arXiv subjects

Rajiv Gupta

Publications and source records attributed to Rajiv Gupta.

At least 19 recordsLinked to original sources

arXiv2026

Nanophotonic control of spatial information in scintillation detectors

X-rays enable non-invasive imaging across medicine, security, materials science, and beyond, yet modern systems remain constrained by the need to resolve finer structures at lower radiation dose. Scintillators are the dominant materials for detecting X-rays, but face a longstanding compromise: thick scintillators absorb X-rays effectively, whereas optical photons generated throughout their volume spread before detection, degrading spatial information. Existing scintillator architectures largely try to preserve resolution by physically confining light using pixels, columnar crystals, or microstructured channels. Here, we show that high-resolution detection does not require the volumetric confinement of scintillation light. A metalens integrated directly with a bulk scintillator uses nanophotonic wavefront control to preferentially transfer high-spatial-frequency information from volumetrically generated scintillation light to the detector, while retaining the X-ray absorption of a thick scintillator. We experimentally recover fine spatial detail in X-ray images of inorganic and biological specimens. In a detector geometry relevant to computed tomography (CT), the experimentally validated model predicts a fivefold reduction in required X-ray dose and a 25-fold increase in resolution bandwidth relative to a state-of-the-art pixelated scintillator. These results establish wavefront engineering as a route to separating efficient X-ray absorption from optical image formation, with the potential for substantially higher-resolution, lower-dose CT.

physics.optics↗

arXiv2026

Video to All-in-focus Image Reconstruction Algorithm for Automated Microscopic Urinalysis

Microscopic urinalysis is a routine diagnostic test at hospitals. Recent studies have demonstrated the effectiveness of deep learning methods to automate microscopic urinalysis. These methods rely on high-quality images of the urine samples in which each cell is clearly identifiable. However, in practice, the urine sample on a glass slide has a multi-layer structure; hence, all the cells are not clearly visible within the depth of field of a lens focused at a particular focal plane. It demands acquiring multiple images at different focal planes to correctly identify each cell in a given urine sample, which is a time-consuming task. In this paper, we propose to simplify the task by recording a video, in place of acquiring multiple images, while gradually changing the focus of the lens manually by hand. A typical length of the video is from 2 to 14 seconds. We reconstruct an all-in-focus image from the recorded video frames and apply a deep learning model to detect and classify urine sediments. As a proof of concept, we conduct experiments on 14 videos acquired by a trained lab technician in a usual diagnostic lab environment and show the effectiveness of the proposed automated urinalysis pipeline with our novel reconstruction algorithm.

eess.IV↗

arXiv2026

Aggregate combat modeling using high-resolution simulation: "the "meeting engagement"scenario as a case study

This paper illustrates a methodology for developing aggregated combat models using high-resolution simulation and the Markovian Lanchester process. The details of the mathematical models involved in a high-resolution simulation process of a Meeting Engagement tactical scenario are presented, along with a theoretical discussion on the Markovian Lanchester model. The output from the discrete event simulation model is used to estimate the attrition rates for an aggregated Lanchester model. A comparative study of various statistical estimation methods suitable for such estimation is also presented.

stat.AP↗

arXiv2026

Solving Subgraph Extraction Problems Using $\Delta$Search

Many NP-hard graph problems can be modeled as optimal subgraph extraction problems with feasibility constraints. From Network Design to Facility Location, from Robotics to Graph Drawing, the subgraph extraction pattern emerges across diverse domains. Despite this commonality, these problems are typically solved with domain-specific heuristics. Usually, these problems balance competing objectives such as maximizing coverage or minimizing cost while satisfying structural constraints such as connectivity, planarity and reachability. In this work, we introduce $\Delta$Search, a general and fast heuristic framework that exploits the insight of Reward-Penalty optimization for solving a large class of subgraph extraction problems. The framework is easy to use as it only requires feasibility constraints and optimality criteria to be provided by the user to express the subgraph extraction problem. We also show how exact methods can be augmented with $\Delta$Search to improve their performance by aggressive pruning of the search space. We evaluate our framework on monotone graph problems such as Maximum Planar Subgraph (MPS) and Minimum Connected Dominating Set, Weighted Monotone problems such as Maximum Weighted Independent Set and Minimum Weighted Steiner Tree, and non-monotone graph problems such as Prize Collecting Vertex Cover (PCVC) and Uncapacitated Facility Location Problem (UFLP). Our results show that $\Delta$Search matches or surpasses state of the art heuristics for MPS, UFLP and PCVC problems with similar runtime. For the remaining problems, $\Delta$Search achieves approximately 89% of the solution quality of the state-of-the-art algorithms without any problem-specific tuning

