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Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches.

Testing whether a sample survey is a credible representation of the population is an important question to ensure the validity of any downstream research. While this problem, in general, does not have an efficient solution, one might take a task-based approach and aim to understand whether a certain data analysis tool, like linear regression, would yield similar answers both on the population and the sample survey. In this paper, we design an algorithm to test the credibility of a sample survey in terms of linear regression. In other words, we design an algorithm that can certify if a sample survey is good enough to guarantee the correctness of data analysis done using linear regression tools. Nowadays, one is naturally concerned about data privacy in surveys. Thus, we further test the credibility of surveys published in a differentially private manner. Specifically, we focus on Local Differential Privacy (LDP), which is a standard technique to ensure privacy in surveys where the survey participants might not trust the aggregator. We extend our algorithm to work even when the data analysis has been done using surveys with LDP. In the process, we also propose an algorithm that learns with high probability the guarantees a linear regression model on a survey published with LDP. Our algorithm also serves as a mechanism to learn linear regression models from data corrupted with noise coming from any subexponential distribution. We prove that it achieves the optimal estimation error bound for $\ell_1$ linear regression, which might be of broader interest. We prove the theoretical correctness of our algorithms while trying to reduce the sample complexity for both public and private surveys. We also numerically demonstrate the performance of our algorithms on real and synthetic datasets.

Bianchi type III (BIII) metric is an interesting anisotropic model for studying cosmic anisotropy as it has an additional exponential term multiplied to a directional scale factor. Thus, the cosmological parameters obtained for this BIII metric with the conventional energy-momentum tensor within the framework of a modified gravity theory and the estimation of their values with the help of Hubble, Pantheon plus and other observational data may provide some new information in cosmic evolution. In this work, we have studied the BIII metric under the framework of $f(R,T)$ gravity theory and estimated the values of the cosmological parameters for three different models of this gravity theory by using the Bayesian technique. In our study, we found that all the models show consistent results with the current observations but show deviations in the early stage of the Universe. In one model we have found a sharp discontinuity in the matter-dominated phase of the Universe. Hence through this study, we have found that all the $f(R,T)$ gravity models may not be suitable for studying evolutions and early stages of the Universe in the BIII metric even though they show consistent results with the current observations., Comment: 27 pages, 16 figures

We formulate theoretical modeling approaches and develop practical computational simulation methods for investigating the non-equilibrium statistical mechanics of fluid interfaces with passive and active immersed particles. Our approaches capture phenomena taking into account thermal and dissipative energy exchanges, hydrodynamic coupling, and correlated spontaneous fluctuations. Our methods allow for modeling non-uniform time-varying temperature fields, fluid momentum fields, and their impacts on particle drift-diffusion dynamics. We show how practical stochastic numerical methods can be developed for these systems by performing analysis to factor operators analytically to obtain efficient algorithms for generating the fluctuating fields. We demonstrate our methods in a few simulation studies showing how they can be used to capture particle heating of the interface and the roles of thermal gradients on hydrodynamic fluctuations. The methods developed provide modeling and simulation approaches that can be used for further investigations of non-equilibrium phenomena in active soft materials, complex fluids, and biophysical systems., Comment: 21 pages, 5 figures

Reliable prediction of the All India Summer Monsoon Rainfall (AISMR) is pivotal for informed policymaking for the country, impacting the lives of billions of people. However, accurate simulation of AISMR has been a persistent challenge due to the complex interplay of various muti-scale factors and the inherent variability of the monsoon system. This research focuses on adapting and fine-tuning the latest LLM model, PatchTST, to accurately predict AISMR with a lead time of three months. The fine-tuned PatchTST model, trained with historical AISMR data, the Ni\~no3.4 index, and categorical Indian Ocean Dipole values, outperforms several popular neural network models and statistical models. This fine-tuned LLM model exhibits an exceptionally low RMSE percentage of 0.07% and a Spearman correlation of 0.976. This is particularly impressive, since it is nearly 80% more accurate than the best-performing NN models. The model predicts an above-normal monsoon for the year 2024, with an accumulated rainfall of 921.6 mm in the month of June-September for the entire country., Comment: 3 figures

This paper investigates the existence and uniqueness of the mild solutions and the exact null controllability for a class of non-autonomous parabolic evolution systems with nonlocal conditions in Hilbert spaces. We present sufficient conditions for achieving exact null controllability in these systems using the theory of linear evolution systems and the Schauder fixed point theorem. Importantly, our results do not require the compactness or Lipschitz conditions for the function \( g \) in the nonlocal conditions, which are often needed in other studies. We also provide an example to demonstrate the practical application of our results., Comment: 20 pages, 0 figure

