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This article presents a generalization of the $q$-numerical range of an operator $T \in \mathcal{B}(\mathcal{H})$ in the context of semi-Hilbertian spaces. Significant findings regarding the q-numerical range and radius have been established.

The effectiveness of zero-shot classification in large vision-language models (VLMs), such as Contrastive Language-Image Pre-training (CLIP), depends on access to extensive, well-aligned text-image datasets. In this work, we introduce two complementary data sources, one generated by large language models (LLMs) to describe the stages of fungal growth and another comprising a diverse set of synthetic fungi images. These datasets are designed to enhance CLIPs zero-shot classification capabilities for fungi-related tasks. To ensure effective alignment between text and image data, we project them into CLIPs shared representation space, focusing on different fungal growth stages. We generate text using LLaMA3.2 to bridge modality gaps and synthetically create fungi images. Furthermore, we investigate knowledge transfer by comparing text outputs from different LLM techniques to refine classification across growth stages., Comment: 11 pages, 5 Figures, 1 Table

In this paper, we show that photoluminescence from CrCl3 bulk crystal exhibits a preferential polarization direction when films are magnetically ordered or strained. We verify the magnetization as responsible for the polarization by measuring the signal as a function of the temperature in Voigt configuration while applying the in-plane magnetic field. We show that phonon coupling contributing to vibronic transitions depolarizes the signal compared with zero phonon lines. We explain the data using DFT calculations, which reveal a magnetization-selective occupation of the low energy d-orbital triplet state of Cr3+ upon photon absorption. In addition, the calculations find that the excitation of one electron results in the excited state acquiring out-of-plane components of spin and very large orbital magnetic moments, in addition to a lattice deformation., Comment: 21 pages, 12 figures

Experimental deployment of quantum communication over Earth-satellite channels opens the way to a secure global quantum Internet. In this work, we present results from a real-time prototype quantum key distribution (QKD) system, which entails the development of optical systems including the encoding of entangled photon pairs, the development of transmitters for quantum signaling through an emulated Earth-satellite channel, and the development of quantum-decoding receivers. A unique aspect of our system is the integration of QKD with existing cryptographic methods to ensure quantum-resistant security, even at low-key rates. In addition, we report the use of specially designed error-reconciliation codes that optimize the security versus key-rate trade-off. Our work demonstrates, for the first time, a deployment of the BBM92 protocol that offers both post-quantum security via the advanced encryption standard (AES) and quantum security via an entanglement-based QKD protocol. If either the AES or the QKD is compromised through some adversary attack, our system still delivers state-of-the-art communications secure against future quantum computers., Comment: 9 pages, 5 figures, 1 table, IEEE, International Conference on Quantum Communications, Networking, and Computing 2025

Large Language Models (LLMs) have been used in cybersecurity in many ways, including their recent use as intelligent agent systems for autonomous security analysis. Capture the Flag (CTF) challenges serve as benchmarks for assessing the automated task-planning abilities of LLM agents across various cybersecurity skill sets. Early attempts to apply LLMs for solving CTF challenges relied on single-agent systems, where feedback was restricted to a single reasoning-action loop. This approach proved inadequate for handling complex CTF tasks. Drawing inspiration from real-world CTF competitions, where teams of experts collaborate, we introduce the D-CIPHER multi-agent LLM framework for collaborative CTF challenge solving. D-CIPHER integrates agents with distinct roles, enabling dynamic feedback loops to enhance reasoning on CTF challenges. It introduces the Planner-Executor agent system, consisting of a Planner agent for overall problem-solving along with multiple heterogeneous Executor agents for individual tasks, facilitating efficient allocation of responsibilities among the LLMs. Additionally, D-CIPHER incorporates an Auto-prompter agent, which improves problem-solving by exploring the challenge environment and generating a highly relevant initial prompt. We evaluate D-CIPHER on CTF benchmarks using multiple LLM models and conduct comprehensive studies to highlight the impact of our enhancements. Our results demonstrate that the multi-agent D-CIPHER system achieves a significant improvement in challenges solved, setting a state-of-the-art performance on three benchmarks: 22.0% on NYU CTF Bench, 22.5% on Cybench, and 44.0% on HackTheBox. D-CIPHER is available at https://github.com/NYU-LLM-CTF/nyuctf_agents as the nyuctf_multiagent package.

