arXiv subjects

Vivek Kumar Singh

Publications and source records attributed to Vivek Kumar Singh.

At least 73 records · Page 4Linked to original sources

arXiv2020

Fluctuations of identified particle yields using the ν dyn variable at energies available at the BNL Relativistic Heavy Ion Collider

We study the fluctuations of net-charge, net-pion, net-kaon and net-proton using $D$-measure and $ν_{dyn}$ variables in heavy-ion jet interaction generator (HIJING), ultra-relativistic quantum molecular dynamics (UrQMD), and hadron resonance gas (HRG) model at different collision energies \sqsn. It has been observed that, the values of $D$ strongly dependent on $Δη$ in HIJING and UrQMD models and independent in HRG model. The diffusion coefficients ($σ$) of identified particles are estimated at various \sqsn. It is observed that, the $σ$ values are independent of collision energies but emphasizes the particle species dependence of diffusion coefficient in the QGP medium. This study provides a realistic baseline for comparison with the experimental data.

hep-ph↗

arXiv2019

Highly cited references in PLOS ONE and their in-text usage over time

In this article, we describe highly cited publications in a PLOS ONE full-text corpus. For these publications, we analyse the citation contexts concerning their position in the text and their age at the time of citing. By selecting the perspective of highly cited papers, we can distinguish them based on the context during citation even if we do not have any other information source or metrics. We describe the top cited references based on how, when and in which context they are cited. The focus of this study is on a time perspective to explain the nature of the reception of highly cited papers. We have found that these references are distinguishable by the IMRaD sections of their citation. And further, we can show that the section usage of highly cited papers is time-dependent: the longer the citation interval, the higher the probability that a reference is cited in a method section.

cs.DL↗

arXiv2019

Disciplinary Variations in Altmetric Coverage of Scholarly Articles

The popular social media platforms are now making it possible for scholarly articles to be shared rapidly in different forms, which in turn can significantly improve the visibility and reach of articles. Many authors are now utilizing the social media platforms to disseminate their scholarly articles (often as pre- or post- prints) beyond the paywalls of journals. It is however not very well established if the level of social media coverage and attention of scholarly articles is same across all research disciplines or there exist discipline-wise variations. This paper aims to explore the disciplinary variations in coverage and altmetric attention by analyzing a significantly large amount of data from Web of Science and Altmetric.com. Results obtained show interesting patterns. Medical Sciences and Biology are found to account for more than 50% of all instances in Altmetrics. In terms of coverage, disciplines like Biology, Medical Science and Multidisciplinary Sciences have more than 60% of their articles covered in Altmetrics, whereas disciplines like Engineering, Mathematics and Material Science have less than 25% of their articles covered in Altmetrics. The coverage percentages further vary across different altmetric platforms, with Twitter and Mendeley having much higher overall coverage than Facebook and News. Disciplinary variations in coverage are also found in different altmetric platforms, with variations as large as 7.5% for Engineering discipline to 55.7% for Multidisciplinary in Twitter. The paper also looks into the possible role of source of publication in altmetric coverage level of articles. Interestingly, some journals are found to have a higher altmetric coverage in comparison to the average altmetric coverage level of that discipline.

cs.DL↗

arXiv2019

How much research output from India gets social media attention?

Scholarly articles are now increasingly being mentioned and discussed in social media platforms, sometimes even as pre- or post-print version uploads. Measures of social media mentions and coverage are now emerging as an alternative indicator of impact of scholarly articles. This article aims to explore how much scholarly research output from India is covered in different social media platforms, and how similar or different it is from the world average. It also analyses the discipline-wise variations in coverage and altmetric attention for Indian research output, including a comparison with the world average. Results obtained show interesting patterns. Only 28.5% of the total research output from India is covered in social media platforms, which is about 18% less than the world average. ResearchGate and Mendeley are the most popular social media platforms in India for scholarly article coverage. In terms of discipline-wise variation, medical sciences and biological sciences have relatively higher coverage across different platforms compared to disciplines like information science and engineering.

cs.DL↗

arXiv2019

Entanglement on multiple $S^2$ boundaries in Chern-Simons theory

Topological entanglement structure amongst disjoint torus boundaries of three manifolds have already been studied within the context of Chern-Simons theory. In this work, we study the topological entanglement due to interaction between the quasiparticles inside three-manifolds with one or more disjoint $S^2$ boundaries in SU($N$) Chern-Simons theory. We focus on the world-lines of quasiparticles (Wilson lines), carrying SU($N$) representations, creating four punctures on every $S^2$. We compute the entanglement entropy by partial tracing some of the boundaries. In fact, the entanglement entropy depends on the SU($N$) representations on these four-punctured $S^2$ boundaries. Further, we observe interesting features on the GHZ-like and W-like entanglement structures. Such a distinction crucially depends on the multiplicity of the irreducible representations in the tensor product of SU($N$) representations.