Collaborative human-AI annotation is a promising approach for various tasks with large-scale and complex data. Tools and methods to support effective human-AI collaboration for data annotation are an important direction for research. In this paper, we present CHAIRA: a Collaborative Human-AI Risk Annotation tool that enables human and AI agents to collaboratively annotate online incivility. We leveraged Large Language Models (LLMs) to facilitate the interaction between human and AI annotators and examine four different prompting strategies. The developed CHAIRA system combines multiple prompting approaches with human-AI collaboration for online incivility data annotation. We evaluated CHAIRA on 457 user comments with ground truth labels based on the inter-rater agreement between human and AI coders. We found that the most collaborative prompt supported a high level of agreement between a human agent and AI, comparable to that of two human coders. While the AI missed some implicit incivility that human coders easily identified, it also spotted politically nuanced incivility that human coders overlooked. Our study reveals the benefits and challenges of using AI agents for incivility annotation and provides design implications and best practices for human-AI collaboration in subjective data annotation.

An inherent fragility of quadrotor systems stems from model inaccuracies and external disturbances. These factors hinder performance and compromise the stability of the system, making precise control challenging. Existing model-based approaches either make deterministic assumptions, utilize Gaussian-based representations of uncertainty, or rely on nominal models, all of which often fall short in capturing the complex, multimodal nature of real-world dynamics. This work introduces DroneDiffusion, a novel framework that leverages conditional diffusion models to learn quadrotor dynamics, formulated as a sequence generation task. DroneDiffusion achieves superior generalization to unseen, complex scenarios by capturing the temporal nature of uncertainties and mitigating error propagation. We integrate the learned dynamics with an adaptive controller for trajectory tracking with stability guarantees. Extensive experiments in both simulation and real-world flights demonstrate the robustness of the framework across a range of scenarios, including unfamiliar flight paths and varying payloads, velocities, and wind disturbances.

The capability of standard Geant4 PhysicsLists to address the fragmentation of $^{12}$C$-^{12}$C was assessed through a comparative analysis with experimental cross sections reported by Divay et al. and Dudouet et al. The standard PhysicsLists were found to be inadequate in explaining the fragmentation systematics. To address this limitation, the breakup component of fragmentation was systematically integrated into the standard PhysicsList, which successfully replicated the differential and double differential cross sections for $\alpha$ production. This breakup component was modeled using fresco CDCC-CRC calculations. This novel physics process was then incorporated into the Geant4 framework, facilitating the calculation of dose distributions in water and tissue. The application of this method demonstrated a precise reproduction of the dose deposited at the Bragg peak region, corroborating the experimental data from Liedner et al., thereby enhancing the accurate visibility of dose tailing.

Heavy neutral leptons (HNLs), depending on their mass and mixing, can be efficiently produced in meson decays from the target or absorber in short- to medium-baseline accelerator neutrino experiments, leaving detectable signals through their decays inside the neutrino detectors. We show that the currently running ICARUS experiment at Fermilab can reconstruct the HNL mass and explore new HNL parameter space in the mass range of 70-190 MeV. The mass reconstruction is enabled by two ingredients: (i) simple two-body kinematics of HNL production from stopped kaon decays at the NuMI absorber, followed by HNL decay into a charged-lepton pair and neutrino at the detector, and (ii) high resolution of Liquid Argon Time Projection Chamber (LArTPC) detectors in reconstructing final state particles. Our mass reconstruction method is robust under realistic energy resolution and angular smearing of the charged leptons, and is applicable to any LArTPC detector. We also discuss the synergy between ICARUS and future facilities like DUNE near detector and PIP-II beam dump in probing the HNL parameter space., Comment: 14 pages, 6 figures, 1 table

Ireland's coastline, a critical and dynamic resource, is facing challenges such as erosion, sedimentation, and human activities. Monitoring these changes is a complex task we approach using a combination of satellite imagery and deep learning methods. However, limited research exists in this area, particularly for Ireland. This paper presents the Landsat Irish Coastal Segmentation (LICS) dataset, which aims to facilitate the development of deep learning methods for coastal water body segmentation while addressing modelling challenges specific to Irish meteorology and coastal types. The dataset is used to evaluate various automated approaches for segmentation, with U-NET achieving the highest accuracy of 95.0% among deep learning methods. Nevertheless, the Normalised Difference Water Index (NDWI) benchmark outperformed U-NET with an average accuracy of 97.2%. The study suggests that deep learning approaches can be further improved with more accurate training data and by considering alternative measurements of erosion. The LICS dataset and code are freely available to support reproducible research and further advancements in coastal monitoring efforts.

