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Deepak Kumar

Publications and source records attributed to Deepak Kumar.

At least 19 recordsLinked to original sources
arXiv2026

Baryon Number Susceptibilities of Hot and Dense Nuclear Matter in Relativistic Mean-Field Models

We develop a fully analytical framework for calculating baryon number susceptibilities in relativistic mean-field (RMF) models of hot and dense nuclear matter at finite temperature and baryon chemical potential. The formalism is applied to representative nonlinear RMF parameterization sets to investigate the model dependence of baryon number fluctuations over a wide range of temperatures and baryon chemical potentials. All the considered RMF parameterizations reproduce the overall qualitative beam-energy dependence of the cumulant ratios. The observed skewness and kurtosis within the RMF models satisfy the experimental values. Specifically, the FSU-family parameterizations exhibit relatively small deviations from the measured heavy-ion observables i.e. cumulant ratios.

nucl-th↗
arXiv2026

Privacy Preference Controls for Agentic Systems

In response to vast and widespread online tracking, regulators and technologists alike have worked to develop laws and tools that allow people to express their privacy preferences. These measures seek to offer users greater control over the sale and sharing of personal information, putting agency in their hands to protect their privacy. However, the web is undergoing a seismic shift, away from strictly user-driven behavior towards agentic behavior, where AI agents take autonomous actions on behalf of users. In this study, we ask: What should privacy preference controls look like in the agentic web and agentic systems more generally? To answer this question, we develop a taxonomy of enforceable privacy preferences for agentic systems. Our taxonomy is informed both by laws and empirical studies of existing privacy tools as well as an audit of 30 leading AI assistants and agent platforms. To explore how our taxonomy can be implemented, we design and develop eight open-source prototypes that mirror real-world agentic applications and demonstrate how major aspects of our taxonomy can be structurally integrated in agentic protocols, such as the Model Control Protocol and Agent2Agent protocol. Ultimately, our study seeks to provide the groundwork for ensuring end-user privacy and autonomy is not lost in the newly arriving agentic world.

cs.CY↗
arXiv2026

Entropy bounded compact stencil Runge-Kutta discontinuous Galerkin method for hyperbolic conservation laws

In this paper we introduce a compact stencil Runge-Kutta discontinuous Galerkin method for the Euler equations of compressible gas dynamics, which preserves a minimum entropy principle similar to that introduced by Tadmor for entropy solution of the Euler equations [4]. Compact stencil RKDG, introduced by Chen et al. in [15], is a variant of standard RKDG method which is known for its smaller stencil size and parallel efficiency. The main challenge is that the cRKDG method has to be expressed in Butcher form and not every Runge-Kutta method of this form is a convex combination of forward-Euler steps. Therefore, in order to establish this property we leverage the bound preserving framework of the cRKDG schemes [8], an entropy bounded limiter [6] and the classical positivity preserving limiter. In the last section numerical experiments are provided to demonstrate the robustness of the method.

math.NA↗
arXiv2026

Computing Multi-Slot First-Price Pacing Equilibria at Scale via Entropic Smoothing

Pacing equilibria describe how budget-constrained advertisers clear an ad market, but computing one at production scale is hard when every request fills several slots: the allocation jumps whenever two campaigns' scores cross. We consider the first-price pacing equilibrium (FPPE) of auctions with $K$ homogeneous slots and unit-demand campaigns, whose Eisenberg-Gale dual reduces, via a TopK identity, to a convex program over bids. Regularizing each request's allocation with the binary (Fermi-Dirac) entropy gives a smooth, strictly convex dual whose gradient is exactly spend minus budget and whose evaluation splits into independent one-dimensional threshold solves, one per request. The smoothed equilibrium converges to the hard one at an explicit $O(\sqrt{T})$ rate, and a duality gap certifies how far any bids are from the hard equilibrium. We implement an efficient multi-GPU solver and test it on a production ad workload. We use it to compute an equilibrium price anchor: each campaign's hindsight equilibrium cost per key result.

math.OC↗
arXiv2026

UAV-Mounted Hybrid RIS-Assisted ISAC-RSMA: Performance Analysis with Imperfect CSI and SIC

