arXiv subjects

Nisha Singh

Publications and source records attributed to Nisha Singh.

4 recordsLinked to original sources

arXiv2026

OPTIMUS: Optimization Productivity Tool for Intelligent Management of Utilizable Space

We study department-level retail space optimization, where limited bay capacity must be allocated among planograms (POGs) under business and operational constraints. The problem is formulated as a linear binary knapsack model, with potential SKUs treated as items characterized by space requirements and weighted value contributions from sales, margin, units, and assortment similarity. Dynamic Programming (DP) is employed to obtain exact and reproducible assortment decisions in O(nc) time, avoiding the variance inherent in heuristic approaches. These decisions are integrated with a second-stage bay optimization model formulated as a mixed-integer program. Evaluated end-to-end across ten optimization runs spanning multiple departments and store clusters, the OPTIMUS framework achieves an average sales lift of 11.8% and an average margin lift of 9.5%. Overall, OPTIMUS provides a scalable, interpretable, and profit-driven solution for enterprise-scale retail space management.

math.OC↗

arXiv2025

Ensuring Reliability of Curated EHR-Derived Data: The Validation of Accuracy for LLM/ML-Extracted Information and Data (VALID) Framework

Large language models (LLMs) are increasingly used to extract clinical data from electronic health records (EHRs), offering significant improvements in scalability and efficiency for real-world data (RWD) curation in oncology. However, the adoption of LLMs introduces new challenges in ensuring the reliability, accuracy, and fairness of extracted data, which are essential for research, regulatory, and clinical applications. Existing quality assurance frameworks for RWD and artificial intelligence do not fully address the unique error modes and complexities associated with LLM-extracted data. In this paper, we propose a comprehensive framework for evaluating the quality of clinical data extracted by LLMs. The framework integrates variable-level performance benchmarking against expert human abstraction, automated verification checks for internal consistency and plausibility, and replication analyses comparing LLM-extracted data to human-abstracted datasets or external standards. This multidimensional approach enables the identification of variables most in need of improvement, systematic detection of latent errors, and confirmation of dataset fitness-for-purpose in real-world research. Additionally, the framework supports bias assessment by stratifying metrics across demographic subgroups. By providing a rigorous and transparent method for assessing LLM-extracted RWD, this framework advances industry standards and supports the trustworthy use of AI-powered evidence generation in oncology research and practice.

cs.LG↗

arXiv2022

Light Response of Poly(ethylene 2,6-napthalate) to Neutrons

There is increasing necessity for low background active materials as ton-scale, rare-event and cryogenic detectors are developed. Poly(ethylene-2,6-naphthalate) (PEN) has been considered for these applications because of its robust structural characteristics, and its scintillation light in the blue wavelength region. Radioluminescent properties of PEN have been measured to aid in the evaluation of this material. In this article we present a measurement of PEN's quenching factor using three different neutron sources; neutrons emitted from spontaneous fission in $^{252}$Cf, neutrons generated from a DD generator, and neutrons emitted from the $^{13}$C($\alpha$,n)$^{16}$O and the $^{7}$Li(p,n)$^{7}$Be nuclear reactions. The fission source used time-of-flight to determine the neutron energy, and the neutron energy from the nuclear reactions was defined using thin targets and reaction kinematics. The Birk's factor and scintillation efficiency were found to be $kB = 0.12 \pm 0.01$ mm MeV$^{-1}$ and $S = 1.31\pm0.09$ MeV$_{ee}$ MeV$^{-1}$ from a simultaneous analysis of the data obtained from the three different sources. With these parameters, it is possible to evaluate PEN as a viable material for large-scale, low background physics experiments.

physics.ins-det↗

arXiv2021

Effects of structure and temperature on the nature of excitons in the Mo0.6W0.4S2 alloys

We have studied the nature of excitons in the transition metal dichalcogenide alloy Mo0.6W0.4 S2, compared to pure MoS2 and WS2 grown by atomic layer deposition (ALD). For this, optical absorption/transmission spectroscopy and time-dependent density functional theory (TDDFT) were used. Effects of temperature on the A and B exciton peak energies and linewidths in the optical transmission spectra were compared between the alloy and pure MoS2 and WS2. On increasing the temperature from 25 K to 293 K the energy of the A and B exciton peaks decreases, while their linewidth increases due to exciton-phonon interactions. The exciton-phonon interactions in the alloy are closer to those for MoS2 than WS2. This suggests that the exciton wave functions in the alloy have a larger amplitude on Mo atoms than on W atoms. The experimental absorption spectra could be reproduced by TDDFT calculations. Interestingly, for the alloy the Mo and W atoms had to be distributed over all layers. Conversely, we could not reproduce the experimental alloy spectrum by calculations on a structure with alternating layers, in which every other layer contains only Mo atoms and the layers in between also W atoms. For the latter atomic arrangement, the TDDFT calculations yielded an additional optical absorption peak that could be due to excitons with some charge transfer character. From these results we conclude that ALD yields an alloy in which Mo and W atoms are distributed uniformly among all layers.

cond-mat.mtrl-sci↗