arXiv subjects

Nitish Kumar

Publications and source records attributed to Nitish Kumar.

14 recordsLinked to original sources

arXiv2026

A Systematic Framework for Tabular Data Disentanglement

Tabular data, widely used in various applications such as industrial control systems, finance, and supply chain, often contains complex interrelationships among its attributes. Data disentanglement seeks to transform such data into latent variables with reduced interdependencies, facilitating more effective and efficient processing. Despite the extensive studies on data disentanglement over image, text, or audio data, tabular data disentanglement may require further investigation due to the more intricate attribute interactions typically found in tabular data. Moreover, due to the highly complex interrelationships, direct translation from other data domains results in suboptimal data disentanglement. Existing tabular data disentanglement methods, such as factor analysis, CT-GAN, and VAE face limitations including scalability issues, mode collapse, and poor extrapolation. In this paper, we propose the use of a framework to provide a systematic view on tabular data disentanglement that modularizes the process into four core components: data extraction, data modeling, model analysis, and latent representation extrapolation. We believe this work provides a deeper understanding of tabular data disentanglement and existing methods, and lays the foundation for potential future research in developing robust, efficient, and scalable data disentanglement techniques. Finally, we demonstrate the framework's applicability through a case study on synthetic tabular data generation, showcasing its potential in the particular downstream task of data synthesis.

cs.LG↗

arXiv2026

SUMMIR: A Hallucination-Aware Framework for Ranking Sports Insights from LLMs

With the rapid proliferation of online sports journalism, extracting meaningful pre-game and post-game insights from articles is essential for enhancing user engagement and comprehension. In this paper, we address the task of automatically extracting such insights from articles published before and after matches. We curate a dataset of 7,900 news articles covering 800 matches across four major sports: Cricket, Soccer, Basketball, and Baseball. To ensure contextual relevance, we employ a two-step validation pipeline leveraging both open-source and proprietary large language models (LLMs). We then utilize multiple state-of-the-art LLMs (GPT-4o, Qwen2.5-72B-Instruct, Llama-3.3-70B-Instruct, and Mixtral-8x7B-Instruct-v0.1) to generate comprehensive insights. The factual accuracy of these outputs is rigorously assessed using a FactScore-based methodology, complemented by hallucination detection via the SummaC (Summary Consistency) framework with GPT-4o. Finally, we propose SUMMIR (Sentence Unified Multimetric Model for Importance Ranking), a novel architecture designed to rank insights based on user-specific interests. Our results demonstrate the effectiveness of this approach in generating high-quality, relevant insights, while also revealing significant differences in factual consistency and interestingness across LLMs. This work contributes a robust framework for automated, reliable insight generation from sports news content. The source code is availble here https://github.com/nitish-iitp/SUMMIR.

cs.IR↗

arXiv2026

Design, Fabrication and Testing of a D-Shaped High Temperature Superconducting Magnet

High-temperature technical superconductors are potential candidates for compact and high-field tokamak magnets. The demand for higher fusion power can be met with an on-axis high magnetic field due to toroidal magnets. An R&D activity has been initiated at the Institute for Plasma Research, India, to develop a compact D-shaped superconducting magnet utilizing REBCO high-temperature superconducting tapes. Under this initiative, a toroidal configuration with a major radius of 0.42 m, consisting of eight D-shaped, four poloidal field, and a central solenoid high-temperature superconducting magnets producing an on-axis toroidal magnetic field of 0.23 T has been conceptualized. The fabrication feasibility of a D-shaped coil for this toroidal configuration also envisaged using stacked high-temperature superconducting cable. In this paper, we report the design of a compact D-shaped coil, the fabrication of a long length HTS cable, a winding pack, and its integration with a cryogenic casing and vacuum enclosure. The winding pack terminations, joints, its interfacing with the power supply, and performance testing are also reported in this paper.

physics.acc-ph↗

arXiv2026

Performance of a Deep Learning-Based Segmentation Model for Pancreatic Tumors on Public Endoscopic Ultrasound Datasets

