arXiv subjects

Vikas Sharma

Publications and source records attributed to Vikas Sharma.

6 recordsLinked to original sources

arXiv2026

Directed Neuro-Symbolic Stochastic Execution for Verification of Distributed Parallel AI Programs

Distributed parallel Artificial Intelligence (AI) programs expose reliability gaps that conventional testing cannot close: parallel executions are non-deterministic, and AI workloads bring high-dimensional inputs and non-linear operations that defeat fuzzing and symbolic execution in isolation. We present Directed Neuro-Symbolic Stochastic Execution (DNSSE), a hybrid testing framework that couples schedule prediction guided by a Large Language Model (LLM) with symbolic constraint solving and coverage-guided stochastic mutation. We model distributed AI executions as non-deterministic transition systems, specify correctness in linear temporal logic, and prove soundness, bounded completeness, and probabilistic completeness of the hybrid solver, together with an expected-cost analysis of LLM-guided schedule exploration. A scalable implementation on PyTorch and Ray detects 2.9% more concurrency bugs than the strongest baseline and raises average branch coverage from 68.6 % to 91.6 % across five realistic distributed AI benchmarks.

cs.AI↗

arXiv2026

Electronic and Vibrational Properties of On-Surface Synthesized Gulf-Edged Chiral Graphene Nanoribbons

On-surface synthesis enables graphene nanoribbons (GNRs) with atomic precision, but the structural diversity of chiral GNRs remains constrained by the limited range of precursor architectures. Here, we design a trisnaphthalene-based diiodo precursor and use it to synthesize a gulf-edged (4,2,7)-chGNR on Au(111). Scanning tunneling microscopy and bond-resolved non-contact atomic force microscopy establish the atomically precise ribbon structure, while scanning tunneling spectroscopy and periodic density functional theory calculations identify a closed-shell semiconducting state with an experimental band gap of 1.8 eV. Raman spectroscopy, supported by vibrational calculations, resolves a mode localized predominantly at the gulf-edge C-H groups and reveals rapid spectral degradation following air exposure. These results demonstrate a precursor-design concept for a distinct chGNR architecture and correlate its atomic structure with its electronic, vibrational, and environmental-response properties.

cond-mat.mtrl-sci↗

arXiv2025

Diffusion of gravitactic chiral active Brownian particles in an asymmetric channel

The diffusion of micro- and nanoswimmers in a fluid, confined within irregular structures that impose entropic barriers, is often modeled using overdamped active Brownian dynamics, where viscous effects are paramount and inertia is negligible. Here, we numerically investigate the diffusive behavior of chiral self-propelled particles in a two-dimensional asymmetric channel subjected to an external torque arising from a gravitational field. We reveal the emergence of resonant diffusion when the external torque $\omega$ approaches the intrinsic angular velocity $\omega_{0}$ of particles. This resonance manifests as a pronounced accumulation of particles near the upper-left corner of the channel, accompanied by an enhanced peak in the effective diffusion coefficient. In particular, it is observed only for low rotational diffusion rates and does not persist beyond moderate values of $\omega_{0}$. Prominent transport features, such as rectification at low values of $\omega$, a monotonic increase in average velocity with $\omega$, and a nonmonotonic response of transport characteristics (average velocity and effective diffusion coefficient) as a function of the rotational diffusion rate near the resonance point, are explained. Furthermore, we show that the transport characteristics depend strongly on the aspect ratio of the channel. For instance, the enhanced diffusion peak becomes more pronounced with increasing aspect ratio, and the average velocity saturates at higher values for wider bottleneck openings. It is conceivable that these findings have a great potential for developing microfluidic and laboratory-on-a-chip devices for particle separation, targeted drug delivery, and advanced active materials.

cond-mat.soft↗

arXiv2022

Robust Linear Hybrid Beamforming Designs Relying on Imperfect CSI in mmWave MIMO IoT Networks

Linear hybrid beamformer designs are conceived for the decentralized estimation of a vector parameter in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) Internet of Things network (IoTNe). The proposed designs incorporate both total IoTNe and individual IoTNo power constraints, while also eliminating the need for a baseband receiver combiner at the fusion center (FC). To circumvent the non-convexity of the hybrid beamformer design problem, the proposed approach initially determines the minimum mean square error (MMSE) digital transmit precoder (TPC) weights followed by a simultaneous orthogonal matching pursuit (SOMP)-based framework for obtaining the analog RF and digital baseband TPCs. Robust hybrid beamformers are also derived for the realistic imperfect channel state information (CSI) scenario, utilizing both the stochastic and norm-ball CSI uncertainty frameworks. The centralized MMSE bound derived in this work serves as a lower bound for the estimation performance of the proposed hybrid TPC designs. Finally, our simulation results quantify the benefits of the various designs developed.

eess.SP↗

arXiv2017

Ensemble Sales Forecasting Study in Semiconductor Industry

Sales forecasting plays a prominent role in business planning and business strategy. The value and importance of advance information is a cornerstone of planning activity, and a well-set forecast goal can guide sale-force more efficiently. In this paper CPU sales forecasting of Intel Corporation, a multinational semiconductor industry, was considered. Past sale, future booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality and other indicators were innovatively incorporated into the quantitative modeling. Benefit from the recent advances in computation power and software development, millions of models built upon multiple regressions, time series analysis, random forest and boosting tree were executed in parallel. The models with smaller validation errors were selected to form the ensemble model. To better capture the distinct characteristics, forecasting models were implemented at lead time and lines of business level. The moving windows validation process automatically selected the models which closely represent current market condition. The weekly cadence forecasting schema allowed the model to response effectively to market fluctuation. Generic variable importance analysis was also developed to increase the model interpretability. Rather than assuming fixed distribution, this non-parametric permutation variable importance analysis provided a general framework across methods to evaluate the variable importance. This variable importance framework can further extend to classification problem by modifying the mean absolute percentage error(MAPE) into misclassify error. Please find the demo code at : https://github.com/qx0731/ensemble_forecast_methods

stat.ML↗

arXiv2015

On Optimizing Human-Machine Task Assignments

When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers. However, if researchers wish to integrate the crowd with "off-the-shelf" machine classifiers, this deep integration is not always possible. This work explores two strategies to increase accuracy and decrease cost under this setting. First, we show that reordering tasks presented to the human can create a significant accuracy improvement. Further, we show that greedily choosing parameters to maximize machine accuracy is sub-optimal, and joint optimization of the combined system improves performance.

cs.HC↗