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Anirban Sen

Publications and source records attributed to Anirban Sen.

At least 19 recordsLinked to original sources
arXiv2026

Quantitative Analysis of Media Bias and Stock Price Dynamics: The 2020 Shock

Whether financial news influences stock prices or simply reflects information already incorporated into them remains an open question in financial economics. The COVID-19 pandemic provides an opportunity to revisit this question, as it disrupted both news coverage and financial markets on an unprecedented scale. Existing studies have largely approached the problem through aggregate sentiment measures, leaving it unclear whether the observed relationships also hold at the level of individual firms. We study this question using 6.28 million news headlines covering 26 large United States firms between 2015 and 2025. After filtering the corpus to retain materially relevant firm-specific coverage, we construct daily stance measures and examine how their relationship with stock returns changed around the 2020 shock using panel regressions and vector autoregressions with data-driven structural breaks. Our findings indicate that the relationship between financial news and equity markets is more nuanced than aggregate analyses alone suggest. While we find little evidence of a persistent market-wide change in media stance or stock returns following the pandemic, dynamic relationships emerge for a subset of firms around their own structural breaks. Taken together, these results suggest that understanding media-market interactions requires firm specific analysis and provide a framework for studying how news and prices interact under changing market conditions.

cs.CE↗
arXiv2026

Weighted composition operators on weighted Fock spaces

We provide a complete characterization of the bounded, compact, and Hilbert-Schmidt class weighted composition operators acting on weighted Fock spaces, extending the classical Fock space characterization due to Le [11]. An estimate for the essential norm of these operators is also provided. Our approach relies on the asymptotic behavior of the Mittag-Leffler function. As applications, we establish explicit criteria for weighted composition operators with exponential weights and recover corresponding results for composition operators.

math.FA↗
arXiv2026

Composition-differentiation operators on weighted Dirichlet spaces

We characterize bounded, compact, and Hilbert-Schmidt composition-differentiation operators on weighted Dirichlet spaces. The essential norm is estimated via the asymptotic behavior of a function that involves the generalized Nevanlinna counting function of the inducing map. Norm estimates for particular inducing maps are given, and examples are provided to demonstrate the applicability of the results.

math.FA↗
arXiv2026

MediaGraph: A Network Theoretic Framework to Analyze Reporting Preferences in Indian News Media

We present MediaGraph, a network-theoretic framework for analyzing reporting preferences in news media through entity co-occurrence networks. Using articles from four Indian news-sources, two mainstream (The Times of India and The Indian Express) and two fringe outlets (dna and firstpost), we construct source-specific co-occurrence networks around the 2020-21 and 2024 Farmers Protests. We analyze these networks along three network theoretic axes of centrality, community structure, and co-occurrence link predictability. The link predictability metric is a novel metric proposed that quantifies the consistency of entity associations over time using a GraphSAGE-based model. Our results reveal significant differences in reporting preferences across sources for the same event, and a consistent under-representation of farmer leaders across sources. By shifting the focus from textual signals to relational structures, our approach offers a scalable, label-independent perspective on media analysis and introduces link predictability as a complementary measure of reporting behavior.

cs.SI↗
arXiv2026

Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text

Sarcasm detection in multilingual and code-mixed environments remains a challenging task for natural language processing models due to structural variations, informal expressions, and low-resource linguistic availability. This study compares four large language models, Llama 3.1, Mistral, Gemma 3, and Phi-4, with a fine-tuned DistilBERT model for sarcasm detection in code-mixed Hinglish text. The results indicate that the smaller, sequentially fine-tuned DistilBERT model achieved the highest overall accuracy of 84%, outperforming all of the LLMs in zero and few-shot set ups, using minimal LLM generated code-mixed data used for fine-tuning. These findings indicate that domain-adaptive fine-tuning of smaller transformer based models may significantly improve sarcasm detection over general LLM inference, in low-resource and data scarce settings.

cs.CL↗
arXiv2026

Astra: AI Safety, Trust, & Risk Assessment

This paper argues that existing global AI safety frameworks exhibit contextual blindness towards India's unique socio-technical landscape. With a population of 1.5 billion and a massive informal economy, India's AI integration faces specific challenges such as caste-based discrimination, linguistic exclusion of vernacular speakers, and infrastructure failures in low-connectivity rural zones, that are frequently overlooked by Western, market-centric narratives. We introduce ASTRA, an empirically grounded AI Safety Risk Database designed to categorize risks through a bottom-up, inductive process. Unlike general taxonomies, ASTRA defines AI Safety Risks specifically as hazards stemming from design flaws such as skewed training sets or lack of guardrails that can be mitigated through technical iteration or architectural changes. This framework employs a tripartite causal taxonomy to evaluate risks based on their implementation timing (development, deployment, or usage), the responsible entity (the system or the user), and the nature of the intent (unintentional vs. intentional). Central to the research is a domain-agnostic ontology that organizes 37 leaf-level risk classes into two primary meta-categories: Social Risks and Frontier/Socio-Structural Risks. By focusing initial efforts on the Education and Financial Lending sectors, the paper establishes a scalable foundation for a "living" regulatory utility intended to evolve alongside India's expanding AI ecosystem.

