arXiv subjects

Manoj Kumar

Publications and source records attributed to Manoj Kumar.

At least 19 recordsLinked to original sources

arXiv2026

First Plasma Commissioning and Operational Highlights from India's First Spherical Tokamak at IPR

A compact Spherical Tokamak(ST) is commissioned at Institute for Plasma Research (IPR) to explore low aspect ratio tokamak physics and technologies that complement to the existing high aspect ratio tokamaks namely ADITYA-U and SST-1 by enabling studies on non-inductive startup, current drive in over dense plasmas, and shaped plasma physics on a low cost platform. The device, India's first spherical tokamak has completed major mechanical, magnetic, and electrical integration, and the coil system has been successfully tested with series of integrated commissioning. First plasma experiments have been carried out with a modest Ohmic system assisted by a 2.45GHz microwave system, supported by a centralized control and data acquisition system. An initial diagnostic set comprising visible imaging, spectroscopy, magnetics, and radiation monitors required for machine operation has been installed. This paper presents the integrated commissioning experiences and first plasma experiments of the newly installed machine.

physics.plasm-ph↗

arXiv2026

QEvict: Recoverable Quantized KV Eviction for Attention-Drift-Robust Long-Context Decoding

Autoregressive large language model inference is increasingly constrained by the memory footprint of the Key-Value (KV) cache. A dominant line of work reduces this footprint by evicting tokens that appear unimportant under attention-derived scores. However, such policies make an implicit irreversible decision: once a token is evicted, it cannot become useful again. We show that this assumption is brittle during decoding. Token and window importance drift as generated queries evolve, causing standard eviction policies to permanently discard states that later receive substantial attention under the full-cache model. To characterize this behaviour, we introduce Future Missed Mass and Global LIR, two diagnostics that measure future attention assigned to discarded states and the reactivation of historically inactive regions. We propose QEvict, a three-tier KV-cache management scheme that replaces binary retain-or-delete eviction with recoverable eviction. QEvict maintains high-confidence windows in full precision, stores intermediate windows in a quantized recoverable tier, and deletes only the lowest-confidence windows. During decoding, cumulative attention scores update window importance and when a quantized window becomes important again, it is dequantized and promoted to the full-precision. Under a fixed memory budget, this design preserves broader historical context while retaining exact full precision for the most important regions. Across long-context understanding, retrieval, and reasoning benchmarks, QEvict consistently improves over representative eviction and quantization baselines, reducing missed attention and improving information retention

cs.LG↗

arXiv2026

Technology Transfer Readiness, Explainable AI and Financial Innovation Capability Transitions in Expanded BRICS: Benchmarking Against Advanced Innovation Economies

This study examines the dynamics of technology transfer readiness and financial innovation capability transitions across the expanded BRICS economies, benchmarked against advanced innovation systems through explainable AI. Using a composite Innovation Capability Development-Readiness index (ICDI) constructed through principal component analysis, the paper evaluates the structural conditions enabling knowledge diffusion, industrial upgrading, and financial innovation ecosystem development. A Markov transition framework is employed to analyse how countries evolve across readiness tiers over time, capturing both persistence and mobility in innovation capabilities. The results reveal significant asymmetries in transition probabilities between advanced economies and emerging innovation systems, with several BRICS economies demonstrating gradual upgrading trajectories while others remain structurally locked in lower readiness states. These findings highlight the institutional and policy conditions required to strengthen technology transfer ecosystems. Successful countries in these areas attract foreign investment, participate in global value chains, and profit from technology partnerships. The study contributes to the literature on innovation capability formation and industrial transformation by integrating composite readiness measurement with dynamic transition modelling to inform evidence-based innovation policy.

econ.EM↗

arXiv2026

Prediction of bank transaction fraud using TabNet an adaptive deep learning architecture

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal. We utilize a supervised learning pipeline that incorporates the Synthetic Minority Oversampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives. Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

q-fin.GN↗

arXiv2026

A Survey of Smart Grid Emerging Use Cases and Relevant 5G and 6G Capabilities and Features

The growing complexity of modern energy systems has led to the adoption of Smart Grid (SG) that use advanced communication technologies to facilitate efficient, reliable, secure, and sustainable energy operation and management. Unlike existing surveys that often treat grid and communication domains separately, this work rigorously quantifies service requirements for high-complexity emerging scenarios. It provides a comprehensive overview of SG architecture that integrates digital communication infrastructure with distributed energy resources (DERs), microgrids, energy storage systems, and cybersecurity frameworks. Furthermore, emerging SG use cases such as smart distributed voltage control, real-time fault detection and self-healing, smart and autonomous monitoring, and predictive maintenance are identified, and more importantly, service performance requirements associated with these use cases have been quantified. Additionally, key capabilities and emerging SG enablers of fifth-generation (5G) and sixth-generation (6G) networks are described. These capabilities and enablers include network slicing, edge computing, spectrum management, artificial intelligence (AI) driven optimization, digital twins, and Open-Radio Access Network (O-RAN). Finally, the paper discusses open challenges and future research directions for designing scalable, intelligent, and secure next-generation SG systems.