Background: Pancreatic cancer is one of the most aggressive cancers, with poor survival rates. Endoscopic ultrasound (EUS) is a key diagnostic modality, but its effectiveness is constrained by operator subjectivity. This study evaluates a Vision Transformer-based deep learning segmentation model for pancreatic tumors. Methods: A segmentation model using the USFM framework with a Vision Transformer backbone was trained and validated with 17,367 EUS images (from two public datasets) in 5-fold cross-validation. The model was tested on an independent dataset of 350 EUS images from another public dataset, manually segmented by radiologists. Preprocessing included grayscale conversion, cropping, and resizing to 512x512 pixels. Metrics included Dice similarity coefficient (DSC), intersection over union (IoU), sensitivity, specificity, and accuracy. Results: In 5-fold cross-validation, the model achieved a mean DSC of 0.651 +/- 0.738, IoU of 0.579 +/- 0.658, sensitivity of 69.8%, specificity of 98.8%, and accuracy of 97.5%. For the external validation set, the model achieved a DSC of 0.657 (95% CI: 0.634-0.769), IoU of 0.614 (95% CI: 0.590-0.689), sensitivity of 71.8%, and specificity of 97.7%. Results were consistent, but 9.7% of cases exhibited erroneous multiple predictions. Conclusions: The Vision Transformer-based model demonstrated strong performance for pancreatic tumor segmentation in EUS images. However, dataset heterogeneity and limited external validation highlight the need for further refinement, standardization, and prospective studies.

cs.CV↗

arXiv2025

Real-Time LPV-Based Non-Linear Model Predictive Control for Robust Trajectory Tracking in Autonomous Vehicles

This paper presents the development and implementation of a Model Predictive Control (MPC) framework for trajectory tracking in autonomous vehicles under diverse driving conditions. The proposed approach incorporates a modular architecture that integrates state estimation, vehicle dynamics modeling, and optimization to ensure real-time performance. The state-space equations are formulated in a Linear Parameter Varying (LPV) form, and a curvature-based tuning method is introduced to optimize weight matrices for varying trajectories. The MPC framework is implemented using the Robot Operating System (ROS) for parallel execution of state estimation and control optimization, ensuring scalability and minimal latency. Extensive simulations and real-time experiments were conducted on multiple predefined trajectories, demonstrating high accuracy with minimal cross-track and orientation errors, even under aggressive maneuvers and high-speed conditions. The results highlight the robustness and adaptability of the proposed system, achieving seamless alignment between simulated and real-world performance. This work lays the foundation for dynamic weight tuning and integration into cooperative autonomous navigation systems, paving the way for enhanced safety and efficiency in autonomous driving applications.

cs.RO↗

arXiv2024

Experimental Measurement of Transverse Spin Dynamics in the Nonparaxial Focal Region

The superposition of complex optical fields in three-dimension (3D) is the basis of several non-trivial wave phenomena. Significant among them are the non-uniform polarization distribution and their topological character, leading to the emergence of transverse spin, transverse momentum, and their dynamics. These aspects are experimentally measured in the nonparaxial focal region of a circularly-polarized Gaussian input beam. A dielectric mirror, kept in the focal region, is axially scanned to obtain the phase and polarization variations in the retro-reflected output beam using an interferometer and spatially-resolved Stokes parameter measurements. The identification of phase and polarization singularities in the beam cross-section and their behaviour as a function of the mirror position enabled us to map and study the phase-polarization variations in the nonparaxial focal region. The lemon-monstar type polarization patterns surrounding the C-point singularity in the output beam are identified and tracked to study the transverse spin dynamics for right- and left- circular polarization of the input beam. Direct measurement of the input beam polarization helicity-independent and helicity-dependent aspects of the transverse and longitudinal spin dynamics in the nonparaxial focal region are the significant findings reported here. The proposed and demonstrated measurement method allows us to investigate the nonparaxial focal region in more detail and has the potential to unravel other intricate optical field effects.

physics.optics↗

arXiv2024

Complex far fields and optical singularities due to propagation beyond tight focusing: combined effects of wavefront curvature and aperture diffraction

