arXiv subjects

Subrata Chakraborty

Publications and source records attributed to Subrata Chakraborty.

At least 19 recordsLinked to original sources

arXiv2026

DC Josephson transport in a three-terminal Yu-Shiba-Rusinov system

Scanning tunneling microscopy (STM) of magnetic adatoms on superconducting surfaces has established Yu-Shiba-Rusinov (YSR) states as a versatile platform for studying magnetic-superconducting interactions and phase-coherent quantum transport. Here, we investigate nonreciprocal supercurrent transport in a three-terminal Josephson junction comprising three BCS superconductors (`A', `B' and `C'), each coupled to a magnetic impurity hosting a pair of YSR states. Treating terminals `A' and `C' as primary transport electrodes and terminal `B' as a phase-control node, we demonstrate field-tunable nonreciprocal supercurrent between the primary electrodes. This effect requires both: the control terminal and broken particle-hole symmetry at least in one impurity attached to a primary electrode. Without the control terminal we show that the supercurrent remains reciprocal. Our results establish multi-terminal YSR junctions as a promising platform for engineering nonreciprocal superconducting transport and symmetry-breaking phenomena at the atomic scale.

cond-mat.supr-con↗

arXiv2026

Signature of Magnon-Raman Phonon-Polariton Condensation in a Cavity via Transverse Pumping

Polaritons---hybrid light-matter quasiparticles---provide a versatile platform for dynamically controlling a wide range of condensed matter systems. While conventional polaritonic platforms rely on direct dipole coupling, controlling dipole-forbidden or Raman-active lattice and spin excitations remains challenging due to optical selection rules and the limitations of THz cavities. Here, we propose a theoretical platform for realizing a continuous-wave magnon--Raman phonon--polariton condensate. By embedding a magnetic medium hosting strongly coupled magnon and Raman-active phonon (MRP) modes inside an optical microcavity under continuous-wave transverse laser pumping, we derive the stationary-state phase diagram and demonstrate the emerging signature of distinct MRP condensation phases under critical conditions. Furthermore, since the bare magnon frequency is tunable via an external magnetic field, we show that the transverse pump frequency and the external magnetic field-dependent magnon simultaneously as highly flexible control parameters for exploring and controlling macroscopic quantum phenomena at the interface of cavity quantum optics, lattice dynamics, and quantum magnetism.

physics.optics↗

arXiv2025

ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis

This research proposes "ForCM", a novel approach to forest cover mapping that combines Object-Based Image Analysis (OBIA) with Deep Learning (DL) using multispectral Sentinel-2 imagery. The study explores several DL models, including UNet, UNet++, ResUNet, AttentionUNet, and ResNet50-Segnet, applied to high-resolution Sentinel-2 Level 2A satellite images of the Amazon Rainforest. The datasets comprise three collections: two sets of three-band imagery and one set of four-band imagery. After evaluation, the most effective DL models are individually integrated with the OBIA technique to enhance mapping accuracy. The originality of this work lies in evaluating different deep learning models combined with OBIA and comparing them with traditional OBIA methods. The results show that the proposed ForCM method improves forest cover mapping, achieving overall accuracies of 94.54 percent with ResUNet-OBIA and 95.64 percent with AttentionUNet-OBIA, compared to 92.91 percent using traditional OBIA. This research also demonstrates the potential of free and user-friendly tools such as QGIS for accurate mapping within their limitations, supporting global environmental monitoring and conservation efforts.

cs.CV↗

arXiv2024

Controlling Orbital Magnetism of Band Electron Systems via Bath Engineering

Orbital magnetism arises due to the coherent cyclotron motion of band electrons. System-bath entanglement is expected to disrupt this coherent electronic motion and quench orbital magnetism. Here, we show that a suitably-tailored bath can lead to an enhancement of the orbital diamagnetic susceptibility of a multi-band electron system and can even convert an orbital paramagnetic response into a diamagnetic one as the system-bath coupling is increased. We also demonstrate how a van Hove singularity in the bath density of states can be exploited to generate a giant enhancement of the orbital magnetic susceptibility. Our work opens doors to the possibility of controlling the orbital magnetic response of band electron systems through bath engineering.