eess.SY↗

arXiv2025

Predictive one-zero with vanishing sub-trace texture in neutrino mass matrix in light of dark matter and neutrinoless double beta decay

In this work, we investigate a predictive class of neutrino mass matrices characterized by one texture zero and one vanishing sub-trace within the framework of the scotogenic model, wherein neutrino masses, dark matter, and neutrinoless double beta decay are intrinsically correlated. We analyze twelve viable texture structures -- namely $B_{1,4,5}$, $C_{1,2,\ldots,5}$, $D_{4,5}$, and $F_{5,6}$ -- and examine their implications for the effective Majorana mass $(|M_{ee}|)$ governing neutrinoless double beta decay $(0\nu\beta\beta)$. Remarkably, all non-zero entries of the neutrino mass matrix can be parametrized in terms of this effective Majorana mass, establishing a direct theoretical link between low-energy observables and high-scale parameters of the model. Among the twelve textures, eleven predict dark matter masses of order TeV and yield correlated bounds on $|M_{ee}|$ -- making them testable in current and forthcoming $0\nu\beta\beta$ experiments -- while the textures $D_4$ and $F_{5,6}$ exhibit comparatively weaker correlations. In contrast, the texture $C_5$ is excluded due to its requirement of unrealistically large Yukawa couplings and its inability to realize dark matter in the TeV regime. Our analysis thus identifies a subset of predictive neutrino mass textures that consistently relate dark matter phenomenology and neutrinoless double beta decay observables within the scotogenic paradigm.

hep-ph↗

arXiv2025

Investigation on Structural, Optical, Thermal, and Magnetic Properties of Bismuth Ferrite Nanoparticles Synthesized at Lower Annealing Temperature

Due to its multiferroic properties and narrow optical bandgap, Bismuth ferrite has been widely explored for spintronics, photovoltaics, and photocatalysis applications. Bismuth ferrite can be synthesized in various forms like bulk, thin films, and nanostructures using various synthesis techniques. It is challenging to synthesize the pure BiFeO3 phase due to the volatile nature of bismuth and the very narrow temperature range for forming this phase. So, this work aims to synthesize the pure BiFeO3 phase at lower annealing temperatures using an efficient sol-gel method. We have chosen the annealing temperature from 450 to 650 C, and a detailed analysis of structural and optical properties is performed here. X-ray diffraction is used to confirm the crystalline nature of the material. Single-phase Rietveld analysis of XRD patterns is carried out to study the effect of annealing temperature on structural parameters. All the samples are crystalized in pure rhombohedral BiFeO3 phase with the R3c space group symmetry, except those annealed at higher temperatures, 600 C and 650 C. Strain and dislocation densities were decreasing with an increase in the annealing temperature. From the UV-visible analysis, a strong response is observed below 600 nm in the visible region, and the band gap from the absorption behaviour is estimated in the range of 2.26 - 2.60 eV for these Bismuth ferrite nanoparticles. Fourier transform infrared analysis confirmed the existence of metal-oxygen bonds in Bismuth ferrite nanoparticles. These nanoparticles were found to be thermally stable from the thermal analysis performed using differential scanning calorimetry. Bismuth ferrite nanoparticles were weakly magnetic from the vibrating sample magnetometry analysis.

cond-mat.mtrl-sci↗

arXiv2025

Generalization Bound for a General Class of Neural Ordinary Differential Equations

Neural ordinary differential equations (neural ODEs) are a popular type of deep learning model that operate with continuous-depth architectures. To assess how well such models perform on unseen data, it is crucial to understand their generalization error bounds. Previous research primarily focused on the linear case for the dynamics function in neural ODEs - Marion, P. (2023), or provided bounds for Neural Controlled ODEs that depend on the sampling interval Bleistein et al. (2023). In this work, we analyze a broader class of neural ODEs where the dynamics function is a general nonlinear function, either time dependent or time independent, and is Lipschitz continuous with respect to the state variables. We showed that under this Lipschitz condition, the solutions to neural ODEs have solutions with bounded variations. Based on this observation, we establish generalization bounds for both time-dependent and time-independent cases and investigate how overparameterization and domain constraints influence these bounds. To our knowledge, this is the first derivation of generalization bounds for neural ODEs with general nonlinear dynamics.

cs.LG↗

arXiv2025

White LED-based photocatalytic treatment using recoverable cobalt ferrite nanoparticles