In integrated sensing and communication (ISAC) systems, strict sensing requirements can substantially constrain the transmit power available for communication. A hybrid reconfigurable intelligent surface (HRIS) with passive reflection and active amplification capabilities mounted on an unmanned aerial vehicle (UAV) can enhance coverage and strengthen communication links in such power-limited regimes. Meanwhile, rate-splitting multiple access (RSMA) provides an effective transmission strategy for managing inter-user interference, particularly in dense multiuser wireless scenarios. Motivated by this, we analyze the performance of a UAV-mounted HRIS-assisted monostatic ISAC system employing RSMA scheme. We consider the imperfections in the channel state information and successive interference cancellation in the RSMA, which are inevitable in practical system implementations. We then derive highly accurate expressions for outage probability, ergodic rate, and throughput to evaluate communication performance over Nakagami-$m$ fading channels. These expressions reveal key insights into the diversity order and coding gain of the system in the high signal-to-noise ratio regime. To characterize the sensing performance, we derive expressions for the probability of detection, the probability of false alarm, and the Cramér-Rao lower bound. Numerical results validate the theoretical findings and highlight the performance gains of the proposed system over conventional multiple-access schemes and RIS-based systems.

eess.SP↗
arXiv2026

From Scroll to Sale: Exploring the Impact of Interaction Type and Device Price on TikTok Advertisements

Companies and brands increasingly use dynamic pricing, including targeting social media ads to users based on their income. In this work we audit TikTok's feed using 56 automated accounts, which collect data on over 80,000 videos, across two studies. We test the impact of device price on ad load and ad types, using 12 phones of low ($0-$250), medium ($400-$650), and high ($750-$1,000+) price as a proxy for income. We also test the impact of interaction type (i.e., like, comment, share), age, and gender on the frequency and content of ads. Overall, the ad load was 29.4%, but liking and sharing content increased ad load significantly. We also found that as accounts spend more time on TikTok, the ad load steadily increases. We found some evidence that device price impacts both ad load and content -- more expensive devices were targeted with fewer ads, while the least expensive devices received more discounts.

cs.CY↗
arXiv2026

Something to Talk About: Social Media as a Lens on Healthcare Ransomware Events

In the modern era, ransomware attacks on critical healthcare organizations---like hospitals and insurers---are frequent, with impacts ranging from leaked private health information all the way to serious disruptions of urgent, time-sensitive clinical operations. Unfortunately, legal and economic incentives make it uncommon for hospitals to share basic information about these attacks, their scope, and the downstream impacts to patient care. In this paper, we explore the use of public social media posts---authored both by hospitals and by individuals---to garner more detailed insights about these attacks and their effects. We collect 1,628 Facebook and Reddit posts from 2018--2024, design and evaluate techniques to match post data to 212 ground-truth ransomware attacks, and conduct quantitative and qualitative analyses that explore the impacts such attacks have on critical hospital infrastructure, patient care, and providers. We conclude by discussing the promise and limitations of leveraging social data to study the impact of ransomware attacks and highlight areas of future research.

cs.CR↗
arXiv2026

COILD: An Indic-Centric Parallel Corpus and Benchmark for Machine Translation Across Indian Languages

Machine translation (MT) for Indian languages remains constrained by the limited availability of high-quality, Indic-centric parallel corpora and evaluation benchmarks. Existing multilingual resources are largely constructed from English-pivot content and often fail to capture the linguistic diversity, cultural complexity, and domain-specific characteristics of Indian languages. We present COILD, an Indic-centric parallel corpus comprising over 1.16 million human-translated and human-verified sentence pairs, covering 20 Indian language pairs across the Indo-Aryan, Dravidian, Tibeto-Burman, and Austro-Asiatic language families. The corpus is built entirely from original Indian language sources collected from licensed repositories spanning eight domains with direct real-world applicability. Furthermore, we introduce a domain-centric benchmark comprising 2,000 expert-verified sentences to enable consistent multilingual and cross-lingual evaluation across Indian language pairs. To validate the effectiveness of COILD, we fine-tune two representative multilingual neural machine translation models, IndicTrans2-Distilled and NLLB-200. Experimental results demonstrate consistent improvements across language pairs, domains, automatic evaluation metrics, and human evaluation, highlighting the effectiveness of high-quality Indic-centric supervision. COILD provides a valuable training and evaluation resource for advancing multilingual machine translation and future multilingual language models for Indian languages.

