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We investigate the astrophysical consequences of black holes in asymptotically safe quantum gravity, characterized by the parameters $S_0$, $S_2$, $m_0$, and $m_2$, in addition to the black hole mass $M$. To evaluate the physical validity of the fundamental asymptotically safe theory of quantum gravity black hole solutions, we analyze their gravitational lensing properties in the strong field regime. Specifically, we examine the shadow cast by the asymptotically safe quantum gravity black hole and constrain its parameters using observational data from the M87* and Sgr A* supermassive black holes. Our analysis reveals that, within the $1\sigma$ confidence level, a significant portion of the parameter space for asymptotically safe quantum gravity black holes is consistent with the Event Horizon Telescope (EHT) observations of M87* and Sgr A*. This suggests these black holes are plausible candidates for describing astrophysical black holes. As an additional observational test, we perform a detailed investigation of the strong gravitational lensing properties of these black holes. We explore the fundamental strong lensing observables in detail, including the angular positions and separations of the lensed images, the relative magnifications, the radius of the outermost Einstein ring, and the relativistic time delay between images. We compare the predictions of the asymptotically safe quantum gravity black hole for each observable with those of the classical Schwarzschild solution using realistic astrophysical data. Our findings provide a pathway for testing asymptotically safe quantum gravity at galactic and extragalactic scales, offering new insights into the observational properties of black hole solutions within this framework., Comment: 26 pages, 38 figures

Ophthalmic diseases represent a significant global health issue, necessitating the use of advanced precise diagnostic tools. Optical Coherence Tomography (OCT) imagery which offers high-resolution cross-sectional images of the retina has become a pivotal imaging modality in ophthalmology. Traditionally physicians have manually detected various diseases and biomarkers from such diagnostic imagery. In recent times, deep learning techniques have been extensively used for medical diagnostic tasks enabling fast and precise diagnosis. This paper presents a novel approach for ophthalmic biomarker detection using an ensemble of Convolutional Neural Network (CNN) and Vision Transformer. While CNNs are good for feature extraction within the local context of the image, transformers are known for their ability to extract features from the global context of the image. Using an ensemble of both techniques allows us to harness the best of both worlds. Our method has been implemented on the OLIVES dataset to detect 6 major biomarkers from the OCT images and shows significant improvement of the macro averaged F1 score on the dataset., Comment: 5 pages

In our study, we examine a 2D radiation, relativistic, magnetohydrodynamics (Rad-RMHD) accretion flows around a spinning supermassive black hole. We begin by setting an initial equilibrium torus around the black hole, with an embedded initial magnetic field inside the torus. The strength of the initial magnetic field is determined by the plasma beta parameter, which is the ratio of the gas pressure to the magnetic pressure. In this paper, we perform a comparative study of the `magnetically arrested disc (MAD)' and `standard and normal evolution (SANE)' states. We observe that MAD state is possible for comparatively high initial magnetic field strength flow. Additionally, we also adopt a self-consistent two-temperature model to evaluate the luminosity and energy spectrum for our model. We observe that the total luminosity is mostly dominated by bremsstrahlung luminosity compared to the synchrotron luminosity due to the presence of highly dense torus. We also identify similar quasi-periodic oscillations (QPOs) for both MAD and SANE states based on power density spectrum analysis. Furthermore, our comparative study of the energy spectrum does not reveal any characteristic differences between MAD and SANE states. Lastly, we note that the MAD state is possible for both prograde and retrograde accretion flow., Comment: 21 pages, 12 figures, Accepted for publication in ApJ

Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full potential of the quantum state space. It is thus a crucial guidepost to know when selecting a particular PQC ansatz. However, the existing technique for expressibility computation through statistical estimation requires a large number of samples, which poses significant challenges due to time and computational resource constraints. This paper introduces a novel approach for expressibility estimation of PQCs using Graph Neural Networks (GNNs). We demonstrate the predictive power of our GNN model with a dataset consisting of 25,000 samples from the noiseless IBM QASM Simulator and 12,000 samples from three distinct noisy quantum backends. The model accurately estimates expressibility, with root mean square errors (RMSE) of 0.05 and 0.06 for the noiseless and noisy backends, respectively. We compare our model's predictions with reference circuits [Sim and others, QuTe'2019] and IBM Qiskit's hardware-efficient ansatz sets to further evaluate our model's performance. Our experimental evaluation in noiseless and noisy scenarios reveals a close alignment with ground truth expressibility values, highlighting the model's efficacy. Moreover, our model exhibits promising extrapolation capabilities, predicting expressibility values with low RMSE for out-of-range qubit circuits trained solely on only up to 5-qubit circuit sets. This work thus provides a reliable means of efficiently evaluating the expressibility of diverse PQCs on noiseless simulators and hardware.

