arXiv subjects

Abhijeet Kumar

Publications and source records attributed to Abhijeet Kumar.

At least 19 recordsLinked to original sources

arXiv2026

Perturbation responses on topological synchrony in simplicial Kuramoto model

Synchronization is conventionally understood as the emergence of a phase-locked collective state among interacting dynamical units. However, extending this notion to systems whose dynamical variables are defined on higher-dimensional simplices introduces fundamentally new constraints arising from the topology of the underlying simplicial complex. In the simplicial Kuramoto model, nontrivial topological cycles give rise to a higher dimensional harmonic subspace that is unaffected by the coupling and can therefore drift indefinitely, preventing a globally synchronized state. To resolve this we employ Hodge decomposition on simplicial Kuramoto dynamics and investigated the components. Despite this topological obstruction, we show that the simplicial Kuramoto model admits fixed-point states in the exact and coexact sectors of the Hodge decomposition above critical coupling strengths, which we derive analytically for both sectors. This decomposition provides a generalized notion of synchronization in which the non-harmonic components converge to fixed states. We further investigate the robustness of these fixed-point states to external perturbations. Excluding the drifting and non-interacting harmonic component, the perturbation response is governed by the spectrum of weighted Laplacian and recovers a Kirchhoff index dependence analogous to standard Kuramoto case. In contrast, the harmonic sector is non-dissipative, and consequently the fragility of the system increases with the dimension of this topological subspace. We characterize this effect numerically using triangulated tori with varying first Betti number and find a superlinear scaling relation between system fragility and the dimension of the harmonic sector.

nlin.AO↗

arXiv2026

Quantum Thermal Field Effect Transistor

We propose and analyse a quantum thermal field-effect transistor (qtFET) composed of left-qubit, middle-qutrit, and right-qubit subsystems. In this architecture, the left qubit is coupled to the middle qutrit, which in turn interacts with the right qubit. Each subsystem interacts independently with its respective baths. The middle subsystem serves as a modulator. We have shown that the qtFET exhibits functionality analogous to that of a conventional electronic field-effect transistor (eFET). The left, right, and middle subsystems of the qtFET correspond to the drain, source, and gate of an eFET in a common gate configuration, respectively. Our results show that the qtFET can precisely modulate thermal currents, highlighting its potential as a fundamental building block for quantum thermal devices and amplifiers in emerging quantum technologies.

quant-ph↗

arXiv2026

Effect of LH and ECR waves on plasma parameters in ADITYA Upgrade tokamak

The plasma discharges in ADITYA Upgrade Tokamak are produced by means of transformer action, in which Ohmically created plasma is driven by means of a secondary loop voltage. Due to reduction of plasma resistivity after a certain level of plasma temperature, Ohmic heating becomes poor and further achievement of temperature needs other heating techniques. ADITYA-U tokamak is facilitated with a 42 GHz-500 kW Electron Cyclotron Resonant Heating (ECRH) system. Also, there is a Lower Hybrid Current Drive (LHCD) system installed and operated at 3.7 GHz for driving non-inductive plasma current followed by the Ohmic current drive. Though an eventual impact in the rise of plasma temperature and plasma current due to the application of ECRH and LHCD respectively are very obvious, their energy coupling with the plasma results in several interesting outcomes in a number of experimentally measured plasma parameters. The present work addresses such impactful observations that are noticed and reported for the first time in ADITYA-U Tokamak.

physics.plasm-ph↗

arXiv2025

Psychological stress during Examination and its estimation by handwriting in answer script

This research explores the fusion of graphology and artificial intelligence to quantify psychological stress levels in students by analyzing their handwritten examination scripts. By leveraging Optical Character Recognition and transformer based sentiment analysis models, we present a data driven approach that transcends traditional grading systems, offering deeper insights into cognitive and emotional states during examinations. The system integrates high resolution image processing, TrOCR, and sentiment entropy fusion using RoBERTa based models to generate a numerical Stress Index. Our method achieves robustness through a five model voting mechanism and unsupervised anomaly detection, making it an innovative framework in academic forensics.

