arXiv subjects

Sumit Kumar

Publications and source records attributed to Sumit Kumar.

At least 37 records · Page 2Linked to original sources

arXiv2025

Density Functional Theory Analysis of Na3AgO: Assessing its Viability as a Sustainable Material for Solar Energy Applications

This study mainly emphasis the fascinating features of inverse perovskites Na3AgO using density functional theory (DFT). Inverse perovskite (IP) Na3AgO structural features have been examined, and the space group and cubic structure of Pm-3m (221) have been confirmed. The experimental formulation and thermal stability of IP have been confirmed by the formation energy. Phonon dispersion curves were used to assess dynamic stability. The dynamic stability of the examined IP and the bonding strength against cubic structure deformation are confirmed by the lack of negative frequencies. The energy gap or the characteristics of semiconducting behaviour have been predicted by the electronic properties of Na3AgO with a band gap of 1.273 eV. In order to confirmthe viability of solar cells, the light-dependent properties have also been identified. Born stability criteria are also used to verify the mechanical stability, and additional elastic characteristics are identified in order to forecast the anisotropy, ductility, strength, and hardness. These anti-perovskites, which possess intriguing characteristics, have the potential to be effective materials for photovoltaic applications, as indicated by the analysed findings.

cond-mat.mtrl-sci↗

arXiv2025

Exploring PdCrAs Half-Heusler Alloy for Sustainable Energy Solutions: An Ab-initio Study

This work presents a comprehensive investigation of the HH alloy PdCrAs using first - principles methods, highlighting its potential applications in various fields, including spintronics, thermoelectrics, and optoelectronics. We employed density functional theory (DFT) within the full potential linearized augmented plane wave (FLAPW) framework. Structural optimizations indicate that the alloy stabilizes in the ferromagnetic phase. Both mechanical and dynamical stability have been confirmed through analyses of elastic constants and phonon dispersion. Our calculations of the electronic band structure and density of states (DOS) reveal that PdCrAs exhibits half-metallic behavior, with a spin-polarized band gap of 0.670 eV in the minority spin channel. The magnetic moment aligns with the Slater Pauling (SP) rule, indicating robust ferromagnetism. Mechanical analysis shows that the material is ductile in nature. Thermodynamic analysis highlights the alloy's resilience, supported by consistent trends in entropy, heat capacity, and Debye temperature.Its optical response demonstrates strong absorption in the visible and ultraviolet (UV) regions, along with pronounced dielectric and plasmonic features, suggesting potential applications in optoelectronics and refracective coatings. Furthermore, evaluations of the transport properties reveal high Seebeck coefficients and a significantly tunable figure of merit (ZT), with values approaching 0.9 across the temperature range of 300 - 1500 K, indicating excellent thermoelectric characteristics. Overall, these findings position PdCrAs as a promising multifunctional material suitable sustainable energy solutions.

cond-mat.mtrl-sci↗

arXiv2025

Thermoelectric Potential of NaVAs Half-Heusler Alloy: Insights from Ab-initio Calculations

This work presents a comprehensive investigation of the HH alloy NaVAs using first - principles methods, emphasizing its potential applications in various fields, including spintronics, thermoelectrics, and optoelectronics. We utilized density functional theory (DFT) within the full-potential linearized augmented plane wave (FLAPW) framework. Structural optimizations indicate that the alloy stabilizes in the ferromagnetic phase. Both mechanical and dynamical stability have been confirmed through analysis of elastic constants and phonon dispersion. Our calculations of the electronic band structure and density of states (DOS) reveal that NaVAs exhibits half-metallic behavior, with a spin-polarized band gap of 2.77 eV in the minority spin channel. The magnetic moment aligns with the Slater Pauling (SP) rule, demonstrating robust ferromagnetism. Mechanical analysis shows that the material is brittle in nature. The thermodynamic analysis highlights the alloy's resilience, supported by consistent trends in entropy, heat capacity, and Debye temperature. Its optical response indicates strong absorption in the visible and ultraviolet (UV) regions, along with pronounced dielectric and plasmonic features, suggesting potential for applications in optoelectronics and refective coatings. Furthermore, evaluations of the transport properties show high Seebeck coefficients and a significantly tunable figure of merit (ZT). ZT values approach 1.0 across the temperature range of 600 - 1500 K, demonstrating excellent thermoelectric characteristics. Overall, these findings position NaVAs as a promising multifunctional material suitable for advanced technological applications in green energy area.

cond-mat.mtrl-sci↗

arXiv2025

Simulating LLM training workloads for heterogeneous compute and network infrastructure

