arXiv subjects

Jyotirmoy Sarkar

Publications and source records attributed to Jyotirmoy Sarkar.

9 recordsLinked to original sources

arXiv2026

Nitrogen-Vacancy-Mediated Magnetism in Sputtered GdN Thin Films

Among rare-earth nitrides (RENs), gadolinium nitride (GdN) stands out as a promising material for spintronics owing to its distinctive combination of semiconducting behavior, strong exchange interactions, and intrinsically soft ferromagnetism. Its relatively high Curie temperature and large saturation magnetization make it an attractive candidate for device concepts such as non-volatile memory elements and spin-based transistors, motivating efforts toward low-cost, uniform, and compositionally controlled thin-film growth. In this work, we deposited GdN thin films on SiO2/AlN substrates using DC sputtering under reactive nitridation conditions, with thicknesses varying from 18 to 180 nm, and systematically investigated their structural and magnetic properties. The films exhibit soft ferromagnetic ordering, characterized by a coercive field of approximately 200 Oe and a Curie temperature (Tc) near 70 K. Structural analysis reveals lattice distortions and local strain associated with nitrogen-vacancy defects, whose concentration varies with film thickness. Our theoretical studies establish a direct correlation between the observed Raman modes of the GdN lattice and the reduced magnetization induced by nitrogen vacancies. These vacancies give rise to defect-mediated ferromagnetism, leading to a measurable enhancement of Tc from 68 K to 82 K across the studied thickness range. The observed magnetic behavior is well described by the bound magnetic polaron (BMP) model, confirming that nitrogen vacancies are key contributors to ferromagnetic ordering while preserving the soft-magnetic character intrinsic to GdN. This study underscores the pivotal role of defect engineering in optimizing GdN thin films for spintronics applications.

cond-mat.mtrl-sci↗

arXiv2025

Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond

Detection of anomalous situations for complex mission-critical systems hold paramount importance when their service continuity needs to be ensured. A major challenge in detecting anomalies from the operational data arises due to the imbalanced class distribution problem since the anomalies are supposed to be rare events. This paper evaluates a diverse array of Machine Learning (ML)-based anomaly detection algorithms through a comprehensive benchmark study. The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical ML, including various tree-based approaches to Deep Learning (DL) and outlier detection methods. The inclusion of 104 publicly available enhances the diversity of the study, allowing a more realistic evaluation of algorithm performance and emphasizing the importance of adaptability to real-world scenarios. The paper evaluates the general notion of DL as a universal solution, showing that, while powerful, it is not always the best fit for every scenario. The findings reveal that recently proposed tree-based evolutionary algorithms match DL methods and sometimes outperform them in many instances of univariate data where the size of the data is small and number of anomalies are less than 10%. Specifically, tree-based approaches successfully detect singleton anomalies in datasets where DL falls short. To the best of the authors' knowledge, such a study on a large number of state-of-the-art algorithms using diverse data sets, with the objective of guiding researchers and practitioners in making informed algorithmic choices, has not been attempted earlier.

cs.LG↗

arXiv2024

An optimal replacement policy under variable shocks and self-healing patterns

We study a system that experiences damaging external shocks at stochastic intervals, continuous degradation, and self-healing. The motivation for such a system comes from real-life applications based on micro-electro-mechanical systems (MEMS). The system fails if the cumulative damage exceeds a time-dependent threshold. We develop a preventive maintenance policy to replace the system such that its lifetime is prudently utilized. Further, three variations on the healing pattern have been considered: (i) shocks heal for a fixed duration $τ$; (ii) a fixed proportion of shocks are non-healable (that is, $τ=0$); (iii) there are two types of shocks -- self healable shocks heal for a finite duration, and nonhealable shocks inflict a random system degradation. We implement a proposed preventive maintenance policy and compare the optimal replacement times in these new cases to that of the original case where all shocks heal indefinitely and thereby enable the system manager to take necessary decisions in generalized system set-ups.

stat.ME↗

arXiv2023

Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data

Anomalies refer to the departure of systems and devices from their normal behaviour in standard operating conditions. An anomaly in an industrial device can indicate an upcoming failure, often in the temporal direction. In this paper, we make two contributions: 1) we estimate conditional quantiles and consider three different ways to define anomalies based on the estimated quantiles. 2) we use a new learnable activation function in the popular Long Short Term Memory networks (LSTM) architecture to model temporal long-range dependency. In particular, we propose Parametric Elliot Function (PEF) as an activation function (AF) inside LSTM, which saturates lately compared to sigmoid and tanh. The proposed algorithms are compared with other well-known anomaly detection algorithms, such as Isolation Forest (iForest), Elliptic Envelope, Autoencoder, and modern Deep Learning models such as Deep Autoencoding Gaussian Mixture Model (DAGMM), Generative Adversarial Networks (GAN). The algorithms are evaluated in terms of various performance metrics, such as Precision and Recall. The algorithms have been tested on multiple industrial time-series datasets such as Yahoo, AWS, GE, and machine sensors. We have found that the LSTM-based quantile algorithms are very effective and outperformed the existing algorithms in identifying anomalies.

