arXiv subjects

Prashant Kumar

Publications and source records attributed to Prashant Kumar.

At least 55 records · Page 3Linked to original sources

arXiv2021

On Graph Induced Symbolic Systems

\begin{abstract} In this paper, we investigate a shift arising from graph $G$. We prove that any $k$-dimensional shift of finite type can be generated through a $k$-dimensional graph. We investigate the structure of the shift space using the generating matrices for the shift space. We prove that a two dimensional shift space has a horizontally (vertically) periodic point if and only if it possesses a $(m,n)$-periodic point (for some $m,n\in \mathbb{Z}\setminus \{0\}$). We prove that a shift space is finite if and only if it can be generated by permutation matrices. We study the non-emptiness problem and existence of periodic points in terms of the generating matrices.

math.DS↗

arXiv2021

A simulation driven optimization algorithm for scheduling sorting center operations

Parcel sorting operations in logistics enterprises aim to achieve a high throughput of parcels through sorting centers. These sorting centers are composed of large circular conveyor belts on which incoming parcels are placed, with multiple arms known as chutes for sorting the parcels by destination, followed by packing into roller cages and loading onto outbound trucks. Modern sorting systems need to complement their hardware innovations with sophisticated algorithms and software to map destinations and workforce to specific chutes. While state of the art systems operate with fixed mappings, we propose an optimization approach that runs before every shift, and uses real-time forecast of destination demand and labor availability in order to maximize throughput. We use simulation to improve the performance and robustness of the optimization solution to stochasticity in the environment, through closed-loop tuning of the optimization parameters.

eess.SY↗

arXiv2021

Structured Prediction in NLP -- A survey

Over the last several years, the field of Structured prediction in NLP has had seen huge advancements with sophisticated probabilistic graphical models, energy-based networks, and its combination with deep learning-based approaches. This survey provides a brief of major techniques in structured prediction and its applications in the NLP domains like parsing, sequence labeling, text generation, and sequence to sequence tasks. We also deep-dived into energy-based and attention-based techniques in structured prediction, identified some relevant open issues and gaps in the current state-of-the-art research, and have come up with some detailed ideas for future research in these fields.

cs.CL↗

arXiv2021

Anisotropic quantum Hall states in the presence of interactions with fourfold rotational symmetry

We study the effects of anisotropic interactions in the quantum Hall effect in the presence of a fourfold discrete rotational ($C_4$) symmetry. Employing the density matrix renormalization group technique on an infinite cylinder geometry (iDMRG), we calculate the anisotropy response of the Laughlin state at $ν=1/3$ and the composite-Fermi liquid (CFL) state at $ν=1/2$. We find that the anisotropy transferred from the interaction potential to the $ν=1/3$ state is stronger when compared to the complementary case of an anisotropic band. Further, the strength of anisotropy reduces as the interaction is made shorter ranged. Quite surprisingly, at $ν=1/2$, we find that the deformation in the CF Fermi-surface changes sign as the interaction range is reduced. Our results imply that the short-distance and long-distance parts of the interaction potential have opposite effects on the CFL state in the presence of $C_4$-symmetric anisotropy.

cond-mat.str-el↗

arXiv2021

fybrrLink: Efficient QoS-aware Routing in SDN enabled Next-Gen Satellite Networks

Providing high-speed Internet service using satellite network has attracted researchers from both academia and industry mainly due to the characteristics of Low Earth Orbit (LEO) satellite networks such as global coverage, scalability, and lower transmission delay. With the recent advancements in the Software-Defined Network (SDN), implementation of SDN in Non-Terrestrial Networks (NTN) can help to achieve the set goals for 5G and beyond networks. Since satellite networks have a distinct architecture, some of the traditional protocols no longer remain useful. Therefore, to satisfy the diverse Quality of Service (QoS) requirements for a variety of applications, we propose a novel and centralized QoS-aware routing algorithm, called fybrrLink in which the global view of the network in SDN is utilized. We implement a modified Bresenham's algorithm and Dijkstra's algorithm to find the optimal path in a significantly reduced computation time. Also, taking advantage of the deterministic satellite constellation, we propose a flow rule transfer algorithm and a topology monitoring algorithm. Further, fybrrLink is evaluated with multiple NS3 simulations, and results confirm its supremacy over other state-of-the-art algorithms.

cs.NI↗

arXiv2021

DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has been attempted to address DST for LiDAR scans. The problem is of critical importance due to wide-spread adoption of LiDAR in Autonomous Vehicles. We show that state-of the art methods developed for the visual domain when adapted for LiDAR scans perform poorly. We develop DSLR, a deep generative model which learns a mapping between dynamic scan to its static counterpart through an adversarially trained autoencoder. Our model yields the first solution for DST on LiDAR that generates static scans without using explicit segmentation labels. DSLR cannot always be applied to real world data due to lack of paired dynamic-static scans. Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. Additionally, if segmentation information is available, we extend DSLR to DSLR-Seg to further improve the reconstruction quality. DSLR gives the state of the art performance on simulated and real-world datasets and also shows at least 4x improvement. We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.