Observations of the 21 cm signal face significant challenges due to bright astrophysical foregrounds that are several orders of magnitude higher than the brightness of the hydrogen line, along with various systematics. Successful 21 cm experiments require accurate calibration and foreground mitigation. Errors introduced during the calibration process such as systematics, can disrupt the intrinsic frequency smoothness of the foregrounds, leading to power leakage into the Epoch of Reionisation (EoR) window. Therefore, it is essential to develop strategies to effectively address these challenges. In this work, we adopt a stringent approach to identify and address suspected systematics, including malfunctioning antennas, frequency channels corrupted by radio frequency interference (RFI), and other dominant effects. We implement a statistical framework that utilises various data products from the data processing pipeline to derive specific criteria and filters. These criteria and filters are applied at intermediate stages to mitigate systematic propagation from the early stages of data processing. Our analysis focuses on observations from the Murchison Widefield Array (MWA) Phase I configuration. Out of the observations processed by the pipeline, our approach selects 18%, totalling 58 hours, that exhibit fewer systematic effects. The successful selection of observations with reduced systematic dominance enhances our confidence in achieving 21 cm measurements., Comment: 18 pages, 17 figures, 2 tables (accepted for publication in PASA)

Mechanical systems exhibit complex dynamical behavior from harmonic oscillations to chaotic motion. The dynamics undergo qualitative changes due to changes to internal system parameters like stiffness and changes to external forcing. Mapping out complete bifurcation diagrams numerically or experimentally is resource-consuming, or even infeasible. This study uses a data-driven approach to investigate how bifurcations can be learned from a few system response measurements. Particularly, the concept of reservoir computing (RC) is employed. As proof of concept, a minimal training dataset under the resource constraint problem of a Duffing oscillator with harmonic external forcing is provided as training data. Our results indicate that the RC not only learns to represent the system dynamics for the external forcing seen during training, but it also provides qualitatively accurate and robust system response predictions for completely unknown multi-parameter regimes outside the training data. Particularly, while being trained solely on regular period-2 cycle dynamics, the proposed framework correctly predicts higher-order periodic and even chaotic dynamics for out-of-distribution forcing signals., Comment: 11 pages, 4 main figures, 1 supplementary figure

This paper explores key aspects of the theory and applications of conformable fractional order systems. It begins by establishing the existence and uniqueness of the evolution operator for a class of non-autonomous homogeneous systems. Using the Schauder fixed point theorem and the theory of linear evolution systems, we delve into the existence of mild solutions for a class of non-autonomous conformable fractional semi-linear systems. Additionally, the paper addresses the exact null controllability of abstract systems. We present an example to demonstrate the efficiency of the results., Comment: 24 Pages, 0 figures

Oral squamous cell carcinoma (OSCC) represents a significant global health concern, with increasing incidence rates and challenges in early diagnosis and treatment planning. Early detection is crucial for improving patient outcomes and survival rates. Deep learning, a subset of machine learning, has shown remarkable progress in extracting and analyzing crucial information from medical imaging data.EfficientNetB3, an advanced convolutional neural network architecture, has emerged as a leading model for image classification tasks, including medical imaging. Its superior performance, characterized by high accuracy, precision, and recall, makes it particularly promising for OSCC detection and diagnosis. EfficientNetB3 achieved an accuracy of 0.9833, precision of 0.9782, and recall of 0.9782 in our analysis. By leveraging EfficientNetB3 and other deep learning technologies, clinicians can potentially improve the accuracy and efficiency of OSCC diagnosis, leading to more timely interventions and better patient outcomes. This article also discusses the role of deep learning in advancing precision medicine for OSCC and provides insights into prospects and challenges in leveraging this technology for enhanced cancer care., Comment: This paper is 13 pages and 9 picture

Kubernetes, an open-source platform for automating the deployment, scaling, and management of containerized applications, is widely used for its efficiency and scalability. However, its complexity and extensive configuration options often lead to security vulnerabilities if not managed properly. This paper presents a detailed analysis of misconfigurations in Kubernetes environments and their significant impact on system reliability and security. A centralized logging solution was developed to detect such misconfigurations, detailing the integration process with a Kubernetes cluster and the implementation of role-based access control. Utilizing a combination of open-source tools, the solution systematically identifies misconfigurations and aggregates diagnostic data into a central repository. The effectiveness of the solution was evaluated using specific metrics, such as the total cycle time for running the central logging solution against the individual open source tools., Comment: Keywords: Central Logging Solution, Cloud Native Computing Foundation, Amazon Web Services, Application Programming Interface, Command Line Interface, Internet Protocol, Media Access Control, Domain Name System, Common Vulnerabilities Exposures, Information Technology, Role-Based Access Control

