arXiv subjects

Arvind Kumar

Publications and source records attributed to Arvind Kumar.

At least 37 records · Page 2Linked to original sources

arXiv2026

Pion Parton Distribution Functions in the Light-Cone Quark Model and Experimental Constraints

In this work, we investigate the valence quark parton distribution functions (PDFs) of the pion within the light-cone quark model. The initial quark PDFs are calculated by solving the quark-quark correlation function for the pseudoscalar mesons. The initial quark PDFs have been evolved to higher energy scales through the Dokshitzer,Gribov,Lipatov,Altarelli,Parisi (DGLAP) evolution equations. We also find that our calculated evolved PDFs match experimental and available theoretical extraction data. For the first time, we have also predicted the $F_2$ structure function at next-to-leading (NLO) order accuracy. The calculated $F_2$ structure function has been compared with the available ZEUS and H1 experimental data at DESY-HERA over a wide range of energy scales. Additionally, we display the forward pion production cross-section for the Drell-Yan process caused by pions using the pion PDFs that were calculated and the target nucleon PDFs from the LHAPDF nucleus datasets. The evolved $F_2$ structure function of the pion have been studied at the upcoming electron-ion collider energy kinematics. Overall, it was observed that the quark PDFs of pions computed using the light-cone quark model consistent with the experimental results.

hep-ph↗

arXiv2026

Pursuit of biomarkers of brain diseases: Beyond cohort comparisons

Despite the diversity and volume of brain data acquired and advanced AI-based algorithms to analyze them, brain features are rarely used in clinics for diagnosis and prognosis. Here we argue that the field continues to rely on cohort comparisons to seek biomarkers, despite the well-established degeneracy of brain features. Using a thought experiment (Brain Swap), we show that more data and more powerful algorithms will not be sufficient to identify biomarkers of brain diseases. We argue that instead of comparing patient versus healthy controls using single data type, we should use multimodal (e.g. brain activity, neurotransmitters, neuromodulators, brain imaging) and longitudinal brain data to guide the grouping before defining multidimensional biomarkers for brain diseases.

q-bio.NC↗

arXiv2026

Magnetic moments of decuplet baryons in asymmetric magnetized nuclear matter

Understanding the novel QCD phenomenon under high external magnetic fields of hot and dense medium help us to develop a better understanding of the underlying quark dynamics of baryons. Using a hybrid approach based on the effective field theory that treats quarks as the fundamental degrees of freedom and calculating the individual contribution of valence, sea and orbital angular moment of sea quark, the magnetic moment of a given baryon of the decuplet family is calculated. The incorporation of Landau quantization in the vector and scalar densities of baryons help us to obtain the impact of external magnetic field on the properties of baryons within the chiral SU(3) quark mean field model (CQMF). In the present study, effective masses of the baryons are calculated using CQMF while the framework of chiral constituent quark model ($χ$CQM), extended to SU(4) sector, is used to obtain the effective magnetic moments of decuplet baryons under the influence of magnetic field.

hep-ph↗

arXiv2026

Generalized Hamming weights and symbolic powers of Stanley-Reisner ideals of matroids

It is well-known that the first generalized Hamming weight of a linear code, more commonly called \textit{the minimum distance} of the linear code, corresponds to the initial degree of the Stanley-Reisner ideal of the matroid of the dual code. Our starting point in this paper is a generalization of this fact -- namely, the $r$-th generalized Hamming weight of a matroid is the smallest degree of a squarefree monomial in the $r$-th symbolic power of the Stanley-Reisner ideal of the matroid (in the appropriate range for $r$). We show that the squarefree monomials in successive symbolic powers of the Stanley-Reisner ideal of a matroid suffice to describe all symbolic powers of the Stanley-Reisner ideal. Hence, we provide explicit expressions for initial degree statistics of symbolic powers of the Stanley-Reisner ideal of a matroid in terms of its generalized Hamming weights. A key aspect of our approach is a careful study of duality. If the generalized Hamming weights of a matroid and its dual are both subadditive, we prove a simple expression for the initial degree of every symbolic power of the Stanley-Reisner ideal of the matroid, which closely mirrors that of a uniform matroid. This has unexpectedly far-reaching consequences - we prove the generalized Hamming weights of a matroid and its dual are both subadditive for many interesting classes of matroids and codes, including sparse paving matroids, perfect matroid designs, matroids arising from Steiner systems, first-order affine and projective Reed-Muller codes, constant weight codes, Griesmer codes, and perfect codes. As an application, we study the resurgence and asymptotic resurgence of the matroid configurations introduced by Geramita-Harbourne-Migliore-Nagel. In particular, we explicitly compute the asymptotic resurgence of a matroid configuration of points arising from a perfect matroid design.

