arXiv subjects

Deepak Kumar

Publications and source records attributed to Deepak Kumar.

At least 73 records · Page 4Linked to original sources

arXiv2025

Axion effects on the non-radial oscillations of neutron stars

The effects of axions on quark matter equation of state (EOS) is studied within the three flavor Nambu--Jona-Lasinio model and its effects on on the non-radial oscillations of neutron stars is investigated. Using such an EOS for quark matter with axions and a EOS for hadronic matter within the relativistic mean field (RMF) theory, we discuss the hadron-quark phase transition (HQPT) using the Gibbs construction. The EOS so obtained is used to investigate the structure of hybrid neutron star (NS)s. It is found that the presence of axions in the core of compact stars stabilizes hybrid NSs in agreement with modern astrophysical constraints. It is further observed that the quadrupolar fundamental modes ($f$-modes) for such hybrid NSs get substantial enhancements both due to a larger quark core in the presence of axions and from the hyperons as compared to a canonical nucleonic neutron stars.

hep-ph↗

arXiv2025

Enhanced Patterned Fluorescence from Polystyrene through Focused Electron Beam Irradiation under Various Gases

We report on a novel method for tuning and enhancing fluorescence from irradiated polystyrene through electron-beam exposure in gaseous environments. We describe the effect of electron dose and ambient gas on the photoluminescence spectra and yield of irradiated PS films on insulating and conductive substrates. PS films were exposed in an environmental scanning electron microscope using a 20 keV electron beam, ambient gas pressures from high vacuum to 3 mbar, and electron doses from 1.8 to 45 mC cm-2. Irradiated PS films were characterized using confocal microscopy, TEM, EDS and FTIR spectroscopy. From emission spectra collected using confocal microscopy we found that the emission wavelength and photon yield of the irradiated film can be tuned by both dose and gas pressure. The emission wavelength blue-shifts with increasing pressure and red-shifts with increasing dose enabling an overall tuning range of 451 - 544 nm. Significant enhancement in the PL intensity, up to 18 times on sapphire substrates under helium when compared to high vacuum, are observed. Overall, the highest PL yield is observed on soda lime glass substrates under argon. Also, the photon-yield on conductive substrates is significantly smaller than that yield from insulating substrates. TEM images revealed e-beam irradiated PS is amorphous in nature and elemental mapping EDS revealed no signs of film oxidation. FTIR spectroscopy revealed that under gaseous environments the decay of aromatic and aliphatic C-H stretches is reduced compared to the high vacuum exposure; in all cases, features associated with the phenyl rings are preserved. Localized e-beam synthesis of fluorophores in PS can be controlled by both dose and by ambient gas pressure. This technique could enable new approaches to photonics where fluorophores with tunable emission properties can be locally introduced by e-beam patterning.

cond-mat.mes-hall↗

arXiv2025

Constraining the neutron star equation of state by including the isoscalar-vector and isovector-vector coupling using the Bayesian analysis

We constrain the nuclear matter equation of state within the relativistic mean field model by including the isoscalar-vector and isovector-vector coupling term at a fundamental level using the Bayesian analysis. We used the nuclear saturation properties and recent astrophysical observations to constrain the dense matter equation of state. We obtained about 20000 sets of equations of states out of sampling about 60 millions sets of equations of states. All 20000 equations of states satisfy nuclear matter saturation properties at saturation densities and produces high mass neutron stars. In our findings, we find that the non-zero value of isoscalar-vector and isovector-vector coupling parameter and negative value of sigma meson self-coupling stiffen the equation of state. Our sets of equations of state produces neutron stars of mass larger than 2.5 M$_{\odot}$ to include the recent gravitational waves observation GW190419.

nucl-th↗

arXiv2025

Use of Metric Learning for the Recognition of Handwritten Digits, and its Application to Increase the Outreach of Voice-based Communication Platforms

Initiation, monitoring, and evaluation of development programmes can involve field-based data collection about project activities. This data collection through digital devices may not always be feasible though, for reasons such as unaffordability of smartphones and tablets by field-based cadre, or shortfalls in their training and capacity building. Paper-based data collection has been argued to be more appropriate in several contexts, with automated digitization of the paper forms through OCR (Optical Character Recognition) and OMR (Optical Mark Recognition) techniques. We contribute with providing a large dataset of handwritten digits, and deep learning based models and methods built using this data, that are effective in real-world environments. We demonstrate the deployment of these tools in the context of a maternal and child health and nutrition awareness project, which uses IVR (Interactive Voice Response) systems to provide awareness information to rural women SHG (Self Help Group) members in north India. Paper forms were used to collect phone numbers of the SHG members at scale, which were digitized using the OCR tools developed by us, and used to push almost 4 million phone calls. The data, model, and code have been released in the open-source domain.