Soft interfaces are ubiquitous in nature, governing quintessential hydrodynamics functions, like lubrication, stability and cargo transport. It is shown here how a magnetic force field at a magnetic-nonmagnetic fluid interface results in an ultra-soft interface with nonlinear elasticity and tunable viscous shear properties. The balance between magnetic pressure, viscous stress and Laplace pressure results in a deformed and stable liquid-in-liquid tube with apparent elasticity in the range 2 kPa -10 kPa, possibly extended by a proper choice of liquid properties. Such highly deformable liquid-liquid interfaces of arbitrary shape with vanishing viscous shear open doors to unique microfluidic phenomena, biomaterial flows and complex biosystems mimicking., Comment: 7 pages , 5 figures. arXiv admin note: text overlap with arXiv:2310.14280

We study the kinetic cooling (heating) of old neutron stars due to coherent scattering with relic neutrinos (sterile neutrino dark matter) via Standard Model neutral-current interactions. We take into account several important physical effects, such as gravitational clustering, coherent enhancement, neutron degeneracy and Pauli blocking. We find that the anomalous cooling of nearby neutron stars due to relic neutrino scattering might actually be observable by current and future telescopes operating in the optical to near-infrared frequency band, such as the James Webb Space Telescope (JWST), provided there is a large local relic overdensity that is still allowed. Similarly, the anomalous heating of neutron stars due to coherent scattering with keV-scale sterile neutrino dark matter, could also be observed by JWST or future telescopes, which would probe hitherto unexplored parameter space in the sterile neutrino mass-mixing plane., Comment: 15 pages + references, 7 figures; added references, minor changes are made

Recently, large language models (LLMs) have been gaining a lot of interest due to their adaptability and extensibility in emerging applications, including communication networks. It is anticipated that 6G mobile edge computing networks will be able to support LLMs as a service, as they provide ultra reliable low-latency communications and closed loop massive connectivity. However, LLMs are vulnerable to data and model privacy issues that affect the trustworthiness of LLMs to be deployed for user-based services. In this paper, we explore the security vulnerabilities associated with fine-tuning LLMs in 6G networks, in particular the membership inference attack. We define the characteristics of an attack network that can perform a membership inference attack if the attacker has access to the fine-tuned model for the downstream task. We show that the membership inference attacks are effective for any downstream task, which can lead to a personal data breach when using LLM as a service. The experimental results show that the attack success rate of maximum 92% can be achieved on named entity recognition task. Based on the experimental analysis, we discuss possible defense mechanisms and present possible research directions to make the LLMs more trustworthy in the context of 6G networks., Comment: 7 pages, 4 figures

The cross sections for the $^{12}\mathrm{C}(\gamma,\alpha)^{8}\mathrm{Be}\rightarrow 3\alpha$ reaction have been successfully measured using exclusive coincidence between three $\alpha$ particles, minimizing Compton background. Sequential breakup kinematics are evident, and the cross sections are presented as locally averaged histogram values. Theoretical \textsc{Fresco} CDCC-CRC calculations reproduce the experimental data, showing that the process involves electromagnetic coupling to both $^{8}\mathrm{Be}^{0^+}$ and $^{8}\mathrm{Be}^{2^+}$ states. This study confirms that the $^{12}\mathrm{C}(\gamma,\alpha)^{8}\mathrm{Be}\rightarrow 3\alpha$ reaction proceeds via a sequential mechanism, crucial for understanding its significance in radiotherapy dosimetry.

The Bumblebee vector model of spontaneous Lorentz symmetry breaking (LSB) in Bianchi type I (BI) Universe to observe its effect on cosmological evolution is an interesting aspect of study in anisotropic cosmology. In this study, we have considered a Bumblebee field under vacuum expectation value condition (VEV) with BI metric and studied the cosmological parameters along with observational data. Further, we have studied the effect of anisotropy and the Bumblebee field in cosmic evolution. We have also studied the effect of both anisotropy and Bumblebee field while considering the Universe as a dynamical system. We have found that there are some prominent roles of both anisotropy and the Bumblebee field in cosmic evolutions. We have also observed an elongated matter-dominated phase as compared to standard cosmology. Moreover, while studying the dynamical system analysis, we have also observed the shift of critical points from standard $\Lambda$CDM results showing the anisotropy and the Bumblebee field effect., Comment: 20 pages, 7 figures