Let $A \in \mathbb{M}_m(S)$, where $S$ is a commutative ring and $m>1$. For a positive integer $n$ if $A^n \mathbb{M}_m(S)= A^{n+1}\mathbb{M}_m(S)$, then we prove that $\mathbb{M}_m(S)A^n = \mathbb{M}_m(S)A^{n+1}$. For $n = 1$ this validates a recent conjecture [3, Conjecture 3.7].

Konrad Burnik suggests a structure-preserving QR factorization for centrosymmetric matrices, known as QX factorization. In this article, we obtain the explicit expressions for rigorous perturbation bounds of the QX factorization when the original matrix is perturbed, either norm-wise or component-wise. First, using the matrix-equation approach, weak rigorous perturbation bounds are derived. Then, strong rigorous perturbation bounds are obtained by combining the modified matrix-vector equation approach with the strategy for the Lyapunov majorant function and the Banach fixed-point theorem. The mixed and component-wise condition numbers and their upper bounds are also explicitly expressed. Numerical tests illustrate the validity of the obtained results., Comment: 21

Astrophysical observations of the cosmos allow us to probe extreme physics and answer foundational questions on our universe. Modern astronomy is increasingly operating under a holistic approach, probing the same question with multiple diagnostics including how sources vary over time, how they appear across the electromagnetic spectrum, and through their other signatures, including gravitational waves, neutrinos, cosmic rays, and dust on Earth. Astrophysical observations are now reaching the point where approximate physics models are insufficient. Key sources of interest are explosive transients, whose understanding requires multidisciplinary studies at the intersection of astrophysics, gravity, nuclear science, plasma physics, fluid dynamics and turbulence, computation, particle physics, atomic, molecular, and optical science, condensed matter and materials science, radiation transport, and high energy density physics. This white paper provides an overview of the major scientific advances that lay at the intersection of physics and astronomy and are best probed through time-domain and multimessenger astrophysics, an exploration of how multidisciplinary science can be fostered, and introductory descriptions of the relevant scientific disciplines and key astrophysical sources of interest., Comment: This white paper is the product of the Third Time-Domain And Multimessenger Astrophysics workshop: Multidisciplinary Science in the Multimessenger Era, hosted by Louisiana State University with additional support from DOE, NSF, and NASA

The integration of Large Language Models (LLMs) such as GitHub Copilot, ChatGPT, Cursor AI, and Codeium AI into software development has revolutionized the coding landscape, offering significant productivity gains, automation, and enhanced debugging capabilities. These tools have proven invaluable for generating code snippets, refactoring existing code, and providing real-time support to developers. However, their widespread adoption also presents notable challenges, particularly in terms of security vulnerabilities, code quality, and ethical concerns. This paper provides a comprehensive analysis of the benefits and risks associated with AI-powered coding tools, drawing on user feedback, security analyses, and practical use cases. We explore the potential for these tools to replicate insecure coding practices, introduce biases, and generate incorrect or non-sensical code (hallucinations). In addition, we discuss the risks of data leaks, intellectual property violations and the need for robust security measures to mitigate these threats. By comparing the features and performance of these tools, we aim to guide developers in making informed decisions about their use, ensuring that the benefits of AI-assisted coding are maximized while minimizing associated risks.