hep-th↗

arXiv2019

Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation

In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization. In the generator network of the adversarial framework, spatial pyramid pooling, kernel factorization and squeeze excitation block are employed to enhance the feature representation in spatial domain on different scales with reduced computational complexity. In turn, the discriminator network of the adversarial framework is formulated by combining convolutional layers with an additional squeeze excitation block to differentiate the generated segmentation mask from its respective ground truth. Before feeding the images to the network, we pre-processed them by using edge sharpening and Gaussian regularization to reach an optimized solution for vessel segmentation. The output of the trained model is post-processed using morphological operations to remove the small speckles of noise. The proposed method qualitatively and quantitatively outperforms state-of-the-art vessel segmentation methods using DRIVE and STARE datasets.

eess.IV↗

arXiv2019

An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning

This paper proposes an efficient solution for tumor segmentation and classification in breast ultrasound (BUS) images. We propose to add an atrous convolution layer to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images. To automatically re-balance the relative impact of each of the highest level encoded features, we also propose to add a channel-wise weighting block in the network. In addition, the SSIM and L1-norm loss with the typical adversarial loss are used as a loss function to train the model. Our model outperforms the state-of-the-art segmentation models in terms of the Dice and IoU metrics, achieving top scores of 93.76% and 88.82%, respectively. In the classification stage, we show that few statistics features extracted from the shape of the boundaries of the predicted masks can properly discriminate between benign and malignant tumors with an accuracy of 85%$

eess.IV↗

arXiv2019

Bi-partite vertex model and multi-colored link invariants

Construction of representations of braid group generators from $N$-state vertex models provide an elegant route to study knot and link invariants. Using such a braid group representation, an algebraic formula for the link invariants was put forth when the same spin $(N-1)/2$ are placed on all the component knots. In this paper, we generalise the procedure to deduce representations of braiding generators from bi-partite vertex models. Such a representation allows the study of multi-colored link invariants where the component knots carry different spins. We propose a multi-colored link invariant formula in terms of braiding generators derived from $R$ matrices of bi-partite vertex models.

hep-th↗

arXiv2018

Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network

Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast masses, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram. The generative network learns to recognize the breast mass area and to create the binary mask that outlines the breast mass. In turn, the adversarial network learns to distinguish between real (ground truth) and synthetic segmentations, thus enforcing the generative network to create binary masks as realistic as possible. The cGAN works well even when the number of training samples are limited. Therefore, the proposed method outperforms several state-of-the-art approaches. This hypothesis is corroborated by diverse experiments performed on two datasets, the public INbreast and a private in-house dataset. The proposed segmentation model provides a high Dice coefficient and Intersection over Union (IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a Convolutional Neural Network (CNN) is proposed to classify the generated masks into four mass shapes: irregular, lobular, oval and round. The proposed shape descriptor was trained on Digital Database for Screening Mammography (DDSM) yielding an overall accuracy of 80%, which outperforms the current state-of-the-art.

cs.CV↗

arXiv2018

REFUGE CHALLENGE 2018-Task 2:Deep Optic Disc and Cup Segmentation in Fundus Images Using U-Net and Multi-scale Feature Matching Networks

In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network. The proposed method targets task 2 of the REFUGE challenge 2018. In order to solve the segmentation problem of task 2, we firstly crop the input image using single shot multibox detector (SSD). The cropped image is then passed to an encoder-decoder network with skip connections also known as generator. Afterwards, both the ground truth and generated images are fed to a convolution neural network (CNN) to extract their multi-level features. A dice loss function is then used to match the features of the two images by minimizing the error at each layer. The aggregation of error from each layer is back-propagated through the generator network to enforce it to generate a segmented image closer to the ground truth. The CNN network improves the performance of the generator network without increasing the complexity of the model.

cs.CV↗

arXiv2018

Equivalence of One Loop Diagrams in Covariant and Light Front QED Revisited

We revisit the proof of equivalence of one loop expressions for fermion self-energy and vertex correction in light-front QED and Covariant QED at the Feynman diagram level and generalize, to all components, the proof of equivalence for the one loop vertex correction diagram which was presented earlier by us only for the $+$ component of $Λ^μ$. We demonstrate, in the general case also, that the equivalence cannot be established without the third term in the three-term photon propagator in light cone gauge.