Neutrinophilic mediators are well-motivated messenger particles that can probe some of the least known sectors of fundamental physics involving nonstandard interactions of neutrinos with themselves and potentially with dark matter. In particular, light mediators coupling to the active neutrinos will induce new decay modes of the Standard Model mesons (e.g., $\pi^\pm, K^\pm \to \ell^\pm + \nu + \phi$), charged leptons (e.g., $\tau^\pm \to \pi^\pm + \nu + \phi$), and gauge bosons (e.g., $Z \to \nu + \bar{\nu} + \phi$). A common lore is that these decays suffer from infrared divergences in the limit of the vanishing mediator mass, i.e., $m_\phi \to 0$. Here, we show for the first time that including the 1-loop contributions of these mediators to the standard 2-body decays (e.g., $\pi^\pm,\,K^\pm \to \ell^\pm + \nu$, etc.), the infrared divergence from the 3-body decay cancels out exactly by virtue of the Kinoshita-Lee-Nauenberg theorem. Including these cancellation effects, we then update the existing laboratory constraints on neutrinophilic scalar mediators, thereby extending the limits far beyond the decaying parent particle mass and excluding a wider range of parameter space. These new "physical" limits derived here have significant implications for the future detection prospects of nonstandard neutrino (self-)interactions., Comment: 7+3 pages, 4+0 figures and 0+1 table

Metaverse virtual reality (VR) applications enable users to socialise, work, entertain, and study online with immersive experiences beyond the classic PC-based interactions. While the 360-degree immersion enables users to be fully engaged in a virtual scenario, suboptimal Quality-of-Experience (QoE) like poorly displayed 3D graphics, disruptive loading time, or motion lagging caused by degraded network Quality-of-Service (QoS) can be perceived by users much worse (such as dizziness) than a monitor visualisation. This paper empirically measures user QoE of metaverse VR caused by network QoS. Specifically, by focusing on both public social hubs and private user-created events in three popular metaverse VR applications (Rec Room, VRChat and MultiverseVR), we first identify three metrics, including environment freeze level, peripheral content loading time, and control response time, that describe metaverse user experience. By tuning three network QoS parameters (bandwidth, latency, and packet loss), we benchmark each QoE metric's level from excellent to unplayable. Key insights are revealed, such as freeze of metaverse virtual environment is resilient to latency but sensitive to packet loss, and private user-created events demand better network conditions than public social hubs, providing a reference for ISPs to optimise their network QoS for superlative metaverse user experience., Comment: Accepted in Proc. IEEE MetaCom, Hong Kong, China, Aug 2024

Various generalizations of the scalar, axisymmetric Aretakis "horizon hair" for extremal black holes have recently appeared in the literature. In this paper, we present an expression for a non-axisymmetric Aretakis gravitational charge and its potentially observable imprint at a finite distance from the horizon (Ori-coefficient) in extremal Kerr black hole backgrounds. We offer strong and robust numerical evidence for the existence of this hair and its properties. Specifically, we consider the time evolution of horizon penetrating, quadrupolar and (subdominant) octupolar gravitational perturbations with compact support on extremal Kerr (EK) spacetime. We do this by numerically solving the Teukolsky equation and determining the Aretakis charge values on the horizon and at a finite distance from the black hole., Comment: 7 pages, 9 figures

We perform a comprehensive analysis of neutrinoless double beta decay and its interplay with low-energy flavor observables in a radiative neutrino mass model with scalar leptoquarks $S_1(\bar{3},1,1/3)$ and $\widetilde{R}_2(3,2,1/6)$. We carve out the parameter region consistent with constraints from neutrino mass and mixing, collider searches, as well as measurements of several flavor observables, such as muon and electron anomalous magnetic moments, charged lepton flavor violation and rare (semi)leptonic kaon and $B$-meson decays, including the recent anomalies in $R_{D^{(*)}}$ and $B\to K\nu\bar{\nu}$ observables. We perform a global analysis to all existing constraints and show the (anti)correlations between all relevant Yukawa couplings satisfying these restrictions. We find that the most stringent constraint on the parameter space comes from $\mu \to e$ conversion in nuclei and $K^{+} \rightarrow\, \pi^{+}\nu \bar{\nu}$ decay. We also point out a tension between the muon and electron $(g-2)$ anomalies in this context. Taking benchmark points from the combined allowed regions, we study the implications for neutrinoless double beta decay including both the canonical light neutrino and the leptoquark contributions. We find that for normal ordering of neutrino masses, the leptoquark contribution removes the cancellation region that occurs for the canonical case. The effective mass in presence of leptoquarks can lie in the desert region between the standard normal and inverted ordering cases, and this can be probed in future ton-scale experiments like LEGEND-1000 and nEXO., Comment: 43 pages, 9 figures and 6 tables