Contamination of freshwater sources has been alarming due to the widespread use of toxic chemicals in various industries. Advanced oxidation processes (AOPs) such as photocatalysis are widely explored to tackle such problems. In photocatalysis, highly oxidative species such as hydroxyl radicals (*OH) are produced with the help of some semiconductor photocatalysts and light. A photocatalyst decomposes these toxic organic compounds in the presence of light. Spinel ferrite (MFe2O4, M = Co, Ni, Cu, Zn, etc.) materials are an important candidate as a photocatalyst due to their semiconducting behaviour and narrow optical bandgap. In this work, we have synthesized cobalt ferrite (CoFe2O4) nanoparticles using the sol-gel method and subsequently annealed at 500{\deg}C. The nanoparticles are characterized using X-ray diffraction, scanning electron microscopy, Raman, and Infrared spectroscopy for structural analysis. The band gap of the material is evaluated using UV-visible spectroscopy. The photocatalytic activity of the material is investigated using methyl orange and methylene blue aqueous solutions as a model dye and a low-power white LED as a light source. The material could decompose 95 % of the dye after 150 minutes of irradiation. Adding hydrogen peroxide further improves the decomposition rate, with over 90 % decomposition achieved within 90 minutes.

cond-mat.mtrl-sci↗

arXiv2025

Identification and Characterization of a New Disruption Regime in ADITYA-U Tokamak

Disruptions continue to pose a significant challenge to the stable operation and future design of tokamak reactors. A comprehensive statistical investigation carried out on the ADITYA-U tokamak has led to the observation and characterization of a novel disruption regime. In contrast to the conventional Locked Mode Disruption (LMD), the newly identified disruption exhibits a distinctive two-phase evolution: an initial phase characterized by a steady rise in mode frequency with a nonlinearly saturated amplitude, followed by a sudden frequency collapse accompanied by a pronounced increase in amplitude. This behaviour signifies the onset of the precursor phase on a significantly shorter timescale. Clear empirical thresholds have been identified to distinguish this disruption type from conventional LMD events, including edge safety factor, current decay coefficient, current quench (CQ) time, and CQ rate. The newly identified disruption regime is predominantly governed by the (m/n = 2/1) drift-tearing mode (DTM), which, in contrast to typical disruptions in the ADITYA-U tokamak that involve both m/n = 2/1 and 3/1 modes, consistently manifests as the sole dominant instability. Initiated by core temperature hollowing, the growth of this mode is significantly enhanced by a synergistic interplay between a strongly localized pressure gradient and the pronounced steepening of the current density profile in the vicinity of the mode rational surface.

physics.plasm-ph↗

arXiv2025

Ultraslow Growth of Domains in a Random-Field System With Correlated Disorder

We study domain growth kinetics in a random-field system in the presence of a spatially correlated disorder $h_{i}(\vec r)$ after an instantaneous quench at a finite temperature $T$ from a random initial state corresponding to $T=\infty$. The correlated disorder field $h_{i}(\vec r)$ arises due to the presence of magnetic impurities, decaying spatially in a power-law fashion. We use Glauber spin-flip dynamics to simulate the kinetics at the microscopic level. The system evolves via the formation of ordered magnetic domains. We characterize the morphology of domains using the equal-time correlation function $C(r,t)$ and structure factor $S(k,t)$. In the large-$k$ limit, $S(k, t)$ obeys Porod's law: $S(k, t)\sim k^{-(d+1)}$. The average domain size $L(t)$ asymptotically follows \textit{double logarithmic growth behavior}.

cond-mat.stat-mech↗

arXiv2025

Temperature switchable self-propulsion activity of liquid crystalline microdroplets

We report on a switchable emulsion droplet microswimmer by utilizing a temperature-dependent transition of the droplet phase. The droplets, made from a liquid crystalline (LC) smectic phase material ($T =$ 25 $^{\circ}$C), self-propel only in their nematic and isotropic phases at elevated temperatures ($T\ge$ 33.5 $^{\circ}$C). This transition between motile and non-motile states is fully reversible - in the motile state, the droplets exhibit persistent motion and directional memory over multiple heating-cooling cycles. Further, we distinguish the state of rest from the state of motion by characterizing the chemical and hydrodynamic fields of the droplets. Next, we map the motility behaviour of the droplets across varying surfactant concentrations and temperatures, observing that swimming occurs only at sufficiently high surfactant concentrations above and temperatures above the smectic-nematic phase transition temperature $\textit{i.e.}$ $T\ge$ 33.5 $^{\circ}$C. Our work envisions the potential of LC emulsion droplets as switchable microswimmers.