cs.CL↗
arXiv2026

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

Repeated prompt prefixes are increasingly common in LLM serving workloads, appearing in system prompts, templated retrieval-augmented generation pipelines, agent frameworks, and multi-turn conversations. Modern inference runtimes such as vLLM and TensorRT-LLM provide mechanisms for reusing previously computed KV-cache state across requests, yet it remains unclear when prefix reuse materially improves serving performance on contemporary accelerators and when its benefits are limited by scheduling, cache granularity, concurrency, or memory pressure. This paper presents PrefixBench-H100, a reproducible benchmark and measurement framework for characterizing prefix reuse on a single NVIDIA H100. PrefixBench-H100 combines controlled synthetic traces with chat-style and retrieval-style workloads, and evaluates two widely used LLM serving runtimes under matched workload conditions. The benchmark varies shared-prefix length, suffix diversity, request arrival pattern, concurrency, output length, and cache configuration, while collecting time-to-first-token, inter-token latency, end-to-end latency, throughput, cache-hit statistics, GPU memory usage, and selected profiling traces. The goal of PrefixBench-H100 is not to introduce a new caching algorithm, but to expose the practical operating envelope of prefix reuse for H100-class LLM serving. The study identifies the regime where prefix reuse provides substantial first-token latency reductions and the regime where cache pressure erodes them, while showing that cache effectiveness itself is largely insensitive to concurrency and output length; the cross-runtime differences that remain arise above the cache, in the scheduling layer.

cs.PF↗
arXiv2026

Outage Analysis of RSMA-Enabled Terrestrial Users Under Co-Channel Interference from HAPS

The coexistence of terrestrial networks (TNs) with high-altitude platform station (HAPS)-based non-terrestrial networks (NTNs) is a promising approach for extending 6G connectivity, but the resulting cross-network interference can significantly affect TN reliability. This paper investigates an rate-splitting multiple access (RSMA)-based TN operating under interference from a multibeam HAPS-NTN system. The desired TN links are modeled using Nakagami-m fading, while the aggregate HAPS interference is characterized by shadowed-Rician fading. Closed-form outage probability expressions are derived by characterizing the aggregate interference and accounting for both perfect and imperfect successive interference cancellation (SIC). Monte Carlo simulations are used to verify the analytical results and to examine the effects of HAPS transmit power, the number of interfering beams, and SIC imperfections. The results demonstrate that the interference-to-signal power scaling is a key factor determining TN outage behavior, with jointly scaled HAPS interference leading to interference-limited performance. Moreover, RSMA provides improved outage performance compared with non-orthogonal multiple access (NOMA), particularly in the presence of HAPS interference

eess.SP↗
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

Hyperonic compact stars with vector portal dark matter

The appearance of hyperons in the core of neutron stars generally softens the equation of state (EOS), posing a longstanding challenge to the existence of observed two-solar-mass compact stars. We investigate whether repulsive interactions mediated by a dark-sector vector portal can provide an additional source of high-density pressure and thereby modify the structure of hyperonic compact stars. The baryonic sector is described within the modified quark-meson coupling (MQMC) model, in which the octet baryons are treated as confined relativistic constituent-quark systems interacting self-consistently through the $σ$, $ω$, and $ρ$ fields within a mean field approximation. The dark sector consists of a fermionic dark matter coupled to baryonic matter through a neutral vector mediator $Z^\prime$, generating an additional repulsive contribution to the dense-matter EOS. We investigate the resulting equation of state, mass--radius relation, tidal deformability, and moment of inertia. The resulting mass--radius relations satisfy the observational bounds from massive pulsars, including PSR J0740 + 6620, with maximum neutron star masses reaching approximately $2 M_{\odot}$. The resulting changes in tidal and rotational observables provide additional avenues for testing the dark-sector interaction through multimessenger observations.