Incorporating responsible practices into software engineering (SE) for AI is essential to ensure ethical principles, societal impact, and accountability remain at the forefront of AI system design and deployment. This study investigates the ethical challenges and complexities inherent in responsible software engineering (RSE) for AI, underscoring the need for practical,scenario-driven operational guidelines. Given the complexity of AI and the relative inexperience of professionals in this rapidly evolving field, continuous learning and market adaptation are crucial. Through qualitative interviews with seven practitioners(conducted until saturation), quantitative surveys of 51 practitioners, and static validation of results with four industry experts in AI, this study explores how personal values, emerging roles, and awareness of AIs societal impact influence responsible decision-making in RSE for AI. A key finding is the gap between the current state of the art and actual practice in RSE for AI, particularly in the failure to operationalize ethical and responsible decision-making within the software engineering life cycle for AI. While ethical issues in RSE for AI largely mirror those found in broader SE process, the study highlights a distinct lack of operational frameworks and resources to guide RSE practices for AI effectively. The results reveal that current ethical guidelines are insufficiently implemented at the operational level, reinforcing the complexity of embedding ethics throughout the software engineering life cycle. The study concludes that interdisciplinary collaboration, H-shaped competencies(Ethical-Technical dual competence), and a strong organizational culture of ethics are critical for fostering RSE practices for AI, with a particular focus on transparency and accountability.

This article focuses on several significant bounds of $q$-numerical radius $w_q(A)$ for sectorial matrix $A$ which refine and generalize previously established bounds. One of the significant bounds we have derived is as follows: \[\frac{|q|^2\cos^2\alpha}{2} \|A^*A+AA^*\| \le w_q^2(A)\le \frac{\left(\sqrt{(1-|q|^2)\left(1+2sin^2(\alpha)\right)}+ |q|\right)^2}{2} \|A^*A+AA^*\|,\] where $ A $ is a sectorial matrix. Also, upper bounds for commutator and anti-commutator matrices and relations between $w_q(A^t)$ and $w_q^t(A)$ for non-integral power $t\in [0,1]$ are also obtained. Moreover, a few significant estimations of $q$-numerical radius of off-diagonal $2\times2$ operator matrices are developed.

In this paper, we study the rotation curves of the Milky Way galaxy (MW) and Andromeda galaxy (M31) by considering its bulge, disk, and halo components. We model the bulge region by the widely accepted de Vaucouleur's law and the disk region by the well established exponential profile. In order to understand the distribution of dark matter in the halo region, we consider three different dark matter profiles in the framework of the standard $\Lambda$CDM model namely, Navarro-Frenk-White (NFW), Hernquist and Einasto profiles. We use recent datasets of rotation curves of the Milky Way and Andromeda galaxy. The data consist of rotation velocities of the stars and gas in the galaxy as a function of the radial distance from the center. Using Bayesian statistics, we perform an overall fit including all the components, i.e., bulge, disk and halo with the data. Our results indicate that the NFW and Hernquist profiles are in concordance with the observational data points. However, the Einasto profile poorly explains the behaviour of dark matter in both the galaxies., Comment: 22 pages, 8 figures, and 2 tables. Comments are welcome!

Variability studies offer a compelling glimpse into black hole dynamics, and NICER's (Neutron Star Interior Composition Explorer) remarkable temporal resolution propels us even further. NICER observations of an Active Galactic Nucleus (AGN), NGC 4051, have charted the geometry of the emission region of the central supermassive black hole. Our investigation of X-ray variability in NGC 4051 has detected extreme variations spanning a factor of 40 to 50 over a mere 10 to 12 hours. For the first time, we have constrained the X-ray Power Spectral Density (PSD) of the source to 0.1 Hz, corresponding to a temporal frequency of 10,000 Hz in a galactic X-ray binary (GXRB) with a mass of 10 M_{\odot}. No extra high-frequency break/bend or any quasi-periodic oscillations are found. Through detailed analysis of energy-dependent PSDs, we found that the PSD normalization, the high-frequency PSD slope as well as the bending frequency remains consistent across all energies within the 0.3-3 keV band, revealing the presence of a constant temperature corona. These significant findings impose critical constraints on current models of X-ray emission and variability in AGN., Comment: ApJ Letters (in press), 11 pages, 8 figures