cs.AI↗

arXiv2025

Training neural networks without backpropagation using particles

Neural networks are a group of neurons stacked together in multiple layers to mimic the biological neurons in a human brain. Neural networks have been trained using the backpropagation algorithm based on gradient descent strategy for several decades. Several variants have been developed to improve the backpropagation algorithm. The loss function for the neural network is optimized through backpropagation, but several local minima exist in the manifold of the constructed neural network. We obtain several solutions matching the minima. The gradient descent strategy cannot avoid the problem of local minima and gets stuck in the minima due to the initialization. Particle swarm optimization (PSO) was proposed to select the best local minima among the search space of the loss function. The search space is limited to the instantiated particles in the PSO algorithm, and sometimes it cannot select the best solution. In the proposed approach, we overcome the problem of gradient descent and the limitation of the PSO algorithm by training individual neurons separately, capable of collectively solving the problem as a group of neurons forming a network. Our code and data are available at https://github.com/dipkmr/train-nn-wobp/

cs.NE↗

arXiv2025

Generalization of Some Well-Known Polynomial Inequalities for the Modified Smirnov Operator

Let $P(z)$ be a polynomial of degree $n$. In this paper, we consider the modified Smirnov operator, which carries a polynomial $P(z)$ into $\tilde{\mathbb{S}}_a[P](z)=(1+az)P'(z)-naP(z),$ where $a$ is an arbitrary number in $\overline{\mathbb{D}}$. We estimate minimum and maximum moduli of modified Smirnov operator of $P(z)$ on the unit circle with restricted zeros and thereby obtain a generalization of some results of Dewan and Hans \cite{dewan2013some}. This study includes compact generalization of some well-known polynomial inequalities.

math.CV↗

arXiv2025

CP violation in cold dense quark matter and axion effects on the non-radial oscillations of neutron stars

Charge-conjugation and parity violation in strong interaction for cold dense quark matter is studied with axions of quantum chromodynamic within the three flavor Nambu--Jona-Lasinio model that includes the coupling of axions to quarks. We first calculate the effective potential for axions at finite baryon density and zero temperature including the effects of a first order chiral phase transition. Using the equation of state for quark matter with axions and a hadronic matter equation of state in the ambit of a relativistic mean field theory in quantum hadrodynamics, we discuss the hadron-quark phase transition. Inclusion of axions reduces the critical density for chiral transition. We use a Gibbs construct for the hadron-quark phase transition satisfying the constraints of beta equilibrium and charge neutrality as appropriate for the neutron star matter. The equation of state so obtained is used to investigate the structure of hybrid neutron stars. It is found that with the presence of axions, it is possible to have stable hybrid neutron stars having an inner core of quark matter both in pure quark matter phase as well as in a mixed phase with hyperonic matter along with a outer core of hyperonic matter and is in agreement with modern astrophysical constraints. We also discuss the properties of non-radial oscillations of such hybrid neutron stars. It is observed that the quadrupolar fundamental modes ($f$-modes) for such hybrid neutron stars get substantial enhancements both due to a larger quark core in the presence of axions and from the hyperons as compared to a canonical nucleonic neutron stars.

hep-ph↗

arXiv2025

Explainable, Multi-modal Wound Infection Classification from Images Augmented with Generated Captions

Infections in Diabetic Foot Ulcers (DFUs) can cause severe complications, including tissue death and limb amputation, highlighting the need for accurate, timely diagnosis. Previous machine learning methods have focused on identifying infections by analyzing wound images alone, without utilizing additional metadata such as medical notes. In this study, we aim to improve infection detection by introducing Synthetic Caption Augmented Retrieval for Wound Infection Detection (SCARWID), a novel deep learning framework that leverages synthetic textual descriptions to augment DFU images. SCARWID consists of two components: (1) Wound-BLIP, a Vision-Language Model (VLM) fine-tuned on GPT-4o-generated descriptions to synthesize consistent captions from images; and (2) an Image-Text Fusion module that uses cross-attention to extract cross-modal embeddings from an image and its corresponding Wound-BLIP caption. Infection status is determined by retrieving the top-k similar items from a labeled support set. To enhance the diversity of training data, we utilized a latent diffusion model to generate additional wound images. As a result, SCARWID outperformed state-of-the-art models, achieving average sensitivity, specificity, and accuracy of 0.85, 0.78, and 0.81, respectively, for wound infection classification. Displaying the generated captions alongside the wound images and infection detection results enhances interpretability and trust, enabling nurses to align SCARWID outputs with their medical knowledge. This is particularly valuable when wound notes are unavailable or when assisting novice nurses who may find it difficult to identify visual attributes of wound infection.

