arXiv subjects

Shashank Sharma

Publications and source records attributed to Shashank Sharma.

15 recordsLinked to original sources

arXiv2026

Probing Criticality Using GMM-Based Potentials

Spin models with a given symmetry are easier to sample than scalar theories with the same symmetry on a lattice, as the constrained nature of spin variables enables cheap heat-bath updates. However, this constraint suppresses radial fluctuations, and consequently, spin models cannot be used to study the phenomena of spontaneous symmetry breaking, such as the Higgs phenomenon. To address this, we propose a class of scalar potentials based on Gaussian Mixture Models (GMMs) that are as easy to sample as spin models with a given symmetry. These potentials can be designed to belong to the same universality class as the theory of interest, thereby reproducing its critical properties while enabling efficient sampling. We construct such models for global $\mathbb{Z}_2$ symmetry, $U(1)$ gauge symmetry, and disordered systems. We also verify by numerical experiment that the case of $\mathbb{Z}_2$ symmetry in two dimensions lies in the two-dimensional Ising universality class.

hep-lat↗

arXiv2026

Hybrid HPC-Quantum Simulations: DFT-Quantum Embedding for Molecular Systems

Scientific simulations demand methods combining scalability with predictive accuracy. Density Functional Theory (DFT) on High-Performance Computing (HPC) enables large-scale electronic-structure simulations but is limited by approximations affecting strongly correlated systems and band-gap predictions. Quantum computing offers a pathway to address this, though current Noisy Intermediate-Scale Quantum (NISQ) hardware remains constrained by qubit resources, noise, and execution cost. This work presents a hybrid DFT-Quantum Embedding (QDFT) framework integrating classical HPC-based DFT with a quantum electronic-structure solver. Large systems are partitioned to isolate a chemically relevant active space, treated via the Variational Quantum Eigensolver (VQE), while the remaining degrees of freedom are described by DFT. The framework incorporates active-space selection, embedded Hamiltonian construction, symmetry preservation, operator mapping, self-consistent density updating, and modular classical-quantum coupling. We focus on noiseless quantum simulation to systematically evaluate accuracy, convergence, active-space dependence, computational cost, and HPC scalability without hardware noise. Detailed profiling identifies computational bottlenecks and highlights limitations of CPU-based quantum simulation. A QPU runtime-estimation methodology is additionally developed to assess execution requirements on actual quantum hardware. Results demonstrate quantum embedding's potential to improve selected electronic-structure properties while retaining classical HPC's scalability. Noisy quantum simulation and QPU execution remain key future directions, providing a pathway toward practical, scalable HPC-quantum hybrid simulations as hardware matures.

quant-ph↗

arXiv2026

QmDFT for Polycyclic Aromatics: Balancing Embedding Ground-State Fidelity and Experimental Gap Estimation

Quantum Embedding density functional theory (QmDFT) embedding offers a highly scalable approach to improve treatment for large highly correlated pi conjugated systems. However, estimating advanced electronic structure properties in polycyclic aromatic hydrocarbons (PAHs) needs advanced exchange correlation functionals that frequently trigger convergence instabilities during the embedding cycle. In this work, we introduce an adaptive damping and direct inversion in the iterative subspace (DIIS) accelerated protocol that stabilizes the embedding procedure, enabling robust integration of hybrid functionals like B3LYP and CAM B3LYP. Using 10 selected PAHs (linear and fused) molecules as a benchmark. We demonstrate a clear functional-dependent ground-state energetics and frontier-orbital gap estimation. While LDA based approaches yield near-quantitative agreement with FCI in DFT reference energies and is further supported by thermochemical isomerization benchmarks, while B3LYP provide significantly improved agreement with experimental E0-0 transition values. This mapping allows us to bypass explicit excited-state calculations for E0 0 values, thereby significantly reducing computational overhead. Among the hybrid functionals screened, CAM B3LYP offers a balanced overall performance. Our results establish a stable QmDFT framework and provide useful guidance for functional selection for quantum embedding studies of PAHs and related low-dimensional pi-conjugated materials.

