arXiv subjects

Nitesh Kumar

Publications and source records attributed to Nitesh Kumar.

At least 19 recordsLinked to original sources

arXiv2026

Longitudinal-Motion-Aware Lateral Control for Autonomous Vehicles: A Robust Nonlinear Control Framework

As autonomous vehicles (AVs) operate in increasingly dynamic traffic conditions, lateral control must be performed while longitudinal speed and acceleration vary. Yet many existing lateral controllers rely on constant-speed or operating-point-based assumptions, which can degrade performance during transient longitudinal maneuvers. Moreover, most methods assume precisely known vehicle parameters, despite real-world parametric uncertainties. To address these limitations, this paper presents a longitudinal-motion-aware robust nonlinear lateral control framework for AVs. It first derives a tracking error model that depends on varying longitudinal speed and acceleration. Using this model, feedback linearization is employed to obtain a linear input-output relation for lateral error tracking while embedding longitudinal motion into the control law. The resulting internal dynamics are then analyzed to ensure overall system stability. To address parameter uncertainty, two robust control designs with distinct implementation trade-offs are proposed: (i) a Lyapunov redesign (LR) approach inspired by sliding mode control, and (ii) an incremental nonlinear dynamic inversion (INDI) method. Both are rigorously analyzed and proven to ensure ultimate boundedness, with key robustness-tuning parameters explicitly identified. Simulations demonstrate enhanced tracking accuracy, consistent performance across varying speeds and accelerations, and robustness to model uncertainties, while also examining the effects of the robustness-related parameters. Real-vehicle tests further confirm real-time implementation and practical path-tracking performance on actual hardware.

cs.RO↗

arXiv2026

Privacy-Preserving Decentralized Cooperative Localization with Range-Only Measurements: A Convex Optimization Based Approach

Cooperative localization using range-based measurements is critical for multi-robot systems operating in GPS-denied and unstructured environments. However, traditional cooperative approaches require sharing explicit spatial coordinates across the network, presenting a severe security vulnerability in privacy-sensitive missions. While recent literature has explored privacy-preserving alternatives, these methods typically rely on accuracy-degrading noise injection or computationally prohibitive cryptographic protocols. To overcome these limitations, we propose a novel, natively privacy-preserving Decentralized Cooperative Localization (DCL) framework based on convex optimization. Discarding probabilistic noise models, we assume strictly bounded measurement noise and formulate the localization problem via Semi-Definite Programming (SDP) to compute a Maximum-Volume Inscribed Ellipsoid (MVE). Our approach introduces novel intersection-plane constraints derived from landmark measurements to significantly tighten individual spatial bounds. To incorporate inter-robot range measurements securely, we uniquely decompose coupling constraints into localized Linear Matrix Inequalities (LMIs). Agents achieve fleet-wide spatial consensus by iteratively exchanging only abstract dual variables, completely avoiding the transmission of explicit primal position estimates. Extensive 3D Monte Carlo simulations demonstrate that our DCL framework outperforms existing SDP-based localization method in accuracy, while guaranteeing operational privacy and maintaining highly scalable, parallelizable computation.

cs.RO↗

arXiv2026

Beyond Logprobs: A Multi-Signal Confidence Engine for LLM-Based Document Field Extraction

In high-stakes document processing pipelines, including financial reconciliation, compliance verification, and procurement automation, an LLM extraction that is silently wrong is more dangerous than one that is visibly absent. The central challenge is not extraction accuracy alone but reliable confidence estimation: knowing, field by field, whether an extraction can be trusted for automation or deferred to human review. Token-level log-probabilities, verbalized confidence, and multi-sample self-consistency all collapse toward all-positive behaviour at practical thresholds, offering no reliable separation between trustworthy and untrustworthy extractions. We present ExtractConf, a cross-domain, field-agnostic confidence engine that grounds confidence estimation in two structurally different readings of the same document. A field-guided Hunter call extracts each field under schema-slot completion pressure; a document-guided Mapper call scans holistically and surfaces values grounded in document content. This asymmetry yields different failure modes: Hunter hallucinates values for absent fields, while Mapper misses visually non-salient ones. Their disagreement is independently informative. ExtractConf fuses cross-call disagreement, LLM-internal uncertainty, OCR, image quality, and spatial layout into a classifier requiring no domain-specific rules or retraining. On DocILE (55-field invoices, 26% failure rate), it achieves 0.928 ROC AUC and reduces selective prediction risk by 70% over logprob-mean. At 80% coverage, accuracy reaches 99.1%, enabling a practical human-in-the-loop workflow. Zero-shot transfer to CORD receipts achieves 0.858 AUC; lightweight Lasso recalibration reduces ECE by 89% and Brier by 43%, confirming the signals generalise across document domains.