E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that leverages the above data sources to transform initial user queries into pseudo-product representations. By tapping into these external data sources, our model can infer users' potential purchase intent from their limited queries and capture query relevant product features. We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval in both controlled and real-world experiments. Our ablation studies confirm that the proposed transformer architecture and integrated learning strategies enable the mining of key data sources to infer purchase intent, extract product features, and enhance the transformation pipeline from queries to more accurate pseudo-product representations., Comment: Published in IEEE Big Data Conference 2024, Washington DC

A 2.1-year periodic oscillation of the gamma-ray flux from the blazar PG 1553+113 has previously been tentatively identified in almost 7 year of data from the Fermi Large Area Telescope. After 15 years of Fermi sky-survey observations, doubling the total time range, we report >7 cycle gamma-ray modulation with an estimated significance of 4 sigma against stochastic red noise. Independent determinations of oscillation period and phase in the earlier and the new data are in close agreement (chance probability <0.01). Pulse timing over the full light curve is also consistent with a coherent periodicity. Multiwavelength new data from Swift X-Ray Telescope, Burst Alert Telescope, and UVOT, and from KAIT, Catalina Sky Survey, All-Sky Automated Survey for Supernovae, and Owens Valley Radio Observatory ground-based observatories as well as archival Rossi X-Ray Timing Explorer satellite-All Sky Monitor data, published optical data of Tuorla, and optical historical Harvard plates data are included in our work. Optical and radio light curves show clear correlations with the gamma-ray modulation, possibly with a nonconstant time lag for the radio flux. We interpret the gamma-ray periodicity as possibly arising from a pulsational accretion flow in a sub-parsec binary supermassive black hole system of elevated mass ratio, with orbital modulation of the supplied material and energy in the jet. Other astrophysical scenarios introduced include instabilities, disk and jet precession, rotation or nutation, and perturbations by massive stars or intermediate-mass black holes in polar orbit., Comment: 23 pages, 14 figures, 20 plots, 1 table, accepted and published by The Astrophysical Journal. Article produced by the Fermi Large Area Telescope (LAT) Collaboration

CTA 102 is a $\gamma$-ray bright blazar that exhibited multiple flares in observations by the Large Area Telescope on board the Fermi Gamma-Ray Space Telescope during the period of 2016-2018. We present results from the analysis of multi-wavelength light curves aiming at revealing the nature of $\gamma$-ray flares from the relativistic jet in the blazar. We analyse radio, optical, X-ray, and $\gamma$-ray data obtained in a period from 2012 September 29 to 2018 October 8. We identify six flares in the $\gamma$-ray light curve, showing a harder-when-brighter-trend in the $\gamma$-ray spectra. We perform a cross-correlation analysis of the multi-wavelength light curves. We find nearly zero time lags between the $\gamma$-ray and optical and X-ray light curves, implying a common spatial origin for the emission in these bands. We find significant correlations between the $\gamma$-ray and radio light curves as well as negative/positive time lags with the $\gamma$-ray emission lagging/leading the radio during different flaring periods. The time lags between $\gamma$-ray and radio emission propose the presence of multiple $\gamma$-ray emission sites in the source. As seen in 43 GHz images from the Very Long Baseline Array, two moving disturbances (or shocks) were newly ejected from the radio core. The $\gamma$-ray flares from 2016 to 2017 are temporally coincident with the interaction between a traveling shock and a quasi-stationary one at $\sim$0.1 mas from the core. The other shock is found to emerge from the core nearly simultaneous with the $\gamma$-ray flare in 2018. Our results suggest that the $\gamma$-ray flares originated from shock-shock interactions., Comment: 14 pages, 15 figures, 4 tables, accepted for publication in A&A

The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing medical-related human activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions that are critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method for MRHA detection, leveraging multi-stage deep learning techniques integrated with IoT. The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions (MBConv) blocks, followed by ConvLSTM to capture spatio-temporal patterns. A classification module with global average pooling, a fully connected layer, and a dropout layer generates the final predictions. The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets, focusing on MRHA, such as sneezing, falling, walking, sitting, etc. It achieves 94.85% accuracy for cross-subject evaluations and 96.45% for cross-view evaluations on NTU RGB+D 120, along with 89.00% accuracy on HMDB51. Additionally, the system integrates IoT capabilities using a Raspberry Pi and GSM module, delivering real-time alerts via Twilios SMS service to caregivers and patients. This scalable and efficient solution bridges the gap between HMR and IoT, advancing patient monitoring, improving healthcare outcomes, and reducing costs.