cs.CV↗

arXiv2025

Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker

Traditional information extraction systems face challenges with text only language models as it does not consider infographics (visual elements of information) such as tables, charts, images etc. often used to convey complex information to readers. Multimodal LLM (MLLM) face challenges of finding needle in the haystack problem i.e., either longer context length or substantial number of documents as search space. Late interaction mechanism over visual language models has shown state of the art performance in retrieval-based vision augmented Q&A tasks. There are yet few challenges using it for RAG based multi-modal Q&A. Firstly, many popular and widely adopted vector databases do not support native multi-vector retrieval. Secondly, late interaction requires computation which inflates space footprint and can hinder enterprise adoption. Lastly, the current state of late interaction mechanism does not leverage the approximate neighbor search indexing methods for large speed ups in retrieval process. This paper explores a pragmatic approach to make vision retrieval process scalable and efficient without compromising on performance quality. We propose multi-step custom implementation utilizing widely adopted hybrid search (metadata & embedding) and state of the art late interaction re-ranker to retrieve best matching pages. Finally, MLLM are prompted as reader to generate answers from contextualized best matching pages. Through experiments, we observe that the proposed design is scalable (significant speed up) and stable (without degrading performance quality), hence can be used as production systems at enterprises.

cs.IR↗

arXiv2025

Building Entity Association Mining Framework for Knowledge Discovery

Extracting useful signals or pattern to support important business decisions for example analyzing investment product traction and discovering customer preference, risk monitoring etc. from unstructured text is a challenging task. Capturing interaction of entities or concepts and association mining is a crucial component in text mining, enabling information extraction and reasoning over and knowledge discovery from text. Furthermore, it can be used to enrich or filter knowledge graphs to guide exploration processes, descriptive analytics and uncover hidden stories in the text. In this paper, we introduce a domain independent pipeline i.e., generalized framework to enable document filtering, entity extraction using various sources (or techniques) as plug-ins and association mining to build any text mining business use-case and quantitatively define a scoring metric for ranking purpose. The proposed framework has three major components a) Document filtering: filtering documents/text of interest from massive amount of texts b) Configurable entity extraction pipeline: include entity extraction techniques i.e., i) DBpedia Spotlight, ii) Spacy NER, iii) Custom Entity Matcher, iv) Phrase extraction (or dictionary) based c) Association Relationship Mining: To generates co-occurrence graph to analyse potential relationships among entities, concepts. Further, co-occurrence count based frequency statistics provide a holistic window to observe association trends or buzz rate in specific business context. The paper demonstrates the usage of framework as fundamental building box in two financial use-cases namely brand product discovery and vendor risk monitoring. We aim that such framework will remove duplicated effort, minimize the development effort, and encourage reusability and rapid prototyping in association mining business applications for institutions.

cs.CL↗

arXiv2025

NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results

This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images. This challenge received a wide range of impressive solutions, which are developed and evaluated using our collected real-world Raindrop Clarity dataset. Unlike existing deraining datasets, our Raindrop Clarity dataset is more diverse and challenging in degradation types and contents, which includes day raindrop-focused, day background-focused, night raindrop-focused, and night background-focused degradations. This dataset is divided into three subsets for competition: 14,139 images for training, 240 images for validation, and 731 images for testing. The primary objective of this challenge is to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions. There are a total of 361 participants in the competition, and 32 teams submitting valid solutions and fact sheets for the final testing phase. These submissions achieved state-of-the-art (SOTA) performance on the Raindrop Clarity dataset. The project can be found at https://lixinustc.github.io/CVPR-NTIRE2025-RainDrop-Competition.github.io/.

cs.CV↗

arXiv2025

Leveraging Retrieval Augmented Generative LLMs For Automated Metadata Description Generation to Enhance Data Catalogs