math.AC↗

arXiv2026

Spectroscopy of $ρ$-meson in symmetric nuclear medium

In this work, we investigate the behavior of the light vector $ρ$ meson in the presence of a symmetric nuclear medium at zero temperature. We calculate the mass and decay constant of the $ρ$-meson as well as the leading twist distribution amplitudes (DAs) in the light-front quark model in vacuum, which are further investigated at different baryonic densities. We also predict the Mellin moments of the DAs and decay width of the $ρ^0 \to e^+ e^-$ process in both vacuum and medium. The evolution of DAs is carried out by the leading order (LO) Efremov-Radyushkin-Brodsky-Lepage method and compared with available predictions. For better understanding of medium effects on $ρ$-meson, we have also predicted the in-medium charge ($G_C(Q^2)$), magnetic ($G_M(Q^2)$), and quadrupole ($G_Q(Q^2)$) form factors. The in-medium charge radii, magnetic moment, and quadrupole moment have also been predicted in this work. We have found that the nuclear medium induces appreciable modifications on the mass, weak decay constant, decay width, and distribution amplitudes of the $ρ$ meson. However, the charge radii, magnetic moment, and quadrupole moment are observed to exhibit weaker sensitivity to changes in baryonic density.

hep-ph↗

arXiv2025

SHARP-QoS: Sparsely-gated Hierarchical Adaptive Routing for joint Prediction of QoS

Dependable service-oriented computing relies on multiple Quality of Service (QoS) parameters that are essential to assess service optimality. However, real-world QoS data are extremely sparse, noisy, and shaped by hierarchical dependencies arising from QoS interactions, and geographical and network-level factors, making accurate QoS prediction challenging. Existing methods often predict each QoS parameter separately, requiring multiple similar models, which increases computational cost and leads to poor generalization. Although recent joint QoS prediction studies have explored shared architectures, they suffer from negative transfer due to loss-scaling caused by inconsistent numerical ranges across QoS parameters and further struggle with inadequate representation learning, resulting in degraded accuracy. This paper presents an unified strategy for joint QoS prediction, called SHARP-QoS, that addresses these issues using three components. First, we introduce a dual mechanism to extract the hierarchical features from both QoS and contextual structures via hyperbolic convolution formulated in the Poincaré ball. Second, we propose an adaptive feature-sharing mechanism that allows feature exchange across informative QoS and contextual signals. A gated feature fusion module is employed to support dynamic feature selection among structural and shared representations. Third, we design an EMA-based loss balancing strategy that allows stable joint optimization, thereby mitigating the negative transfer. Evaluations on three datasets with two, three, and four QoS parameters demonstrate that SHARP-QoS outperforms both single- and multi-task baselines. Extensive study shows that our model effectively addresses major challenges, including sparsity, robustness to outliers, and cold-start, while maintaining moderate computational overhead, underscoring its capability for reliable joint QoS prediction.

cs.LG↗

arXiv2025

Distinguishing Siegel modular forms

Let $f$ and $f'$ be genus $2$ cuspidal Siegel paramodular newforms. We prove that if their Hecke eigenvalues $a_p$ and $a_p'$ satisfy a non-trivial polynomial relation $P(a_p, a_p') = 0$ for a set of primes $p$ of positive density, then $f$ is a scalar multiple of a quadratic twist of $f'$. This result extends the strong multiplicity one theorem, which handles the case $P(x,y) = x - y$, to arbitrary polynomial relations. Our proof analyses the image of the product Galois representation attached to the pair $(f, f')$: we show that this image is as large as possible, unless $f$ is a twist of $f'$. Our results also apply to elliptic modular forms. They therefore provide a unified method for distinguishing both elliptic and Siegel modular forms based on their Hecke data, including their Hecke eigenvalues, Satake parameters, Sato--Tate angles, and the coefficients of their $L$-functions. We apply our methods to recover and generalise a range of existing results and to prove new ones in both the elliptic and Siegel settings.

math.NT↗

arXiv2025

Millikelvin Si-MOSFETs for Quantum Electronics

Large power consumption of silicon CMOS electronics is a challenge in very-large-scale integrated circuits and a major roadblock to fault-tolerant quantum computation. Matching the power dissipation of Si-MOSFETs to the thermal budget at deep cryogenic temperatures, below 1 K, requires switching performance beyond levels facilitated by currently available CMOS technologies. We have manufactured fully depleted silicon-on-insulator MOSFETs tailored for overcoming the power dissipation barrier towards sub-1 K applications. With these cryo-optimized transistors we achieve a major milestone of reaching subthreshold swing of 0.3 mV/dec at 420 mK, thereby enabling very-large-scale integration of cryo-CMOS electronics for ultra-low temperature applications.