cs.PF↗

arXiv2026

AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve

Post-link optimizers (PLOs) such as Propeller and BOLT have demonstrated that precise, profile-guided code layout can extract significant performance gains from heavily optimized binaries. However, these systems are currently restricted to intraprocedural techniques, leaving the global potential of interprocedural layout largely untapped. Interprocedural code layout is historically difficult due to a combinatorially intractable search space and complex call-return semantics that are challenging to model. Consequently, the performance potential of fine-grained interprocedural layout remains unproven in practice. AI-PROPELLER uses Magellan, an agentic workflow that evolves the compiler heuristic in Propeller into a fine-grained interprocedural optimizer and fine-tunes the resulting policy hyperparameters. To ensure high-fidelity, we move away from approximate static cost models and the agentic workflow generates multiple layout variants that are executed on actual hardware to measure real performance counters, providing a precise reward signal for the evolutionary loop. AI-PROPELLER has been evaluated on several benchmarks including large warehouse-scale applications and experiments show performance improvements of 0.23% to 1.6% optimized with state-of-the-art FDO and PLO which is significant for real-world binaries. This is the first time ever that large warehouse-scale applications in industrial settings have been optimized with fine-grained interprocedural code layout.

cs.SE↗

arXiv2026

SemLoc: Structured Grounding of Free-Form LLM Reasoning for Fault Localization

Fault localization identifies program locations responsible for observed failures. Existing techniques rank suspicious code using syntactic spectra--signals derived from execution structure such as statement coverage, control-flow divergence, or dependency reachability. These signals collapse for semantic bugs, where failing and passing executions follow identical code paths and differ only in whether semantic intent is satisfied. Recent LLM-based approaches introduce semantic reasoning but produce stochastic, unverifiable outputs that cannot be systematically cross-referenced across tests or distinguish root causes from cascading effects. We present SemLoc, a fault localization framework based on structured semantic grounding. SemLoc converts free-form LLM reasoning into a closed intermediate representation that binds each inferred property to a typed program anchor, enabling runtime checking and attribution to program structure. It executes instrumented programs to construct a semantic violation spectrum--a constraint-by-test matrix--from which suspiciousness scores are derived analogously to coverage-based methods. A counterfactual verification step further prunes over-approximate constraints and isolates primary causal violations. We evaluate SemLoc on SemFault-250, a corpus of 250 Python programs with single semantic faults. SemLoc outperforms five coverage-, reduction-, and LLM-based baselines, achieving Top-1 accuracy of 42.8% and Top-3 of 68%, while reducing inspection to 7.6% of executable lines. Counterfactual verification provides an additional 12% accuracy gain and identifies primary causal semantic constraints.

cs.SE↗

arXiv2026

Effect of hole pitch reduction on electron transport and diffusion: A comparative simulation study of Triple GEM detectors

Advances in fabrication techniques and high-performance electronics have facilitated the development of fine-pitch Gas Electron Multipliers (GEMs). Earlier experimental and simulation findings suggest that these reduced-pitch GEMs can outperform the standard configuration in terms of effective gain, collection efficiency, and position resolution. However, a noticeable fraction of avalanche electrons is lost within the GEM systems, resulting in a degradation of charge collection efficiency. Therefore, a comprehensive simulation-based study is essential to provide deeper insights into the extent of degradation and its contributing factors. In this context, we employ ANSYS and Garfield++ to model the Triple GEM detectors with reduced pitch sizes of 90 and 60 $\mu$m, and perform a comparative performance analysis with the standard configuration (pitch size: 140 $\mu$m). At first, the simulation framework is validated by comparing the results of the standard configuration with available experimental data and previously reported simulation outcomes. Despite the characteristic gain offset, the framework remains physically consistent and reliable in capturing microscopic avalanche dynamics, reproducing the experimental trend. Following validation, we investigate electron losses at the metal electrodes and within the Kapton holes, electron transmission through the transfer and induction regions, electron diffusion on the induction electrode, and the overall collection efficiency. These parameters are analyzed as functions of GEM potential, outer hole diameter, inner hole diameter, Kapton thickness, metal thickness, and gas composition, thereby offering insights for designing efficient GEM detectors.