cond-mat.str-el↗

arXiv2024

A new count model based on Poisson-Transmuted Geometric convolution

A novel over-dispersed discrete distribution, namely the PoiTG distribution is derived by the convolution of a Poisson variate and an independently distributed transmuted geometric random variable. This distribution generalizes the geometric, transmuted geometric, and PoiG distributions. Various important statistical properties of this count model, such as the probability generating function, the moment generating function, the moments, the survival function, and the hazard rate function are investigated. Stochastic ordering for the proposed model are also studied in details. The maximum likelihood estimators of the parameters are obtained using general optimization approach and the EM algorithm approach. It is envisaged that the proposed distribution may prove to be useful for the practitioners for modelling over-dispersed count data compared to its closest competitors.

math.ST↗

arXiv2024

A new over-dispersed count model

A new two-parameter discrete distribution, namely the PoiG distribution is derived by the convolution of a Poisson variate and an independently distributed geometric random variable. This distribution generalizes both the Poisson and geometric distributions and can be used for modelling over-dispersed as well as equi-dispersed count data. A number of important statistical properties of the proposed count model, such as the probability generating function, the moment generating function, the moments, the survival function and the hazard rate function. Monotonic properties are studied, such as the log concavity and the stochastic ordering are also investigated in detail. Method of moment and the maximum likelihood estimators of the parameters of the proposed model are presented. It is envisaged that the proposed distribution may prove to be useful for the practitioners for modelling over-dispersed count data compared to its closest competitors.

stat.ME↗

arXiv2024

Signature of long-ranged spin triplets across a two-dimensional superconductor/helimagnet van der Waals interface

The combination of a superconductor with a magnetically inhomogeneous material has been established as an efficient mechanism for the generation of long-ranged spin-polarized (spin-triplet) Cooper pairs. Evidence for this mechanism, however, has been established based on studies done on three-dimensional systems, where the strong bonds existing at the interface between the superconductor and the magnetic material should in principle enhance proximity effects and strengthen any electronic correlations. Here, we fabricate devices based on van der Waals stacks of flakes of the two-dimensional superconductor $NbS_2$ combined with flakes of $Cr_{1/3}NbS_2$, which has a built-in magnetic inhomogeneity due to its helimagnetic spin texture at low temperatures. We find that the critical temperature of these vdW bilayers is strongly dependent on the magnetic state of $Cr_{1/3}NbS_2$, whose degree of magnetic inhomogeneity can be controlled via an applied magnetic field. Our results demonstrate evidence for the generation of long-ranged spin-triplet pairs across the $Cr_{1/3}NbS_2$/$NbS_2$ vdW interface.

cond-mat.supr-con↗

arXiv2024

Lessons Learned in Performing a Trustworthy AI and Fundamental Rights Assessment

This report shares the experiences, results and lessons learned in conducting a pilot project ``Responsible use of AI'' in cooperation with the Province of Friesland, Rijks ICT Gilde-part of the Ministry of the Interior and Kingdom Relations (BZK) (both in The Netherlands) and a group of members of the Z-Inspection$^{\small{\circledR}}$ Initiative. The pilot project took place from May 2022 through January 2023. During the pilot, the practical application of a deep learning algorithm from the province of Frŷslan was assessed. The AI maps heathland grassland by means of satellite images for monitoring nature reserves. Environmental monitoring is one of the crucial activities carried on by society for several purposes ranging from maintaining standards on drinkable water to quantifying the CO2 emissions of a particular state or region. Using satellite imagery and machine learning to support decisions is becoming an important part of environmental monitoring. The main focus of this report is to share the experiences, results and lessons learned from performing both a Trustworthy AI assessment using the Z-Inspection$^{\small{\circledR}}$ process and the EU framework for Trustworthy AI, and combining it with a Fundamental Rights assessment using the Fundamental Rights and Algorithms Impact Assessment (FRAIA) as recommended by the Dutch government for the use of AI algorithms by the Dutch public authorities.