We present the first detailed study of Standard Model (SM) neutrino tridents involving tau leptons at the near detectors of accelerator neutrino facilities. These processes were previously thought to be negligible, even at future facilities like DUNE, based on approximations that underestimated the tau trident cross sections. Our full $2\to 4$ calculation, including both coherent and incoherent scatterings, reveals that the DUNE near detector will actually get a non-negligible number of tau tridents, which is an important background to new physics searches. We identify promising kinematic features that may allow distinction of tau tridents from the usual neutrino charged-current background at DUNE, and thus could establish the observation of tau tridents for the first time. We also comment on the detection prospects at other accelerator and collider neutrino experiments., Comment: 14 pages, 12 figures, 1 table

We show that if neutrinos are pseudo-Dirac, they can potentially affect the flavor ratio predictions for the high-energy astrophysical neutrino flux observed by IceCube. In this context, we point out a novel matter effect induced by the cosmic neutrino background (C$\nu$B) on the flavor ratio composition. Specifically, the active-sterile neutrino oscillations over the astrophysical baseline lead to an energy-dependent flavor ratio at Earth due to the C$\nu$B matter effect, which is distinguishable from the vacuum oscillation effect, provided there is a local C$\nu$B overdensity. Considering the projected precision of the 3-neutrino oscillation parameter measurements and improved flavor triangle measurements, we show that the next-generation neutrino telescopes, such as IceCube-Gen2 and KM3NeT, can probe the pseudo-Dirac neutrino hypothesis in a distinctive way., Comment: 14 pages, 6 figures; added references in v2

We derive novel black hole solutions in a modified gravity theory, namely the Hu-Sawicki model of $f(R)$ gravity. After obtaining the black hole solution, we study the horizon radius of the black hole from the metric and then analyse the dependence of the model parameters on the horizon. We then use the 6th-order WKB method to study the quasinormal modes of oscillations (QNMs) of the black hole perturbed by a scalar field. The dependence of the amplitude and damping part of the QNMs are analysed with respect to variations in model parameters and the errors associated with the QNMs are also computed. After that, we study some thermodynamic properties associated with the black hole such as its thermodynamic temperature as well as greybody factors. It is found that the black hole has the possibility of showcasing negative temperatures. We also analyse the geodesics and derive the photon sphere radius as well as the shadow radius of the black hole. The photon radius is independent of the model parameters while the shadow radius showed a fair amount of dependence on the model parameters. We tried to constrain the parameters with the help of Keck and VLTI observational data and obtained some bounds on $m$ and $c_{2}$ parameters., Comment: 14 pages, 9 figures; published version

We present multi-frequency (5-345 GHz) and multi-resolution radio observations of 1ES 1927+654, widely considered one of the most unusual and extreme changing-look active galactic nuclei (CL-AGN). The source was first designated a CL-AGN after an optical outburst in late 2017 and has since displayed considerable changes in X-ray emission, including the destruction and rebuilding of the X-ray corona in 2019-2020. Radio observations prior to 2023 show a faint and compact radio source not unusual for radio-quiet AGN. Starting in February 2023, 1ES 1927+654 began exhibiting a radio flare with a steep exponential rise, reaching a peak 60 times previous flux levels, and has maintained this higher level of radio emission for nearly a year. The 5-23 GHz spectrum is broadly similar to gigahertz-peaked radio sources, which are understood to be young radio jets less than ~1000 years old. Recent high-resolution VLBA observations at 23.5 GHz now show resolved extensions on either side of the core, with a separation of 0.14 pc, consistent with a new and mildly relativistic bipolar outflow. A steady increase in the soft X-ray band (0.3-2 keV) concurrent with the radio may be consistent with jet-driven shocked gas, though further observations are needed to test alternate scenarios. This source joins a growing number of CL-AGN and tidal disruption events which show late-time radio activity, years after the initial outburst., Comment: Submitted to ApJ Letters 24 June 2023. 15 pages, 4 figures, 1 table

We investigate the effects of magnetic diffusion on the spectrum of ultra-high energy cosmic rays (UHECRs) from a cosmological perspective. To this end, we consider two modified theories of gravity (MTGs), namely, the $f(R)$ gravity and a symmetric teleparallel gravity, also known as $f(Q)$ gravity. Utilizing these two MTGs, we calculate the suppression in the flux of UHECRs for a collection of sources. Non-evolution (NE) and cosmic star formation rate (SFR) scenarios have been considered in our calculation of the suppression factor. This study also includes a mixed composition scenario involving the nuclei upto iron (Fe). Furthermore, we provide a parameterization of the suppression factor for the proton and also for the mixed compositions within the $f(R)$ and $f(Q)$ theories, considering both NE and SFR scenarios. The influence of the turbulent magnetic field on the suppression factor is also incorporated in our work. Comparative analysis of all our results with the standard $\Lambda$CDM model reveals significant effects of MTGs on the suppression factor that the $f(R)$ power-law model predicts the lowest suppression factor, while the $f(Q)$ model predicts the highest, and interestingly the results from the standard model fall within the range predicted by these two cosmological models., Comment: 19 pages, 12 figures