physics.ins-det↗

arXiv2025

Investigating the effects of acceptor removal mechanism and impact ionization on proton irradiated 300 $\mu$m thick LGAD

Low-Gain Avalanche Detectors (LGADs) are the leading 4D sensing technology selected for use in the High Luminosity Large Hadron Collider (HL-LHC). However, their proximity to the interaction point makes them highly susceptible to radiation-induced damage. Such degradation effects can be effectively studied through TCAD simulations. In this work, we extend the validation of a previously developed proton damage model for transitional sensors. The enhanced model for LGAD also incorporates an acceptor removal mechanism and modifications in impact ionization behavior, resulting in a more comprehensive and reliable tool for fabrication and performance analysis.

physics.ins-det↗

arXiv2025

Study of anisotropic flow of heavy hadrons in Au + Au collisions at $\sqrt{s_{NN}} =$ 200 GeV using HYDJET++ framework

A comprehensive study of the anisotropic flow of heavy hadrons ($D^{0}$, $D^{\pm}$, and $\Lambda_{c}$) in Au + Au collisions at $\sqrt{s_{NN}} = 200$ GeV using the HYDJET++ model is presented. This study aims to explore the collective behavior and thermalization of charm hadrons at RHIC energy. The modeling of anisotropic flow is performed using a centrality-dependent parameterization of anisotropic parameters. Our model results are consistent with STAR experimental results up to $p_{T} = 4$ GeV/c. It captures the centrality-dependent shifts of the flow peak towards lower $p_{T}$ as collisions become more peripheral. This shift is attributed to the weaker radial flow in peripheral collisions. The model also reproduces important features such as the number-of-constituent-quark scaling, mass ordering, and baryon-meson grouping, all consistent with experimental observations. Furthermore, we present comparisons with other models, like DUKE, SUBATECH, TAMU, and AMPT. Overall, our results highlight the efficacy of HYDJET++ in describing the collective dynamics of heavy-flavor hadrons in the quark-gluon plasma medium.

hep-ph↗

arXiv2025

Simulation-based performance comparison of varied pitch sizes GEM detectors

Gas Electron Multiplier (GEM) detectors, typically featuring a standard pitch size of 140 $\mu$m and an inner hole diameter of 50 $\mu$m, are extensively utilized in high-energy physics experiments for tracking, triggering, and timing measurements. Their characteristics, such as high gain, good position resolution, improved temporal resolution, low discharge probability, radiation hardness, and high rate capabilities, make them highly favoured. Recent experimental studies have shown that triple-GEM detectors with a reduced pitch size of 90 $\mu$m and a smaller hole diameter of 40 $\mu$m can perform better than standard-pitch GEM detectors. To assess the effectiveness of these reduced dimensions, we conducted a simulation-based study using ANSYS and Garfield++. As a first step, we validated the simulation framework by modelling a standard single GEM detector and comparing the results with previous simulations and experimental data. Following validation, we designed GEM structures with reduced pitch sizes of 90 $\mu$m and 60 $\mu$m. We then performed a comparative analysis, focusing on key performance parameters like effective gain, electron transparency, and position resolution. These parameters were varied against an increase in GEM potential, drift electric field, induction electric field, drift gap, induction gap, and gas composition to optimize the performance of the detectors.

hep-ex↗

arXiv2025

Study of Heavy Hadron Production in Au + Au Collisions at a Center-of-Mass Energy of $\sqrt{s_{NN}}=200$ GeV