The growing demand for large-scale GPU clusters in distributed model training presents a significant barrier to innovation, particularly in model optimization, performance tuning, and system-level enhancements. To address this challenge, LLM training simulators are employed to estimate training time and guide design decisions. However, the state-of-the-art LLM training simulators assume homogeneous compute and network infrastructure. In practice, device heterogeneity is inevitable due to resource sharing in cloud environments, frequent shifts in device generations, and inherent intra-chip interconnect heterogeneity. To address the gap between state-of-the-art and practical requirements, we propose the design of a heterogeneity-aware distributed LLM simulator capable of predicting training time while enabling abstractions to specify custom configurations for device groups and device-to-parallelism mapping. We present the design requirements and challenges in building a heterogeneity-aware distributed ML training simulator, and design components such as non-uniform workload partitioning. Our initial simulation results demonstrate the impact of heterogeneity on the model computation and communication time.

cs.DC↗

arXiv2025

Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts

The Internet is poised to host billions to trillions of autonomous AI agents that negotiate, delegate, and migrate in milliseconds and workloads that will strain DNS-centred identity and discovery. In this paper, we describe the NANDA index architecture, which we envision as a means for discoverability, identifiability and authentication in the internet of AI agents. We present an architecture where a minimal lean index resolves to dynamic, cryptographically verifiable AgentFacts that supports multi-endpoint routing, load balancing, privacy-preserving access, and credentialed capability assertions. Our architecture design delivers five concrete guarantees: (1) A quilt-like index proposal that supports both NANDA-native agents as well as third party agents being discoverable via the index, (2) rapid global resolution for newly spawned AI agents, (3) sub-second revocation and key rotation, (4) schema-validated capability assertions, and (5) privacy-preserving discovery across organisational boundaries via verifiable, least-disclosure queries. We formalize the AgentFacts schema, specify a CRDT-based update protocol, and prototype adaptive resolvers. The result is a lightweight, horizontally scalable foundation that unlocks secure, trust-aware collaboration for the next generation of the Internet of AI agents, without abandoning existing web infrastructure.

cs.NI↗

arXiv2025

Balancing Semantic Relevance and Engagement in Related Video Recommendations

Related video recommendations commonly use collaborative filtering (CF) driven by co-engagement signals, often resulting in recommendations lacking semantic coherence and exhibiting strong popularity bias. This paper introduces a novel multi-objective retrieval framework, enhancing standard two-tower models to explicitly balance semantic relevance and user engagement. Our approach uniquely combines: (a) multi-task learning (MTL) to jointly optimize co-engagement and semantic relevance, explicitly prioritizing topical coherence; (b) fusion of multimodal content features (textual and visual embeddings) for richer semantic understanding; and (c) off-policy correction (OPC) via inverse propensity weighting to effectively mitigate popularity bias. Evaluation on industrial-scale data and a two-week live A/B test reveals our framework's efficacy. We observed significant improvements in semantic relevance (from 51% to 63% topic match rate), a reduction in popular item distribution (-13.8% popular video recommendations), and a +0.04% improvement in our topline user engagement metric. Our method successfully achieves better semantic coherence, balanced engagement, and practical scalability for real-world deployment.

cs.IR↗

arXiv2025

SHAPE -- A Spectro-Polarimeter Onboard Propulsion Module of Chandrayaan-3 Mission

SHAPE (Spectro-polarimetry of HAbitable Planet Earth) is an experiment onboard the Chandrayaan-3 Mission, designed to study the spectro-polarimetric signatures of the habitable planet Earth in the near-infrared (NIR) wavelength range (1.0 - 1.7 $μ$m). The spectro-polarimeter is the only scientific payload (experimental in nature) on the Propulsion Module (PM) of the Chandrayaan-3 mission. The instrument is a compact and lightweight spectro-polarimeter with an Acousto-Optic Tunable Filter (AOTF) at its core. The AOTF operates in the frequency range of 80 MHz to 135 MHz with a power of 0.5 - 2.0 Watts. The two output beams (e-beam and o-beam) from the AOTF are focused onto two InGaAs detectors (pixelated, 1D linear array) with the help of focusing optics. The primary (aperture) optics, with a diameter of $\sim$2 mm, collects the NIR light for input to the AOTF, defining the field of view (FOV) of 2.6$^\circ$. The payload has a mass of 4.8 kg and operates at a power of 25 Watts. This manuscript highlights some of the ground-based results, including the post-launch initial performance of the payload while orbiting around the Moon to observe Earth.

