arXiv subjects

Mayukh Bagchi

Publications and source records attributed to Mayukh Bagchi.

At least 19 recordsLinked to original sources

arXiv2026

An RFSoC-based Backend and Timing System for the Balloon-borne Very Long Baseline Interferometry Experiment

We present the design and performance characterization of the digital backend and precision-timing system for the Balloon-borne Very Long Baseline Interferometry Experiment (BVEX), a pathfinder for high-frequency stratospheric VLBI at 22 GHz. The backend uses one of the four 14-bit analog-to-digital converter inputs on an AMD-Xilinx RFSoC 4x2. Although the converters support sampling rates up to 5 GSPS, the flight configuration digitizes the 2-4 GHz intermediate frequency at 4.096 GSPS. CASPER firmware provides both a high-resolution spectrometer for pointing and receiver verification, and a VLBI acquisition chain with two-bit requantization that records at a rate of about 8.2 Gbps. The timestamped data packets are sent over 100 Gigabit Ethernet (GbE) to a 16 TB NVMe array in a storage computer that draws approximately 70-80 W. The timing chain uses a Rakon oven-controlled crystal oscillator as a timing reference while a time-interval counter measures its drift relative to a GPS reference with approximately 60 ps resolution. This is the first deployment of an RFSoC-based VLBI backend and precision-timing system on a stratospheric balloon. Ground tests validated the backend, spectrometer, and timing chain. The August 2025 CSA STRATOS flight ended before reaching the target float altitude because of a balloon failure, and as a result no science observations were obtained. For the planned 2027 reflight, we are developing a conduction-cooled data storage computer with 24 TB of NVMe capacity and a direct data path from the 100 GbE interface to the NVMe array.

astro-ph.IM↗

arXiv2026

The Balloon-borne VLBI Experiment (BVEX) Radio Telescope and Position Tracking System

Very Long Baseline Interferometry (VLBI) is a technique in radio astronomy that vastly increases the diffraction limited angular resolution of radio observations by correlating simultaneous observations of radio telescopes separated by thousands of kilometers. Ground-based high-frequency VLBI is limited in baseline length by the diameter of the Earth and in sensitivity by molecular absorption of the Earth's atmosphere. One of the ways to improve VLBI is with balloon telescopes, which are much cheaper than space VLBI telescopes. In addition balloon-VLBI stations could also improve image quality due to increased uv-coverage. As a first step to achieving VLBI from a balloon-borne platform we present the Balloon-borne VLBI Experiment (BVEX). This is a novel, first-generation balloon-borne VLBI station observing at K-band (22 GHz) which launched as part of the Canadian Space Agency (CSA) STRATOS campaign from Timmins, Ontario, Canada in August 2025, with the aim to demonstrate VLBI from a balloon-borne platform by correlating with a large ground based telescope. We discuss the design of the 36 inch (91 cm) radio telescope and receiver with a 2 GHz bandwidth built from commercial-off-the-shelf (COTS) components as well as the telescope pointing system capable of interfacing with the CSA's CARMENCITA gondola. We also present a novel high precision position tracking system built from COTS components giving <1 mm precision on 1 s time scales which is required for achieving phase tracking at 22 GHz. We further show on-the-ground testing results from the 2025 Timmins campaign. The 2025 flight from Timmins was not successful due to a leak in the balloon preventing the payload from reaching its target altitude where accurate gondola pointing is possible. We therefore wrap up by presenting plans to upgrade the telescope for a potential next flight opportunity from Palmas, Tocantins, Brazil in 2027.

astro-ph.IM↗

arXiv2026

What can Computer Vision learn from Ranganathan?

The Semantic Gap Problem (SGP) in Computer Vision (CV) arises from the misalignment between visual and lexical semantics leading to flawed CV dataset design and CV benchmarks. This paper proposes that classification principles of S.R. Ranganathan can offer a principled starting point to address SGP and design high-quality CV datasets. We elucidate how these principles, suitably adapted, underpin the vTelos CV annotation methodology. The paper also briefly presents experimental evidence showing improvements in CV annotation and accuracy, thereby, validating vTelos.