Using the Monte Carlo HYDJET++ model, the transverse momentum ($p_{T}$) spectra of heavy hadrons ($D^{0}$, $\overline{D}^{0}$, $D^{+}$, $D^{-}$ and $\Lambda_{c}$), as well as the nuclear modification factors of $D^{0}$ and $D^{\pm}$, produced in Au + Au collisions at $\sqrt{s_{NN}} = 200$ GeV RHIC energy across various centrality bins, are presented. This study is motivated by the need to understand the centrality dependence of the charm enhancement factor ($\gamma_{c}$) and the roles of different hadronization mechanisms such as coalescence and fragmentation, in charm hadron production. To achieve the best description of heavy hadron production, several input parameters in both the soft and hard components of the model are tuned. The study finds a decreasing trend of $\gamma_{c}$ from central to peripheral collisions and a mass dependence across charm hadrons. Moreover, the model effectively reproduces experimental data of $p_{T}$ spectra at low and intermediate $p_{T}$, capturing key features of charm hadron production in the quark-gluon plasma medium. However, it overpredicts the data at high $p_{T}$, indicating the need for improvements in modeling heavy quark energy loss mechanisms. Further, the nuclear modification factors ($R_{AA}$ and $R_{CP}$) for $D^{0}$ mesons exhibit significant suppression in central collisions, which matches with experimental observations. This highlights the roles of collisional and radiative energy loss due to collective effects, such as coalescence and radial flow. The antiparticle-to-particle and mixed particle ratios are also presented, showing good agreement with experimental data and revealing limitations in baryon production due to the absence of heavy quark coalescence in HYDJET++.

hep-ph↗

arXiv2025

Nanophotonic thermal management in X-ray tubes

In X-ray tubes, more than 99% of the kilowatts of power supplied to generate X-rays via bremsstrahlung are lost in the form of heat generation in the anode. Therefore, thermal management is a critical barrier to the development of more powerful X-ray tubes with higher brightness and spatial coherence, which are needed to translate imaging modalities such as phase-contrast imaging to the clinic. In rotating anode X-ray tubes, the most common design, thermal radiation is a bottleneck that prevents efficient cooling of the anode$\unicode{x2014}$the hottest part of the device by far. We predict that nanophotonically patterning the anode of an X-ray tube enhances heat dissipation via thermal radiation, enabling it to operate at higher powers without increasing in temperature. The focal spot size, which is related to the spatial coherence of generated X-rays, can also be made smaller at a constant temperature. A major advantage of our "nanophotonic thermal management" approach is that in principle, it allows for complete control over the spectrum and direction of thermal radiation, which can lead to optimal thermal routing and improved performance.

physics.optics↗

arXiv2025

Multi-Source Static CT with Adaptive Fluence Modulation to Minimize Hallucinations in Generative Reconstructions

Multi-source static Computed Tomography (CT) systems have introduced novel opportunities for adaptive imaging techniques. This work presents an innovative method of fluence field modulation using spotlight collimators. These instruments block positive or negative fan angles of even and odd indexed sources, respectively. Spotlight collimators enable volume of interest imaging by increasing relative exposure for the overlapping views. To achieve high quality reconstructions from sparse-view low-dose data, we introduce a generative reconstruction algorithm called Langevin Posterior Sampling (LPS), which uses a score based diffusion prior and physics based likelihood model to sample a posterior random walk. We conduct simulation-based experiments of head CT imaging for stroke detection and we demonstrate that spotlight collimators can effectively reduce the standard deviation and worst-case scenario hallucinations in reconstructed images. Compared to uniform fluence, our approach shows a significant reduction in posterior standard deviation. This highlights the potential for spotlight collimators and generative reconstructions to improve image quality and diagnostic accuracy of multi-source static CT.

physics.med-ph↗

arXiv2025

Analysis of Stable Vertex Values: Fast Query Evaluation Over An Evolving Graph

Evaluating a query over a large, irregular graph is inherently challenging. This challenge intensifies when solving a query over a sequence of snapshots of an evolving graph, where changes occur through the addition and deletion of edges. We carried out a study that shows that due to the gradually changing nature of evolving graphs, when a vertex-specific query (e.g., SSSP) is evaluated over a sequence of 25 to 100 snapshots, for 53.2% to 99.8% of vertices, the query results remain unchanged across all snapshots. Therefore, the Unchanged Vertex Values (UVVs) can be computed once and then minimal analysis can be performed for each snapshot to obtain the results for the remaining vertices in that snapshot. We develop a novel intersection-union analysis that very accurately computes lower and upper bounds of vertex values across all snapshots. When the lower and upper bounds for a vertex are found to be equal, we can safely conclude that the value found for the vertex remains the same across all snapshots. Therefore, the rest of our query evaluation is limited to computing values across snapshots for vertices whose bounds do not match. We optimize this latter step evaluation by concurrently performing incremental computations on all snapshots over a significantly smaller subgraph. Our experiments with several benchmarks and graphs show that we need to carry out per snapshot incremental analysis for under 42% vertices on a graph with under 32% of edges. Our approach delivers speedups of 2.01-12.23x when compared to the state-of-the-art RisGraph implementation of the KickStarter-based incremental algorithm for 64 snapshots.