cs.CV↗

arXiv2025

Krutrim LLM: Multilingual Foundational Model for over a Billion People

India is a diverse society with unique challenges in developing AI systems, including linguistic diversity, oral traditions, data accessibility, and scalability. Existing foundation models are primarily trained on English, limiting their effectiveness for India's population. Indic languages comprise only 1 percent of Common Crawl corpora despite India representing 18 percent of the global population, leading to linguistic biases. Thousands of regional languages, dialects, and code mixing create additional representation challenges due to sparse training data. We introduce Krutrim LLM, a 2 trillion token multilingual model designed for India's linguistic landscape. It incorporates the largest known Indic dataset, mitigating data scarcity and ensuring balanced performance across dialects. Krutrim outperforms or matches state-of-the-art models on Indic benchmarks while maintaining competitive English performance. Despite being significantly smaller in training flops, Krutrim LLM matches or exceeds models like LLAMA-2 on 10 out of 16 tasks, with an average score of 0.57 versus 0.55. This evidences Krutrim's flexible multilingual fluency across diverse linguistic contexts. Krutrim is integrated with real-time search to improve factual accuracy in conversational AI applications. This enhances accessibility for over 1 billion users worldwide. Through intentional design choices addressing data imbalances, Krutrim LLM signifies meaningful progress in building ethical, globally representative AI models.

cs.CL↗

arXiv2025

The footprint of nuclear saturation properties on the neutron star $f$ mode oscillation frequencies: a machine learning approach

We investigate the intricate relationships between the non-radial $f$ mode oscillation frequencies of neutron stars (NS)s and the corresponding nuclear matter equation of state (EOS) using a machine learning (ML) approach within the ambit of the relativistic mean field (RMF) framework for nuclear matter. With two distinct parameterizations of the Walecka model, namely, (1) with non-linear self interactions of the scalar field (NL) and, (2) a density dependent Bayesian model (DDB), we perform a thorough examination of the $f$ mode frequency in relation to various nuclear saturation properties. The correlations between the $f$ mode frequencies and nuclear saturation properties reveal, through various analytical and ML methods, the complex nature of NSs and their potential as the cosmic laboratory for studying extreme states of matter. A principal component analysis (PCA) has been performed using mixed datasets from DDB and NL models to discriminate the relative importance of the different components of the EOS on the $f$ mode frequencies. Additionally, a {\it Random forest feature importance} analysis also elucidates the distinct roles of these properties in determining the $f$ mode frequency across a spectrum of NS masses. Our findings are further supported by symbolic regression searches, yielding high-accuracy relations with strong Pearson coefficients and minimal errors. These relations suggest new methodologies for probing NS core characteristics, such as energy density, pressure, and speed of sound from observations of non-radial $f$ mode oscillations of NSs.

nucl-th↗

arXiv2025

Uplink Rate Splitting Multiple Access with Imperfect Channel State Information and Interference Cancellation

This article investigates the performance of uplink rate splitting multiple access (RSMA) in a two-user scenario, addressing an under-explored domain compared to its downlink counterpart. With the increasing demand for uplink communication in applications like the Internet-of-Things, it is essential to account for practical imperfections, such as inaccuracies in channel state information at the receiver (CSIR) and limitations in successive interference cancellation (SIC), to provide realistic assessments of system performance. Specifically, we derive closed-form expressions for the outage probability, throughput, and asymptotic outage behavior of uplink users, considering imperfect CSIR and SIC. We validate the accuracy of these derived expressions using Monte Carlo simulations. Our findings reveal that at low transmit power levels, imperfect CSIR significantly affects system performance more severely than SIC imperfections. However, as the transmit power increases, the impact of imperfect CSIR diminishes, while the influence of SIC imperfections becomes more pronounced. Moreover, we highlight the impact of the rate allocation factor on user performance. Finally, our comparison with non-orthogonal multiple access (NOMA) highlights the outage performance trade-offs between RSMA and NOMA. RSMA proves to be more effective in managing imperfect CSIR and enhances performance through strategic message splitting, resulting in more robust communication.