cs.CV↗

arXiv2025

Information Ecosystem Reengineering via Public Sector Knowledge Representation

Information Ecosystem Reengineering (IER) -- the technological reconditioning of information sources, services, and systems within a complex information ecosystem -- is a foundational challenge in the digital transformation of public sector services and smart governance platforms. From a semantic knowledge management perspective, IER becomes especially entangled due to the potentially infinite number of possibilities in its conceptualization, namely, as a result of manifoldness in the multi-level mix of perception, language and conceptual interlinkage implicit in all agents involved in such an effort. This paper proposes a novel approach -- Representation Disentanglement -- to disentangle these multiple layers of knowledge representation complexity hindering effective reengineering decision making. The approach is based on the theoretically grounded and implementationally robust ontology-driven conceptual modeling paradigm which has been widely adopted in systems analysis and (re)engineering. We argue that such a framework is essential to achieve explainability, traceability and semantic transparency in public sector knowledge representation and to support auditable decision workflows in governance ecosystems increasingly driven by Artificial Intelligence (AI) and data-centric architectures.

cs.DL↗

arXiv2025

Language and Knowledge Representation: A Stratified Approach

The thesis proposes the problem of representation heterogeneity to emphasize the fact that heterogeneity is an intrinsic property of any representation, wherein, different observers encode different representations of the same target reality in a stratified manner using different concepts, language and knowledge (as well as data). The thesis then advances a top-down solution approach to the above stratified problem of representation heterogeneity in terms of several solution components, namely: (i) a representation formalism stratified into concept level, language level, knowledge level and data level to accommodate representation heterogeneity, (ii) a top-down language representation using Universal Knowledge Core (UKC), UKC namespaces and domain languages to tackle the conceptual and language level heterogeneity, (iii) a top-down knowledge representation using the notions of language teleontology and knowledge teleontology to tackle the knowledge level heterogeneity, (iv) the usage and further development of the existing LiveKnowledge catalog for enforcing iterative reuse and sharing of language and knowledge representations, and, (v) the kTelos methodology integrating the solution components above to iteratively generate the language and knowledge representations absolving representation heterogeneity. The thesis also includes proof-of-concepts of the language and knowledge representations developed for two international research projects - DataScientia (data catalogs) and JIDEP (materials modelling). Finally, the thesis concludes with future lines of research.

cs.DB↗

arXiv2025

A Generative AI-driven Metadata Modelling Approach

Since decades, the modelling of metadata has been core to the functioning of any academic library. Its importance has only enhanced with the increasing pervasiveness of Generative Artificial Intelligence (AI)-driven information activities and services which constitute a library's outreach. However, with the rising importance of metadata, there arose several outstanding problems with the process of designing a library metadata model impacting its reusability, crosswalk and interoperability with other metadata models. This paper posits that the above problems stem from an underlying thesis that there should only be a few core metadata models which would be necessary and sufficient for any information service using them, irrespective of the heterogeneity of intra-domain or inter-domain settings. To that end, this paper advances a contrary view of the above thesis and substantiates its argument in three key steps. First, it introduces a novel way of thinking about a library metadata model as an ontology-driven composition of five functionally interlinked representation levels from perception to its intensional definition via properties. Second, it introduces the representational manifoldness implicit in each of the five levels which cumulatively contributes to a conceptually entangled library metadata model. Finally, and most importantly, it proposes a Generative AI-driven Human-Large Language Model (LLM) collaboration based metadata modelling approach to disentangle the entanglement inherent in each representation level leading to the generation of a conceptually disentangled metadata model. Throughout the paper, the arguments are exemplified by motivating scenarios and examples from representative libraries handling cancer information.

cs.DL↗

arXiv2024

From Knowledge Organization to Knowledge Representation and Back

Knowledge Organization (KO) and Knowledge Representation (KR) have been the two mainstream methodologies of knowledge modelling in the Information Science community and the Artificial Intelligence community, respectively. The facet-analytical tradition of KO has developed an exhaustive set of guiding canons for ensuring quality in organising and managing knowledge but has remained limited in terms of technology-driven activities to expand its scope and services beyond the bibliographic universe of knowledge. KR, on the other hand, boasts of a robust ecosystem of technologies and technology-driven service design which can be tailored to model any entity or scale to any service in the entire universe of knowledge. This paper elucidates both the facet-analytical KO and KR methodologies in detail and provides a functional mapping between them. Out of the mapping, the paper proposes an integrated KR-enriched KO methodology with all the standard components of a KO methodology plus the advanced technologies provided by the KR approach. The practical benefits of the methodological integration has been exemplified through the flagship application of the Digital University at the University of Trento, Italy.

