brownian particle aggregates on porous walls

Motility-induced wall aggregation of active Brownian particles (ABPs) is a well-studied phenomenon. Here, we study the aggregation of ABPs on porous walls, which allows the particles to penetrate through at large motility. We show that the active aggregates undergo a morphological transition from a connected dense-phase to disconnected droplets with an increase in wall porosity and the particle self-motility, similar to wetting-dewetting transitions in equilibrium fluids. We show that both morphologically distinct states are stable, and independent of initial conditions at least in some parameter regions. Our analysis reveals that changes in wall porosity affect the intrinsic properties of the aggregates and changes the effective wall-aggregate interfacial tension, consistent with the appearance of the morphological transition. Accordingly, a close analysis of the density, as well as orientational distribution, indicates that the underlying reason for such morphological transitions is not necessarily specific to the systems with porous walls, and it can be possible to observe in a larger class of confined, active systems by tuning the properties of confining walls.

cond-mat.soft↗

arXiv2020

Aggregate morphology of active Brownian particles on porous, circular walls

We study the motility-induced aggregation of active Brownian particles (ABPs) on a porous, circular wall. We observe that the morphology of aggregated dense-phase on a static wall depends on the wall porosity, particle motility, and the radius of the circular wall. Our analysis reveals two morphologically distinct, dense aggregates; a connected dense cluster that spreads uniformly on the circular wall and a localized cluster that breaks the rotational symmetry of the system. These distinct morphological states are similar to the macroscopic structures observed in aggregates on planar, porous walls. We systematically analyze the parameter regimes where the different morphological states are observed. We further extend our analysis to motile circular rings. We show that the motile ring propels almost ballistically due to the force applied by the active particles when they form a localized cluster, whereas it moves diffusively when the active particles form a continuous cluster. This property demonstrates the possibility of extracting useful work from a system of ABPs, even without artificially breaking the rotational symmetry.

cond-mat.soft↗

arXiv2020

Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach

Here, we propose an unsupervised fuzzy rule-based dimensionality reduction method primarily for data visualization. It considers the following important issues relevant to dimensionality reduction-based data visualization: (i) preservation of neighborhood relationships, (ii) handling data on a non-linear manifold, (iii) the capability of predicting projections for new test data points, (iv) interpretability of the system, and (v) the ability to reject test points if required. For this, we use a first-order Takagi-Sugeno type model. We generate rule antecedents using clusters in the input data. In this context, we also propose a new variant of the Geodesic c-means clustering algorithm. We estimate the rule parameters by minimizing an error function that preserves the inter-point geodesic distances (distances over the manifold) as Euclidean distances on the projected space. We apply the proposed method on three synthetic and three real-world data sets and visually compare the results with four other standard data visualization methods. The obtained results show that the proposed method behaves desirably and performs better than or comparable to the methods compared with. The proposed method is found to be robust to the initial conditions. The predictability of the proposed method for test points is validated by experiments. We also assess the ability of our method to reject output points when it should. Then, we extend this concept to provide a general framework for learning an unsupervised fuzzy model for data projection with different objective functions. To the best of our knowledge, this is the first attempt to manifold learning using unsupervised fuzzy modeling.

cs.LG↗

arXiv2017

Some Ageing Properties of Dynamic Additive Mean Residual Life Model

Although proportional hazard rate model is a very popular model to analyze failure time data, sometimes it becomes important to study the additive hazard rate model. Again, sometimes the concept of the hazard rate function is abstract, in comparison to the concept of mean residual life function. A new model called `dynamic additive mean residual life model' where the covariates are time-dependent has been defined in the literature. Here we study the closure properties of the model for different positive and negative ageing classes under certain condition(s). Quite a few examples are presented to illustrate different properties of the model.

math.ST↗