cond-mat.mes-hall↗

arXiv2025

Neural Brain: A Neuroscience-inspired Framework for Embodied Agents

The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments. Despite advancements in pattern recognition and symbolic reasoning, current AI systems, such as large language models, remain disembodied, unable to physically engage with the world. This limitation has driven the rise of embodied AI, where autonomous agents, such as humanoid robots, must navigate and manipulate unstructured environments with human-like adaptability. At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability. A Neural Brain must seamlessly integrate multimodal sensing and perception with cognitive capabilities. Achieving this also requires an adaptive memory system and energy-efficient hardware-software co-design, enabling real-time action in dynamic environments. This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges: (1) defining the core components of Neural Brain and (2) bridging the gap between static AI models and the dynamic adaptability required for real-world deployment. To this end, we propose a biologically inspired architecture that integrates multimodal active sensing, perception-cognition-action function, neuroplasticity-based memory storage and updating, and neuromorphic hardware/software optimization. Furthermore, we also review the latest research on embodied agents across these four aspects and analyze the gap between current AI systems and human intelligence. By synthesizing insights from neuroscience, we outline a roadmap towards the development of generalizable, autonomous agents capable of human-level intelligence in real-world scenarios.

cs.RO↗

arXiv2025

Impact of anisotropy on QCD phase structure and transport coefficients of quark matter

Employing the Polyakov chiral SU(3) mean field (PCQMF) model, we investigate how momentum-space anisotropy, characteristic of quark-gluon plasma (QGP) in ultrarelativistic heavy-ion collisions (uRHIC), impacts the thermodynamic behavior and transport coefficients of strongly interacting quark matter. The momentum anisotropy is introduced via a small deformation in the momentum distribution, quantified by a spheroidal parameter $ξ$, which deforms the distribution functions and captures anisotropic effects to linear order. The PCQMF model captures key non-perturbative aspects of QCD, like chiral symmetry breaking, deconfinement dynamics through Polyakov loop potential, and is extended here to accommodate momentum-space anisotropy. We compute the modifications induced by momentum-space anisotropy to key thermodynamic observables including pressure $p$, energy density $ε$, entropy density $s$, speed of sound squared $c_s^2$, and specific heat $c_v$, alongside key transport coefficients, such as shear viscosity $η$, bulk viscosity $ζ_b$, and electrical conductivity $σ_{el}$. These coefficients are derived using the relativistic Boltzmann equation (RBE) under the relaxation time approximation (RTA). We find that even a weak anisotropy can lead to significant modifications in the thermodynamic response and transport behavior of quark matter. This underscores the importance of including momentum anisotropy for realistic modeling of the QCD medium across all energy regimes, from current studies at RHIC and LHC to future explorations of the high-density frontier at FAIR, NICA, and J-PARC.

hep-ph↗

arXiv2025

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective

Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning, yet their spatial reasoning abilities remain limited and underexplored, largely constrained to symbolic and sequential processing. In contrast, human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments. Therefore, bridging this gap is critical for advancing Agentic Spatial Intelligence toward better interaction with the physical 3D world. To this end, we first start from scrutinizing the spatial neural models as studied in computational neuroscience, and accordingly introduce a novel computational framework grounded in neuroscience principles. This framework maps core biological functions to six essential computation modules: bio-inspired multimodal sensing, multi-sensory integration, egocentric-allocentric conversion, an artificial cognitive map, spatial memory, and spatial reasoning. Together, these modules form a perspective landscape for agentic spatial reasoning capability across both virtual and physical environments. On top, we conduct a framework-guided analysis of recent methods, evaluating their relevance to each module and identifying critical gaps that hinder the development of more neuroscience-grounded spatial reasoning modules. We further examine emerging benchmarks and datasets and explore potential application domains ranging from virtual to embodied systems, such as robotics. Finally, we outline potential research directions, emphasizing the promising roadmap that can generalize spatial reasoning across dynamic or unstructured environments. We hope this work will benefit the research community with a neuroscience-grounded perspective and a structured pathway. Our project page can be found at Github.

cs.AI↗

arXiv2025

Symbolic Powers of Classical Varieties

Let $R=\mathbb{K}[x_1,\dots,x_n]$ and let $\mathfrak{a}_1,\dots,\mathfrak{a}_m$ be homogeneous ideals satisfying certain properties, which include a description of the Noetherian symbolic Rees algebra. We give a solution to a question of Harbourne and Huneke for this set of ideals. We also compute the Waldschmidt constant and resurgence and show that it exhibits a stronger version of the Chudnovsky and Demailly-type bounds. We further show that these properties are satisfied for classical varieties such as the generic determinantal ideals, minors of generic symmetric matrices, generic extended Hankel matrices, and ideal of pfaffians of skew-symmetric matrices.

