arXiv subjects

Vivek Kumar Singh

Publications and source records attributed to Vivek Kumar Singh.

At least 37 records · Page 2Linked to original sources

arXiv2024

BPS Spectra of complex knots

Marino's conjecture remains underexplored within the framework of $SO(N )$ string dualities. In this article, we investigated the reformulated invariants of a one-parameter family of knots $\left[ K\right]_p$ derived from tangle surgery on Manolescu's quasi-alternating knot diagrams. Within topological string dualities, we have verified Marino's integrality conjecture for these families of knots up to the Young diagram representation ${\bf R}$, with ${|\bf R|}\leq 2$. Furthermore, through our analysis, we have conjectured the closed structure of extremal refined BPS integers for the torus knots $ \left[{\bf 3_1}\right]_{2p+1}$ and $ \left[{\bf 8_{20}}\right]_{2p+1}$, $p \in \mathbb{Z}_{\geq 0}$. As the parameter $p$ of the knot diagram increases, the total crossing number of a knot exceeds $16$, which we describe as a complex knot. Interestingly, we discovered a maximum number of gaps in the BPS spectra associated with complex knot families. Moreover, our observations indicated that as $p$ increases, the size of these gaps also expands.

hep-th↗

arXiv2024

Unitary tetrahedron quantum gates

Quantum simulations of many-body systems using 2-qubit Yang-Baxter gates offer a benchmark for quantum hardware. This can be extended to the higher dimensional case with $n$-qubit generalisations of Yang-Baxter gates called $n$-simplex operators. Such multi-qubit gates potentially lead to shallower and more efficient quantum circuits as well. Finding them amounts to identifying unitary solutions of the $n$-simplex equations, the building blocks of higher dimensional integrable systems. These are a set of highly non-linear and over determined system of equations making it notoriously hard to solve even when the local Hilbert spaces are spanned by qubits. We systematically overcome this for higher simplex operators constructed using two methods: from Clifford algebras and by lifting Yang-Baxter operators. The $n=3$ or the tetrahedron case is analyzed in detail. For the qubit case our methods produce 13 inequivalent families of unitary tetrahedron operators. 12 of these families are obtained by appending the 5 unitary families of 4 by 4 constant Yang-Baxter operators of Dye-Hietarinta, with a single qubit operator. As applications, universal sets of single, two and three qubit gates are realized using such unitary tetrahedron operators. The ideas presented in this work can be naturally extended to the higher simplex cases.

quant-ph↗

arXiv2024

A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images

The advancements in computer vision and image processing techniques have led to emergence of new application in the domain of visual surveillance, targeted advertisement, content-based searching, and human-computer interaction etc. Out of the various techniques in computer vision, face analysis, in particular, has gained much attention. Several previous studies have tried to explore different applications of facial feature processing for a variety of tasks, including age and gender classification. However, despite several previous studies having explored the problem, the age and gender classification of in-wild human faces is still far from the achieving the desired levels of accuracy required for real-world applications. This paper, therefore, attempts to bridge this gap by proposing a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. The proposed models performance is compared with several state-of-the-art model proposed so far. An improvement of approximately 10percent and 6percent over the state-of-the-art implementations for age and gender classification, respectively, are noted for the proposed model. The proposed model is thus found to achieve superior performance and is found to provide a more generalized learning. The model can, therefore, be applied as a core classification component in various image processing and computer vision problems.

cs.CV↗

arXiv2024

Knot-Quiver correspondence for double twist knots

We obtain a quiver representation for a family of knots called double twist knots $K(p,-m)$. Particularly, we exploit the reverse engineering of Melvin-Morton-Rozansky(MMR) formalism to deduce the pattern of the charge matrix for these quivers.

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arXiv2024

Exploring topological entanglement through Dehn surgery

We compute the $\text{PSL}(2,\mathbb{C})$ Chern-Simons partition function of a closed 3-manifold obtained from Dehn fillings of the link complement $\mathbf S^3\backslash {\mathcal{L}}$, where $\mathcal{L}=\mathcal{K}# H$ is the connected sum of the knot $\mathcal {K}$ with the Hopf link $H$. Motivated by our earlier work on topological entanglement and the reduced density matrix $σ$ for such link complements, we wanted to determine a choice of Dehn filling so that the trace of the matrix $σ$ becomes equal to the $\text{PSL}(2,\mathbb{C})$ partition function of the closed 3-manifold. We use the SnapPy program and numerical techniques to show this equivalence up to the leading order. We have given explicit results for all hyperbolic knots $\mathcal{K}$ up to six crossings.