Data catalogs serve as repositories for organizing and accessing diverse collection of data assets, but their effectiveness hinges on the ease with which business users can look-up relevant content. Unfortunately, many data catalogs within organizations suffer from limited searchability due to inadequate metadata like asset descriptions. Hence, there is a need of content generation solution to enrich and curate metadata in a scalable way. This paper explores the challenges associated with metadata creation and proposes a unique prompt enrichment idea of leveraging existing metadata content using retrieval based few-shot technique tied with generative large language models (LLM). The literature also considers finetuning an LLM on existing content and studies the behavior of few-shot pretrained LLM (Llama, GPT3.5) vis-\`a-vis few-shot finetuned LLM (Llama2-7b) by evaluating their performance based on accuracy, factual grounding, and toxicity. Our preliminary results exhibit more than 80% Rouge-1 F1 for the generated content. This implied 87%- 88% of instances accepted as is or curated with minor edits by data stewards. By automatically generating descriptions for tables and columns in most accurate way, the research attempts to provide an overall framework for enterprises to effectively scale metadata curation and enrich its data catalog thereby vastly improving the data catalog searchability and overall usability.

cs.IR↗

arXiv2025

A multi-purpose reciprocating probe drive system for studying the effect of gas-puffs on edge plasma dynamics in the ADITYA-U tokamak

This article reports the development of a versatile high-speed reciprocating drive system (HRDS) with interchangeable probe heads to characterize the edge plasma region of ADITYA-U tokamak. This reciprocating probe drive system consisting of Langmuir and magnetic probe heads, is designed, fabricated, installed, and operated for studying the extent of fuel/impurity gas propagation and its influence on plasma dynamics in the far-edge region inside the last closed magnetic flux surface (LCFS). The HRDS is driven by a highly accurate, easy-to-control, dynamic, brushless, permanently excited synchronous servo motor operated by a PXI-commanded controller. The system is remotely operated and allows for precise control of the speed, acceleration, and distance traveled of the probe head on a shot-to-shot basis, facilitating seamless control of operations according to experimental requirements. Using this system, consisting of a linear array of Langmuir probes, measurements of plasma density, temperature, potential, and their fluctuations revealed that the fuel gas-puff impact these mean and fluctuating parameters up to three to four cm inside the LCFS. Attaching an array of magnetic probes to this system led to measurements of magnetic fluctuations inside the LCFS. The HRDS system is fully operational and serves as an important diagnostic tool for ADITYA-U tokamak.

physics.plasm-ph↗

arXiv2024

Massimo: Public Queue Monitoring and Management using Mass-Spring Model

An efficient system of a queue control and regulation in public spaces is very important in order to avoid the traffic jams and to improve the customer satisfaction. This article offers a detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places. Through the utilization of different technologies i.e. computer vision, machine learning algorithms, deep learning our system provide accurate information about the place is crowded or not and the necessary efforts to be taken.

cs.CV↗

arXiv2024

Measuring Sustainability Intention of ESG Fund Disclosure using Few-Shot Learning

Global sustainable fund universe encompasses open-end funds and exchange-traded funds (ETF) that, by prospectus or other regulatory filings, claim to focus on Environment, Social and Governance (ESG). Challengingly, the claims can only be confirmed by examining the textual disclosures to check if there is presence of intentionality and ESG focus on its investment strategy. Currently, there is no regulation to enforce sustainability in ESG products space. This paper proposes a unique method and system to classify and score the fund prospectuses in the sustainable universe regarding specificity and transparency of language. We aim to employ few-shot learners to identify specific, ambiguous, and generic sustainable investment-related language. Additionally, we construct a ratio metric to determine language score and rating to rank products and quantify sustainability claims for US sustainable universe. As a by-product, we publish manually annotated quality training dataset on Hugging Face (ESG-Prospectus-Clarity-Category under cc-by-nc-sa-4.0) of more than 1K ESG textual statements. The performance of the few-shot finetuning approach is compared with zero-shot models e.g., Llama-13B, GPT 3.5 Turbo etc. We found that prompting large language models are not accurate for domain specific tasks due to misalignment issues. The few-shot finetuning techniques outperform zero-shot models by large margins of more than absolute ~30% in precision, recall and F1 metrics on completely unseen ESG languages (test set). Overall, the paper attempts to establish a systematic and scalable approach to measure and rate sustainability intention quantitatively for sustainable funds using texts in prospectus. Regulatory bodies, investors, and advisors may utilize the findings of this research to reduce cognitive load in investigating or screening of ESG funds which accurately reflects the ESG intention.