cs.PF↗

arXiv2025

Noise Controlled CT Super-Resolution with Conditional Diffusion Model

Improving the spatial resolution of CT images is a meaningful yet challenging task, often accompanied by the issue of noise amplification. This article introduces an innovative framework for noise-controlled CT super-resolution utilizing the conditional diffusion model. The model is trained on hybrid datasets, combining noise-matched simulation data with segmented details from real data. Experimental results with real CT images validate the effectiveness of our proposed framework, showing its potential for practical applications in CT imaging.

cs.CV↗

arXiv2024

Realistic wave-optics simulation of X-ray dark-field imaging at a human scale

Background: X-ray dark-field imaging (XDFI) has been explored to provide superior performance over the conventional X-ray imaging for the diagnosis of many pathologic conditions. A simulation tool to reliably predict clinical XDFI images at a human scale, however, is currently missing. Purpose: In this paper, we demonstrate XDFI simulation at a human scale for the first time to the best of our knowledge. Using the developed simulation tool, we demonstrate the strengths and limitations of XDFI for the diagnosis of emphysema, fibrosis, atelectasis, edema, and pneumonia. Methods: We augment the XCAT phantom with Voronoi grids to simulate alveolar substructure, responsible for the dark-field signal from lungs, assign material properties to each tissue type, and simulate X-ray wave propagation through the augmented XCAT phantom using a multi-layer wave-optics propagation. Altering the density and thickness of the Voronoi grids as well as the material properties, we simulate XDFI images of normal and diseased lungs. Results: Our simulation framework can generate realistic XDFI images of a human chest with normal or diseased lungs. The simulation confirms that the normal, emphysematous, and fibrotic lungs show clearly distinct dark-field signals. It also shows that alveolar fluid accumulation in pneumonia, wall thickening in interstitial edema, and deflation in atelectasis result in a similar reduction in dark-field signal. Conclusions: It is feasible to augment XCAT with pulmonary substructure and generate realistic XDFI images using multi-layer wave optics. By providing the most realistic XDFI images of lung pathologies, the developed simulation framework will enable in-silico clinical trials and the optimization of both hardware and software for XDFI.

physics.med-ph↗

arXiv2023

Graph Analytics on Evolving Data (Abstract)

We consider the problem of graph analytics on evolving graphs. In this scenario, a query typically needs to be applied to different snapshots of the graph over an extended time window. We propose CommonGraph, an approach for efficient processing of queries on evolving graphs. We first observe that edge deletions are significantly more expensive than addition operations. CommonGraph converts all deletions to additions by finding a common graph that exists across all snapshots. After computing the query on this graph, to reach any snapshot, we simply need to add the missing edges and incrementally update the query results. CommonGraph also allows sharing of common additions among snapshots that require them, and breaks the sequential dependency inherent in the traditional streaming approach where snapshots are processed in sequence, enabling additional opportunities for parallelism. We incorporate the CommonGraph approach by extending the KickStarter streaming framework. CommonGraph achieves 1.38x-8.17x improvement in performance over Kickstarter across multiple benchmarks.

cs.DB↗

arXiv2022

Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited. Achieving containment demands accurate and timely collection of the infected individual's location and contact history. Traditionally, this process is labor intensive, susceptible to memory errors, and fraught with privacy concerns. With the recent almost ubiquitous availability of smart phones, many people carry a tool which can be utilized to quickly identify an infected individual's contacts during an epidemic, such as the current 2019 novel Coronavirus crisis. Unfortunately, the very same first-generation contact tracing tools have been used to expand mass surveillance, limit individual freedoms and expose the most private details about individuals. We seek to outline the different technological approaches to mobile-phone based contact-tracing to date and elaborate on the opportunities and the risks that these technologies pose to individuals and societies. We describe advanced security enhancing approaches that can mitigate these risks and describe trade-offs one must make when developing and deploying any mass contact-tracing technology. With this paper, our aim is to continue to grow the conversation regarding contact-tracing for epidemic and pandemic containment and discuss opportunities to advance this space. We invite feedback and discussion.

cs.CR↗