astro-ph.HE↗
arXiv2026

CADET: Context-Conditioned Ads CTR Prediction With a Decoder-Only Transformer

Click-through rate (CTR) prediction is fundamental to online advertising systems. While Deep Learning Recommendation Models (DLRMs) with explicit feature interactions have long dominated this domain, recent advances in generative recommenders have shown promising results in content recommendation. However, adapting these transformer-based architectures to ads CTR prediction still presents unique challenges, including handling post-scoring contextual signals, maintaining offline-online consistency, and scaling to industrial workloads. We present CADET (Context-Conditioned Ads Decoder-Only Transformer), an end-to-end decoder-only transformer for ads CTR prediction deployed at LinkedIn. Our approach introduces several key innovations: (1) a context-conditioned decoding architecture with multi-tower prediction heads that explicitly model post-scoring signals such as ad position, resolving the chicken-and-egg problem between predicted CTR and ranking; (2) a self-gated attention mechanism that stabilizes training by adaptively regulating information flow at both representation and interaction levels; (3) a timestamp-based variant of Rotary Position Embedding (RoPE) that captures temporal relationships across timescales from seconds to months; (4) session masking strategies that prevent the model from learning dependencies on unavailable in-session events, addressing train-serve skew; and (5) production engineering techniques including tensor packing, sequence chunking, and custom Flash Attention kernels that enable efficient training and serving at scale. In online A/B testing, CADET achieves a 11.04\% CTR lift compared to the production LiRank baseline model, a hybrid ensemble of DCNv2 and sequential encoders. The system has been successfully deployed on LinkedIn's advertising platform, serving the main traffic for homefeed sponsored updates.

cs.LG↗
arXiv2026

Calibrated uncertainty for wide-angle crustal models: how firmly is the Dharwar Craton Moho actually constrained?

Depth uncertainties for wide-angle crustal models are usually obtained by propagating an assumed picking error, yet picks from one shot gather are mutually dependent and velocity-depth trade-offs act at the level of the model class. We revisit the 210-km Perur-Chikmagalur profile across the Dharwar Craton using a permanently identified ledger of 2,000 primary P1-P2 first arrivals and 9,919 reflections from seven shots, with 852 later-turning P4 observations held aside for a conditional test. At fixed production velocity, a reciprocity-based fast-marching table makes it affordable to refit all six reflectors inside nested complete-shot validation and a receiver-coordinate block bootstrap. Withheld reflections are predicted to 68 ms RMS with standardized mean-squared residual 2.07 and 85 per cent coverage inside nominal 95 per cent limits; calibration is strongly phase-dependent, the Moho reflection specifically giving 73 ms and 2.12. First arrivals degrade from a 17 ms all-data RMS to 103 ms on untouched shots, driven by aperture extrapolation and a parameterization blind to the weathered near-surface. Exact window means place the Moho at 46.9 km beneath the western block and 39.4 km beneath the central block, a raw contrast of 7.47 km. Resampling receivers within shots gives a +/-0.10 km conditional spread; deleting whole shots gives +/-0.40 km, and two independent experiments agree the raw contrast is biased low by 0.36-0.48 km. Admissible velocity models span 6.46-8.64 km. Paired LVL-boundary reflections at 1,260 receivers separate beyond uncertainty everywhere, though the internal velocity shape stays unresolved. Sampling uncertainty and model-class uncertainty must be reported apart.

physics.geo-ph↗
arXiv2026

Reliability-calibrated deep residual full-waveform inversion using geometry-invariant physics encoding: synthetic validation and zero-shot Marmousi-2 testing

Neural networks that map seismic data directly to velocity models tend to memorize the acquisition geometry they were trained on, and their uncertainty estimates are rarely trustworthy once the data drift away from the training distribution. We describe a laptop-scale pipeline that addresses both problems. The network never sees shot gathers. Instead, variable acquisition geometries are mapped into a fixed model-space representation computed by classical physics operators: the starting model, a regularized classical inversion, two misfit-gradient images, and six illumination and wavenumber-coverage maps from fast-marching traveltimes. The learned component is not a replacement for FWI; it is a calibrated residual corrector applied to this physics-derived prior. A six-member heterogeneous ensemble with per-pixel variance heads is calibrated by physics-conditioned conformal prediction, giving finite-sample pixel-wise marginal coverage on the calibration distribution. Beyond that distribution, a held-out-shot physics audit simulates shots the inversion never used through samples of the predictive distribution and rescales interval width where their data-space coverage peaks; no ground truth is involved. On a corpus of 1000 synthetic models spanning six acquisition families, the ensemble reduces error by 38% relative to its classical prior and transfers to never-seen geometries without measurable degradation. Applied zero-shot to the full 17 km Marmousi-2 line, it lowers the error from 354 to 304 m/s while raw coverage collapses to 0.42; the audit restores coverage to 0.89-0.91 across eleven corruption conditions covering noise, wavelet error, and shot decimation, and its peak height cleanly separates physics mismatch from benign corruptions. Budget-matched gather-based baselines underperform substantially off their training acquisition. Code and checkpoints will be archived on Zenodo.