cs.CY↗

arXiv2023

Microscopic theory of supercurrent suppression by gate-controlled surface depairing

Recently gate-mediated supercurrent suppression in superconducting nano-bridges has been reported in many experiments. This could be either a direct or an indirect gate effect. The microscopic understanding of this observation is not clear till now. Using the quasiclassical Green's function method, we show that a small concentration of magnetic impurities at the surface of the bridges can significantly help to suppress superconductivity and hence the supercurrent inside the systems while applying a gate field. This is because the gate field can enhance the depairing through the exchange interaction between the magnetic impurities at the surface and the superconductor. We also obtain a \emph{symmetric} suppression of the supercurrent with respect to the gate field, a signature of a direct gate effect. Future experiments can verify our predictions by modifying the surface with magnetic impurities.

cond-mat.mes-hall↗

arXiv2023

Solving the multiplication problem of a large language model system using a graph-based method

The generative pre-trained transformer (GPT)-based chatbot software ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems, especially multiplication. Its GPT structure uses a computational graph for multiplication, which has limited accuracy beyond simple multiplication operations. We developed a graph-based multiplication algorithm that emulated human-like numerical operations by incorporating a 10k operator, where k represents the maximum power to base 10 of the larger of two input numbers. Our proposed algorithm attained 100% accuracy for 1,000,000 large number multiplication tasks, effectively solving the multiplication challenge of GPT-based and other large language models. Our work highlights the importance of blending simple human insights into the design of artificial intelligence algorithms. Keywords: Graph-based multiplication; ChatGPT; Multiplication problem

cs.OH↗

arXiv2023

DC Josephson effect between two Yu-Shiba-Rusinov bound states

Motivated by recent experiments [Nat. Phys. $\textbf{16}$, 1227 (2020)], we present here a theoretical study of the DC Josephson effect in a system comprising two magnetic impurities coupled to their respective superconducting electrodes and which exhibit Yu-Shiba-Rusinov (YSR) states. We make use of a mean-field Anderson model with broken spin symmetry to compute the supercurrent in this system for an arbitrary range of parameters (coupling between the impurities, orientation of the impurity spins, etc.). We predict a variety of physical phenomena such as (i) the occurrence of multiple $0-π$ transitions in the regime of weak coupling that can be induced by changing the energy of the YSR states or the temperature; (ii) the critical current strongly depends on the relative orientation of the impurity spins and it is maximized when the spins are either parallel or antiparallel, depending on the ground state of the impurities; and (iii) upon increasing the coupling between impurities, triplet superconductivity is generated in the system and it is manifested in a highly nonsinusoidal current-phase relation. In principle, these predictions can be tested experimentally with the existing realization of this system and the main lessons of this work are of great relevance for the field of superconducting spintronics.

cond-mat.supr-con↗

arXiv2023

Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and spatial filtering, but with a sub-second inference time that is faster than other methods. The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7.6.

eess.IV↗

arXiv2023

Bivariate binomial conditionals distributions with positive and negative correlations: A statistical study

In this article, we discuss a bivariate distribution whose conditionals are univariate binomial distributions and the marginals are not binomial that exhibits negative correlation. Some useful structural properties of this distribution namely marginals, moments, generating functions, stochastic ordering are investigated. Simple proofs of negative correlation, marginal over-dispersion, distribution of sum and conditional given the sum are also derived. The distribution is shown to be a member of the multi-parameter exponential family and some natural but useful consequences are also outlined. The proposed distribution tends to a recently investigated conditional Poisson distribution studied by Ghosh et al. (2020). Finally, the distribution is fitted to two bivariate count data sets with an inherent negative correlation to illustrate its suitability.