Fungi undergo dynamic morphological transformations throughout their lifecycle, forming intricate networks as they transition from spores to mature mycelium structures. To support the study of these time-dependent processes, we present a synthetic, time-aligned image dataset that models key stages of fungal growth. This dataset systematically captures phenomena such as spore size reduction, branching dynamics, and the emergence of complex mycelium networks. The controlled generation process ensures temporal consistency, scalability, and structural alignment, addressing the limitations of real-world fungal datasets. Optimized for deep learning (DL) applications, this dataset facilitates the development of models for classifying growth stages, predicting fungal development, and analyzing morphological patterns over time. With applications spanning agriculture, medicine, and industrial mycology, this resource provides a robust foundation for automating fungal analysis, enhancing disease monitoring, and advancing fungal biology research through artificial intelligence., Comment: 8 pages, 3 figures, 1 table, 1 algorithm

The Solar Ultraviolet Imaging Telescope (SUIT) is an instrument on the Aditya-L1 mission of the Indian Space Research Organization (ISRO) launched on September 02, 2023. SUIT continuously provides, near-simultaneous full-disk and region-of-interest images of the Sun, slicing through the photosphere and chromosphere and covering a field of view up to 1.5 solar radii. For this purpose, SUIT uses 11 filters tuned at different wavelengths in the 200{--}400~nm range, including the Mg~{\sc ii} h~and~k and Ca~{\sc ii}~H spectral lines. The observations made by SUIT help us understand the magnetic coupling of the lower and middle solar atmosphere. In addition, for the first time, it allows the measurements of spatially resolved solar broad-band radiation in the near and mid ultraviolet, which will help constrain the variability of the solar ultraviolet irradiance in a wavelength range that is central for the chemistry of the Earth's atmosphere. This paper discusses the details of the instrument and data products., Comment: 37 pages, Accepted for Publication in Solar Physics

The rise in cybercrime and the complexity of multilingual and code-mixed complaints present significant challenges for law enforcement and cybersecurity agencies. These organizations need automated, scalable methods to identify crime types, enabling efficient processing and prioritization of large complaint volumes. Manual triaging is inefficient, and traditional machine learning methods fail to capture the semantic and contextual nuances of textual cybercrime complaints. Moreover, the lack of publicly available datasets and privacy concerns hinder the research to present robust solutions. To address these challenges, we propose a framework for automated cybercrime complaint classification. The framework leverages Hinglish-adapted transformers, such as HingBERT and HingRoBERTa, to handle code-mixed inputs effectively. We employ the real-world dataset provided by Indian Cybercrime Coordination Centre (I4C) during CyberGuard AI Hackathon 2024. We employ GenAI open source model-based data augmentation method to address class imbalance. We also employ privacy-aware preprocessing to ensure compliance with ethical standards while maintaining data integrity. Our solution achieves significant performance improvements, with HingRoBERTa attaining an accuracy of 74.41% and an F1-score of 71.49%. We also develop ready-to-use tool by integrating Django REST backend with a modern frontend. The developed tool is scalable and ready for real-world deployment in platforms like the National Cyber Crime Reporting Portal. This work bridges critical gaps in cybercrime complaint management, offering a scalable, privacy-conscious, and adaptable solution for modern cybersecurity challenges.