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arXiv2023

Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from Hematoxylin and Eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation.

eess.IV↗

arXiv2023

Dyck Paths and Topological Quantum Computation

The fusion basis of Fibonacci anyons supports unitary braid representations that can be utilized for universal quantum computation. We show a mapping between the fusion basis of three Fibonacci anyons, $\{|1\rangle, |τ\rangle\}$, and the two length 4 Dyck paths via an isomorphism between the two dimensional braid group representations on the fusion basis and the braid group representation built on the standard $(2,2)$ Young diagrams using the Jones construction. This correspondence helps us construct the fusion basis of the Fibonacci anyons using Dyck paths as the number of standard $(N,N)$ Young tableaux is the Catalan number, $C_N$ . We then use the local Fredkin moves to construct a spin chain that contains precisely those Dyck paths that correspond to the Fibonacci fusion basis, as a degenerate set. We show that the system is gapped and examine its stability to random noise thereby establishing its usefulness as a platform for topological quantum computation. Finally, we show braidwords in this rotated space that efficiently enable the execution of any desired single-qubit operation, achieving the desired level of precision($\sim 10^{-3}$).

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arXiv2023

Detection of Homophobia & Transphobia in Dravidian Languages: Exploring Deep Learning Methods

The increase in abusive content on online social media platforms is impacting the social life of online users. Use of offensive and hate speech has been making so-cial media toxic. Homophobia and transphobia constitute offensive comments against LGBT+ community. It becomes imperative to detect and handle these comments, to timely flag or issue a warning to users indulging in such behaviour. However, automated detection of such content is a challenging task, more so in Dravidian languages which are identified as low resource languages. Motivated by this, the paper attempts to explore applicability of different deep learning mod-els for classification of the social media comments in Malayalam and Tamil lan-guages as homophobic, transphobic and non-anti-LGBT+content. The popularly used deep learning models- Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) using GloVe embedding and transformer-based learning models (Multilingual BERT and IndicBERT) are applied to the classification problem. Results obtained show that IndicBERT outperforms the other imple-mented models, with obtained weighted average F1-score of 0.86 and 0.77 for Malayalam and Tamil, respectively. Therefore, the present work confirms higher performance of IndicBERT on the given task in selected Dravidian languages.

cs.CL↗

arXiv2023

Exploring Deep Learning Methods for Classification of SAR Images: Towards NextGen Convolutions via Transformers

Images generated by high-resolution SAR have vast areas of application as they can work better in adverse light and weather conditions. One such area of application is in the military systems. This study is an attempt to explore the suitability of current state-of-the-art models introduced in the domain of computer vision for SAR target classification (MSTAR). Since the application of any solution produced for military systems would be strategic and real-time, accuracy is often not the only criterion to measure its performance. Other important parameters like prediction time and input resiliency are equally important. The paper deals with these issues in the context of SAR images. Experimental results show that deep learning models can be suitably applied in the domain of SAR image classification with the desired performance levels.

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arXiv2022

Measuring and Characterizing International Collaboration Patterns in Indian Scientific Research

Scientific collaboration at international level has increased manifolds during last two decades. Collaboration is not only associated with higher research productivity but has also been found to be positively correlated with impact. Considering the benefits and advantages of international collaboration for the national science and technology systems of a country, policymakers in different countries have designed programs to promote international collaboration in science. This paper attempts to measure and characterize the international collaboration patterns in Indian scientific research. Research publication data for the last 20 years (2001-2020), obtained from Web of Science, is analysed for the purpose. Analytical results show that India's international collaboration has grown at a rate of 12.27% during this period, rising from only 20.73% internationally collaborated papers in 2001 to 32.35% internationally collaborated papers in 2020. USA, Germany, England, South Korea and China are found to be the top collaborating partners for India during his period, however, some subject area-wise variations are also seen. Among the internationally collaborated papers, about 50% papers have an Indian researcher as lead (first) author, and more than 50% authors of such internationally collaborated papers are from India. The internationally collaborated papers from India are also found to have a slight advantage in terms of citation impact and social media visibility. The probable factors shaping the Indian international collaboration and the major policy implications are discussed.

cs.DL↗

arXiv2022

Institutional Collaboration Recommendation: An expertise-based framework using NLP and Network Analysis

