Search

Understanding the origins of massive black hole seeds and their co-evolution with their host galaxy requires studying intermediate-mass black holes (IMBHs) and estimating their mass. However, measuring the mass of these IMBHs is challenging due to the high spatial resolution requirement. A spectrophotometric reverberation monitoring is performed for a low-luminosity Seyfert 1 galaxy NGC 4395 to measure the size of the broad line region (BLR) and black hole mass. The data were collected using the 1.3-m Devasthal fast optical telescope (DFOT) and 3.6-m Devasthal optical telescope (DOT) at ARIES, Nainital, over two consecutive days in March 2022. The analysis revealed strong emission lines in the spectra and light curves of merged 5100{\AA} spectroscopic continuum flux ($f_{\mathrm{5100}}$) with photometric continuum V-band and H$\alpha$, with fractional variabilities of 6.38\% and 6.31\% respectively. In comparison to several previous studies with lag estimation $<$ 90 minutes, our calculated H$\alpha$ lag supersedes by $125.0^{+6.2}_{-6.1}$ minutes using ICCF and {\small JAVELIN} methods. The velocity dispersion ($\sigma_{\mathrm{line}}$) of the broad line clouds is measured to be $544.7^{+22.4}_{-25.1}$ km s$^{-1}$, yielding a black hole mass of $\sim$ $2.2^{+0.2}_{-0.2}\times 10^{4}M_{\mathrm{\odot}}$ and an Eddington ratio of 0.06., Comment: 17 pages, 8 figures, accepted for publication in ApJ

In the present work, we have predicted the half-lives for the $\beta^{-}$ decay for higher forbidden unique transitions in the mass range of nuclei from A = 40-138. For these transitions, the experimental data for half-lives are not available except for a few cases. The calculations for half-lives are performed within the framework of the nuclear shell model (SM). We have used the effective interactions sdpf-mu, gxpf1a, gwbxg, G-matrix, snet, sn100pn, and jj56pnb to perform the SM calculations in different mass regions. A comprehensive discussion has been made between the SM-predicted half-lives and the scaled half-lives from proton-neutron quasiparticle random-phase approximation (pnQRPA). The results of the present study will be useful to plan new experiments to measure half-lives for these higher forbidden unique $\beta^{-}$ transitions., Comment: 8 pages, 2 figures

In this work, the cosmic solutions, particularly the well-known $\Lambda$CDM model, are investigated in the framework of the Gauss-Bonnet gravity, where the gravitational action incorporates the Gauss-Bonnet invariant function. We utilize a specialized formulation of the deceleration parameter in terms of the Hubble parameter $H$, given by $q = -1 - \frac{\dot{H}}{H^2}$, to solve the field equations. To identify the appropriate model parameters, we align them to the most recent observational datasets, which include 31 data points from the Cosmic Chronometers, Pantheon+, and BAO datasets. The physical characteristics of the cosmographic parameters, such as pressure and energy density, that correlate to the limited values of the model parameters, are examined. The evolution of the deceleration parameter suggests a transition from a decelerated to an accelerated phase of the universe. Additionally, we examine the stability of the assumed model and provide an explanation for late-time acceleration using the energy conditions. The behavior of the equation of state parameter has been analyzed through dynamical variables by constraining various parameters in light of the recent observational data. This study has resulted in a quintessence-like evolution., Comment: 15 pages, 12 figures

Across the technology industry, many companies have expressed their commitments to AI ethics and created dedicated roles responsible for translating high-level ethics principles into product. Yet it is unclear how effective this has been in leading to meaningful product changes. Through semi-structured interviews with 26 professionals working on AI ethics in industry, we uncover challenges and strategies of institutionalizing ethics work along with translation into product impact. We ultimately find that AI ethics professionals are highly agile and opportunistic, as they attempt to create standardized and reusable processes and tools in a corporate environment in which they have little traditional power. In negotiations with product teams, they face challenges rooted in their lack of authority and ownership over product, but can push forward ethics work by leveraging narratives of regulatory response and ethics as product quality assurance. However, this strategy leaves us with a minimum viable ethics, a narrowly scoped industry AI ethics that is limited in its capacity to address normative issues separate from compliance or product quality. Potential future regulation may help bridge this gap.