Color centers in diamond, such as the GeV center, are promising candidates for quantum-based applications. Here, we investigate the impact of strain on the zero-phonon line (ZPL) position of GeV$^0$. Both hydrostatic and linear strain are modeled using density functional theory for GeV$^0$ concentrations of $1.61$ \% down to $0.10$ \%. We present qualitative and quantitative differences between the two strain types: for hydrostatic tensile and compressive strain, red-and blue-shifted ZPL positions are expected, respectively, with a linear relation between the ZPL shift and the experienced stress. By calculating the ZPL shift for varying GeV$^0$ concentrations, a shift of $0.15$ nm/GPa ($0.38$ meV/GPa) is obtained at experimentally relevant concentrations using a hybrid functional. In contrast, only red-shifted ZPL are found for tensile and compressive linear strain along the $\langle100\rangle$ direction. The calculated ZPL shift exceeds that of hydrostatic strain by at least one order of magnitude, with a significant difference between tensile and compressive strains: $3.2$ and $4.8$ nm/GPa ($8.1$ and $11.7$ meV/GPa), respectively. In addition, a peak broadening is expected due to the lifted degeneracy of the GeV$^0$ $e_g$ state, calculated to be about $6$ meV/GPa. These calculated results are placed in perspective with experimental observations, showing values of ZPL shifts and splittings of comparable magnitude., Comment: 13 pages, 6 figures, 1 table

Magnons can endow quantum devices with new functionalities. Assessing their potential requires precise characterization of magnon properties. Here, we use a superconducting qubit to probe magnons in a ferrimagnet over a range of about 2000 excitations. Using qubit control and parametrically induced qubit-magnon interactions we demonstrate few-excitation sensitive detection of magnons and are able to accurately resolve their decay. These results introduce quantum circuits as high-dynamic range probes for magnons and provide an avenue toward sensitive detection of nontrivial magnon dynamics., Comment: 11 pages, 9 Figures

The BB84 quantum key distribution protocol set the foundation for achieving secure quantum communication. Since its inception, significant advancements have aimed to overcome experimental challenges and enhance security. In this paper, we report the implementation of a passive polarization-encoded BB84 protocol using a heralded single-photon source. By passively and randomly encoding polarization states with beam splitters and half-wave plates, the setup avoids active modulation, simplifying design and enhancing security against side-channel attacks. The heralded single-photon source ensures a low probability of multi-photon emissions, eliminating the need for decoy states and mitigating photon number splitting vulnerabilities. The quality of the single-photon source is certified by measuring the second-order correlation function at zero delay, $g^{2}(0)=0.0408\pm0.0008$, confirming a very low probability of multi-photon events. Compared to conventional BB84 or BBM92 protocols, our protocol provides optimized resource trade-offs, with fewer detectors (compared to BBM92) and no reliance on external quantum random number generators (compared to typical BB84) to drive Alice's encoding scheme. Our implementation achieved a quantum bit error rate of 7% and a secure key rate of 5 kbps. These results underscore the practical, secure, and resource-efficient framework our protocol offers for scalable quantum communication technologies., Comment: 7 pages, 7 figures

Two-dimensional (2D) transition metal di-chalcogenide layers with high electrical conductivity and spin-orbit coupling (SOC) can find huge potential in spintronic devices. With limited success of 2D spin Hall material development, we demonstrate vanadium (V) substitutionally doped monolayer MoS2 (VMS) as a potential spin Hall material having tunable electrical conductivity, SOC strength, and room temperature magnetism. Systematic enhancement in the electrical conductivity is observed with the extent of V doping, where it is enhanced from ~0.3 S/m of MoS2 to ~100000 S/m upon doping to the level of 9 atomic%. Ferromagnetic resonance (FMR) based spin-pumping experiments indicate the spin transport across the junction of permalloy (Py) and VMS. Spin-torque FMR measurements demonstrate the suggesting latter's potential as a spin-orbit torque layer in 2D spintronic devices.