cs.AI↗

arXiv2024

From Knowledge Representation to Knowledge Organization and Back

Knowledge Representation (KR) and facet-analytical Knowledge Organization (KO) have been the two most prominent methodologies of data and knowledge modelling in the Artificial Intelligence community and the Information Science community, respectively. KR boasts of a robust and scalable ecosystem of technologies to support knowledge modelling while, often, underemphasizing the quality of its models (and model-based data). KO, on the other hand, is less technology-driven but has developed a robust framework of guiding principles (canons) for ensuring modelling (and model-based data) quality. This paper elucidates both the KR and facet-analytical KO methodologies in detail and provides a functional mapping between them. Out of the mapping, the paper proposes an integrated KO-enriched KR methodology with all the standard components of a KR methodology plus the guiding canons of modelling quality provided by KO. The practical benefits of the methodological integration has been exemplified through a prominent case study of KR-based image annotation exercise.

cs.AI↗

arXiv2023

Towards a Gateway for Knowledge Graph Schemas Collection, Analysis, and Embedding

One of the significant barriers to the training of statistical models on knowledge graphs is the difficulty that scientists have in finding the best input data to address their prediction goal. In addition to this, a key challenge is to determine how to manipulate these relational data, which are often in the form of particular triples (i.e., subject, predicate, object), to enable the learning process. Currently, many high-quality catalogs of knowledge graphs, are available. However, their primary goal is the re-usability of these resources, and their interconnection, in the context of the Semantic Web. This paper describes the LiveSchema initiative, namely, a first version of a gateway that has the main scope of leveraging the gold mine of data collected by many existing catalogs collecting relational data like ontologies and knowledge graphs. At the current state, LiveSchema contains - 1000 datasets from 4 main sources and offers some key facilities, which allow to: i) evolving LiveSchema, by aggregating other source catalogs and repositories as input sources; ii) querying all the collected resources; iii) transforming each given dataset into formal concept analysis matrices that enable analysis and visualization services; iv) generating models and tensors from each given dataset.

cs.AI↗

arXiv2023

A semantics-driven methodology for high-quality image annotation

Recent work in Machine Learning and Computer Vision has highlighted the presence of various types of systematic flaws inside ground truth object recognition benchmark datasets. Our basic tenet is that these flaws are rooted in the many-to-many mappings which exist between the visual information encoded in images and the intended semantics of the labels annotating them. The net consequence is that the current annotation process is largely under-specified, thus leaving too much freedom to the subjective judgment of annotators. In this paper, we propose vTelos, an integrated Natural Language Processing, Knowledge Representation, and Computer Vision methodology whose main goal is to make explicit the (otherwise implicit) intended annotation semantics, thus minimizing the number and role of subjective choices. A key element of vTelos is the exploitation of the WordNet lexico-semantic hierarchy as the main means for providing the meaning of natural language labels and, as a consequence, for driving the annotation of images based on the objects and the visual properties they depict. The methodology is validated on images populating a subset of the ImageNet hierarchy.

cs.CV↗

arXiv2023

Building Interoperable Electronic Health Records as Purpose-Driven Knowledge Graphs

When building a new application we are increasingly confronted with the need of reusing and integrating pre-existing knowledge. Nevertheless, it is a fact that this prior knowledge is virtually impossible to reuse as-is. This is true also in domains, e.g., eHealth, where a lot of effort has been put into developing high-quality standards and reference ontologies, e.g. FHIR1. In this paper, we propose an integrated methodology, called iTelos, which enables data and knowledge reuse towards the construction of Interoperable Electronic Health Records (iEHR). The key intuition is that the data level and the schema level of an application should be developed independently, thus allowing for maximum flexibility in the reuse of the prior knowledge, but under the overall guidance of the needs to be satisfied, formalized as competence queries. This intuition is implemented by codifying all the requirements, including those concerning reuse, as part of a purpose defined a priori, which is then used to drive a middle-out development process where the application schema and data are continuously aligned. The proposed methodology is validated through its application to a large-scale case study.

cs.AI↗

arXiv2023

Incremental Image Labeling via Iterative Refinement

Data quality is critical for multimedia tasks, while various types of systematic flaws are found in image benchmark datasets, as discussed in recent work. In particular, the existence of the semantic gap problem leads to a many-to-many mapping between the information extracted from an image and its linguistic description. This unavoidable bias further leads to poor performance on current computer vision tasks. To address this issue, we introduce a Knowledge Representation (KR)-based methodology to provide guidelines driving the labeling process, thereby indirectly introducing intended semantics in ML models. Specifically, an iterative refinement-based annotation method is proposed to optimize data labeling by organizing objects in a classification hierarchy according to their visual properties, ensuring that they are aligned with their linguistic descriptions. Preliminary results verify the effectiveness of the proposed method.