math.AC↗

arXiv2025

Binomial edge ideals of crown graphs

In this article, we explore the class of graphs for which the projective dimension of the quotient of the binomial edge ideals matches the big height of that ideal. Additionally, we investigate the Vasconcelos number of binomial edge ideals for cycles and crown graphs. We also provide proof for [Conjecture 4.13, 3], which is related to the Vasconcelos number of binomial edge ideals for cycles.

math.AC↗

arXiv2025

(Generalized) binomial edge ideals and their regularity

In this article, we survey the recent results on the Castelnuovo-Mumford regularity of binomial edge ideals and generalized binomial edge ideals. We also generalize some of the known upper bounds for binomial edge ideals to the case of generalized binomial edge ideals.

math.AC↗

arXiv2025

Renormalization group analysis of noisy neural field

Neurons in the brain show great diversity in their individual properties and their connections to other neurons. To develop an understanding of how neuronal diversity contributes to brain dynamics and function at large scales we start with a linearized version of the Wilson-Kowan model and introduce a random anisotropy to inter-neuron connection. The resultant model is Edwards-Wilkinson model with a random anisotropic term. Averaging over the quenched randomness with the replica method we obtain a bi-quadratic nonlinearity. We use Wilsonian dynamic renormalization group to analyze this model. We find that, up to one loop order, for dimensions higher than two, the effect of the noise is to change dynamic exponent from two to one.

cond-mat.dis-nn↗

arXiv2025

Strange quark stars in modified vector MIT bag model: role of $ρ$ and $ϕ$ mesons

In the present work, we study the properties of strange quark stars (SQSs) using the vector MIT bag model with modification in vector channels. Unlike recent studies which only consider interactions through $ω$ mesons, we analyze the possibility of $ρ$ and $ϕ$ vector channels. We consider two types of higher order non-linear self-interaction terms for the vector mesons. With these modifications, we computed the equation of state (EoS) and mass-radius of strange stars for different values of vector coupling strength. Considerations of $ρ$ and $ϕ$ vector mesons along with $ω$, as well as an increase in the strength of vector coupling $g_v$, enhance the mass and radius of SQSs. For two kind of non-linear self-interactions of vector mesons considered in the present calculations, we observe the SQSs with maximum mass $2.48$ and $2.42 M_{\odot}$ for the vector coupling $g_v = 3$. Corresponding radii of these SQSs are found to be $12.27$ and $12.18$ km, respectively. We also calculate the tidal deformability parameter $Λ$, the Love number $k_2$ and the gravitational redshift of SQSs. The tidal deformability parameter $Λ$ is observed to increase with $g_v$, with appreciable effect for low mass stars.

hep-ph↗

arXiv2025

Kaon structure modifications in strange hadronic matter

We present the valence quark distributions of the kaons in an isospin asymmetric dense strange medium consisting of nucleons and hyperons. The comparative analysis of in-medium parton distribution functions, electromagnetic form factors, and charge densities with respect to the free space distributions is studied in the light-cone quark model. The medium effects are incorporated in these distribution functions by using the effective quark masses, computed from the chiral SU(3) quark mean field model for finite values of baryonic density, isospin asymmetry, and strangeness fraction parameters. We observe a suppression of the kaon electromagnetic form factors and a redistribution of charge density in high-density strange matter.

hep-ph↗

arXiv2025

A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma OSCC is a major global health burden, particularly in several regions across Asia, Africa, and South America, where it accounts for a significant proportion of cancer cases. Early detection dramatically improves outcomes, with stage I cancers achieving up to 90 percent survival. However, traditional diagnosis based on histopathology has limited accessibility in low-resource settings because it is invasive, resource-intensive, and reliant on expert pathologists. On the other hand, oral cytology of brush biopsy offers a minimally invasive and lower cost alternative, provided that the remaining challenges, inter observer variability and unavailability of expert pathologists can be addressed using artificial intelligence. Development and validation of robust AI solutions requires access to large, labeled, and multi-source datasets to train high capacity models that generalize across domain shifts. We introduce the first large and multicenter oral cytology dataset, comprising annotated slides stained with Papanicolaou(PAP) and May-Grunwald-Giemsa(MGG) protocols, collected from ten tertiary medical centers in India. The dataset is labeled and annotated by expert pathologists for cellular anomaly classification and detection, is designed to advance AI driven diagnostic methods. By filling the gap in publicly available oral cytology datasets, this resource aims to enhance automated detection, reduce diagnostic errors, and improve early OSCC diagnosis in resource-constrained settings, ultimately contributing to reduced mortality and better patient outcomes worldwide.

eess.IV↗