The integration of Distributed Energy Resources (DERs) into power distribution systems has made microgrids foundational to grid modernization. These DERs, connected through power electronic inverters, create power electronics dominated grid architecture, introducing unique challenges for fault detection. While external line faults are widely studied, inverter faults remain a critical yet underexplored issue. This paper proposes various data mining techniques for the effective detection and localization of inverter faults-essential for preventing catastrophic grid failures. Furthermore, the difficulty of differentiating between system anomalies and internal inverter faults within Power Electronics-Driven Grids (PEDGs) is addressed. To enhance grid resilience, this work applies advanced artificial intelligence methods to distinguish anomalies from true internal faults, identifying the specific malfunctioning switch. The proposed FaultNet-ML methodology is validated on a 9-bus system dominated by inverters, illustrating its robustness in a PEDG environment., Comment: 5 pages, 2 figures, submitted to 2025 IEEE Power and Energy Society General Meeting (PESGM 2025), Austin, TX

Clinical diagnosis of breast malignancy (BM) is a challenging problem in the recent era. In particular, Deep learning (DL) models have continued to offer important solutions for early BM diagnosis but their performance experiences overfitting due to the limited volume of breast ultrasound (BUS) image data. Further, large BUS datasets are difficult to manage due to privacy and legal concerns. Hence, image augmentation is a necessary and challenging step to improve the performance of the DL models. However, the current DL-based augmentation models are inadequate and operate as a black box resulting lack of information and justifications about their suitability and efficacy. Additionally, pre and post-augmentation need high-performance computational resources and time to produce the augmented image and evaluate the model performance. Thus, this study aims to develop a novel efficient augmentation approach for BUS images with advanced neural style transfer (NST) and Explainable AI (XAI) harnessing GPU-based parallel infrastructure. We scale and distribute the training of the augmentation model across 8 GPUs using the Horovod framework on a DGX cluster, achieving a 5.09 speedup while maintaining the model's accuracy. The proposed model is evaluated on 800 (348 benign and 452 malignant) BUS images and its performance is analyzed with other progressive techniques, using different quantitative analyses. The result indicates that the proposed approach can successfully augment the BUS images with 92.47% accuracy.

This research paper examines the feasibility and stability of compact stars in the context of $f(\mathcal{Q})$ theory, where $\mathcal{Q}$ represents the non-metricity scalar. To achieve this objective, a static spherical line element is assumed in the interior region and the Schwarzschild spacetime is used in the exterior region of the star. The unknown constants are determined by using the Darmois junction conditions. We consider a specific model of this theory to investigate the viability of compact stars through various physical quantities such as matter contents, energy bounds, anisotropy and state parameters. The stability states for the stellar objects under consideration are determined by the speed of sound and adiabatic index, respectively. The resulting data indicate that the compact stars in this modified framework are physically viable and stable., Comment: 33 pages, 12 figures

The metric dimension of a graph measures how uniquely vertices may be identified using a set of landmark vertices. This concept is frequently used in the study of network architecture, location-based problems and communication. Given a graph $G$, the metric dimension, denoted as $\dim(G)$, is the minimum size of a resolving set, a subset of vertices such that for every pair of vertices in $G$, there exists a vertex in the resolving set whose shortest path distance to the two vertices is different. This subset of vertices helps to uniquely determine the location of other vertices in the graph. A basis is a resolving set with a least cardinality. Finding a basis is a problem with practical applications in network design, where it is important to efficiently locate and identify nodes based on a limited set of reference points. The Cartesian product of $P_m$ and $P_n$ is the grid network in network science. In this paper, we investigate two novel types of grids in network science: the Villarceau grid Type I and Type II. For each of these grid types, we find the precise metric dimension.

Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news for many among the younger population. Fact verification, thus, becomes an important task and many media sites contribute to the cause. Manual fact verification is a tedious task, given the volume of fake news online. The Factify5WQA shared task aims to increase research towards automated fake news detection by providing a dataset with an aspect-based question answering based fact verification method. Each claim and its supporting document is associated with 5W questions that help compare the two information sources. The objective performance measure in the task is done by comparing answers using BLEU score to measure the accuracy of the answers, followed by an accuracy measure of the classification. The task had submissions using custom training setup and pre-trained language-models among others. The best performing team posted an accuracy of 69.56%, which is a near 35% improvement over the baseline., Comment: Accepted at defactify3@aaai2024

Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security.