cs.CL↗

arXiv2026

Unconventional topological Hall response and anisotropic magnetotransport properties of a helical magnet EuAuAs

Topological magnets with nontrivial spin texture have attracted considerable interest because they display a rich spectrum of emergent quantum phenomena. Here, we present a combined experimental and theoretical investigation of the magnetic and magnetotransport properties of EuAuAs, an antiferromagnet with N\'eel temperature ($T_\mathrm{N}$) $\sim$ 6 K. The temperature and magnetic field dependence of electrical resistivity and magnetization demonstrate that the charge transport in EuAuAs is strongly influenced by the spin configuration of local Eu moments. Below $T_\mathrm{N}$, both longitudinal magnetoresistance (LMR) and transverse magnetoresistance (TMR) are positive at low fields but large and negative at high fields. With increasing temperature, TMR becomes positive above 60 K, whereas LMR remains negative up to 100 K. The low-field positive LMR and TMR originate from weak antilocalization (WAL). The WAL contribution in TMR is well captured by the Hikami-Larkin-Nagaoka model, whereas the LMR data are described by a generalized Altshuler-Aronov framework. Moreover, we observe a giant topological Hall effect arising from the scalar spin chirality, which is further supported by the helical magnetic structure obtained from the ab-initio calculations. The observed anisotropy in longitudinal resistivity and magnetoresistance underscores the very nature of the Fermi surface of the EuAuAs, as confirmed by first-principles calculations. These results establish EuAuAs as a unique platform for exploring the interplay between electronic structure and noncoplanar spin texture in a centrosymmetric helical magnet.

cond-mat.mtrl-sci↗

arXiv2026

Photometric metallicity of Galactic RR Lyrae stars in the Gaia DR3 era

We present a new, calibrated $G$-band relationship between pulsation period $P$, Fourier parameter $\phi_{31}$, and metallicity [Fe/H] for galactic RR Lyrae stars from the Gaia survey. A set of 72 fundamental mode RR Lyrae stars were identified for deriving the relation in the $G$-band, after visual examination of their light curves. Unlike recent large-scale calibrations, our relation prioritizes calibration purity by anchoring exclusively to a homogeneously analyzed sample of high-resolution spectroscopic metallicities from the literature. Our best fit relation is $\text{[Fe/H]} = (-6.93 \pm 0.58) - (6.04 \pm 0.37)P + (1.65 \pm 0.11)\phi_{31}$. We compare the [Fe/H] predicted by our relation for the stars in our calibration sample with that obtained from previously established relations in the $G$-band using different approaches. Our calibrated $G$-band $P$-$\phi_{31}$-[Fe/H] relationship demonstrates high reliability when validated against spectroscopic data, achieving a negligible bias of $0.00$ dex and an empirical RMS scatter of 0.26 dex. Furthermore, by applying an Orthogonal Distance Regression (ODR) routine that fully propagates parameter covariance, we establish a mathematically strict empirical baseline whose theoretical uncertainties perfectly align with this observed dispersion. We find that the inclusion of the $R_{21}$ Fourier parameter offers no significant improvement in metallicity estimation. Comparisons with literature confirm that our linear relation aligns closely with other Gaia DR3-based studies, while offering improved precision over older DR2-based relations.

astro-ph.SR↗

arXiv2026

Anomalous magnetotransport in a non-collinear correlated kagome ferromagnet MgMn6Sn6

Magnetic kagome metals provide a fertile platform for exploring unusual magnetotransport phenomena arising from the intricate interplay between electronic topology, electron correlations, and magnetic order. MgMn6Sn6 is a room-temperature kagome ferromagnet with strong in-plane magnetic anisotropy. Here, we report a combined study of single-crystal neutron diffraction (SCND) and magnetotransport properties of MgMn6Sn6, supported by first-principles calculations. Our SCND measurements reveal a non-collinear arrangement of Mn magnetic moments within the basal plane of the kagome bilayer. The Hall conductivity shows a substantial intrinsic contribution of approximately 0.29 e^2/h per kagome layer, which is nearly isotropic with respect to the field orientation. At low temperatures, the anomalous Hall conductivity develops a pronounced anisotropic extrinsic component, highlighting the directional sensitivity of scattering processes. The significantly large value of the Sommerfeld coefficient, in the absence of f-electrons, underscores enhanced electron correlation. Therefore, the non-collinear kagome ferromagnet MgMn6Sn6 is a promising candidate for studying the effects of electron correlation on magnetotransport properties.