stat.ME↗

arXiv2022

2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips

The ever-growing volume of satellite imagery data presents a challenge for industry and governments making data-driven decisions based on the timely analysis of very large data sets. Commonly used deep learning algorithms for automatic classification of satellite images are time and resource-intensive to train. The cost of retraining in the context of Big Data presents a practical challenge when new image data and/or classes are added to a training corpus. Recognizing the need for an adaptable, accurate, and scalable satellite image chip classification scheme, in this research we present an ensemble of: i) a slow to train but high accuracy vision transformer; and ii) a fast to train, low-parameter convolutional neural network. The vision transformer model provides a scalable and accurate foundation model. The high-speed CNN provides an efficient means of incorporating newly labelled data into analysis, at the expense of lower accuracy. To simulate incremental data, the very large (~400,000 images) So2Sat LCZ42 satellite image chip dataset is divided into four intervals, with the high-speed CNN retrained every interval and the vision transformer trained every half interval. This experimental setup mimics an increase in data volume and diversity over time. For the task of automated land-cover/land-use classification, the ensemble models for each data increment outperform each of the component models, with best accuracy of 65% against a holdout test partition of the So2Sat dataset. The proposed ensemble and staggered training schedule provide a scalable and cost-effective satellite image classification scheme that is optimized to process very large volumes of satellite data.

cs.CV↗

arXiv2022

Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection

Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups being located in rural areas far from medical facilities. Reaching these populations would require a scaled approach that combines mobility, low cost, speed, accuracy, and privacy. We can resolve these issues by combining the chest X-ray imaging mode with a federated deep-learning approach, provided that the federated model is trained on homogenous data to ensure that no single data source can adversely bias the model at any point in time. In this study we show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization, paving the way for a low-cost and accessible deep learning-based clinical system for lung cancer screening. An evolutionary pruning mechanism is used to train a nodule detection deep learning model on the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is performed using all combinations of lung field segmentation, close cropping, and rib suppression operators. We show that this pre-processing pipeline results in deep learning models that successfully generalize an independent lung nodule dataset using ablation studies to assess the contribution of each operator in this pipeline. In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.

cs.CV↗

arXiv2021

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the black-box nature of deep learning models. Additionally, most lung nodules visible on chest X-ray are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two, independent data sets for which malignancy metadata is available. We mine multi-variate predictive models by fitting shallow decision trees to the malignancy stratified datasets and interrogate a range of metrics to determine the best model. Our best decision tree model achieves sensitivity and specificity of 86.7% and 80.0% respectively with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

eess.IV↗

arXiv2021

Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focusing on diagnosis and stratification of COVID-19 from medical images. Despite this large-scale research effort, these models have found limited practical application due in part to unproven generalization of these models beyond their source study. This study investigates the generalizability of key published models using the publicly available COVID-19 Computed Tomography data through cross dataset validation. We then assess the predictive ability of these models for COVID-19 severity using an independent new dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. The study shows high variability in the generalization of models trained on these datasets due to varied sample image provenances and acquisition processes amongst other factors. We show that under certain conditions, an internally consistent dataset can generalize well to an external dataset despite structural differences between these datasets with f1 scores up to 86%. Our best performing model shows high predictive accuracy for lung involvement score for an independent dataset for which expertly labelled lung involvement stratification is available. Creating an ensemble of our best model for disease positive prediction with our best model for disease negative prediction using a min-max function resulted in a superior model for lung involvement prediction with average predictive accuracy of 75% for zero lung involvement and 96% for 75-100% lung involvement with almost linear relationship between these stratifications.

cs.CV↗

arXiv2021

Modeling proportion of success in high school leaving examination- A comparative study of Inflated Unit Lindley and Inflated Beta distribution

In this article, we first introduced the inflated unit Lindley distribution considering zero or/and one inflation scenario and studied its basic distributional and structural properties. Both the distributions are shown to be members of exponential family with full rank. Different parameter estimation methods are discussed and supporting simulation studies to check their efficacy are also presented. Proportion of students passing the high school leaving examination for the schools across the state of Manipur in India for the year 2020 are then modeled using the proposed distributions and compared with the inflated beta distribution to justify its benefits.

stat.ME↗