Neural Radiance Fields (NeRFs) have revolutionized the reconstruction of static scenes and objects in 3D, offering unprecedented quality. However, extending NeRFs to model dynamic objects or object articulations remains a challenging problem. Previous works have tackled this issue by focusing on part-level reconstruction and motion estimation for objects, but they often rely on heuristics regarding the number of moving parts or object categories, which can limit their practical use. In this work, we introduce LEIA, a novel approach for representing dynamic 3D objects. Our method involves observing the object at distinct time steps or "states" and conditioning a hypernetwork on the current state, using this to parameterize our NeRF. This approach allows us to learn a view-invariant latent representation for each state. We further demonstrate that by interpolating between these states, we can generate novel articulation configurations in 3D space that were previously unseen. Our experimental results highlight the effectiveness of our method in articulating objects in a manner that is independent of the viewing angle and joint configuration. Notably, our approach outperforms previous methods that rely on motion information for articulation registration., Comment: Accepted to ECCV 2024. Project Website at https://archana1998.github.io/leia/

We propose an image-conditioned diffusion model to estimate high angular resolution diffusion weighted imaging (DWI) from a low angular resolution acquisition. Our model, which we call QID$^2$, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction. We leverage a U-Net architecture with cross-attention to preserve the positional information of the reference images, further guiding the target image generation. We train and evaluate QID$^2$ on single-shell DWI samples curated from the Human Connectome Project (HCP) dataset. Specifically, we sub-sample the HCP gradient directions to produce low angular resolution DWI data and train QID$^2$ to reconstruct the missing high angular resolution samples. We compare QID$^2$ with two state-of-the-art GAN models. Our results demonstrate that QID$^2$ not only achieves higher-quality generated images, but it consistently outperforms the GAN models in downstream tensor estimation across multiple metrics. Taken together, this study highlights the potential of diffusion models, and QID$^2$ in particular, for q-space up-sampling, thus offering a promising toolkit for clinical and research applications., Comment: Accepted at MICCAI 2024 International Workshop on Computational Diffusion MRI. Zijian Chen and Jueqi Wang contributed equally to this work

We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes functional connectivity between brain regions, a lesion encoding module, and a subgraph learning module that leverages functional similarities for prediction. We use synthetic data derived from the Human Connectome Project (HCP) for hyperparameter tuning and model pretraining. We then evaluate the performance using repeated 10-fold cross-validation on an in-house neuroimaging dataset of post-stroke aphasia. Our results demonstrate that LEGNet outperforms baseline deep learning methods in predicting language ability. LEGNet also exhibits superior generalization ability when tested on a second in-house dataset that was acquired under a slightly different neuroimaging protocol. Taken together, the results of this study highlight the potential of LEGNet in effectively learning the relationships between rs-fMRI connectivity and language ability in a patient cohort with brain lesions for improved post-stroke aphasia evaluation., Comment: Accepted at MICCAI 2024 International Workshop on Machine Learning in Clinical Neuroimaging (MLCN)

Star clusters (SCs) are potential cosmic-ray (CR) accelerators and therefore are expected to emit high-energy radiation. However, a clear detection of gamma-ray emission from this source class has only been possible for a handful of cases. This could in principle result from two different reasons: either detectable SCs are limited to a small fraction of the total number of Galactic SCs, or gamma-ray-emitting SCs are not recognized as such and therefore are listed in the ensemble of unidentified sources. In this Letter we investigate this latter scenario, by comparing available catalogs of SCs and HII regions, obtained from Gaia and WISE observations, to the gamma-ray GeV and TeV catalogs built from Fermi-LAT, H.E.S.S. and LHAASO data. The significance of the correlation between catalogs is evaluated by comparing the results with simulations of synthetic populations. A strong correlation emerges between Fermi-LAT unidentified sources and HII regions which trace massive SCs in the earliest (< 1-2 Myr) phase of their life, where no supernova explosions have happened yet, confirming that winds of massive stars can alone accelerate particles and produce gamma-ray emission at least up to GeV energies. The association with TeV-energies sources is less evident. Similarly, no significant association is found between Gaia SCs and GeV nor TeV sources. We ascribe this fact to the larger extension of these objects, but also to an intrinsic bias in the Gaia selection towards SCs surrounded by a lower target gas density, that would otherwise hinder the detection in the optical waveband., Comment: Accepted for publication in ApJL