cond-mat.mtrl-sci↗

arXiv2026

SeqShield: A Behavioral Analysis Approach to Uncover Rootkits

Rootkits are among the most elusive types of malware, capable of bypassing traditional static analysis methods due to their metamorphic behavior. Signature-based detection techniques struggle against these threats, necessitating a shift toward dynamic analysis approaches. We propose SeqShield, a behavior-based rootkit detection approach designed specifically for the Windows OS, leveraging API call sequences for dynamic behavior analysis. Instead of relying on static signatures, SeqShield examines the execution patterns of API calls, which inherently reflect malicious intent. Analyzing API sequences, we can effectively identify rootkit-like behavior. We also employed a metamorphic code engine to generate 10X mutated variants of rootkits, demonstrating their obfuscation strategies. SeqShield applies n-gram analysis to extract bigram and trigram features from these API call sequences, enabling effective detection of rootkit-like activity. Among the models tested, Random Forest achieves the highest accuracy of 97.27% (bigram) and 96.17% (trigram). To optimize performance and decrease the dimension, we apply feature importance ranking using the Gini Impurity Index, iteratively selecting the most significant features. The optimized lower-dimensional feature matrix significantly enhances detection efficiency without sacrificing accuracy. Using the optimized feature set, our approach achieves 96.72% accuracy for bigrams and 97.81% accuracy for trigrams.

cs.CR↗

arXiv2026

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse datasets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000 times faster than DFT. While previous MLIP training datasets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) dataset provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pre-trained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation-solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion-correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations.

physics.chem-ph↗

arXiv2026

Complex spin dynamics induced metamagnetic phase transitions in Dirac semimetal EuAuBi

We report a comprehensive investigation of the physical properties of the Dirac semimetal compound EuAuBi single crystals, using neutron diffraction, magnetization, electrical transport, and specific heat measurements. EuAuBi crystallizes in a hexagonal structure with space group P63mc (No. 186). First-principles calculations using density functional theory characterize it as a Dirac semimetal, with a notable band-crossing in proximity to the Fermi level (EF ) along the {\Gamma}-A direction. The crystal exhibits three distinct magnetic phases at 4 K (TN1), 3.5 K (TN2), and 2.8 K (TN3)as observed from magnetic and specific heat measurements. However, zero-field neutron diffraction resolves only two magnetic phases: a commensurate antiferromagnetic phase and a canted antiferromagnetic phase. Field-dependent ac and dc magnetization measurements uncover field-induced non-trivial spin textures in the magnetic field range 1.5 to 3 T, manifested as a tilted plateau in the magnetization curves. The interplay between conduction carriers and these spin textures is further evidenced by unique features in the magnetic field-dependent longitudinal resistivity in the system. Finally, we present a comprehensive magnetic phase diagram of EuAuBi, highlighting diverse spin alignments present in the material. EuAuBi thus emerges as a rare material system in which both momentum-space and real-space Berry curvature effects may coexist, providing a unique opportunity to investigate their interplay.

cond-mat.mtrl-sci↗

arXiv2026

A Multiband Photometric Study of RR Lyrae Stars in M53 (NGC 5024)

We present a multiband (UBVRI) time-series photometric study of RR Lyrae (RRL) stars in the globular cluster Messier 53 (NGC 5024) to refine their pulsation properties and determine a precise cluster distance. The archival photometric data includes images taken over 22 years and 3 months using different optical telescopes, providing an excellent time baseline to investigate light curves of variable stars. Using Lomb-Scargle periodogram, we derived accurate periods for 29 fundamental-mode (RRab) and 35 first-overtone (RRc) RRLs. Template-fitting to phase-folded light curves provided robust mean magnitudes and amplitudes. The refined periods confirm M53 as an Oosterhoff II cluster, with a mean period of 0.649 days for RRab and 0.346 days for RRc, and a high RRc fraction (54.7\%). Most RRLs align with the horizontal branch in the color-magnitude diagram, while a few outliers result from blending effects. Period-amplitude diagrams show RRab stars following the Oosterhoff II locus. We derived I-band period-luminosity and multi-band period-Wesenheit relations, comparing them with theoretical predictions. A weighted mean distance modulus of 16.242 $\pm$ 0.05 mag yields a cluster distance of 17.717 $\pm$ 0.408 kpc, in agreement with recent estimates based on parallaxes from Gaia data.

astro-ph.SR↗

arXiv2025

Higgs-like inflation in scalar-torsion $f(T,\phi)$ gravity in light of ACT-SPT-DESI constraints