cs.LG↗

arXiv2021

Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method

A profound shift in the study of cosmology came with the discovery of thousands of exoplanets and the possibility of the existence of billions of them in our Galaxy. The biggest goal in these searches is whether there are other life-harbouring planets. However, the question which of these detected planets are habitable, potentially-habitable, or maybe even inhabited, is still not answered. Some potentially habitable exoplanets have been hypothesized, but since Earth is the only known habitable planet, measures of habitability are necessarily determined with Earth as the reference. Several recent works introduced new habitability metrics based on optimization methods. Classification of potentially habitable exoplanets using supervised learning is another emerging area of study. However, both modeling and supervised learning approaches suffer from drawbacks. We propose an anomaly detection method, the Multi-Stage Memetic Algorithm (MSMA), to detect anomalies and extend it to an unsupervised clustering algorithm MSMVMCA to use it to detect potentially habitable exoplanets as anomalies. The algorithm is based on the postulate that Earth is an anomaly, with the possibility of existence of few other anomalies among thousands of data points. We describe an MSMA-based clustering approach with a novel distance function to detect habitable candidates as anomalies (including Earth). The results are cross-matched with the habitable exoplanet catalog (PHL-HEC) of the Planetary Habitability Laboratory (PHL) with both optimistic and conservative lists of potentially habitable exoplanets.

astro-ph.EP↗

arXiv2019

A Repairable System Supported by Two Spare Units and Serviced by Two Types of Repairers

We study a one-unit repairable system, supported by two identical spare units on cold standby, and serviced by two types of repairers. The model applies, for instance, to ANSI (American National Standard Institute) centrifugal pumps in a chemical plant. The failed unit undergoes repair either by an in-house repairer within a random or deterministic patience time, or else by a visiting expert repairer. The expert repairs one or all failed units before leaving, and does so faster but at a higher cost rate than the regular repairer. Four models arise depending on the number of repairs done by the expert and the nature of the patience time. We compare these models based on the limiting availability $A_{\infty}$, and the limiting profit per unit time $ω$, using semi-Markov processes, when all distributions are exponential. As anticipated, to maximize $A_{\infty}$, the expert should repair all failed units. To maximize $ω$, a suitably chosen deterministic patience time is better than a random patience time. Furthermore, given all cost parameters, we determine the optimum number of repairs the expert should complete, and the optimum patience time given to the regular repairer in order to maximize $ω$.

cs.PF↗

arXiv2018

ALVEC: Auto-scaling by Lotka Volterra Elastic Cloud: A QoS aware Non Linear Dynamical Allocation Model

Elasticity in resource allocation is still a relevant problem in cloud computing. There are many academic and white papers which have investigated the problem and offered solutions.\textbf{Unfortunately, there are scant evidence of determining scaling number dynamically.} Elasticity is defined as the ability to adapt with the changing workloads by provisioning and de-provisioning Cloud resources. We propose ALVEC, a novel model of resource allocation in Cloud data centers, inspired by population dynamics and Mathematical Biology, which addresses dynamic allocation by auto-tuning model parameters. The proposed model, governed by a coupled differential equation known as Lotka Volterra (LV), fares better in Service level agreement (SLA) management and Quality of Services (QoS). We show evidence of true elasticity, in theory and empirical comparisons. Additionally, ALVEC is able to predict the future load and allocate VM's accordingly. The proposed model, ALVEC is the first example of unsupervised resource allocation scheme.

cs.NI↗

arXiv2016

CDSFA Stochastic Frontier Analysis Approach to Revenue Modeling in Large Cloud Data Centers

Enterprises are investing heavily in cloud data centers to meet the ever surging business demand. Data Center is a facility, which houses computer systems and associated components, such as telecommunications and storage systems. It generally includes power supply equipment, communication connections and cooling equipment. A large data center can use as much electricity as a small town. Due to the emergence of data center based computing services, it has become necessary to examine how the costs associated with data centers evolve over time, mainly in view of efficiency issues. We have presented a quasi form of Cobb Douglas model, which addresses revenue and profit issues in running large data centers. The stochastic form has been introduced and explored along with the quasi Cobb Douglas model to understand the behavior of the model in depth. Harrod neutrality and Solow neutrality are incorporated in the model to identify the technological progress in cloud data centers. This allows us to shed light on the stochastic uncertainty of cloud data center operations. A general approach to optimizing the revenue cost of data centers using Cobb Douglas Stochastic Frontier Analysis,CDSFA is presented. Next, we develop the optimization model for large data centers. The mathematical basis of CDSFA has been utilized for cost optimization and profit maximization in data centers. The results are found to be quite useful in view of production reorganization in large data centers around the world.

cs.DC↗

arXiv2006

Random walk on a polygon

A particle moves among the vertices of an $(m+1)$-gon which are labeled clockwise as $0,1,...,m$. The particle starts at 0 and thereafter at each step it moves to the adjacent vertex, going clockwise with a known probability $p$, or counterclockwise with probability $1-p$. The directions of successive movements are independent. What is the expected number of moves needed to visit all vertices? This and other related questions are answered using recursive relations.

math.PR↗