In this paper, we propose the first method to modify the prosodic features of a given speech signal using actor-critic reinforcement learning strategy. Our approach uses a Bayesian framework to identify contiguous segments of importance that links segments of the given utterances to perception of emotions in humans. We train a neural network to produce the variational posterior of a collection of Bernoulli random variables; our model applies a Markov prior on it to ensure continuity. A sample from this distribution is used for downstream emotion prediction. Further, we train the neural network to predict a soft assignment over emotion categories as the target variable. In the next step, we modify the prosodic features (pitch, intensity, and rhythm) of the masked segment to increase the score of target emotion. We employ an actor-critic reinforcement learning to train the prosody modifier by discretizing the space of modifications. Further, it provides a simple solution to the problem of gradient computation through WSOLA operation for rhythm manipulation. Our experiments demonstrate that this framework changes the perceived emotion of a given speech utterance to the target. Further, we show that our unified technique is on par with state-of-the-art emotion conversion models from supervised and unsupervised domains that require pairwise training., Comment: 7 pages, 10 figures

Detecting active black holes in dwarf galaxies has proven to be a challenge due to their small size and weak electromagnetic signatures. Mid-infrared variability has emerged as a promising tool that can be used to detect active low-mass black holes in dwarf galaxies. We analyzed 10.4 years of photometry from the ALL$WISE$/NEO$WISE$ multi-epoch catalogs, identifying 25 objects with AGN-like variability. Independent confirmation of AGN activity was found in 68% of these objects using optical and near-infrared diagnostics. Notably, we discovered a near-infrared coronal line [S IX] $\lambda$ 1.252 $\mu$m in J1205, the galaxy with the lowest stellar mass (log M$_{*}$ = 7.5 M$_{\odot}$) and low metallicity (12 + log(O/H) = 7.46) in our sample. Additionally, we found broad Pa$\alpha$ potentially from the BLR in two targets, and their implied black hole masses are consistent with black hole-stellar mass relations. Comparing non-variable galaxies with similar stellar masses and $WISE$ $W1-W2$ colors, we found no clear trends between variability and large-scale galaxy properties. However, we found that AGN activity likely causes redder $W1-W2$ colors in variable targets, while for the non-variable galaxies, the contribution stems from strong star formation activity. A high incidence of optical broad lines was also observed in variable targets. Our results suggest that mid-infrared variability is an effective method for detecting AGN activity in low-mass galaxies and can help uncover a larger sample of active low-mass ($<$ 10$^{6}$ M$_{\odot}$) black holes in the universe., Comment: 24 pages, 14 figures, Accepted for publication in ApJ

We present an enhanced method for the application of Gaussian Mixture Modelling (GMM) to the coherent WaveBurst (cWB) algorithm in the search for short-duration gravitational wave (GW) transients. The supervised Machine Learning method of GMM allows for the multi-dimensional distributions of noise and signal to be modelled over a set of representative attributes, which aids in the classification of GW signals against noise transients (glitches) in the data. We demonstrate that updating the approach to model construction eliminates bias previously seen in the GMM analysis, increasing the robustness and sensitivity of the analysis over a wider range of burst source populations. The enhanced methodology is applied to the generic burst all-sky short search in the LIGO-Virgo full third observing run (O3), marking the first application of GMM to the 3 detector Livingston-Hanford-Virgo network. For both 2- and 3- detector networks, we observe comparable sensitivities to an array of generic signal morphologies, with significant sensitivity improvements to waveforms in the low Quality factor parameter space at false alarm rates of 1 per 100 years. This proves that GMM can effectively mitigate blip glitches, which are one of the most problematic sources of noise for un-modelled GW searches. The cWB-GMM search recovers similar numbers of compact binary coalescence (CBC) events as other cWB post-production methods, and concludes on no new gravitational wave detection after known CBC events are removed., Comment: 14 pages, 8 figures