astro-ph.IM↗

arXiv2025

Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling

Large Language Models (LLMs) are revolutionizing industries by enhancing efficiency, scalability, and innovation. This paper investigates the potential of LLMs in automating Computer-Aided Design (CAD) workflows, by integrating FreeCAD with LLM as CAD design tool. Traditional CAD processes are often complex and require specialized sketching skills, posing challenges for rapid prototyping and generative design. We propose a framework where LLMs generate initial CAD scripts from natural language descriptions, which are then executed and refined iteratively based on error feedback. Through a series of experiments with increasing complexity, we assess the effectiveness of this approach. Our findings reveal that LLMs perform well for simple to moderately complex designs but struggle with highly constrained models, necessitating multiple refinements. The study highlights the need for improved memory retrieval, adaptive prompt engineering, and hybrid AI techniques to enhance script robustness. Future directions include integrating cloud-based execution and exploring advanced LLM capabilities to further streamline CAD automation. This work underscores the transformative potential of LLMs in design workflows while identifying critical areas for future development.

cs.HC↗

arXiv2025

A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks

Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications on graph data. Because graph data does not follow the independently and identically distributed (i.i.d.) assumption, adversarial manipulations or incorrect data can propagate to other data points through message passing, which deteriorates the model's performance. To allow model developers to remove the adverse effects of manipulated entities from a trained GNN, we study the recently formulated problem of Corrective Unlearning. We find that current graph unlearning methods fail to unlearn the effect of manipulations even when the whole manipulated set is known. We introduce a new graph unlearning method, Cognac, which can unlearn the effect of the manipulation set even when only 5% of it is identified. It recovers most of the performance of a strong oracle with fully corrected training data, even beating retraining from scratch without the deletion set while being 8x more efficient. We hope our work assists GNN developers in mitigating harmful effects caused by issues in real-world data, post-training. Our code is publicly available at https://github.com/cognac-gnn-unlearning/corrective-unlearning-for-gnns

cs.LG↗

arXiv2025

Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation

The task of item-to-item (I2I) retrieval is to identify a set of relevant and highly engaging items based on a given trigger item. It is a crucial component in modern recommendation systems, where users' previously engaged items serve as trigger items to retrieve relevant content for future engagement. However, existing I2I retrieval models in industry are primarily built on co-engagement data and optimized using the recall measure, which overly emphasizes co-engagement patterns while failing to capture semantic relevance. This often leads to overfitting short-term co-engagement trends at the expense of long-term benefits such as discovering novel interests and promoting content diversity. To address this challenge, we propose MTMH, a Multi-Task and Multi-Head I2I retrieval model that achieves both high recall and semantic relevance. Our model consists of two key components: 1) a multi-task learning loss for formally optimizing the trade-off between recall and semantic relevance, and 2) a multi-head I2I retrieval architecture for retrieving both highly co-engaged and semantically relevant items. We evaluate MTMH using proprietary data from a commercial platform serving billions of users and demonstrate that it can improve recall by up to 14.4% and semantic relevance by up to 56.6% compared with prior state-of-the-art models. We also conduct live experiments to verify that MTMH can enhance both short-term consumption metrics and long-term user-experience-related metrics. Our work provides a principled approach for jointly optimizing I2I recall and semantic relevance, which has significant implications for improving the overall performance of recommendation systems.

cs.IR↗

arXiv2025

Recognizing Ornaments in Vocal Indian Art Music with Active Annotation

Ornamentations, embellishments, or microtonal inflections are essential to melodic expression across many musical traditions, adding depth, nuance, and emotional impact to performances. Recognizing ornamentations in singing voices is key to MIR, with potential applications in music pedagogy, singer identification, genre classification, and controlled singing voice generation. However, the lack of annotated datasets and specialized modeling approaches remains a major obstacle for progress in this research area. In this work, we introduce Rāga Ornamentation Detection (ROD), a novel dataset comprising Indian classical music recordings curated by expert musicians. The dataset is annotated using a custom Human-in-the-Loop tool for six vocal ornaments marked as event-based labels. Using this dataset, we develop an ornamentation detection model based on deep time-series analysis, preserving ornament boundaries during the chunking of long audio recordings. We conduct experiments using different train-test configurations within the ROD dataset and also evaluate our approach on a separate, manually annotated dataset of Indian classical concert recordings. Our experimental results support the superior performance of our proposed approach over the baseline CRNN.

eess.AS↗

arXiv2025

Artificial Intelligence implementation of onboard flexible payload and adaptive beamforming using commercial off-the-shelf devices