quant-ph↗

arXiv2026

Hybrid Quantum-Classical Density Functional Theory: A Structured Framework

Density Functional Theory (DFT) is widely used for atomistic simulations. However, its reach stays limited due to several limitations such as lack of accurate exchange-correlation functional, requirement of costly O(N 3) diagonalization etc. Although quantum computing offers paths forward, including variational techniques, embedding strategies, and quantum linear solvers, the discussion remains scattered. Without shared terms or structure, evaluating progress in hybrid quantum-classical DFT efforts becomes challenging. To bring order, we introduce a three-axis scheme based on where the method connects into DFT, whether the quantum part boosts precision or cuts time, alongside intended device type: current noisy machines or future error-corrected ones. Sorting known approaches in this way shows why embedding frameworks fit modern tools better, while faster linear algebra waits for more advanced systems.

quant-ph↗

arXiv2026

OLLM: Options-based Large Language Models

We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a \textit{set of learned options} for the next token, indexed by a discrete latent variable. Instead of relying on temperature or sampling heuristics to induce diversity, OLLM models variation explicitly: a small latent space parametrizes multiple plausible next-token options which can be selected or searched by a downstream policy. Architecturally, OLLM is a lightweight "plug-in" that inserts two layers: an encoder and a decoder, before the output head, allowing almost any pretrained LLM to be converted with minimal additional parameters. We apply OLLM to a 1.7B-parameter backbone (only $1.56\%$ of parameters trainable) trained on OpenMathReasoning and evaluated on OmniMath. The SOTA LoRA-adapted baselines peak at $51\%$ final answer correctness, while OLLM's option set allows up to $\sim 70\%$ under optimal latent selection. We then train a compact policy in the latent space that emits latents to control generation. Operating in a low-dimensional option space makes reward optimization far more sample-efficient and substantially reduces common misalignments (e.g., language switching or degenerate reasoning), as the policy is constrained to options learned during SFT. Crucially, this alignment arises from model structure rather than additional KL or handcrafted alignment losses. Our results demonstrate that optionized next-token modeling enhances controllability, robustness, and efficiency in math reasoning, and highlight latent-space policy learning as a promising direction for reinforcement learning in LLMs.

cs.AI↗

arXiv2025

MRS: Multi-Resolution Skills for HRL Agents

Hierarchical reinforcement learning (HRL) decomposes the policy into a manager and a worker, enabling long-horizon planning but introducing a performance gap on tasks requiring agility. We identify a root cause: in subgoal-based HRL, the manager's goal representation is typically learned without constraints on reachability or temporal distance from the current state, preventing precise local subgoal selection. We further show that the optimal subgoal distance is both task- and state-dependent: nearby subgoals enable precise control but amplify prediction noise, while distant subgoals produce smoother motion at the cost of geometric precision. We propose Multi-Resolution Skills (MRS), which learns multiple goal-prediction modules each specialized to a fixed temporal horizon, with a jointly trained meta-controller that selects among them based on the current state. MRS consistently outperforms fixed-resolution baselines and significantly reduces the performance gap between HRL and non-HRL state-of-the-art on DeepMind Control Suite, Gym-Robotics, and long-horizon AntMaze tasks. [Project page: https://sites.google.com/view/multi-res-skills/home]