cs.CL↗

arXiv2024

Ultrafast Optical Control of Rashba Interactions in a TMDC Heterostructure

We investigate spin relaxation dynamics of interlayer excitons in a MoSe2/MoS2 heterostructure induced by the Rashba effect. In such a system, Rashba interactions arise from an out-of-plane electric field due to photo-generated interlayer excitons inducing a phonon-assisted intravalley spin relaxation. We develop a theoretical description based on a microscopic approach to quantify the magnitude of Rashba interactions and test these predictions via time-resolved Kerr rotation measurements. In agreement with the calculations, we find that the Rashba-induced intravalley spin mixing becomes the dominating spin relaxation channel above T = 50 K. Our work identifies a previously unexplored spin-depolarization channel in heterostructures which can be used for ultrafast spin manipulation.

cond-mat.mes-hall↗

arXiv2024

Strain activation of localized states in WSe2

Single-photon emission centers generated by controlled atomic force microscopy (AFM) indentation in monolayer WSe$_2$ on a flexible polymer substrate are explored for applications in quantum technologies. Here, we study the response of these emitters to the polymer substrate's strain state, which is controlled by selecting the indentation force and by gradually thermally annealing the samples. In the indented areas, we observe sharp new photoluminescence (PL) peaks in the regions 1.62--1.68 eV and 1.70--1.73 eV, characterized by sublinear power dependence and spectral wandering. We find that these peaks arise only when the indentation force exceeds a few $\mu$N and generally redshift as the applied force increases. Conversely, after thermal annealing ($T < 60^{\circ}$C), WSe$_2$ experiences strain relaxation, leading to a blueshift of the peaks' spectral position and their ultimate disappearance. Our analysis of the peaks' positions vs. strain allows us to draw several conclusions about the nature of these emission. Specifically, we elucidate the roles of excitonic confinement and hybridization between free excitons and defect-related states, a process activated by the strain level. Overall, our approach suggests that the energy of localized emitters may be controlled via strain engineering.

physics.app-ph↗

arXiv2023

Strain fingerprinting of exciton valley character

Momentum-indirect excitons composed of electrons and holes in different valleys define optoelectronic properties of many semiconductors, but are challenging to detect due to their weak coupling to light. The identification of an excitons' valley character is further limited by complexities associated with momentum-selective probes. Here, we study the photoluminescence of indirect excitons in controllably strained prototypical 2D semiconductors (WSe$_2$, WS$_2$) at cryogenic temperatures. We find that these excitons i) exhibit valley-specific energy shifts, enabling their valley fingerprinting, and ii) hybridize with bright excitons, becoming directly accessible to optical spectroscopy methods. This approach allows us to identify multiple previously inaccessible excitons with wavefunctions residing in K, $\Gamma$, or Q valleys in the momentum space as well as various types of defect-related excitons. Overall, our approach is well-suited to unravel and tune intervalley excitons in various semiconductors.

cond-mat.mes-hall↗

arXiv2023

Probing the formation of dark interlayer excitons via ultrafast photocurrent

Optically dark excitons determine a wide range of properties of photoexcited semiconductors yet are hard to access via conventional spectroscopies. Here, we develop a time-resolved ultrafast photocurrent technique (trPC) to probe the formation dynamics of optically dark excitons. The nonlinear nature of the trPC makes it particularly sensitive to the formation of excitons occurring at the femtosecond timescale after the excitation. As proof of principle, we extract the interlayer exciton formation time 0.4~ps at 160 $\mu$J/cm$^2$ fluence in a MoS$_2$/MoSe$_2$ heterostructure and show that this time decreases with fluence. In addition, our approach provides access to the dynamics of carriers and their interlayer transport. Overall, our work establishes trPC as a technique to study dark excitons in various systems that are hard to probe by other approaches.