cs.CV↗

arXiv2021

fybrrStream: A WebRTC based Efficient and Scalable P2P Live Streaming Platform

The demand for streaming media and live video conferencing is at peak and expected to grow further, thereby the need for low-cost streaming services with better quality and lower latency is essential. Therefore, in this paper, we propose a novel peer-to-peer (P2P) live streaming platform, called fybrrStream, where a logical mesh and physical tree i.e., hybrid topology-based approach is leveraged for low latency streaming. fybrrStream distributes the load on participating peers in a hierarchical manner by considering their network bandwidth, network latency, and node stability. fybrrStream costs as low as the cost of just hosting a light-weight website and the performance is comparable to the existing state-of-the-art media streaming services. We evaluated and tested the proposed fybrrStream platform with real-field experiments using 50+ users spread across India and results obtained show significant improvements in the live streaming performance over other schemes.

cs.NI↗

arXiv2021

Composite Fermion Nonlinear Sigma Models

We study the integer quantum Hall plateau transition using composite fermion mean-field theory. We show that the topological $θ= π$ term in the associated nonlinear sigma model [P. Kumar et al., Phys. Rev. B 100, 235124 (2019)] is stable against a certain particle-hole symmetry violating perturbation, parameterized by the composite fermion effective mass. This result, which applies to both the Halperin, Lee, and Read and Dirac composite fermion theories, represents an emergent particle-hole symmetry. For a disorder ensemble without particle-hole symmetry, we find that $θ$ can vary continuously within the diffusive regime. Our results call for further study of the universality of the plateau transition.

cond-mat.str-el↗

arXiv2021

Numerical Study of a Dual Representation of the Integer Quantum Hall Transition

We study the critical properties of the non-interacting integer quantum Hall to insulator transition (IQHIT) in a "dual" composite-fermion (CF) representation. A key advantage of the CF representation over electron coordinates is that at criticality, $\textit{CF states are delocalized at all}$ energies. The CF approach thus enables us to study the transition from a new vantage point. Using a lattice representation of CF mean-field theory, we compute the critical and multifractal exponents of the IQHIT. We obtain $ν= 2.56 \pm 0.02$ and $η= 0.51\pm 0.01$, both of which are consistent with the predictions of the Chalker-Coddington network model formulated in the electron representation.

cond-mat.str-el↗

arXiv2021

Approximation of discontinuous functions by Kantorovich exponential sampling series

The Kantorovich exponential sampling series at jump discontinuities of the bounded measurable signal f has been analysed. A representation lemma for the series is established and using this lemma certain approximation theorems for discontinuous signals are proved. The degree of approximation in terms of logarithmic modulus of smoothness for the series is studied. Further a linear prediction of signals based on past sample values has been obtained. Some numerical simulations are performed to validate the approximation of discontinuous signals f by the sampling series.

math.FA↗

arXiv2021

Approximation of Discontinuous Signals by Exponential Sampling Series

We analyse the behaviour of the exponential sampling series $S_{w}^χf$ at jump discontinuity of the bounded signal $f.$ We obtain a representation lemma that is used for analysing the series $S_{w}^χf$ and we establish approximation of jump discontinuity functions by the series $S_{w}^χf.$ The rate of approximation of the exponential sampling series $S_{w}^χf$ is obtained in terms of logarithmic modulus of continuity of functions and the round-off and time-jitter errors are also studied. Finally we give some graphical representation of approximation of discontinuous functions by $S_{w}^χf$ using suitable kernels.

math.FA↗

arXiv2020

Data Processing Software for Chandrayaan-2 Solar X-ray Monitor

Solar X-ray Monitor (XSM) instrument of India's Chandrayaan-2 lunar mission carries out broadband spectroscopy of the Sun in soft X-rays. XSM, with its unique features such as low background, high time cadence, and high spectral resolution, provides the opportunity to characterize transient and quiescent X-ray emission from the Sun even during low activity periods. It records the X-ray spectrum at one-second cadence, and the data recorded on-board are downloaded at regular intervals along with that of other payloads. During ground pre-processing, the XSM data is segregated, and the level-0 data is made available for higher levels of processing at the Payload Operations Center (POC). XSM Data Analysis Software (XSMDAS) is developed to carry out the processing of the level-0 data to higher levels and to generate calibrated light curves and spectra for user-defined binning parameters such that it is suitable for further scientific analysis. A front-end for the XSMDAS named XSM Quick Look Display (XSMQLD) is also developed to facilitate a first look at the data without applying calibration. XSM Data Management-Monitoring System (XSMDMS) is designed to carry out automated data processing at the POC and to maintain an SQLite database with relevant information on the data sets and an internal web application for monitoring data quality and instrument health. All XSM raw and calibrated data products are in FITS format, organized into day-wise files, and the data archive follows Planetary Data System-4 (PDS4) standards. The XSM data will be made available after a lock-in period along with the XSM Data Analysis Software from ISRO Science Data Archive (ISDA) at Indian Space Science Data Center(ISSDC). Here we discuss the design and implementation of all components of the software for the XSM data processing and the contents of the XSM data archive.