The proper planning of different types of public transportation such as metro, highway, waterways, and so on, can increase the efficiency, reduce the congestion and improve the safety of the country. There are certain challenges associated with route planning, such as high cost of implementation, need for adequate resource & infrastructure and resistance to change. The goal of this research is to examine the working, applications, complexity factors, advantages & disadvantages of Floyd- Warshall, Bellman-Ford, Johnson, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), & Grey Wolf Optimizer (GWO), to find the best choice for the above application. In this paper, comparative analysis of above-mentioned algorithms is presented. The Floyd-Warshall method and ACO algorithm are chosen based on the comparisons. Also, a combination of modified Floyd-Warshall with ACO algorithm is proposed. The proposed algorithm showed better results with less time complexity, when applied on randomly structured points within a boundary called quasi-structured points. In addition, this paper also discusses the future works of integrating Floyd-Warshall with ACO to develop a real-time model for overcoming above mentioned-challenges during transportation route planning., Comment: 10 pages, 8 figures

This study introduces an innovative methodology for the planning of metro network routes within the urban environment of Chennai, Tamil Nadu, India. A comparative analysis of the modified Ant Colony Optimization (ACO) method (previously developed) with recent breakthroughs in nature-inspired algorithms demonstrates the modified ACO's superiority over modern techniques. By utilizing the modified ACO algorithm, the most efficient routes connecting the origin and destination of the metro route are generated. Additionally, the model is applied to the existing metro network to highlight variations between the model's results and the current network. The Google Maps platform, integrated with Python, handles real-time data, including land utilization, Geographical Information Systems (GIS) data, census information, and points of interest. This processing enables the identification of stops within the city and along the chosen routes. The resulting metro network showcases substantial benefits compared to conventional route planning methods, with noteworthy enhancements in workforce productivity, decreased planning time, and cost-efficiency. This study significantly enhances the efficiency of urban transport systems, specifically in rapidly changing metropolitan settings such as chennai., Comment: 10 pages, 17 figures

We aim to investigate the global star formation scenario in star-forming sites AFGL 5157, [FSR2007] 0807 (hereafter FSR0807), [HKS2019] E70 (hereafter E70), [KPS2012] MWSC 0620 (hereafter KPS0620), and IRAS 05331+3115 in the outer galactic arm. The distribution of young stellar objects in these sites coincides with a higher extinction and H2 column density, which agrees with the notion that star formation occurs inside the dense molecular cloud cores. We have found two molecular structures at different velocities in this direction; one contains AFGL 5157 and FSR0807, and the other contains E70, [KPS2012] MWSC 0620, and IRAS 05331+3115. All these clusters in our target region are in different evolutionary stages and might form stars through different mechanisms. The E70 cluster seems to be the oldest in our sample; AFGL 5157 and FSR0807 formed later, and KPS0620 and IRAS 05331+3115 are the youngest sites. AFGL 5157 and FSR0807 are physically connected and have cold filamentary structures and dense hub regions. Additionally, the near-infrared photometric analysis shows signatures of massive star formation in these sites. KPS0620 also seems to have cold filamentary structures with the central hub but lacks signatures of massive stars. Our analysis suggests molecular gas flow and the hub filamentary star formation scenario in these regions. IRAS 05331+3115 is a single clump of molecular gas favoring low-mass star formation. Our study suggests that the selected area is a menagerie of star-forming sites where the formation of the stars happens through different processes., Comment: 19 pages, 13 figures, and 3 tables; Accepted for publication in AJ