Controlling light propagation through complex media plays a significant role in a wide range of applications ranging from astronomical observations to microscopy. Although, several advances have been made based on adaptive optics, optical phase conjugation and wavefront shaping, but many of these involve challenges. Recently, controlling light propagation in complex media by simply structuring light has shown promising capabilities. We present experimental and numerical investigations of abruptly autofocusing of circular Airy derivative beams (CADBs) in complex media. We find that up to a relatively high turbulence strength, CADB possesses relatively good abrupt autofocusing, however, efficiency and autofocusing position vary with the strength of turbulence. Further, the spatial distortions in CADB caused by turbulence are quantified by an overlap integral, which shows that CADB possesses reasonably good resilience against the turbulence. The diffraction efficiency of CADB changes by a factor of ~ 1.7 with increasing strength of turbulence from zero to high, indicating good confinement of intensity at autofocusing. The focused beam spot size grows gradually with increasing the strength of turbulence, specifically, it grows by a factor of ~ 2 for a strong turbulence, indicating reasonably good focusing abilities. The results of CADB are compared with a Gaussian beam, and find that CADB possesses superior focusing abilities in turbulent media. We have carried out a detailed analysis of these observations based on Zernike polynomials, which reveals that different kinds of aberrations present in turbulent media leads to distortions in the spatial structure as well as other properties of CADBs. Our results can be used for various applications, such as in biomedical treatment, seismology, optical tweezers and material processing., Comment: 20 pages, 23 figures

Two-way quantum computing (2WQC) represents a novel approach to quantum computing that introduces a CPT version of state preparation. This paper analyses the influence of this approach on Grover's algorithm and compares the behaviour of typical Grover and its 2WQC version in the presence of noise in the system. Our findings indicate that, in an ideal scenario without noise, the 2WQC Grover algorithm exhibits a constant complexity of $\mathcal{O}(1)$. In the presence of noise, the 2WQC Grover algorithm demonstrates greater resilience to different noise models than the standard Grover's algorithm., Comment: Preliminary version of the article, we welcome your comments and suggestions

In this paper, we discuss the capable and isoclinic properties of the tensor square in the context of multiplicative Lie algebras. We also developed the concept of isoclinic extensions and proved several results for multiplicative Lie algebras. Consequently, we demonstrate that covers of a multiplicative Lie algebra are mutually isoclinic.

The tango tree is the first proven $O(\lg \lg n)$-competitive binary search tree (BST). We present the first ever experimental implementation of tango trees and compare the running time of the tango tree with the multi-splay tree and the splay tree on a variety of families of access sequences. We construct access sequences that are intended to test specific properties of BSTs. The results of the other experiments demonstrate the optimality of the splay tree and multi-splay tree on these accesses, while simultaneously demonstrating the tango trees inability to achieve optimality. We prove that the running time of tango trees on the sequential access is $\Theta(n \lg \lg n)$, which provides insight into why the $\Theta(\lg \lg n)$ slow down exists on many access sequences. Motivated by experimental results, we conduct a deeper analysis of the working set access on multi-splay trees, leading to new insights about multi-splay tree behavior. Finally, all of the experiments also reveal insights about large constants and lower order terms in the multi-splay tree, which make it less practical than the splay tree, even though its proven competitive bound is tighter.

We review the progress made on bounding the number of independent sets in $d$-regular and irregular graphs over the last 31 years. We particularly focus on contributions from Kahn, Zhao, and Sah et al. in incrementally proving stronger and more general versions of the upper bound. We reproduce the main results of these works, particularly focusing on the unweighted special case (with fugacity $\lambda = 1$), which allows us to provide more intuitive and clear explanations of the key ideas that have been developed in the field over three decades.

We interpret a deep-learning semantic segmentation model used to classify coastline satellite images into land and water. This is to build trust in the model and gain new insight into the process of coastal water body extraction. Specifically, we seek to understand which spectral bands are important for predicting segmentation masks. This is done using a permutation importance approach. Results show that the NIR is the most important spectral band. Permuting this band lead to a decrease in accuracy of 38.12 percentage points. This is followed by Water Vapour, SWIR 1, and Blue bands with 2.58, 0.78 and 0.19 respectively. Water Vapour is not typically used in water indices and these results suggest it may be useful for water body extraction. Permuting, the Coastal Aerosol, Green, Red, RE1, RE2, RE3, RE4, and SWIR 2 bands did not decrease accuracy. This suggests they could be excluded from future model builds reducing complexity and computational requirements.