cs.CY↗
arXiv2026

Numerical range and Berezin range of weighted composition operators on weighted Dirichlet spaces

We investigate the numerical ranges of weighted composition operators on weighted Dirichlet spaces, focusing on the properties of the inducing functions. We identify conditions on these functions under which the origin lies in the interior of the numerical range. The geometric structure of the numerical range is also analyzed, determining when it contains a circular or elliptical disc and computing the corresponding radius. Next, we introduce a class of Weyl-type weighted composition operators and obtain their Berezin range and Berezin number. Finally, we characterize the convexity of the Berezin range for weighted composition operators on these spaces.

math.FA↗
arXiv2026

Weighted composition operators on weighted Dirichlet spaces: boundedness, compactness and spectral properties

We establish necessary and sufficient conditions for the boundedness and compactness of weighted composition operators acting on weighted Dirichlet spaces and determine the spectrum of a certain class of such operators. Our results extend earlier work on unweighted composition operators and highlight the close interplay between the operator theoretic behavior of weighted composition operators and the function theoretic properties of their inducing functions. Several examples are provided to illustrate the applicability of the obtained results.

math.FA↗
arXiv2026

An introduction of Berezin sectorial operators and its application to Berezin number inequalities

We introduce a new class of operators, called Berezin sectorial operators, which generalizes classical sectorial operators. We provide examples on the Hardy-Hilbert space showing that there exist operators that are Berezin sectorial but not sectorial and that the Berezin sectorial index can be strictly smaller than the classical one. We derive Berezin number inequalities for this class, including a weak version of the power inequality, and study geometric properties of the Berezin range for finite-rank and weighted shift operators on the Dirichlet space. We also raise the question of whether similar constructions are possible for composition-differentiation operators on the Dirichlet space.

math.FA↗
arXiv2025

PolicyStory: Leveraging Large Language Models to Generate Comprehensible Summaries of Policy-News in India

In the era of information overload, traditional news consumption through both online and print media often fails to provide a structured and longitudinal understanding of complex sociopolitical issues. To address this gap, we present PolicyStory, an information tool designed to offer lucid, chronological, and summarized insights into Indian policy issues. PolicyStory collects news articles from diverse sources, clusters them by topic, and generates three levels of summaries from longitudinal media discourse on policies, leveraging open source large language models. A user study around the tool indicated that PolicyStory effectively aided users in grasping policy developments over time, with positive feedback highlighting its usability and clarity of summaries. By providing users a birds' eye view of complex policy topics, PolicyStory serves as a valuable resource.

cs.CY↗
arXiv2025

Extending FKG.in: Towards a Food Claim Traceability Network

The global food landscape is rife with scientific, cultural, and commercial claims about what foods are, what they do, what they should not do, or should not do. These range from rigorously studied health benefits (probiotics improve gut health) and misrepresentations (soaked almonds make one smarter) to vague promises (superfoods boost immunity) and culturally rooted beliefs (cold foods cause coughs). Despite their widespread influence, the infrastructure for tracing, verifying, and contextualizing these claims remains fragmented and underdeveloped. In this paper, we propose a Food Claim-Traceability Network (FCN) as an extension of FKG[.]in, a knowledge graph of Indian food that we have been incrementally building. We also present the ontology design and the semi-automated knowledge curation workflow that we used to develop a proof of concept of FKG[.]in-FCN using Reddit data and Large Language Models. FCN integrates curated data inputs, structured schemas, and provenance-aware pipelines for food-related claim extraction and validation. While directly linked to the Indian food knowledge graph as an application, our methodology remains application-agnostic and adaptable to other geographic, culinary, or regulatory settings. By modeling food claims and their traceability in a structured, verifiable, and explainable way, we aim to contribute to more transparent and accountable food knowledge ecosystems, supporting researchers, policymakers, and most importantly, everyday consumers in navigating a world saturated with dietary assertions.

cs.AI↗
arXiv2025

On the Berezin range of Toeplitz and weighted composition operators on weighted Bergman spaces

In this article, we completely characterize the Berezin range of Toeplitz operators with harmonic symbols acting on weighted Bergman spaces, illustrating the necessity of the harmonicity condition through examples. We then introduce a new class of weighted composition operators on these spaces, investigating their fundamental properties and determining their Berezin range and Berezin number. Finally, we study the convexity of the Berezin range of composition operators on weighted Bergman spaces and show that the origin lies in its closure of Berezin range but not in the range itself.