We study Higgs-like inflation in the framework of scalar-torsion gravity, focusing on the general class of $f(T,\phi)$ theories in which gravitation is mediated by torsion rather than curvature. Motivated by the increasing precision of cosmic microwave background and large-scale-structure observations, we examine whether Higgs-like inflation remains compatible with current data in this extended gravitational setting. Working within the slow-roll approximation, we analyze the inflationary dynamics both analytically and numerically. In the dominant-coupling regime we derive closed-form expressions for the scalar spectral index and the tensor-to-scalar ratio as functions of the number of e-folds, and we subsequently relax this assumption by numerically solving the slow-roll equations. Confrontation with the latest constraints from Planck 2018, ACT DR6, DESI DR1, and BICEP/Keck shows that Higgs-like inflation in $f(T,\phi)$ gravity is fully consistent with current bounds, naturally accommodating the preferred shift in the scalar spectral index and leading to distinctive tensor-sector signatures.

gr-qc↗

arXiv2025

The Open Polymers 2026 (OPoly26) Dataset and Evaluations

Polymers-macromolecular systems composed of repeating chemical units-constitute the molecular foundation of living organisms, while their synthetic counterparts drive transformative advances across medicine, consumer products, and energy technologies. While machine learning (ML) models have been trained on millions of quantum chemical atomistic simulations for materials and/or small molecular structures to enable efficient, accurate, and transferable predictions of chemical properties, polymers have largely not been included in prior datasets due to the computational expense of high quality electronic structure calculations on representative polymeric structures. Here, we address this shortcoming with the creation of the Open Polymers 2026 (OPoly26) dataset, which contains more than 6.57 million density functional theory (DFT) calculations on up to 360 atom clusters derived from polymeric systems, comprising over 1.2 billion total atoms. OPoly26 captures the chemical diversity that makes polymers intrinsically tunable and versatile materials, encompassing variations in monomer composition, degree of polymerization, chain architectures, and solvation environments. We show that augmenting ML model training with the OPoly26 dataset improves model performance for polymer prediction tasks. We also publicly release the OPoly26 dataset to help further the development of ML models for polymers, and more broadly, strive towards universal atomistic models.

physics.chem-ph↗

arXiv2025

Ferromagnetic Resonance Spectroscopy on the Kagome Magnet MgMn$_6$Sn$_6$

MgMn$_6$Sn$_6$ is the itinerant ferromagnet on the kagome lattice with high ordering temperature featuring complex electronic properties due to the nontrivial topological electronic band structure, where the spin-orbit coupling (SOC) plays a crucial role. Here, we report a detailed ferromagnetic resonance (FMR) spectroscopic study of MgMn$_6$Sn$_6$ aimed to elucidate and quantify the intrinsic magnetocrystalline anisotropy that is responsible for the alignment of the Mn magnetic moments in the kagome plane. By analyzing the frequency, magnetic field, and temperature dependences of the FMR modes, we have quantified the magnetocrystalline anisotropy energy density that reaches the value of approximately $ 3.5\cdot 10^6$ erg/cm$^3$ at $T = 3$ K and reduces to about $1\cdot 10^6$ erg/cm$^3$ at $T = 300$ K. The revealed significantly strong magnetic anisotropy suggests a sizable contribution of the orbital magnetic moment to the spin magnetic moment of Mn, supporting the scenario of the essential role of SOC for the nontrivial electronic properties of MgMn$_6$Sn$_6$.

cond-mat.str-el↗

arXiv2025

Extracting Conceptual Spaces from LLMs Using Prototype Embeddings

Conceptual spaces represent entities and concepts using cognitively meaningful dimensions, typically referring to perceptual features. Such representations are widely used in cognitive science and have the potential to serve as a cornerstone for explainable AI. Unfortunately, they have proven notoriously difficult to learn, although recent LLMs appear to capture the required perceptual features to a remarkable extent. Nonetheless, practical methods for extracting the corresponding conceptual spaces are currently still lacking. While various methods exist for extracting embeddings from LLMs, extracting conceptual spaces also requires us to encode the underlying features. In this paper, we propose a strategy in which features (e.g. sweetness) are encoded by embedding the description of a corresponding prototype (e.g. a very sweet food). To improve this strategy, we fine-tune the LLM to align the prototype embeddings with the corresponding conceptual space dimensions. Our empirical analysis finds this approach to be highly effective.

cs.CL↗

arXiv2025

Signatures of two ferromagnetic states and goniopolarity in LaCrGe3 in the Hall effect