cs.AI↗

arXiv2025

3D Characterization of Smoke Plume Dispersion Using Multi-View Drone Swarm

This study presents an advanced multi-view drone swarm imaging system for the three-dimensional characterization of smoke plume dispersion dynamics. The system comprises a manager drone and four worker drones, each equipped with high-resolution cameras and precise GPS modules. The manager drone uses image feedback to autonomously detect and position itself above the plume, then commands the worker drones to orbit the area in a synchronized circular flight pattern, capturing multi-angle images. The camera poses of these images are first estimated, then the images are grouped in batches and processed using Neural Radiance Fields (NeRF) to generate high-resolution 3D reconstructions of plume dynamics over time. Field tests demonstrated the ability of the system to capture critical plume characteristics including volume dynamics, wind-driven directional shifts, and lofting behavior at a temporal resolution of about 1 s. The 3D reconstructions generated by this system provide unique field data for enhancing the predictive models of smoke plume dispersion and fire spread. Broadly, the drone swarm system offers a versatile platform for high resolution measurements of pollutant emissions and transport in wildfires, volcanic eruptions, prescribed burns, and industrial processes, ultimately supporting more effective fire control decisions and mitigating wildfire risks.

cs.RO↗

arXiv2025

Autonomous Drone for Dynamic Smoke Plume Tracking

This paper presents a novel autonomous drone-based smoke plume tracking system capable of navigating and tracking plumes in highly unsteady atmospheric conditions. The system integrates advanced hardware and software and a comprehensive simulation environment to ensure robust performance in controlled and real-world settings. The quadrotor, equipped with a high-resolution imaging system and an advanced onboard computing unit, performs precise maneuvers while accurately detecting and tracking dynamic smoke plumes under fluctuating conditions. Our software implements a two-phase flight operation, i.e., descending into the smoke plume upon detection and continuously monitoring the smoke movement during in-plume tracking. Leveraging Proportional Integral-Derivative (PID) control and a Proximal Policy Optimization based Deep Reinforcement Learning (DRL) controller enables adaptation to plume dynamics. Unreal Engine simulation evaluates performance under various smoke-wind scenarios, from steady flow to complex, unsteady fluctuations, showing that while the PID controller performs adequately in simpler scenarios, the DRL-based controller excels in more challenging environments. Field tests corroborate these findings. This system opens new possibilities for drone-based monitoring in areas like wildfire management and air quality assessment. The successful integration of DRL for real-time decision-making advances autonomous drone control for dynamic environments.

cs.RO↗

arXiv2025

DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents

Hierarchical Reinforcement Learning (HRL) agents often struggle with long-horizon visual planning due to their reliance on error-prone distance metrics. We propose Discrete Hierarchical Planning (DHP), a method that replaces continuous distance estimates with discrete reachability checks to evaluate subgoal feasibility. DHP recursively constructs tree-structured plans by decomposing long-term goals into sequences of simpler subtasks, using a novel advantage estimation strategy that inherently rewards shorter plans and generalizes beyond training depths. In addition, to address the data efficiency challenge, we introduce an exploration strategy that generates targeted training examples for the planning modules without needing expert data. Experiments in 25-room navigation environments demonstrate a 100% success rate (vs. 90% baseline). We also present an offline variant that achieves state-of-the-art results on OGBench benchmarks, with up to 71% absolute gains on giant HumanoidMaze tasks, demonstrating our core contributions are architecture-agnostic. The method also generalizes to momentum-based control tasks and requires only log N steps for replanning. Theoretical analysis and ablations validate our design choices.

cs.RO↗

arXiv2024

Advanced Smart City Monitoring: Real-Time Identification of Indian Citizen Attributes

This project focuses on creating a smart surveillance system for Indian cities that can identify and analyze people's attributes in real time. Using advanced technologies like artificial intelligence and machine learning, the system can recognize attributes such as upper body color, what the person is wearing, accessories they are wearing, headgear, etc., and analyze behavior through cameras installed around the city.

cs.CV↗

arXiv2023

Rust for Embedded Systems: Current State, Challenges and Open Problems (Extended Report)