We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we also share the IndicInstruct dataset, which is a collection of diverse instruction-tuning datasets to enable further research for Indic LLMs. Additionally, we present evaluation benchmarks and a framework for assessing LLM performance across tasks in Hindi. Currently, Airavata supports Hindi, but we plan to expand this to all 22 scheduled Indic languages. You can access all artifacts at https://ai4bharat.github.io/airavata., Comment: Work in progress

This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages that use non-Roman scripts. We propose an approach that utilizes the romanized form of text as an interface for LLMs, hypothesizing that its frequent informal use and shared tokens with English enhance cross-lingual alignment. Our approach involves the continual pretraining of an English LLM like Llama 2 on romanized text of non-English, non-Roman script languages, followed by instruction tuning on romanized data. The results indicate that romanized text not only reduces token fertility by 2x-4x but also matches or outperforms native script representation across various NLU, NLG, and MT tasks. Moreover, the embeddings computed on romanized text exhibit closer alignment with their English translations than those from the native script. Our approach presents a promising direction for leveraging the power of English LLMs in languages traditionally underrepresented in NLP. Our code is available on https://github.com/AI4Bharat/romansetu., Comment: Accepted to ACL 2024

Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about the architecture decisions made in quantum software development, which have a significant influence on the functionality, performance, scalability, and reliability of these systems. The study aims to empirically investigate and analyze architecture decisions made during the development of quantum software systems, identifying prevalent challenges and limitations by using the posts and issues from Stack Exchange and GitHub. We used a qualitative approach to analyze the obtained data from Stack Exchange Sites and GitHub projects. Specifically, we collected data from 385 issues (from 87 GitHub projects) and 70 posts (from three Stack Exchange sites) related to architecture decisions in quantum software development. The results show that in quantum software development (1) architecture decisions are articulated in six linguistic patterns, the most common of which are Solution Proposal and Information Giving, (2) the two major categories of architectural decisions are Implementation Decision and Technology Decision, (3) Softwar Development Tools are the most common application domain among the twenty application domains identified, (4) Maintainability is the most frequently considered quality attribute, and (5) Design Issues and High Error Rates are the major limitations and challenges that practitioners face when making architecture decisions in quantum software development. Our results show that the limitations and challenges encountered in architecture decision-making during the development of quantum software systems are strongly linked to the particular features (e.g., quantum entanglement, superposition, and decoherence) of those systems., Comment: Preprint accepted for publication in Information and Software Technology, 2024

The state of security demands innovative solutions to defend against targeted attacks due to the growing sophistication of cyber threats. This study explores the nefarious tactic known as "watering hole attacks using supervised neural networks to detect and prevent these attacks. The neural network identifies patterns in website behavior and network traffic associated with such attacks. Testing on a dataset of confirmed attacks shows a 99% detection rate with a mere 0.1% false positive rate, demonstrating the model's effectiveness. In terms of prevention, the model successfully stops 95% of attacks, providing robust user protection. The study also suggests mitigation strategies, including web filtering solutions, user education, and security controls. Overall, this research presents a promising solution for countering watering hole attacks, offering strong detection, prevention, and mitigation strategies.

We perform axisymmetric, two-dimensional magnetohydrodynamic (MHD) simulations to investigate accretion flows around spinning AGN. To mimic the space-time geometry of spinning black holes, we consider effective Kerr potential, and the mass of the black holes is $10^8 M_{\odot}$. We initialize the accretion disc with a magnetized torus by adopting the toroidal component of the magnetic vector potential. The initial magnetic field strength is set by using the plasma beta parameter ($\beta_0$). We observe self-consistent turbulence generated by magneto rotational instability (MRI) in the disc. The MRI turbulence transports angular momentum in the disc, resulting in an angular momentum distribution that approaches a Keplerian distribution. We investigate the effect of the magnetic field on the dynamics of the torus and associated mass outflow from the disc around a maximally spinning black hole $(a_k = 0.99)$. For the purpose of our analysis, we investigate the magnetic state of our simulation model. The model $\beta_0 = 10$ indicates the behaviour similar to the "magnetically arrested disk (MAD)'' state, and all the other low magnetic model remains in the SANE state. We observe that mass outflow rates are significantly enhanced with the increased magnetic field in the disc. We find a positive correlation between the magnetic field and mass outflow rates. We also investigate the effect of black hole spin on the magnetized torus evolution. However, we have not found any significant effect of black hole spin on mass outflows in our model. Finally, we discuss the possible astrophysical applications of our simulation results., Comment: 15 pages, 13 figures (2 appendix figures), Accepted for publication in MNRAS

We investigate the properties of spiral shocks in a steady, adiabatic, non-axisymmetric, self-gravitating, mass-outflowing accretion disk around a compact object. We obtain the accretion-ejection solutions in a gaseous galactic disk and apply them to the spiral galaxies to investigate the possible physical connections between some galaxy observational quantities. The self-gravitating disk potential is considered following Mestel's (1963) prescription. The spiral shock-induced accretion-ejection solutions are obtained following the point-wise self-similar approach. We observe that the self-gravitating disk profoundly affects the dynamics of the spiral structure of the disk and the properties of the spiral shocks. We find that the observational dispersion between the pitch angle and shear rate and between the pitch angle and star formation rate in spiral galaxies contains some important physical information. There are large differences in star formation rates among galaxies with similar pitch angles, which may be explained by the different star formation efficiencies caused by the distinct galactic ambient conditions., Comment: 13 pages, 7 figures, accepted for publication in Astronomy & Astrophysics