eess.SP↗

arXiv2025

Characterizing the MrDeepFakes Sexual Deepfake Marketplace

The prevalence of sexual deepfake material has exploded over the past several years. Attackers create and utilize deepfakes for many reasons: to seek sexual gratification, to harass and humiliate targets, or to exert power over an intimate partner. In part enabling this growth, several markets have emerged to support the buying and selling of sexual deepfake material. In this paper, we systematically characterize the most prominent and mainstream marketplace, MrDeepFakes. We analyze the marketplace economics, the targets of created media, and user discussions of how to create deepfakes, which we use to understand the current state-of-the-art in deepfake creation. Our work uncovers little enforcement of posted rules (e.g., limiting targeting to well-established celebrities), previously undocumented attacker motivations, and unexplored attacker tactics for acquiring resources to create sexual deepfakes.

cs.CY↗

arXiv2025

Multimodal AI on Wound Images and Clinical Notes for Home Patient Referral

Chronic wounds affect 8.5 million Americans, particularly the elderly and patients with diabetes. These wounds can take up to nine months to heal, making regular care essential to ensure healing and prevent severe outcomes like limb amputations. Many patients receive care at home from visiting nurses with varying levels of wound expertise, leading to inconsistent care. Problematic, non-healing wounds should be referred to wound specialists, but referral decisions in non-clinical settings are often erroneous, delayed, or unnecessary. This paper introduces the Deep Multimodal Wound Assessment Tool (DM-WAT), a machine learning framework designed to assist visiting nurses in deciding whether to refer chronic wound patients. DM-WAT analyzes smartphone-captured wound images and clinical notes from Electronic Health Records (EHRs). It uses DeiT-Base-Distilled, a Vision Transformer (ViT), to extract visual features from images and DeBERTa-base to extract text features from clinical notes. DM-WAT combines visual and text features using an intermediate fusion approach. To address challenges posed by a small and imbalanced dataset, it integrates image and text augmentation with transfer learning to achieve high performance. In evaluations, DM-WAT achieved 77% with std 3% accuracy and a 70% with std 2% F1 score, outperforming prior approaches. Score-CAM and Captum interpretation algorithms provide insights into specific parts of image and text inputs that influence recommendations, enhancing interpretability and trust.

cs.LG↗

arXiv2025

Effect of Magnetic Field on Aqueous Humor Flows Inside Anterior Chamber of Human Eye

Aqueous humor (AH) dynamics is responsible for maintaining intraocular pressure, ocular health and targeted drug delivery within the eye. This study investigates the flow of AH within the anterior chamber (AC) under the combined influence of a uniform magnetic field and natural convection. Different orientations of the magnetic field and temperaature gradient are considered. A lubrication approximation is employed and the resulting equations are solved using regular perturbation method. The analytical solutions are validated using numerical simulations performed in COMSOL Multiphysics 6.2. In the standing position, AH flow field is characterised by a single vortex, while in the supine position, it forms two counter-rotating vortices. The velocity is found to be higher in standing position. The effect of a uniform magnetic field on the velocity is more significant in the supine position. The magnetic field does not change the flow field qualitatively as buoyancy is the primary driving force. In the standing position a magnetic field oriented perpendicular to the eye resulted in a greatest reduction of AH velocity, as compared to a magnetic field along the eye. This study is a step towards holistic approach for targeted drug delivery using magnetic fields in eye.