The shift from 'trust-based funding' to 'performance-based funding' is one of the factors that has forced institutions to strive for continuous improvement of performance. Several studies have established the importance of collaboration in enhancing the performance of paired institutions. However, identification of suitable institutions for collaboration is sometimes difficult and therefore institutional collaboration recommendation systems can be vital. Currently, there are no well-developed institutional collaboration recommendation systems. In order to bridge this gap, we design a framework that recognizes thematic strengths and core competencies of institutions, which can in turn be used for collaboration recommendations. The framework, based on NLP and network analysis techniques, is capable of determining the strengths of an institution in different thematic areas within a field and thereby determining the core competency and potential core competency areas of that institution. A major advantage of the system is that it can help to determine and improve the research portfolio of an institution within a field through suitable collaboration, which may lead to the overall improvement of the performance of the institution in that field. The framework is demonstrated by analyzing the performance of 195 Indian institutions in the field of 'Computer Science'. Upon validation using standard metrics for novelty, coverage and diversity of recommendation systems, the framework is found to be of sufficient coverage and capable of tossing novel and diverse recommendations. The article thus presents an institutional collaboration recommendation system which can be used by institutions to identify potential collaborators.

cs.DL↗

arXiv2022

Research Contribution of major Centrally Funded Institution Systems of India

India is now among the major knowledge producers of the world, ranking among the top 5 countries in total research output, as per some recent reports. The institutional setup for Research & Development (R&D) in India comprises a diverse set of Institutions, including Universities, government departments, research laboratories, and private sector institutions etc. It may be noted that more than 45% share of India's Gross Expenditure on Research and Development (GERD) comes from the central government. In this context, this article attempts to explore the quantum of research contribution of centrally funded institutions and institution systems of India. The volume, proportionate share and growth patterns of research publications from the major centrally funded institutions, organised in 16 groups, is analysed. These institutions taken together account for 67.54% of Indian research output during 2001 to 2020. The research output of the centrally funded institutions in India has increased steadily since 2001 with a good value for CAGR. The paper presents noteworthy insights about scientific research production of India that may be useful to policymakers, researchers and science practitioners in India. It presents a case for increased activity by the state governments and private sector to further the cause of sustainable and inclusive research and development in the country.

cs.DL↗

arXiv2022

Bibliometric Patterns and Concept Evolution Trajectories in research publications in Future Generation Computer Systems

Future Generation Computer Systems (FGCS), published by Elsevier, is a prestigious peer reviewed journal that started in 1984. As on date of writing this article, the journal is in its 137th volume. Owing to its publication quality and continued academic standards, it has been indexed by major academic databases such as Web of Science, Scopus, DOAJ etc. It is now ranked among the top journals in the area of Computer Science (General). Motivated by the long history, academic reputation and prestige of the journal, the present study attempts to do a bibliometric and text-based analysis of publications in the journal during the period of 1984-2020 (37 years). Bibliometric analysis helped to identify publishing and citation patterns, authorship and collaboration structure, funding patterns of the published research, open access and altmetric impact, gender distribution and SDG connections etc. The text-integrated path analysis helped in identifying the thematic structure and major concept evolution trajectories. The results of the analysis indicate that the FGCS journal is a high-quality research journal that has grown over time in terms of publications, citations and rankings. It publishes articles in specified thematic areas, with cloud, grid, IoT, Blockchain etc. found as the major themes addressed. Knowledge extension from cloud computing to multi-domain networks is witnessed and a divergence towards service function chains, distributed machine learning and edge computing systems is visible. The article thus presents very informative and useful insight about research publication trends in FGCS.

cs.DL↗

arXiv2022

Exploring the relationship between journals indexed from a country and its research output: An empirical investigation

Scientific journals are currently the primary medium used by researchers to report their research findings. The transformation of print journals into e-journals has simplified the process of submissions to journals and also their access has become wider. Journals are usually published by commercial publishers, learned societies as well as Universities. There are different number of journals published from different countries. This paper attempts to explore whether the number of journals published from a country influences its research output. Scopus master journal list is analysed to identify journals published from 50 selected countries with significant volume of research output. The following relationship are analysed: (a) number of journals from a country and its research output, (b) growth rate of journals and research output for different countries, (c) global share of journals and research output for different countries, and (d) subject area-wise number of journals and research output in that subject area for different countries. Factors like journal packing density are also analysed. The results obtained show that for majority of the countries, the number of journals is positively correlated to their research output volume, though some other factors also play a role in growth of research output. The study at the end presents a discussion of the analytical outcomes and provides useful suggestions on policy perspectives for different countries.

cs.DL↗

arXiv2022

ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?