Very High Throughput satellites typically provide multibeam coverage, however, a common problem is that there can be a mismatch between the capacity of each beam and the traffic demand: some beams may fall short, while others exceed the requirements. This challenge can be addressed by integrating machine learning with flexible payload and adaptive beamforming techniques. These methods allow for dynamic allocation of payload resources based on real-time capacity needs. As artificial intelligence advances, its ability to automate tasks, enhance efficiency, and increase precision is proving invaluable, especially in satellite communications, where traditional optimization methods are often computationally intensive. AI-driven solutions offer faster, more effective ways to handle complex satellite communication tasks. Artificial intelligence in space has more constraints than other fields, considering the radiation effects, the spaceship power capabilities, mass, and area. Current onboard processing uses legacy space-certified general-purpose processors, costly application-specific integrated circuits, or field-programmable gate arrays subjected to a highly stringent certification process. The increased performance demands of onboard processors to satisfy the accelerated data rates and autonomy requirements have rendered current space-graded processors obsolete. This work is focused on transforming the satellite payload using artificial intelligence and machine learning methodologies over available commercial off-the-shelf chips for onboard processing. The objectives include validating artificial intelligence-driven scenarios, focusing on flexible payload and adaptive beamforming as machine learning models onboard. Results show that machine learning models significantly improve signal quality, spectral efficiency, and throughput compared to conventional payload.

eess.SP↗

arXiv2025

A 2-6 GHz Ultra-Wideband CMOS Transceiver for Radar Applications

This paper presents a low power, low cost transceiver architecture to implement radar-on-a-chip. The transceiver comprises of a full ultra-wideband (UWB) transmitter and a full UWB band receiver. A design methodology to maximize the tuning range of the voltage-controlled oscillator (VCO) is presented. At the transmitter side, a sub-harmonic mixer is used for signal up-conversion. The receiver low noise amplifier (LNA) has a 2 to 6 GHz input matching bandwidth with a power gain of 9 dB and a noise figure of 2.5 dB. The transceiver is implemented in Cadence EDA tools using 65nm CMOS technology. The system achieves a total dc power consumption of 50 mW. Good noise figure performance; good wide-band matching; gain; high level of integration; low power; low cost of the proposed UWB radar transceiver front-end make it a highly competitive SoC solution for low power UWB transceivers.

eess.SY↗

arXiv2025

Parameter Estimation with Nonstationary Noise in Gravitational-wave Data

The sensitivity of gravitational-wave (GW) detectors is characterized by their noise curves, which determine the detector's reach and ability to measure the parameters of astrophysical sources accurately. The detector noise is typically modeled as stationary and Gaussian for many practical purposes and is characterized by its Power Spectral Density (PSD). However, due to environmental and instrumental factors, physical changes in the state of detectors may introduce non-stationarity into the noise. Misestimation of the noise behavior directly impacts the posterior width of the signal parameters. It becomes an issue for studies that depend on accurate localization volumes, such as i) probing cosmological parameters (e.g., Hubble constant) using cross-correlation methods with galaxies, ii) doing electromagnetic follow-up using localization information from parameter estimation (PE) done from pre-merger data. We study the effects of dynamical noise on the PE of the GW events. We develop a new method to correct dynamical noise by estimating a locally valid pseudo-PSD normalized along a potential signal's time-frequency track. We do simulations by injecting binary neutron star (BNS) merger signals in various scenarios where the detector goes through a period of non-stationarity with reference noise curves of third-generation detectors (Cosmic Explorer, Einstein telescope). As an example, for a source where mis-modeling of the noise biases the signal-to-noise estimate by even $10\%$, one would expect the estimated sky localization to be either under or over-reported by $\sim 20\%$; errors like this, especially in low-latency, could potentially cause follow-up campaigns to miss the actual source location.

astro-ph.IM↗

arXiv2025

Cyber security of OT networks: A tutorial, survey of attacks and overview of current state of defense tools, protocols, & challenges