All optical systems, which involve the collimation of a reflected, transmitted or scattered wave subsequent to tight focusing, are subject to two kinds of deviations. One is the wavefront curvature due to inaccurate focal placement of the interface or scatterer particle under consideration, and the other is the diffraction caused by the finite lens aperture. In the present paper we explore these phenomena in detail by considering a rigorous simulated model and an appropriate experimental setup. We hence demonstrate the complicated intensity profiles and optical singularity characteristics of the observed far field. Then we describe ways to minimize these deviations in a general experiment. But more importantly, our analysis proves that these deviations by themselves are significant optical phenomena of fundamental interest. The observed complex field profiles have similarities to standard diffraction-limited tight focal fields, though our field detection is different from the standard schemes. This indicates the relevance of these complex fields to a larger class of systems involving wavefront curvature and aperture diffraction. The detailed analysis and results of the present paper already serve as core explorations of these optical phenomena; and we also suggest future research directions where these system aspects can be purposefully created and explored further.

physics.optics↗

arXiv2024

V2X Enabled Emergency Vehicle Alert System

Today's major concern in traffic management systems includes time-efficient emergency transports. The awareness of environment and vehicle information is necessary for the emergency vehicles as well as the surrounding commercial vehicles that might be driven by inexperienced drivers to act accordingly if they both interact. The information exchange should be quick and accurate along with how much interactive the alerting system is with the drivers. Therefore, technologies like V2X-based alert systems can deal with such emergency situations and hence prevent potential health or social hazards. An alerting system as a part of a smart-connected city is proposed in this paper. The Dedicated Short Range Communication (DSRC) based system has tried to cover the major domain of information about misbehaving vehicles, any pedestrians on the road, and information about the emergency vehicle itself. The commercial vehicle also will have a similar alert system as an application of V2V and V2I. Further in this paper, a realtime monitoring system was developed using grafana dashboard which will be installed in the area's base station to monitor the vehicles in that area.

eess.SP↗

arXiv2022

Optical Singularity Dynamics and Spin-Orbit Interaction due to a Normal-Incident Optical Beam Reflected at a Plane Dielectric Interface

The degenerate case of normal incidence and reflection of an optical beam (both paraxial and non-paraxial) at a plane isotropic dielectric interface, which is azimuthally symmetric in terms of the momentum-spatial variation of Fresnel coefficients but not in terms of the fundamental polarization inhomogeneity of the incident field, requires in-depth analyses. In this paper, we use the reflection and transmission coefficient matrix formalism to derive an exact field expression of a normal-reflected diverging beam. The availability of the exact field information allows controlled variations of the system parameters, leading to significant dynamics of phase and polarization singularities hitherto unanticipated in the literature. We carry out a detailed exploration of these dynamics in our simulated system, and also verify them experimentally by using an appropriate setup. We then use Barnett's formalism to determine the associated orbital angular momentum (OAM) fluxes, leading to a subtle interpretation and mathematical characterization of spin-orbit interaction (SOI) in the system. Our work thus represents a non-trivial unification of the most fundamental electromagnetic reflection/transmission problem at a plane dielectric interface and the emerging areas of optical singularity dynamics with their understanding in terms of OAM flux and SOI. The normal-incidence--retro-reflection geometry being especially amenable to applications, these beam-field phenomena are anticipated to have applications in interface characterization, particle rotation/manipulation and other nano-optical processes.

physics.optics↗

arXiv2022

Entity Alignment For Knowledge Graphs: Progress, Challenges, and Empirical Studies

Entity Alignment (EA) identifies entities across databases that refer to the same entity. Knowledge graph-based embedding methods have recently dominated EA techniques. Such methods map entities to a low-dimension space and align them based on their similarities. With the corpus of EA methodologies growing rapidly, this paper presents a comprehensive analysis of various existing EA methods, elaborating their applications and limitations. Further, we distinguish the methods based on their underlying algorithms and the information they incorporate to learn entity representations. Based on challenges in industrial datasets, we bring forward $4$ research questions (RQs). These RQs empirically analyse the algorithms from the perspective of \textit{Hubness, Degree distribution, Non-isomorphic neighbourhood,} and \textit{Name bias}. For Hubness, where one entity turns up as the nearest neighbour of many other entities, we define an $h$-score to quantify its effect on the performance of various algorithms. Additionally, we try to level the playing field for algorithms that rely primarily on name-bias existing in the benchmarking open-source datasets by creating a low name bias dataset. We further create an open-source repository for $14$ embedding-based EA methods and present the analysis for invoking further research motivations in the field of EA.