physics.flu-dyn↗

arXiv2025

Realistic Total-Body J-PET Geometry Optimization -- Monte Carlo Study

Total-Body PET is one of the most promising medical diagnostics modalities. The high sensitivity provided by Total-Body technology can be advantageous for novel tomography methods like positronium imaging. Several efforts are ongoing to lower the price of the TB-PET systems. Among the alternatives, the Jagiellonian PET (J-PET) technology, based on plastic scintillator strips, offers a low-cost alternative. The work aimed to compare five Total-Body J-PET geometries as a possible next generation J-PET scanner design. We present comparative studies of performance characteristics of the cost-effective Total-Body PET scanners using J-PET technology. We investigated in silico five Total-Body scanner geometries. Monte Carlo simulations of the XCAT phantom, the 2-meter sensitivity line source and positronium sensitivity phantoms were performed. We compared the sensitivity profiles for 2-gamma and 3-gamma tomography, relative cost of the setups and performed quantitative analysis of the reconstructed images. The analysis of the reconstructed XCAT images reveals the superiority of the seven-ring scanners over the three-ring setups. However, the three-ring scanners would be approximately 2-3 times cheaper. The peak sensitivity values for two-gamma vary from 20 to 34 cps/kBq. The sensitivity curves for the positronium tomography have a similar shape to the two-gamma sensitivity profiles. The peak values are lower compared to the two-gamma cases, from about 20-28 times, with a maximum of 1.66 cps/kBq. The results show the feasibility of multi-organ imaging of all the systems to be considered for the next generation of TB J-PET designs. The relative cost for all the scanners is about 10-4 times lower compared to the cost of the uExplorer. These properties coupled together with J-PET cost-effectiveness, make the J-PET technology an attractive solution for broad application in clinics.

physics.med-ph↗

arXiv2024

Statistical mechanics of an active wheel rolling in circles

Vibrated granular matter constitutes a useful system for studying the physics of active matter. Usually, self-propulsion is induced in grains through suitable asymmetry in the particle design. In this paper, we show that a symmetrical mini wheel placed on a vibrating plate self-propels along circular trajectories, showing chiral active dynamics. The chiral activity emerges through a sequence of spontaneous symmetry breaking in the particle's kinetics. The fact that isotropy, fore-aft, and chiral symmetries are broken spontaneously leads to distinct statistics, which include a temporal evolution involving stochastic resetting, a non-Gaussian velocity distribution with multiple peaks, broad power-law curvature distribution, and a bounded chirality probability, along with a phase transition from passive achiral to active chiral state as a function of vibration amplitude. Our study establishes the vibrated wheel as a three-state chiral active system that can serve as a model experimental system to study the non-equilibrium statistical mechanics and stochastic thermodynamics of chiral active systems and can inspire novel locomotion strategies in robotics.

cond-mat.soft↗

arXiv2024

Global Calderón-Zygmund theory for fractional Laplacian type equations

We establish several fine boundary regularity results of weak solutions to non-homogeneous $s$-fractional Laplacian type equations. In particular, we prove sharp Calderón-Zygmund type estimates of $u/d^s$ depending on the regularity assumptions on the associated kernel coefficient including VMO, Dini continuity or the Hölder continuity, where $u$ is a weak solution to such a nonlocal problem and $d$ is the distance to the boundary function of a given domain. Our analysis is based on point-wise behaviors of maximal functions of $u/d^s$.

math.AP↗

arXiv2024

Complexities in the growth and stabilization of polar phase in the Hf$_{0.5}$Zr$_{0.5}$O$_2$ thin films grown by Pulsed Laser Deposition

After the discovery of ferroelectricity in HfO$_2$ based thin films a decade ago, ferroelectric Hf$_{0.5}$Zr$_{0.5}$O$_2$ (HZO) thin films are frequently being utilized in the CMOS (Complementary Metal- Oxide Semiconductor) and logic devices, thanks to their large remnant polarization, high retention and endurance. A great deal of effort has been made towards understanding the origin of ferroelectricity in epitaxial HZO thin films and controlling the microstructure at the atomic level which governs the ferroelectric phase. Nevertheless, the HZO films still suffer from fundamental questions, such as (1) the vagueness of interfacial mechanisms between HZO, buffer layer and the substrate which controls the polar phase; (2) the nature of the metastable polar phase responsible for the ferroelectricity, be it orthorhombic or rhombohedral; which are poorly understood. Here, we have addressed these issues by employing the in-situ reflection high energy electron diffraction -- assisted pulsed laser deposition and mapping the asymmetrical polar maps on high quality HZO films grown on functional perovskite oxide substrates. The interface between La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) and the substrate is shown to be quite important, and a slightly rougher interface of the former destabilizes the ferroelectric phase of HZO irrespective of well-controlled growth of the ferroelectric layers. A rhombohedral-like symmetry of HZO unit cell is extracted through the x-ray diffraction asymmetrical polar maps. The ferroelectric measurements on a nearly 7 nm HZO film on STO(001) substrate display a remnant polarization close to 8 uC/cm$^2$. These results highlight the complexities involved at the atomic scale interface in the binary oxides thin films and can be of importance to the HfO$_2$-based ferroelectric community which is still at its infancy.

cond-mat.mtrl-sci↗