The convergence of Operational Technology (OT) and Information Technology (IT) under Industry~4.0 has widened the cyber-attack surface of critical infrastructure across manufacturing, energy, transportation, water, and healthcare. This survey synthesizes OT/IT cybersecurity along four axes. First, we taxonomize \emph{attack vectors} that traverse the IT--OT boundary, separating IT-side initial access (phishing, exploits, supply-chain compromise, exposed remote access) from OT-side propagation and impact (insecure protocols, weak authentication, firmware tampering, control-logic manipulation). Second, we review \emph{defensive technologies} -- signature-based intrusion detection, AI/ML anomaly detection, Zero Trust Architecture, blockchain-based event logging, digital twins, and OT-aware Security Operations Centers -- and identify remaining \emph{gaps}: OT-specific patch management, dataset scarcity for ML, IoMT segmentation, and the absence of consistent resilience metrics. Third, we compile a cross-validated \emph{historical record} of 69 high-impact incidents spanning 2010--2025, from Stuxnet to Jaguar Land Rover, and quantify their \emph{commercial effects} sector by sector using figures sourced from SEC filings, government post-incident reviews, and primary regulatory disclosures. Fourth, we map the \emph{regulatory landscape}: NIST Cybersecurity Framework2.0 and SP~800-82~Rev.~3, IEC~62443, the EU NIS2 Directive, DORA, the Cyber Resilience Act, NERC~CIP, and healthcare-specific regimes (IEC-80001-1, FDA, NIST-SP-1800-8). A sectoral deep-dive on healthcare illustrates the IT--OT convergence threat model under high-consequence conditions. The result is a single, source-traceable reference on where OT/IT cybersecurity stands, what the historical record costs defenders who lag, and where investment yields the highest marginal return on resilience.

cs.CR↗

arXiv2025

Computational insights into Cobalt-based novel half-Heusler alloy for sustainable energy applications

The quest for efficient and sustainable green energy solutions has led to a growing interest in half Heusler alloys, particularly for thermoelectric and spintronic applications. This study investigates the multifaceted nature of cobalt based half Heusler alloy, CoVAs, employing DFT with advanced computational techniques, such as the FLAPW method. The elastic, electronic, magnetic, thermodynamic, and optical properties of CoVAs are meticulously analyzed. Structural and mechanical evaluations reveal mechanical stability and brittleness under varying pressures. Electronic and magnetic properties are examined through band structure and DOS analysis, revealing a half metallic nature with a minority spin band gap. The total magnetic moment aligns with the Slater Pauling rule, further confirming ferromagnetism and half metallicity. Thermodynamic investigations, based on the quasi-harmonic Debye approximation, provide insights into temperature- and pressure dependent behavior, including thermal expansion, heat capacity, and Debye temperature, establishing CoVAs as a viable candidate for high temperature applications. Additionally, the optical properties underestimate its potential in optoelectronic applications due to high absorption in the UV region, showing a distinct absorption edge corresponding to the electronic band gap. Phonon dispersion relations reflect the stability of the alloy, and the figure of merit confirms the alloy's suitability for thermodynamics applications. The findings highlight the potential of CoVAs as a promising candidate for spintronic photovoltaic and optoelectronic applications, providing insights into its fundamental properties that could facilitate experimental synthesis and industrial implementation for green energy and advanced technological applications.

cond-mat.mtrl-sci↗

arXiv2025

Computational Studies of NaVTe Half Heusler Alloy for Green Energy Applications

To lessen the quick depletion of fossil fuels and the resulting environmental harm, it is necessary to investigate effective and eco-friendly materials that can convert lost energy into electricity. The structural, optical, electronic, thermo-electric, and thermodynamic properties of the novel half-Heusler (HH) material NaVTe were examined in the current work using density functional theory (DFT). The Birch-Murnaghan equations of states were used to confirm the structural stability of the NaVTe HH alloy under investigation. These equations show that the compound in question has structural stability because its ground-state energy levels are negative. For spin-down configurations, NaVTe possesses an energy band gap of 3.2 eV, according to band structure and total density of state analysis. NaVTe is a material that is desirable for optoelectronic applications due to its optical features, which include maximum conductivity and absorption of electromagnetic radiation. The figure of merit and other thermodynamic and thermoelectric parameters are calculated. According to these predicted outcomes, the NaVTe HH alloy would be the ideal option for thermo-electric and renewable energy applications.

cond-mat.mtrl-sci↗

arXiv2025

Relations amongst the distances between $C^{*}$-subalgebras and some canonically associated operator algebras

We prove that the Christensen distance (resp., the Kadison-Kastler distance) between two $C^*$-subalgebras $\mathcal{A}$ and $\mathcal{B}$ of a $C^*$-algebra $\mathcal{C}$ is equal to that between their enveloping von Neumann algebras $\mathcal{A}^{**}$ and $\mathcal{B}^{**}$ (resp., the tensor product algebras $\mathcal{A} \otimes^{\min} \mathcal{D}$ and $\mathcal{B} \otimes^{\min} \mathcal{D}$, for any unital commutative $C^*$-algebra $\mathcal{D}$).

math.OA↗