Biomarkers identify a patients response to treatment. With the recent advances in artificial intelligence based on the Transformer networks, there is only limited research has been done to measure the performance on challenging histopathology images. In this paper, we investigate the efficacy of the numerous state-of-the-art Transformer networks for immune-checkpoint biomarker, Inducible Tcell COStimulator (ICOS) protein cell segmentation in colon cancer from immunohistochemistry (IHC) slides. Extensive and comprehensive experimental results confirm that MiSSFormer achieved the highest Dice score of 74.85% than the rest evaluated Transformer and Efficient U-Net methods.

cs.CV↗

arXiv2022

Colored HOMFLY-PT polynomials of quasi-alternating $3$-braid knots

Obtaining a closed-form expression for the colored HOMFLY-PT polynomials of knots from $3$-strand braids carrying arbitrary $SU(N)$ representation is a challenging problem. In this paper, we confine our interest to twisted generalized hybrid weaving knots which we denote hereafter by $\hat{Q}_3(m_1,-m_2,n,\ell)$. This family of knots not only generalizes the well-known class of weaving knots but also contains an infinite family of quasi-alternating knots. Interestingly, we obtain a closed-form expression for the HOMFLY-PT polynomial of $\hat{Q}_3(m_1,-m_2,n,\ell)$ using a modified version of the Reshitikhin-Turaev method. In addition, we compute the exact coefficients of the Jones polynomials and the Alexander polynomials of quasi-alternating knots $\hat{Q}_3(1,-1,n,\pm 1)$. For these homologically-thin knots, such coefficients are known to be the ranks of their Khovanov and link Floer homologies, respectively. We also show that the asymptotic behaviour of the coefficients of the Alexander polynomial is trapezoidal. On the other hand, we compute the $[r]$-colored HOMFLY-PT polynomials of quasi alternating knots for small values of $r$. Remarkably, the study of the determinants of certain twisted weaving knots leads to establish a connection with enumerative geometry related to $m^{th}$ Lucas numbers, denoted hereafter as $L_{m,2n}$. At the end, we verify that the reformulated invariants satisfy Ooguri-Vafa conjecture and we express certain BPS integers in terms of hyper-geometric functions ${}_2 {\bf F}_1\left[a,b, c;z\right]$.

hep-th↗

arXiv2022

ResearchGate and Google Scholar: How much do they differ in publications, citations and different metrics and why?

ResearchGate has emerged as a popular professional network for scientists and researchers in a very short span of time. Similar to Google Scholar, the ResearchGate indexing uses an automatic crawling algorithm that extracts bibliographic data, citations and other information about scholarly articles from various sources. However, it has been observed that the two platforms often show different publication and citation data for the same institutions, journals and authors. This paper, therefore, attempts to analyse and measure the differences in publication counts, citations and different metrics of the two platforms for a large data set of highly cited authors. The results indicate that there are significantly high differences in publication counts and citations for the same authors in the two platforms, with Google Scholar having higher counts for a vast majority of the cases. The different metrics computed by the two platforms also differ in their values, showing different degrees of correlations. The coverage policy, indexing errors, author attribution mechanism and strategy to deal with predatory publishing are found to be the main probable reasons for the differences in the two platforms.

cs.DL↗

arXiv2021

Topological entanglement and hyperbolic volume

The entanglement entropy of many quantum systems is difficult to compute in general. They are obtained as a limiting case of the Rényi entropy of index $m$, which captures the higher moments of the reduced density matrix. In this work, we study pure bipartite states associated with $S^3$ complements of a two-component link which is a connected sum of a knot $\mathcal{K}$ and the Hopf link. For this class of links, the Chern-Simons theory provides the necessary setting to visualise the $m$-moment of the reduced density matrix as a three-manifold invariant $Z(M_{\mathcal{K}_m})$, which is the partition function of $M_{\mathcal{K}_m}$. Here $M_{\mathcal{K}_m}$ is a closed 3-manifold associated with the knot $\mathcal K_m$, where $\mathcal K_m$ is a connected sum of $m$-copies of $\mathcal{K}$ (i.e., $\mathcal{K}\#\mathcal{K}\ldots\#\mathcal{K}$) which mimics the well-known replica method. We analyse the partition functions $Z(M_{\mathcal{K}_m})$ for SU(2) and SO(3) gauge groups, in the limit of the large Chern-Simons coupling $k$. For SU(2) group, we show that $Z(M_{\mathcal{K}_m})$ can grow at most polynomially in $k$. On the contrary, we conjecture that $Z(M_{\mathcal{K}_m})$ for SO(3) group shows an exponential growth in $k$, where the leading term of $\ln Z(M_{\mathcal{K}_m})$ is the hyperbolic volume of the knot complement $S^3\backslash \mathcal{K}_m$. We further propose that the Rényi entropies associated with SO(3) group converge to a finite value in the large $k$ limit. We present some examples to validate our conjecture and proposal.

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