cond-mat.soft↗

arXiv2025

Ordering transition of the three-dimensional four-state random-field Potts model

Spin systems exposed to the influence of random magnetic fields are paradigmatic examples for studying the effect of quenched disorder on condensed-matter systems. In this context, previous studies have almost exclusively focused on systems with Ising or continuous symmetries, while the Potts symmetry, albeit being of fundamental importance also for the description of realistic physical systems, has received very little attention. In the present study, we use a recently developed quasi-exact method for determining ground states in the random-field Potts model to study the problem with four states. Extending the protocol applied for the three-state model, we use extensive finite-size scaling analyses of the magnetization, Binder parameter, energy cumulant, specific heat, and the connected as well as disconnected susceptibilities to study the magnetic ordering transition of the model. In contrast to the system in the absence of disorder, we find compelling evidence for a continuous transition, and we precisely determine the critical point as well as the critical exponents, which are found to differ from the exponents of the three-state system as well as from those of the random-field Ising model.

cond-mat.stat-mech↗

arXiv2024

Minimal structure for neutrino mass matrix

Taking clue from the minimal structure of texture 4-zero hermitian mass matrices, which are very successful in accommodating quark mixing data, we propose a form of texture 2-zero complex symmetric neutrino mass matrix with only one phase parameter. This minimal mass matrix not only accommodates the available neutrino oscillation data, but also makes interesting predictions for the unknown parameters like the lightest neutrino mass $m_{\nu_1}$ (for normal ordering(NO)), Jarlskog's rephasing invariant $J_{CP}$, Dirac type CP violating phase $\delta_{CP}$ and effective neutrino mass $\left< m_{ee} \right>$. We also explore the correlations between the parameters of the model, that lead us to minimizing the parameters further.

hep-ph↗

arXiv2024

XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision with Learned Depth Point Cloud Registration

Tracking the full body motions of users in XR (AR/VR) devices is a fundamental challenge to bring a sense of authentic social presence. Due to the absence of dedicated leg sensors, currently available body tracking methods adopt a synthesis approach to generate plausible motions given a 3-point signal from the head and controller tracking. In order to enable mixed reality features, modern XR devices are capable of estimating depth information of the headset surroundings using available sensors combined with dedicated machine learning models. Such egocentric depth sensing cannot drive the body directly, as it is not registered and is incomplete due to limited field-of-view and body self-occlusions. For the first time, we propose to leverage the available depth sensing signal combined with self-supervision to learn a multi-modal pose estimation model capable of tracking full body motions in real time on XR devices. We demonstrate how current 3-point motion synthesis models can be extended to point cloud modalities using a semantic point cloud encoder network combined with a residual network for multi-modal pose estimation. These modules are trained jointly in a self-supervised way, leveraging a combination of real unregistered point clouds and simulated data obtained from motion capture. We compare our approach against several state-of-the-art systems for XR body tracking and show that our method accurately tracks a diverse range of body motions. XR-MBT tracks legs in XR for the first time, whereas traditional synthesis approaches based on partial body tracking are blind.

cs.CV↗

arXiv2024

Analysis of a mathematical model for malaria using data-driven approach

Malaria is one of the deadliest diseases in the world, every year millions of people become victims of this disease and many even lose their lives. Medical professionals and the government could take accurate measures to protect the people only when the disease dynamics are understood clearly. In this work, we propose a compartmental model to study the dynamics of malaria. We consider the transmission rate dependent on temperature and altitude. We performed the steady state analysis on the proposed model and checked the stability of the disease-free and endemic steady state. An artificial neural network (ANN) is applied to the formulated model to predict the trajectory of all five compartments following the mathematical analysis. Three different neural network architectures namely Artificial neural network (ANN), convolution neural network (CNN), and Recurrent neural network (RNN) are used to estimate these parameters from the trajectory of the data. To understand the severity of a disease, it is essential to calculate the risk associated with the disease. In this work, the risk is calculated using dynamic mode decomposition(DMD) from the trajectory of the infected people.

q-bio.PE↗

arXiv2024

PaliGemma: A versatile 3B VLM for transfer

PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation.

cs.CV↗

arXiv2024

Modularity aided consistent attributed graph clustering via coarsening

Graph clustering is an important unsupervised learning technique for partitioning graphs with attributes and detecting communities. However, current methods struggle to accurately capture true community structures and intra-cluster relations, be computationally efficient, and identify smaller communities. We address these challenges by integrating coarsening and modularity maximization, effectively leveraging both adjacency and node features to enhance clustering accuracy. We propose a loss function incorporating log-determinant, smoothness, and modularity components using a block majorization-minimization technique, resulting in superior clustering outcomes. The method is theoretically consistent under the Degree-Corrected Stochastic Block Model (DC-SBM), ensuring asymptotic error-free performance and complete label recovery. Our provably convergent and time-efficient algorithm seamlessly integrates with graph neural networks (GNNs) and variational graph autoencoders (VGAEs) to learn enhanced node features and deliver exceptional clustering performance. Extensive experiments on benchmark datasets demonstrate its superiority over existing state-of-the-art methods for both attributed and non-attributed graphs.

cs.LG↗