hep-th↗

arXiv2018

Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

This paper proposed a retinal image segmentation method based on conditional Generative Adversarial Network (cGAN) to segment optic disc. The proposed model consists of two successive networks: generator and discriminator. The generator learns to map information from the observing input (i.e., retinal fundus color image), to the output (i.e., binary mask). Then, the discriminator learns as a loss function to train this mapping by comparing the ground-truth and the predicted output with observing the input image as a condition.Experiments were performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE. The proposed model outperformed state-of-the-art-methods by achieving around 0.96% and 0.98% of Jaccard and Dice coefficients, respectively. Moreover, an image segmentation is performed in less than a second on recent GPU.

cs.CV↗

arXiv2018

Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification

This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when the training data is limited. The generative network learns intrinsic features of tumors while the adversarial network enforces segmentations to be similar to the ground truth. Experiments performed on dozens of malignant tumors extracted from the public DDSM dataset and from our in-house private dataset confirm our hypothesis with very high Dice coefficient and Jaccard index (>94% and >89%, respectively) outperforming the scores obtained by other state-of-the-art approaches. Furthermore, in order to detect portray significant morphological features of the segmented tumor, a specific Convolutional Neural Network (CNN) have also been designed for classifying the segmented tumor areas into four types (irregular, lobular, oval and round), which provides an overall accuracy about 72% with the DDSM dataset.

cs.CV↗

arXiv2018

SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks

Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than $100$ images of size 384x384 per second on a recent GPU.

cs.CV↗

arXiv2018

Multi-Colored Links From 3-strand Braids Carrying Arbitrary Symmetric Representations

Obtaining colored HOMFLY-PT polynomials for knots from 3-strand braid carrying arbitrary $SU(N)$ representation is still tedious. For a class of rank $r$ symmetric representations, $[r]$-colored HOMFLY-PT $H_{[r]}$ evaluation becomes simpler. Recently it was shown that $H_{[r]}$, for such knots from 3-strand braid, can be constructed using the quantum Racah coefficients (6j-symbols) of $U_q(sl_2)$. In this paper, we generalise it to links whose components carry different symmetric representations. We illustrate the technique by evaluating multi-colored link polynomials $H_{[r_1],[r_2]}$ for the two-component link L7a3 whose components carry $[r_1]$ and $[r_2]$ colors.

hep-th↗

arXiv2018

Entanglement on linked boundaries in Chern-Simons theory with generic gauge groups

We study the entanglement for a state on linked torus boundaries in $3d$ Chern-Simons theory with a generic gauge group and present the asymptotic bounds of Rényi entropy at two different limits: (i) large Chern-Simons coupling $k$, and (ii) large rank $r$ of the gauge group. These results show that the Rényi entropies cannot diverge faster than $\ln k$ and $\ln r$, respectively. We focus on torus links $T(2,2n)$ with topological linking number $n$. The Rényi entropy for these links shows a periodic structure in $n$ and vanishes whenever $n = 0 \text{ (mod } \textsf{p})$, where the integer $\textsf{p}$ is a function of coupling $k$ and rank $r$. We highlight that the refined Chern-Simons link invariants can remove such a periodic structure in $n$.

hep-th↗

arXiv2018

Eigenvalue hypothesis for multi-strand braids

Computing polynomial form of the colored HOMFLY-PT for non-arborescent knots obtained from three or more strand braids is still an open problem. One of the efficient methods suggested for the three-strand braids relies on the eigenvalue hypothesis which uses the Yang-Baxter equation to express the answer through the eigenvalues of the ${\cal R}$-matrix. In this paper, we generalize the hypothesis to higher number of strands in the braid where commuting relations of non-neighbouring $\mathcal{R}$ matrices are also incorporated. By solving these equations, we determine the explicit form for $\mathcal{R}$-matrices and the inclusive Racah matrices in terms of braiding eigenvalues (for matrices of size up to 6 by 6). For comparison, we briefly discuss the highest weight method for four-strand braids carrying fundamental and symmetric rank two $SU_q(N)$ representation. Specifically, we present all the inclusive Racah matrices for representation $[2]$ and compare with the matrices obtained from eigenvalue hypothesis.

hep-th↗

arXiv2018

Checks of integrality properties in topological strings

Tests of the integrality properties of a scalar operator in topological strings on a resolved conifold background or orientifold of conifold backgrounds have been performed for arborescent knots and some non-arborescent knots. The recent results on polynomials for those knots colored by SU(N) and SO(N) adjoint representations are useful to verify Marino's integrality conjecture up to two boxes in the Young diagram. In this paper, we review the salient aspects of the integrality properties and tabulate explicitly for an arborescent knot and a link. In our knotebook website, we have put these results for over 100 prime knots available in Rolfsen table and some links. The first application of the obtained results, an observation of the Gaussian distribution of the LMOV invariants is also reported.

hep-th↗