arXiv subjects

Arka Ghosh

Publications and source records attributed to Arka Ghosh.

25 records · Page 2Linked to original sources

arXiv2022

Solvability of orbit-finite systems of linear equations

We study orbit-finite systems of linear equations, in the setting of sets with atoms. Our principal contribution is a decision procedure for solvability of such systems. The procedure works for every field (and even commutative ring) under mild effectiveness assumptions, and reduces a given orbit-finite system to a number of finite ones: exponentially many in general, but polynomially many when atom dimension of input systems is fixed. Towards obtaining the procedure we push further the theory of vector spaces generated by orbit-finite sets, and show that each such vector space admits an orbit-finite basis. This fundamental property is a key tool in our development, but should be also of wider interest.

cs.CL↗

arXiv2021

Nonlinear electromagnetic-wave interactions in pair plasma: (I) Non-relativistic regime

This paper is the first in a series devoted to the numerical study of nonlinear interactions of electromagnetic waves with plasma. We start with non-magnetized pair plasmas, where the primary processes are induced (Compton) scattering and the filamentation instability. In this paper, we consider waves in which electron oscillations are non-relativistic. Here, the numerical results can be compared to analytical theory, facilitating the development of appropriate numerical tools and framework. We distill the analytic theory, reconciling plasma and radiative transfer pictures of induced scattering and developing in detail the kinetic theory of modulation/filamentation instability. We carry out homogeneous numerical simulations using the particle-in-cell codes EPOCH and Tristan-MP, for both monochromatic waves and wave packets. We show that simulations of both processes are consistent with theoretical predictions, setting the stage for analyzing the highly nonlinear regime.

astro-ph.HE↗

arXiv2021

A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers

Constructing adversarial perturbations for deep neural networks is an important direction of research. Crafting image-dependent adversarial perturbations using white-box feedback has hitherto been the norm for such adversarial attacks. However, black-box attacks are much more practical for real-world applications. Universal perturbations applicable across multiple images are gaining popularity due to their innate generalizability. There have also been efforts to restrict the perturbations to a few pixels in the image. This helps to retain visual similarity with the original images making such attacks hard to detect. This paper marks an important step which combines all these directions of research. We propose the DEceit algorithm for constructing effective universal pixel-restricted perturbations using only black-box feedback from the target network. We conduct empirical investigations using the ImageNet validation set on the state-of-the-art deep neural classifiers by varying the number of pixels to be perturbed from a meagre 10 pixels to as high as all pixels in the image. We find that perturbing only about 10% of the pixels in an image using DEceit achieves a commendable and highly transferable Fooling Rate while retaining the visual quality. We further demonstrate that DEceit can be successfully applied to image dependent attacks as well. In both sets of experiments, we outperformed several state-of-the-art methods.

cs.CR↗

arXiv2012

Hybrid Optimized Back propagation Learning Algorithm For Multi-layer Perceptron

Standard neural network based on general back propagation learning using delta method or gradient descent method has some great faults like poor optimization of error-weight objective function, low learning rate, instability .This paper introduces a hybrid supervised back propagation learning algorithm which uses trust-region method of unconstrained optimization of the error objective function by using quasi-newton method .This optimization leads to more accurate weight update system for minimizing the learning error during learning phase of multi-layer perceptron.[13][14][15] In this paper augmented line search is used for finding points which satisfies Wolfe condition. In this paper, This hybrid back propagation algorithm has strong global convergence properties & is robust & efficient in practice.

cs.NE↗

arXiv2012

Dynamic Scheduling for Markov Modulated Single-server Multiclass Queueing Systems in Heavy Traffic

This paper studies a scheduling control problem for a single-server multiclass queueing network in heavy traffic, operating in a changing environment. The changing environment is modeled as a finite state Markov process that modulates the arrival and service rates in the system. Various cases are considered: fast changing environment, fixed environment and slow changing environment. In each of the cases, using weak convergence analysis, in particular functional limit theorems for renewal processes and ergodic Markov processes, it is shown that an appropriate "averaged" version of the classical cμ-policy (the priority policy that favors classes with higher values of the product of holding cost c and service rate μ) is asymptotically optimal for an infinite horizon discounted cost criterion.

math.PR↗

arXiv2012

Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Financial forecasting is an example of a signal processing problem which is challenging due to Small sample sizes, high noise, non-stationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice dataset made available by GoogleFinance.To develop this prediction model Backpropagation method with Gradient Descent learning has been implemented.Finally the Neural Net, learned with said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries.

cs.NE↗

arXiv2011

On a directionally reinforced random walk

We consider a generalized version of a directionally reinforced random walk, which was originally introduced by Mauldin, Monticino, and von Weizsäcker in \cite{drw}. Our main result is a stable limit theorem for the position of the random walk in higher dimensions. This extends a result of Horváth and Shao \cite{limits} that was previously obtained in dimension one only (however, in a more stringent functional form).

math.PR↗