We analyse the effectiveness of RMSE, PSNR, SSIM and FOM for evaluating edge detection algorithms used for automated coastline detection. Typically, the accuracy of detected coastlines is assessed visually. This can be impractical on a large scale leading to the need for objective evaluation metrics. Hence, we conduct an experiment to find reliable metrics. We apply Canny edge detection to 95 coastline satellite images across 49 testing locations. We vary the Hysteresis thresholds and compare metric values to a visual analysis of detected edges. We found that FOM was the most reliable metric for selecting the best threshold. It could select a better threshold 92.6% of the time and the best threshold 66.3% of the time. This is compared RMSE, PSNR and SSIM which could select the best threshold 6.3%, 6.3% and 11.6% of the time respectively. We provide a reason for these results by reformulating RMSE, PSNR and SSIM in terms of confusion matrix measures. This suggests these metrics not only fail for this experiment but are not useful for evaluating edge detection in general.

We analyse the effectiveness of edge detection algorithms for the purpose of automatically extracting coastlines from satellite images. Four algorithms - Canny, Sobel, Scharr and Prewitt are compared visually and using metrics. With an average SSIM of 0.8, Canny detected edges that were closest to the reference edges. However, the algorithm had difficulty distinguishing noisy edges, e.g. due to development, from coastline edges. In addition, histogram equalization and Gaussian blur were shown to improve the effectiveness of the edge detection algorithms by up to 1.5 and 1.6 times respectively.

The effect of mid-T heat treatment on flux trapping sensitivity was measured on several 1.3 GHz single cell cavities subjected to vacuum annealing at temperature of 150 - 400 $^\circ$C for a duration of 3 hours. The cavity was cooldown with residual magnetic field $\sim$0 and $\sim$20 mG in the Dewar with cooldown condition of full flux trapping. The quality factor as a function of accelerating gradient was measured. The results show the correlation between the treatment temperature, quality factor, and sensitivity to flux trapping. Sensitivity increases with increasing heat treatment temperatures within the range of (200 - 325 $^\circ$C/3h). Moreover, variations in the effective penetration depth of the magnetic field and the density of quasi-particles can occur, influencing alterations in the cavity's electromagnetic response and resonance frequency., Comment: to be published in proceeding of IPAC2024

Observed neutrino oscillations imply that the global lepton flavor symmetry of the Standard Model must be broken. Therefore, searches for lepton flavor violation (LFV) are promising probes of new physics beyond the Standard Model. High-energy colliders provide a powerful tool to study LFV effects, which are complementary to the low-energy charged LFV searches. Here we discuss the possibility of LFV signals at colliders arising from exotic Higgs decays, and from leptophilic scalar and vector portal scenarios., Comment: 6 pages, 3 figures; Contribution to the Proceedings of the 17th International Workshop on Tau Lepton Physics (TAU2023)

This study involves the use of Sloan Digital Sky Survey Data Release 12 (SDSS DR12) also referred to as the Legacy Survey to investigate the influence of the galaxy environment on the main sequence of star formation, colour bimodality and the quenching of star formation rate. We classify the galaxies according to the ratio of their emission lines and based on their environment (isolated and non-isolated). We find that for $z\lesssim 0.09$, the fraction of non-isolated galaxies is greater than isolated galaxies, whereas for $z>0.09$ the opposite result is observed. Quenching is observed to be influenced by the environment at $M_\star < 10^{10.7} M_{\odot}$ (mostly for the star-forming and composite galaxies), while for $M_{\star }\geq 10^{10.7} M_{\odot}$ (mostly for Seyfert galaxies and low-ionization nuclear emission-line regions), the effect of the environment is very weak. We observe the decrease in the slope of the star formation main sequence by $\sim\! 0.02$ dex and the intercept by $\sim\! 0.17$ dex for non-isolated galaxies in comparison to isolated galaxies. We also find that star-forming, composite, Seyfert galaxies and low-ionization nuclear emission-line regions form the evolutionary pathways, where most star-forming galaxies ($\sim\! 60\%$) are found in the blue cloud, both composite ($\sim\! 50\%$) and Seyfert ($\sim\! 49\%$) galaxies in the green valley and low-ionization nuclear emission-line regions ($\sim 60\%$) in the red sequence. The study concludes that the environment in which the galaxies reside influences the shape of the star formation main sequence, quenching and hence colour bimodality especially for star-forming and composite galaxies while for Seyfert and low-ionization nuclear emission-line region galaxies, there is a mild impact., Comment: 14 pages, 8 figures