physics.geo-ph↗
arXiv2026

Experimental determination of the Dalitz plot for positronium decay using the J-PET detection system

We present the first measurements of the Dalitz plot for ortho-positronium annihilation to three photons. Our measurements, accurate to about 3% statistical and 2-3% systematic uncertainty in angular representation over almost the entire available phase space, were performed using the Jagiellonian Positron Emission Tomograph (J-PET) based on organic scintillator strips. Until now, the Dalitz plot for the three-body positronium decay has been poorly explored. The new measurements presented here are consistent with both the leading-order and next-to-leading order QED predictions for the Dalitz plot.

nucl-ex↗
arXiv2026

Roadmap Towards Quantum Entanglement Positron Emission Tomography (QE-PET)

Annihilation photons are quantum entangled in their polarization, a property that is not accessible in state-of-the-art clinical Positron Emission Tomography (PET). This roadmap describes the current status of research in the emerging field of quantum entanglement applications involving these photons for medical diagnosis. It outlines the underlying physics phenomena and the development of detector systems that can serve as a foundation for future Quantum Entanglement PET (QE-PET) scanners. These scanners will be capable of utilizing the entanglement between annihilation photons by measuring their Compton scattering on electrons. This roadmap comprises up-to-date experimental results on the study of quantum entanglement and the decoherence of annihilation photons, alongside the current theoretical understanding of these phenomena. The methods and detector technologies described herein are being developed (i) in order to enhance standard PET imaging by suppressing random coincidences in the reconstruction of annihilation site density distributions, (ii) in order to establish the degree of quantum entanglement as a diagnostic biomarker for tissue pathology and oxygenation, and (iii) in order to elaborate a method for pH imaging. Whether entanglement-based imaging and pH mapping can be successfully translated into clinical practice remains an open question and a subject of exciting ongoing research. This roadmap serves as an invitation to the scientific community to join this burgeoning field.

physics.med-ph↗
arXiv2026

Neutron star with dark matter using vector portal

Compact astrophysical objects, such as neutron star, can provide a unique environment where the interplay between strongly interacting nuclear matter and dark matter (DM) can yield possible observable signatures. We investigate here the impact of fermionic DM interacting with nucleons via a vector mediator ($Z'$) portal inside neutron stars using the relativistic mean-field (RMF) framework. Unlike scalar portal DM models, which primarily modify the effective nucleon mass through scalar interactions, vector mediators (Z') introduce additional repulsive interactions that directly affect the baryonic chemical potential and the pressure of dense matter. We show that the precise measurements of neutron star properties, including the mass-radius relation and tidal deformability from gravitational wave observations, X-ray and radio observations of pulsars, can shed light on properties of DM. We study the gross structural properties of a neutron star using the Tolman-Oppenheimer-Volkoff (TOV) equations, employing an equation of state (EOS) for neutron star matter in the presence of vector portal-assisted DM. The resulting stellar configurations consistent with observational bounds from gravitational wave observations (GW170817) in LIGO/Virgo and X-ray observations of pulsar PSR J0030+0451 in NICER, are shown to constrain the vector portal DM parameters. It is observed that, while large portal mass can soften the EOS of the DM admixed neutron star matter, the light portal mass can make the EOS stiffer at large densities resulting in distinct mass-radius relation and the tidal deformability between the two scenarios. The vector portal DM scenario, with DM interaction with quarks via Z' vector boson, can establish a direct connection to terrestrial searches, including direct and indirect detection and collider searches for the Z' boson.

hep-ph↗