We present an optical polarimetric study of a nearby star-forming region, Lambda-Orionis, to map plane-of-the-sky magnetic field geometry to understand the magnetized evolution of the HII region and associated small molecular clouds. We made multi-wavelength polarization observations of 34 bright stars distributed across the region. R-band polarization measurements focused on small molecular clouds BRC 17 and BRC 18 located at the periphery of the HII region are also presented. The magnetic field lines exhibit a large-scale ordered orientation consistent with the Planck sub-mm polarization measurements. The magnetic field lines in both the BRCs are found to be roughly in north-south directions; however, a larger dispersion is noticed in the orientation for BRC 17 compared to BRC 18. Using structure-function analysis, the strength of the plane-of-the-sky component of the magnetic field is estimated as $\sim$28 $\mu$G for BRC 17 and $\sim$40 $\mu$G for BRC 18. The average dust grain size and the mean value of the total-to-selective extinction ratio (R$_{V}$) in the HII region are found to be $\sim$0.51 $\pm$ 0.05 $\mu$m and $\sim$2.9 $\pm$ 0.3, respectively. The distance of the whole HII region is estimated as $\sim$392 $\pm$ 8 pc by combining astrometry information from GAIA EDR3 for YSOs associated with BRCs and confirmed members of central cluster Collinder 69., Comment: 17 pages, Accepted in A&A on 20/05/2024

We address the resource allocation challenges in NextG cellular radio access networks (RAN), where heterogeneous user applications demand guarantees on throughput and service regularity. We leverage the Whittle indexability property to decompose the resource allocation problem, enabling the independent computation of relative priorities for each user. By simply allocating resources in decreasing order of these indices, we transform the combinatorial complexity of resource allocation into a linear one. We propose Windex, a lightweight approach for training neural networks to compute Whittle indices, considering constraint violation, channel quality, and system load. Implemented on a real-time RAN intelligent controller (RIC), our approach enables resource allocation decision times of less than 20$\mu$s per user and efficiently allocates resources in each 1ms scheduling time slot. Evaluation across standardized 3GPP service classes demonstrates significant improvements in service guarantees compared to existing schedulers, validated through simulations and emulations with over-the-air channel traces on a 5G testbed., Comment: 15 pages, 8 figures

Astrophysical observations, theoretical models, and terrestrial experiments probe different regions of neutron star (NS) interior. Therefore, it is essential to consistently combine the information from these sources. This analysis requires multiple evaluations of Tolman Oppenheimer Volkoff equations which can become computationally expensive with a large number of observations. Further, multi-messenger astronomy requires rapid NS characterization via gravitational waves for efficient electromagnetic follow-up. In this work, we develop a novel neural network-based map from the EoS curve to the mass and radius of cold non-rotating NS. We estimate a speed-up of an order of magnitude when compared with the state-of-the-art RePrimAnd solver and an average error of 1e-3 when calculating the mass and radius of the neutron star. Additionally, we also develop neural network solvers for obtaining EoS curves from a physics conforming EoS model, FRZ$\chi_{1.5}$. We utilize this efficient continuous map to measure the sensitivity of model parameters of FRZ$\chi_{1.5}$ towards mass and radius. We show that 8 out of 18 parameters of this model are sensitive by at least three orders of magnitude higher than the remaining 10 parameters. This information will be useful in further speeding up, as well as probing the crucial parameter space, in the parameter estimation from astrophysical observations using this physics-conforming EoS model.

We present a lattice QCD study of the elastic $S$-wave $D\bar{B}$ scattering in search of tetraquark candidates with explicitly exotic flavor content $bc\bar u\bar d$ in the isospin $I\!=\!0$ and $J^P=0^+$ channel. We use four lattice QCD ensembles with dynamical $u/d$, $s$, and $c$ quark fields generated by the MILC Collaboration. A non-relativistic QCD Hamiltonian, including improvement coefficients up to $\mathcal{O}(\alpha_sv^4)$, is utilized for the bottom quarks. For the rest of the valence quarks we employ a relativistic overlap action. Five different valence quark masses are utilized to study the light quark mass dependence of the $D\bar{B}$ scattering amplitude. The finite volume energy spectra are extracted following a variational approach. The elastic $D\bar{B}$ scattering amplitudes are extracted employing L\"{u}scher's prescription. The light quark mass dependence of the continuum extrapolated amplitudes suggests an attractive interaction between the $\bar B$ and $D$ mesons. At the physical pseudoscalar meson mass ($M_{ps}=M_{\pi}$) the $D\bar{B}$ scattering amplitude has a sub-threshold pole corresponding to a binding energy of $-39(^{+4}_{-6})(^{~+8}_{-18}) \mbox{~MeV}$ with respect to the $D\bar{B}$ threshold. The critical $M_{ps}$ at which the $D\bar{B}$ scattering length diverges and the system becomes unbound corresponds to $M^*_{ps}=2.94(15)(5) \mbox{~GeV}$. This result can hold significant experimental relevance in the search for a bound scalar $T_{bc}$ tetraquark, which could well be the next "doubly heavy" bound tetraquark to be discovered with only weak decay modes., Comment: 13 pages, 10 figures