cs.CV↗

arXiv2023

Disentangling Domain Ontologies

In this paper, we introduce and illustrate the novel phenomenon of Conceptual Entanglement which emerges due to the representational manifoldness immanent while incrementally modelling domain ontologies step-by-step across the following five levels: perception, labelling, semantic alignment, hierarchical modelling and intensional definition. In turn, we propose Conceptual Disentanglement, a multi-level conceptual modelling strategy which enforces and explicates, via guiding principles, semantic bijections with respect to each level of conceptual entanglement (across all the above five levels) paving the way for engineering conceptually disentangled domain ontologies. We also briefly argue why state-of-the-art ontology development methodologies and approaches are insufficient with respect to our characterization.

cs.AI↗

arXiv2022

Aligning Visual and Lexical Semantics

We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when using vision to perceive a target reality, lexical semantics focus on how humans build concepts of the same target reality through the use of language. The lack of coincidence between visual and lexical semantics, in turn, has a major impact on CV systems in the form of the Semantic Gap Problem (SGP). The paper, while extensively exemplifying the lack of coincidence as above, introduces a general, domain-agnostic methodology to enforce alignment between visual and lexical semantics.

cs.CV↗

arXiv2022

Popularity Driven Data Integration

More and more, with the growing focus on large scale analytics, we are confronted with the need of integrating data from multiple sources. The problem is that these data are impossible to reuse as-is. The net result is high cost, with the further drawback that the resulting integrated data will again be hardly reusable as-is. iTelos is a general purpose methodology aiming at minimizing the effects of this process. The intuition is that data will be treated differently based on their popularity: the more a certain set of data have been reused, the more they will be reused and the less they will be changed across reuses, thus decreasing the overall data preprocessing costs, while increasing backward compatibility and future sharing

cs.AI↗

arXiv2022

A Diversity-Aware Domain Development Methodology

The development of domain ontological models, though being a mature research arena backed by well-established methodologies, still suffer from two key shortcomings. Firstly, the issues concerning the semantic persistency of ontology concepts and their flexible reuse in domain development employing existing approaches. Secondly, due to the difficulty in understanding and reusing top-level concepts in existing foundational ontologies, the obfuscation regarding the semantic nature of domain representations. The paper grounds the aforementioned shortcomings in representation diversity and proposes a three-fold solution - (i) a pipeline for rendering concepts reuse-ready, (ii) a first characterization of a minimalistic foundational knowledge model, named foundational teleology, semantically explicating foundational distinctions enforcing the static as well as dynamic nature of domain representations, and (iii) a flexible, reuse-native methodology for diversity-aware domain development exploiting solutions (i) and (ii). The preliminary work reported validates the potentiality of the solution components.

cs.AI↗

arXiv2022

LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemas

One of the major barriers to the training of algorithms on knowledge graph schemas, such as vocabularies or ontologies, is the difficulty that scientists have in finding the best input resource to address the target prediction tasks. In addition to this, a key challenge is to determine how to manipulate (and embed) these data, which are often in the form of particular triples (i.e., subject, predicate, object), to enable the learning process. In this paper, we describe the LiveSchema initiative, namely a gateway that offers a family of services to easily access, analyze, transform and exploit knowledge graph schemas, with the main goal of facilitating the reuse of these resources in machine learning use cases. As an early implementation of the initiative, we also advance an online catalog, which relies on more than 800 resources, with the first set of example services.

cs.AI↗

arXiv2022

Representation Heterogeneity

Semantic Heterogeneity is conventionally understood as the existence of variance in the representation of a target reality when modelled, by independent parties, in different databases, schemas and/ or data. We argue that the mere encoding of variance, while being necessary, is not sufficient enough to deal with the problem of representational heterogeneity, given that it is also necessary to encode the unifying basis on which such variance is manifested. To that end, this paper introduces a notion of Representation Heterogeneity in terms of the co-occurrent notions of Representation Unity and Representation Diversity. We have representation unity when two heterogeneous representations model the same target reality, representation diversity otherwise. In turn, this paper also highlights how these two notions get instantiated across the two layers of any representation, i.e., Language and Knowledge.

cs.AI↗