astro-ph.IM↗

arXiv2020

On Bivariate Kantorovich Exponential Sampling Series

We analyse the approximation properties of the bivariate generalization of the family of Kantorovich type exponential sampling series. We derive the point-wise and Voronovskaya type theorem for these sampling type series. Using the modulus of smoothness, we obtain the quantitative estimate of order of convergence of these series. Further, we establish the degree of approximation for these series associated with generalized Boolean sum (GBS) operators. Finally, we provide a few examples of kernels to which the theory can be applied along with the graphical representation and error estimates.

math.FA↗

arXiv2019

Self-duality of the integer quantum Hall to insulator transition: composite fermion description

The integer quantum Hall to insulator transition (IQHIT) is a paradigmatic quantum critical point. Key aspects of this transition, however, remain mysterious, due to the simultaneous effects of quenched disorder and strong interactions. We study this transition using a composite fermion (CF) representation, which incorporates some of the effects of interactions. As we describe, the transition also marks a IQHIT of CFs: this suggests that the transition may exhibit `self-duality'. We show the explicit equivalence of the electron and CF Lagrangians at the critical point via the corresponding non-linear sigma models, revealing the self-dual nature of the transition. We show analytically that the resistivity tensor at the critical point is $ρ^c_{xx} = ρ^c_{xy} = \frac{h}{e^2}$, which are consistent with the expectations of self-duality, and in rough agreement with experiments.

cond-mat.str-el↗

arXiv2019

Formal Methods and Event Notification Systems in Mobile Computing Environment

In this report, we have explored the issues associated with the specification of event-based systems in a mobile environment using Unity \cite{unity}. We used a few constructs and concepts from Mobile UNITY which was proposed as an extension of UNITY by Roman and McCann \cite{intro}. Our aim in this report is to show that some of the constructs proposed in Mobile UNITY are not unnecessary. Those constructs are overly powerful and put a hindrance on the mapping from UNITY specification to particular architectures, which is one of the key simplicity of UNITY specification. Using an example of a message-based event notification system we have shown that a system with a simple modification to the structure of assign section of the UNITY programs could serve well in mapping and implementation at the same time preserve the small and compact proof logic of UNITY.

cs.PL↗

arXiv2019

A machine learning framework for computationally expensive transient models

The promise of machine learning has been explored in a variety of scientific disciplines in the last few years, however, its application on first-principles based computationally expensive tools is still in nascent stage. Even with the advances in computational resources and power, transient simulations of large-scale dynamic systems using a variety of the first-principles based computational tools are still limited. In this work, we propose an ensemble approach where we combine one such computationally expensive tool, called discrete element method (DEM), with a time-series forecasting method called auto-regressive integrated moving average (ARIMA) and machine-learning methods to significantly reduce the computational burden while retaining model accuracy and performance. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing.

physics.data-an↗

arXiv2019

Emergent reflection symmetry from non-relativistic composite fermions

A recent experimental study [Pan et al., arXiv: 1902.10262] has shown that fractional quantum Hall effect gaps are essentially consistent with particle-hole symmetry in the lowest Landau level. Motivated by this result, we consider a clean two dimensional electron system (2DES) from the viewpoint of composite fermion mean-field theory. In this short note, we show that while the experiment is manifestly consistent with a Dirac composite fermion theory proposed recently by Son, it can equally well be explained within the framework of non-relativistic composite fermions, first put forward by Halperin, Lee, and Read.

cond-mat.str-el↗

arXiv2019

A parametric acceleration of multilevel Monte Carlo convergence for nonlinear variably saturated flow

We present a multilevel Monte Carlo (MLMC) method for the uncertainty quantification of variably saturated porous media flow that are modeled using the Richards' equation. We propose a stochastic extension for the empirical models that are typically employed to close the Richards' equations. This is achieved by treating the soil parameters in these models as spatially correlated random fields with appropriately defined marginal distributions. As some of these parameters can only take values in a specific range, non-Gaussian models are utilized. The randomness in these parameters may result in path-wise highly nonlinear systems, so that a robust solver with respect to the random input is required. For this purpose, a solution method based on a combination of the modified Picard iteration and a cell-centered multigrid method for heterogeneous diffusion coefficients is utilized. Moreover, we propose a non-standard MLMC estimator to solve the resulting high-dimensional stochastic Richards' equation. The improved efficiency of this multilevel estimator is achieved by parametric continuation that allows us to incorporate simpler nonlinear problems on coarser levels for variance reduction while the target strongly nonlinear problem is solved only on the finest level. Several numerical experiments are presented showing computational savings obtained by the new estimator compared to the original MC estimator.

math.NA↗