Parameterized Quantum Circuits (PQCs) are essential to quantum machine learning and optimization algorithms. The expressibility of PQCs, which measures their ability to represent a wide range of quantum states, is a critical factor influencing their efficacy in solving quantum problems. However, the existing technique for computing expressibility relies on statistically estimating it through classical simulations, which requires many samples. In this work, we propose a novel method based on Graph Neural Networks (GNNs) for predicting the expressibility of PQCs. By leveraging the graph-based representation of PQCs, our GNN-based model captures intricate relationships between circuit parameters and their resulting expressibility. We train the GNN model on a comprehensive dataset of PQCs annotated with their expressibility values. Experimental evaluation on a four thousand random PQC dataset and IBM Qiskit's hardware efficient ansatz sets demonstrates the superior performance of our approach, achieving a root mean square error (RMSE) of 0.03 and 0.06, respectively.

Angiography is widely used to detect, diagnose, and treat cerebrovascular diseases. While numerous techniques have been proposed to segment the vascular network from different imaging modalities, deep learning (DL) has emerged as a promising approach. However, existing DL methods often depend on proprietary datasets and extensive manual annotation. Moreover, the availability of pre-trained networks specifically for medical domains and 3D volumes is limited. To overcome these challenges, we propose a few-shot learning approach called VesselShot for cerebrovascular segmentation. VesselShot leverages knowledge from a few annotated support images and mitigates the scarcity of labeled data and the need for extensive annotation in cerebral blood vessel segmentation. We evaluated the performance of VesselShot using the publicly available TubeTK dataset for the segmentation task, achieving a mean Dice coefficient (DC) of 0.62(0.03).

In this study, we have used density functional theory (DFT) based first-principles investigation of the physical properties of prospective NbAlB compound for the first time. From the analysis of the cohesive energy and enthalpy of formation, it was found that NbAlB is chemically stable. The physical properties of NbAlB have been compared and contrasted with those obtained for MoAlB. Both these MAB phases are elastically anisotropic, mechanically stable, machinable and brittle materials. Structural and elastic features reflect the layered features. The estimated hardness of NbAlB is 19.0 GPa comparable to that of MoAlB (20.8 GPa) suggesting that predicted NbAlB is a hard compound and is suitable for heavy duty industrial applications. NbAlB is more machinable than MoAlB. Electronic band structure calculations reveal conventional metallic behavior with the electronic density of states at the Fermi level arising mainly due to the Nb 4d orbitals in NbAlB. The electronic density of states at the Fermi level is significantly higher in NbAlB in comparison to MoAlB, indicating that NbAlB is expected to exhibit higher level of electrical conductivity. Electronic dispersion is highly anisotropic for both MoAlB and NbAlB with substantially large electronic effective masses in the out-of-plane directions. The bonding features have been elucidated via the analysis of the band structure and charge density distribution. Both the compounds have mixed covalent, ionic and metallic bonding characteristics. The Fermi surfaces of MoAlB and NbAlB consists of electron and hole like sheets. The Debye temperatures of MoAlB and NbAlB are comparable. The estimated melting temperature of NbAlB is somewhat lower than that of MoAlB. NbAlB shows excellent reflection characteristics suitable to be used as an efficient solar reflector. NbAlB is also expected to absorb ultraviolet radiation very effectively.

We examine the time delay between radio and X-ray and between narrow radio frequency flares in Sagittarius A* (Sgr A*), from analyses of the synchrotron, bremsstrahlung and monochromatic luminosity curves. Using the results of 2D relativistic radiation magnetohydrodynamic (MHD) simulations based on the shock oscillation model, we find three types of time delay between the synchrotron and bremsstrahlung emissions: Type A with a time delay of 2 -- 3 h on the shock descending branch, Type B with no time delay and Type C with an inverse time delay of 0.5 -- 1 h on the shock ascending branch. The time delays in Types A and C are interpreted as a transit time of Alfv\'{e}n and acoustic waves between both emission dominant regions, respectively. The delay times between 22 and 43 GHz flares and between 8 and 10 GHz flares are $\sim$ 13 -- 26 min and 13 min, respectively, while the inverse delay also occurs dependently on the shock location branch. These time delays between the narrow radio bands are interpreted as the transit time of the acoustic wave between the frequency-dependent effective radii $R_{\tau_{\rm \nu=1}}$, at which the optical depth $\tau_{\rm \nu}$ at the accretion disc surface becomes $\sim$ unity. The shock oscillation model explains well the observed delay times of 0.5 -- 5 h between radio and X-ray, 20 -- 30 min between 22 and 43 GHz and $\sim$ 18 min between 8 and 10 GHz in Sgr A*., Comment: 14 pages, 14 figures, (accepted for publication in MNRAS)