cs.AI↗

arXiv2021

Wide elastic wave bandgap metamaterial with single phase constituent

Accomplishing a wide elastic wave bandgap with single phase constituent is of primary interest in developing phononic metamaterials. In the present article, exploiting spatial periodicity, a single phase lattice is configured towards achieving a large frequency bandgap in sonic range. Numerical simulations reveal the presence of a comprehensive bandgap of 18 kHz in the 2 to 22 kHz range with systematically localizing the same constituent material in the lattice. Bloch wave modes unravel the involvement of dipole, monopole, and quadrupole resonances for wide and connected bandgaps. The existence of salient bandgaps is experimentally validated by analyzing the mechanical wave transmission.

physics.app-ph↗

arXiv2021

The Hilti SLAM Challenge Dataset

Research in Simultaneous Localization and Mapping (SLAM) has made outstanding progress over the past years. SLAM systems are nowadays transitioning from academic to real world applications. However, this transition has posed new demanding challenges in terms of accuracy and robustness. To develop new SLAM systems that can address these challenges, new datasets containing cutting-edge hardware and realistic scenarios are required. We propose the Hilti SLAM Challenge Dataset. Our dataset contains indoor sequences of offices, labs, and construction environments and outdoor sequences of construction sites and parking areas. All these sequences are characterized by featureless areas and varying illumination conditions that are typical in real-world scenarios and pose great challenges to SLAM algorithms that have been developed in confined lab environments. Accurate sparse ground truth, at millimeter level, is provided for each sequence. The sensor platform used to record the data includes a number of visual, lidar, and inertial sensors, which are spatially and temporally calibrated. The purpose of this dataset is to foster the research in sensor fusion to develop SLAM algorithms that can be deployed in tasks where high accuracy and robustness are required, e.g., in construction environments. Many academic and industrial groups tested their SLAM systems on the proposed dataset in the Hilti SLAM Challenge. The results of the challenge, which are summarized in this paper, show that the proposed dataset is an important asset in the development of new SLAM algorithms that are ready to be deployed in the real-world.

cs.RO↗

arXiv2019

Trajectory optimization for a class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family

We present a novel class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family which consists of ground mobile bases with non-holonomic constraints. Moreover, these mobile robots are constrained by closed-loop kinematic chains consisting of revolute joints which can be either passive or actuated. We also describe a novel trajectory optimization method which is general with respect to number of mobile robots, topology of the closed-loop kinematic chains and placement of the actuators at the revolute joints. We also extend the standalone trajectory optimization method to optimize concurrently the design parameters and the control policy. We describe various CCMA system examples, in simulation, differing in design, topology, number of mobile robots and actuation space. The simulation results for standalone trajectory optimization with fixed design parameters is presented for CCMA system examples. We also show how this method can be used for tasks other than end-effector positioning such as internal collision avoidance and external obstacle avoidance. The concurrent design and control policy optimization is demonstrated, in simulations, to increase the CCMA system workspace and manipulation capabilities. Finally, the trajectory optimization method is validated in experiments through two 4-DOF prototypes consisting of 3 tracked mobile bases.

cs.RO↗

arXiv2019

An optimization framework for simulation and kinematic control of Constrained Collaborative Mobile Agents (CCMA) system

We present a concept of constrained collaborative mobile agents (CCMA) system, which consists of multiple wheeled mobile agents constrained by a passive kinematic chain. This mobile robotic system is modular in nature, the passive kinematic chain can be easily replaced with different designs and morphologies for different functions and task adaptability. Depending solely on the actuation of the mobile agents, this mobile robotic system can manipulate or position an end-effector. However, the complexity of the system due to presence of several mobile agents, passivity of the kinematic chain and the nature of the constrained collaborative manipulation requires development of an optimization framework. We therefore present an optimization framework for forward simulation and kinematic control of this system. With this optimization framework, the number of deployed mobile agents, actuation schemes, the design and morphology of the passive kinematic chain can be easily changed, which reinforces the modularity and collaborative aspects of the mobile robotic system. We present results, in simulation, for spatial 4-DOF to 6-DOF CCMA system examples. Finally, we present experimental quantitative results for two different fabricated 4-DOF prototypes, which demonstrate different actuation schemes, control and collaborative manipulation of an end-effector.

cs.RO↗