arXiv subjects

Nidhi

Publications and source records attributed to Nidhi.

6 recordsLinked to original sources

arXiv2025

A Surrogate Model for the Forward Design of Multi-layered Metasurface-based Radar Absorbing Structures

Metasurface-based radar absorbing structures (RAS) are highly preferred for applications like stealth technology, electromagnetic (EM) shielding, etc. due to their capability to achieve frequency selective absorption characteristics with minimal thickness and reduced weight penalty. However, the conventional approach for the EM design and optimization of these structures relies on forward simulations, using full wave simulation tools, to predict the electromagnetic (EM) response of candidate meta atoms. This process is computationally intensive, extremely time consuming and requires exploration of large design spaces. To overcome this challenge, we propose a surrogate model that significantly accelerates the prediction of EM responses of multi-layered metasurface-based RAS. A convolutional neural network (CNN) based architecture with Huber loss function has been employed to estimate the reflection characteristics of the RAS model. The proposed model achieved a cosine similarity of 99.9% and a mean square error of 0.001 within 1000 epochs of training. The efficiency of the model has been established via full wave simulations as well as experiment where it demonstrated significant reduction in computational time while maintaining high predictive accuracy.

cs.CV↗

arXiv2024

Normalized solutions to a quasilinear equation involving critical Sobolev exponent

In this paper we study the existence and regularity results of normalized solutions to the following quasilinear elliptic Choquard equation with critical Sobolev exponent and mixed diffusion type operators: \begin{equation*} \begin{array}{rcl} -\Delta_p u+(-\Delta_p)^su & = & \lambda |u|^{p-2}u +|u|^{p^*-2}u+ \mu(I_{\alpha}*|u|^q)|u|^{q-2}u\;\;\text{in } \mathbb{R}^N, \int_{\mathbb{R}^N}|u|^pdx & = & \tau, \end{array} \end{equation*} where $N\geq 3$, $\tau>0$, $\frac{p}{2}(\frac{N+\alpha}{N}) 0$ is a parameter, $(-\Delta_p)^s$ is the fractional p-laplacian operator, $p^*=\frac{Np}{N-p}$ is the critical Sobolev exponent and $\lambda$ appears as a Lagrange multiplier.

math.AP↗

arXiv2024

Ultralow Lattice Thermal Conductivity in Complex Structure Cu26V2Sn6Se32 due to Interaction of Low-Frequency Acoustic-Optical Phonons

Damping of phonon momentum suppresses the lattice thermal conductivity (kl) through low energy acoustic-optical phonon interactions. We studied the thermal transport properties and underlying mechanism of phonon interactions in the large unit cell Cu26V2Sn6Se32. The large number of atoms in the unit cell results in low acoustic phonon cutoff frequency, flat phonon branches, low frequency Raman active modes, localized rattler-like vibrations and strong crystalline anharmonicity. The crystal structure complexity disrupts the phonon propagation through weak bonded Cu atoms, boson peak and poor phonon velocity. The sulfur at selenium sites (Cu26V2Sn6Se30S2) distort the crystal lattice by offering additional scattering mechanism at the anionic sites, thereby increases the power factor and decreases the kl. This strategic manipulation of phonon scattering towards ultra-low kl not only results in improved thermoelectric performance but also offers insights into the fundamental understanding of heat transport in complex structured, large unit cell compounds.

cond-mat.mtrl-sci↗

arXiv2022

Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues

Movies reflect society and also hold power to transform opinions. Social biases and stereotypes present in movies can cause extensive damage due to their reach. These biases are not always found to be the need of storyline but can creep in as the author's bias. Movie production houses would prefer to ascertain that the bias present in a script is the story's demand. Today, when deep learning models can give human-level accuracy in multiple tasks, having an AI solution to identify the biases present in the script at the writing stage can help them avoid the inconvenience of stalled release, lawsuits, etc. Since AI solutions are data intensive and there exists no domain specific data to address the problem of biases in scripts, we introduce a new dataset of movie scripts that are annotated for identity bias. The dataset contains dialogue turns annotated for (i) bias labels for seven categories, viz., gender, race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains biases like body shaming, personality bias, etc. (ii) labels for sensitivity, stereotype, sentiment, emotion, emotion intensity, (iii) all labels annotated with context awareness, (iv) target groups and reason for bias labels and (v) expert-driven group-validation process for high quality annotations. We also report various baseline performances for bias identification and category detection on our dataset.

cs.CL↗

arXiv2021

Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets

Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutralized in their early stages to mitigate the damages. Most of the existing methods for early detection of fake news assume to have enough propagation information for large labeled tweets -- which may not be an ideal setting for cases like COVID-19 where both aspects are largely absent. In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labeled data. We first develop a novel dataset, called CTF for early COVID-19 Twitter fake news, with additional behavioral test sets to validate early detection. We build a heterogeneous graph with follower-followee, user-tweet, and tweet-retweet connections and train a graph embedding model to aggregate propagation information. Graph embeddings and contextual features constitute endogenous, while time-relative web-scraped information constitutes exogenous signals. ENDEMIC is trained in a semi-supervised fashion, overcoming the challenge of limited labeled data. We propose a co-attention mechanism to fuse signal representations optimally. Experimental results on ECTF, PolitiFact, and GossipCop show that ENDEMIC is highly reliable in detecting early fake tweets, outperforming nine state-of-the-art methods significantly.

cs.CL↗

arXiv2012

Performance Evaluation of Realistic Vanet Using Traffic Light Scenario

Vehicular Ad-hoc Networks (VANETs) is attracting considerable attention from the research community and the automotive industry to improve the services of Intelligent Transportation System (ITS). As today's transportation system faces serious challenges in terms of road safety, efficiency, and environmental friendliness, the idea of so called "ITS" has emerged. Due to the expensive cost of deployment and complexity of implementing such a system in real world, research in VANET relies on simulation. This paper attempts to evaluate the performance of VANET in a realistic environment. The paper contributes by generating a real world road Map of JNU using existing Google Earth and GIS tools. Traffic data from a limited region of road Map is collected to capture the realistic mobility. In this work, the entire region has been divided into various smaller routes. The realistic mobility model used here considers the driver's route choice at the run time. It also studies the clustering effect caused by traffic lights used at the intersection to regulate traffic movement at different directions. Finally, the performance of the VANET is evaluated in terms of average delivery ratio, packet loss, and router drop as statistical measures for driver route choice with traffic light scenario. This experiment has provided insight into the performance of vehicular traffic communication for a small realistic scenario.

cs.NI↗