Embedded software is used in safety-critical systems such as medical devices and autonomous vehicles, where software defects, including security vulnerabilities, have severe consequences. Most embedded codebases are developed in unsafe languages, specifically C/C++, and are riddled with memory safety vulnerabilities. To prevent such vulnerabilities, RUST, a performant memory-safe systems language, provides an optimal choice for developing embedded software. RUST interoperability enables developing RUST applications on top of existing C codebases. Despite this, even the most resourceful organizations continue to develop embedded software in C/C++. This paper performs the first systematic study to holistically understand the current state and challenges of using RUST for embedded systems. Our study is organized across three research questions. We collected a dataset of 2,836 RUST embedded software spanning various categories and 5 Static Application Security Testing ( SAST) tools. We performed a systematic analysis of our dataset and surveys with 225 developers to investigate our research questions. We found that existing RUST software support is inadequate, SAST tools cannot handle certain features of RUST embedded software, resulting in failures, and the prevalence of advanced types in existing RUST software makes it challenging to engineer interoperable code. In addition, we found various challenges faced by developers in using RUST for embedded systems development.

cs.CR↗

arXiv2021

Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks

Self- and semi-supervised machine learning techniques leverage unlabeled data for improving downstream task performance. These methods are especially valuable for remote sensing tasks where producing labeled ground truth datasets can be prohibitively expensive but there is easy access to a wealth of unlabeled imagery. We perform a rigorous evaluation of SimCLR, a self-supervised method, and FixMatch, a semi-supervised method, on three remote sensing tasks: riverbed segmentation, land cover mapping, and flood mapping. We quantify performance improvements on these remote sensing segmentation tasks when additional imagery outside of the original supervised dataset is made available for training. We also design experiments to test the effectiveness of these techniques when the test set is domain shifted to sample different geographic areas compared to the training and validation sets. We find that such techniques significantly improve generalization performance when labeled data is limited and there are geographic domain shifts between the training data and the validation/test data.

cs.CV↗

arXiv2018

No Modes left behind: Capturing the data distribution effectively using GANs

Generative adversarial networks (GANs) while being very versatile in realistic image synthesis, still are sensitive to the input distribution. Given a set of data that has an imbalance in the distribution, the networks are susceptible to missing modes and not capturing the data distribution. While various methods have been tried to improve training of GANs, these have not addressed the challenges of covering the full data distribution. Specifically, a generator is not penalized for missing a mode. We show that these are therefore still susceptible to not capturing the full data distribution. In this paper, we propose a simple approach that combines an encoder based objective with novel loss functions for generator and discriminator that improves the solution in terms of capturing missing modes. We validate that the proposed method results in substantial improvements through its detailed analysis on toy and real datasets. The quantitative and qualitative results demonstrate that the proposed method improves the solution for the problem of missing modes and improves training of GANs.

cs.CV↗

arXiv2013

CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Parallel computing using accelerators has gained widespread research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. However, accelerator based computing has signifi- cantly relegated the role of CPUs in computation. As CPUs evolve and also offer matching computational resources, it is important to also include CPUs in the computation. We call this the hybrid computing model. Indeed, most computer systems of the present age offer a degree of heterogeneity and therefore such a model is quite natural. We reevaluate the claim of a recent paper by Lee et al.(ISCA 2010). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, sparse matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-core Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CPU with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good advantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 90% resource efficient on average. Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale systems with significant performance gains and resource efficiency to the large scale user community.

cs.DC↗

arXiv2012

Wireless Public key Infrastructure for Mobile Phones

Mobile devices play an important role in the wireless network environment for providing different services over internet. The business transactions over wireless electronic devices are not secure and hence the messages are prone to be intercepted and modified by an intruder. So, devices supporting wireless internet must be guaranteed at the same level of security as the wired network. PKI (Public Key Infrastructure) used in the wired environment is not suitable for wireless environment because of the less powerful processor and small memory. This arises a need for the development of a Wireless Public Key Infrastructure (WPKI) that provides the similar security level as the wired PKI suitable for mobile phone. In this paper, a discussion of public key infrastructure and an experimental set up for Wireless Public key Infrastructure for mobile phones are made.

cs.CR↗