LaCrGe3 has become a playground to understand quantum critical phenomena in ferromagnetic (FM) materials. It has also garnered attention due to its peculiar two FM phases. Here, we demonstrate the presence of these phases using the Hall effect. Continuous temperature-dependent Hall resistivity measurements at fixed magnetic fields clearly demonstrate the presence of these phases, regardless of the direction of the applied magnetic field. The remanent Hall resistivity and Hall coefficient undergo a maximum and a minimum, respectively, at the boundary between the two phases. We observe significantly large anomalous Hall conductivity of 1160 ohm-1cm-1 at 2 K when the magnetic field is applied along the magnetic easy axis, which is dominated by intrinsic effects, at least in the low-temperature FM phase. In the paramagnetic (PM) phase, hexagonal LaCrGe3 exhibits opposite charge carrier polarities along different crystallographic directions, attributed to the anisotropic Fermi surface geometry, a phenomenon known as "goniopolarity". The coexistence of goniopolar transport and unconventional magnetic phases may lead this material as a promising candidate for future electronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator

Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework that uses feedforward artificial neural networks (ANNs) to infer stellar parameters-mass ($M$), luminosity (log($L/L_\odot$)), effective temperature (log($T_{\rm eff}$)), and metallicity ($Z$)-directly from Transiting Exoplanet Survey Satellite (TESS) light curves. The network is trained on a synthetic grid of RRab light curves generated from hydrodynamical pulsation models spanning a broad range of physical parameters. We validate the model using synthetic self-inversion tests and demonstrate that the ANN accurately recovers the input parameters with minimal bias. We then apply the trained model to RRab stars observed by the TESS. The observed light curves are phase-folded, corrected for extinction, and passed through the ANN to derive physical parameters. Based on these results, we construct an empirical period-luminosity-metallicity (PLZ) relation: log($L/L_\odot$) = (1.458 $\pm$ 0.028) log($P$/days) + (-0.068 $\pm$ 0.007) [Fe/H] + (2.040 $\pm$ 0.007). This work shows that ANN-based light-curve inversion offers an alternative method for extracting stellar parameters from single-band photometry. The approach can be extended to other classes of pulsators such as Cepheids and Miras.

astro-ph.SR↗

arXiv2025

Accessing quasi-flat $\textit{f}$-bands to harvest large Berry curvature in NdGaSi

In typical rare-earth lanthanide compounds, the localized 4\textit{f}-electrons have a weak effect on the electrical conduction, limiting their influence on the Berry curvature and, hence, the intrinsic anomalous Hall effect. A comprehensive study of the magnetic, thermodynamic, and transport properties of single-crystalline NdGaSi, guided by first-principles calculations, reveals a ferromagnetic ground state that induces a splitting of quasi-flat 4\textit{f} electronic bands and positions them near the Fermi energy. The observation of an extraordinarily large intrinsic anomalous Hall conductivity of 1165 $\Omega^{-1}$ cm$^{-1}$ implies the direct involvement of localized states in the generation of non-trivial band crossings around the Fermi energy. The angle-resolved photoemission spectroscopy measurements provide direct evidence of non-trivial crossing of the 4\textit{f}-bands with dispersive bands. These results are remarkable when compared to ferrimagnetic NdAlSi, which differs only in a non-magnetic atom (a change in the principal quantum number \textit{n} of the outer \textit{p }orbital) with the same number of valence electrons and does not exhibit any measurable anomalous Hall conductivity.

cond-mat.mtrl-sci↗

arXiv2025

Physical Parameters of Stars in NGC 6397 Using ANN-Based Interpolation and Full Spectrum Fitting

Stellar spectral interpolation is critical technique employed by fitting software to derive the physical parameters of stars. This approach is necessary because on-the-go generation of synthetic stellar spectra is not possible due to the complex and high cost of computation. The goal of this study is to develop a spectral interpolator for a synthetic spectral library using artificial neural networks (ANNs). The study aims to test the accuracy of the trained interpolator through self-inversion and, subsequently, to utilize the interpolator to derive the physical parameters of stars in the globular cluster NGC 6397 using spectra obtained from the Multi Unit Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT). In this study, ANNs were trained to function as spectral interpolators. The ULySS full-spectrum fitting package, integrated with the trained interpolators, was then used to extract the physical parameters of 1587 spectra of 1063 stars in NGC 6397. The trained ANN interpolator achieved precise determination of stellar parameters with a mean difference of 31 K for $T_{\rm eff}$ and 0.01 dex for [Fe/H] compared to previous studies. This study demonstrates the efficacy of ANN-based spectral interpolation in stellar parameter determination, offering faster and more accurate analysis.

astro-ph.SR↗