math.FA↗
arXiv2025

Numerical range of Toeplitz and Composition operators on weighted Bergman spaces

In this paper we completely describe the numerical range of Toeplitz operators on weighted Bergman spaces with harmonic symbol. We also characterize the numerical range of weighted composition operators on weighted Bergman spaces and classify some sets which are the numerical range of composition operators. We investigate the inclusion of zero in the numerical range, and compute the radius of circle and ellipse contained in the numerical range of weighted composition operators on weighted Bergman spaces.

math.FA↗
arXiv2024

A note on the $A$-numerical range of semi-Hilbertian operators

In this paper we explore the relation between the $A$-numerical range and the $A$-spectrum of $A$-bounded operators in the setting of semi-Hilbertian structure. We introduce a new definition of $A$-normal operator and prove that closure of the $A$-numerical range of an $A$-normal operator is the convex hull of the $A$-spectrum. We further prove Anderson's theorem for the sum of $A$-normal and $A$-compact operators which improves and generalizes the existing result on Anderson's theorem for $A$-compact operators. Finally we introduce strongly $A$-numerically closed class of operators and along with other results prove that the class of $A$-normal operators is strongly $A$-numerically closed.

math.FA↗
arXiv2023

Berezin number and Berezin norm inequalities for operator matrices

We establish new upper bounds for Berezin number and Berezin norm of operator matrices, which are refinements of the existing bounds. Among other bounds, we prove that if $A=[A_{ij}]$ is an $n\times n$ operator matrix with $A_{ij}\in\mathbb{B}(\mathcal{H})$ for $i,j=1,2\dots n$, then $\|A\|_{ber} \leq \left\|\left[\|A_{ij}\|_{ber}\right]\right\|$ and $\textbf{ber}(A) \leq w([a_{ij}]),$ where $a_{ii}=\textbf{ber}(A_{ii}),$ $a_{ij}=\big\||A_{ij}|+|A^*_{ji}|\big\|^{\frac{1}{2}}_{ber} \big\||A_{ji}|+|A^*_{ij}|\big\|^{\frac{1}{2}}_{ber}$ if $i j$. Further, we give some examples for the Berezin number and Berezin norm estimation of operator matrices on the Hardy-Hilbert space.

math.FA↗
arXiv2022

Numerical radius inequalities of sectorial matrices

We obtain several upper and lower bounds for the numerical radius of sectorial matrices. We also develop several numerical radius inequalities of the sum, product and commutator of sectorial matrices. The inequalities obtained here are sharper than the existing related inequalities for general matrices. Among many other results we prove that if $A$ is an $n\times n$ complex matrix with the numerical range $W(A)$ satisfying $W(A)\subseteq\{re^{\pm i\theta}~:~\theta_1\leq\theta\leq\theta_2\},$ where $r>0$ and $\theta_1,\theta_2\in \left[0,\pi/2\right],$ then \begin{eqnarray*} &&(i)\,\, w(A) \geq \frac{csc\gamma}{2}\|A\| + \frac{csc\gamma}{2}\left| \|\Im(A)\|-\|\Re(A)\|\right|,\,\,\text{and} &&(ii)\,\, w^2(A) \geq \frac{csc^2\gamma}{4}\|AA^*+A^*A\| + \frac{csc^2\gamma}{2}\left| \|\Im(A)\|^2-\|\Re(A)\|^2\right|, \end{eqnarray*} where $\gamma=\max\{\theta_2,\pi/2-\theta_1\}$. We also prove that if $A,B$ are sectorial matrices with sectorial index $\gamma \in [0,\pi/2)$ and they are double commuting, then $w(AB)\leq \left(1+\sin^2\gamma\right)w(A)w(B).$

math.FA↗
arXiv2022

Numerical radius inequalities for tensor product of operators

The two well-known numerical radius inequalities for the tensor product $A \otimes B$ acting on $\mathbb{H} \otimes \mathbb{K}$, where $A$ and $B$ are bounded linear operators defined on complex Hilbert spaces $\mathbb{H} $ and $ \mathbb{K},$ respectively are, $ \frac{1}{2} \|A\|\|B\| \leq w(A \otimes B) \leq \|A\|\|B\| $ and $w(A)w(B) \leq w(A \otimes B) \leq \min \{ w(A) \|B\|, w(B) \|A\| \}. $ In this article we develop new lower and upper bounds for the numerical radius $w(A \otimes B)$ of the tensor product $A \otimes B $ and study the equality conditions for those bounds.

math.FA↗