cond-mat.mes-hall↗

arXiv2022

Holder Recommendations using Graph Representation Learning & Link Prediction

Lead recommendations for financial products such as funds or ETF is potentially challenging in investment space due to changing market scenarios, and difficulty in capturing financial holder's mindset and their philosophy. Current methods surface leads based on certain product categorization and attributes like returns, fees, category etc. to suggest similar product to investors which may not capture the holder's investment behavior holistically. Other reported works does subjective analysis of institutional holder's ideology. This paper proposes a comprehensive data driven framework for developing a lead recommendations system in holder's space for financial products like funds by using transactional history, asset flows and product specific attributes. The system assumes holder's interest implicitly by considering all investment transactions made and collects possible meta information to detect holder's investment profile/persona like investment anticipation and investment behavior. This paper focusses on holder recommendation component of framework which employs a bi-partite graph representation of financial holders and funds using variety of attributes and further employs GraphSage model for learning representations followed by link prediction model for ranking recommendation for future period. The performance of the proposed approach is compared with baseline model i.e., content-based filtering approach on metric hits at Top-k (50, 100, 200) recommendations. We found that the proposed graph ML solution outperform baseline by absolute 42%, 22% and 14% with a look ahead bias and by absolute 18%, 19% and 18% on completely unseen holders in terms of hit rate for top-k recommendations: 50, 100 and 200 respectively.

q-fin.GN↗

arXiv2022

Strain control of hybridization between dark and localized excitons in a 2D semiconductor

Mechanical strain is a powerful tuning knob for excitons, Coulomb-bound electron-hole complexes dominating optical properties of two-dimensional semiconductors. While the strain response of bright free excitons is broadly understood, the behavior of dark free excitons (long-lived excitations that generally do not couple to light due to spin and momentum conservation) or localized excitons related to defects remains mostly unexplored. Here, we develop a technique capable of straining pristine suspended WSe2 kept at cryogenic temperatures up to 3\% to study the strain behavior of these fragile many-body states. We find that under the application of strain, dark and localized excitons in monolayer WSe2 - a prototypical 2D semiconductor - are brought into energetic resonance, forming a new hybrid state that inherits the properties of the constituent species. The characteristics of the hybridized state, including an order-of-magnitude enhanced light/matter coupling, avoided-crossing energy shifts, and strain tunability of many-body interactions, are all supported by first-principles calculations. The hybridized exciton reported here may play a critical role in the operation of single quantum emitters based on WSe2. Furthermore, the techniques we developed may be used to fingerprint unidentified excitonic states

cond-mat.mtrl-sci↗

arXiv2021

Spin/valley coupled dynamics of electrons and holes at the ${\text{MoS}_{2}-\text{MoSe}_{2}}$ interface

The coupled spin and valley degrees of freedom in transition metal dichalcogenides (TMDs) are considered a promising platform for information processing. Here, we use a TMD heterostructure ${\text{MoS}_{2}-\text{MoSe}_{2}}$ to study optical pumping of spin/valley polarized carriers across the interface and to elucidate the mechanisms governing their subsequent relaxation. By applying time-resolved Kerr and reflectivity spectroscopies, we find that the photoexcited carriers conserve their spin for both tunneling directions across the interface. Following this, we measure dramatically different spin/valley depolarization rates for electrons and holes, $\sim 30\,{\text{ns}}^{-1}$ and $< 1\,{\text{ns}}^{-1}$, respectively and show that this difference relates to the disparity in the spin-orbit splitting in conduction and valence bands of TMDs. Our work provides insights into the spin/valley dynamics of free carriers unaffected by complex excitonic processes and establishes TMD heterostructures as generators of spin currents in spin/valleytronic devices.

cond-mat.mes-hall↗