Media streaming is the dominant application over wireless edge (access) networks. The increasing softwarization of such networks has led to efforts at intelligent control, wherein application-specific actions may be dynamically taken to enhance the user experience. The goal of this work is to develop and demonstrate learning-based policies for optimal decision making to determine which clients to dynamically prioritize in a video streaming setting. We formulate the policy design question as a constrained Markov decision problem (CMDP), and observe that by using a Lagrangian relaxation we can decompose it into single-client problems. Further, the optimal policy takes a threshold form in the video buffer length, which enables us to design an efficient constrained reinforcement learning (CRL) algorithm to learn it. Specifically, we show that a natural policy gradient (NPG) based algorithm that is derived using the structure of our problem converges to the globally optimal policy. We then develop a simulation environment for training, and a real-world intelligent controller attached to a WiFi access point for evaluation. We empirically show that the structured learning approach enables fast learning. Furthermore, such a structured policy can be easily deployed due to low computational complexity, leading to policy execution taking only about 15$\mu$s. Using YouTube streaming experiments in a resource constrained scenario, we demonstrate that the CRL approach can increase quality of experience (QOE) by over 30\%., Comment: 15 pages, 14 figures

This is a sequel to our earlier work in which we described an implicit leapfrog scheme in conjunction with a higher order mixed finite element discretization of a system of Maxwell's equations. In our earlier work, we focussed on providing the error analysis for both the semidiscretization of the Maxwell's equations using an implicit leapfrog scheme that we invented as well as providing the error analysis for the full discretization using this time domain scheme in conjunction with higher order mixed finite elements from finite element exterior calculus. In this work, we record our initial results with extending our implicit leapfrog scheme from being a discretization that is second order accurate in time to an arbitrary (even) order accurate in time method. Towards this end, we provide here the complete error analysis for the semidiscretization in time and full discretization of the Maxwell's equations for the fourth order scheme. We leave the completion of our efforts in providing all the necessary proofs for the general scheme to an immediate future update of this work., Comment: arXiv admin note: substantial text overlap with arXiv:2310.20310

We study the kinematics of a pillar, namely G287.76-0.87, using three rotational lines of $^{12}$CO(5-4), $^{12}$CO(8-7), $^{12}$CO(11-10), and a fine structure line of [OI] $63\,\mu$m Southern Carina observed by SOFIA/GREAT. This pillar is irradiated by the associated massive star cluster Trumpler 16, which includes $\eta$~Carina. Our analysis shows that the relative velocity of the pillar with respect to this ionization source is small, $\sim 1\,\rm km\,s^{-1}$, and the gas motion in the tail is more turbulent than in the head. We also performed analytical calculations to estimate the gas column density in local thermal equilibrium (LTE) conditions, which yields $N_{\rm CO}$ as $(\sim 0.2 -5)\times 10^{17}\,\rm cm^{-2}$. We further constrain the gas's physical properties in non-LTE conditions using RADEX. The non-LTE estimations result in $n_{\rm H_{2}} \simeq 10^{5}\,\rm cm^{-3}$ and $N_{\rm CO} \simeq 10^{16}\,\rm cm^{-2}$. We found that the thermal pressure within the G287.76-0.87 pillar is sufficiently high to make it stable for the surrounding hot gas and radiation feedback if the winds are not active. While they are active, stellar winds from the clustered stars sculpt the surrounding molecular cloud into pillars within the giant bubble around $\eta$~Carina., Comment: 17 pages, 14 figures, accepted to ApJ

A quantum system that interacts with an environment generally undergoes non-unitary evolution described by a non-Markovian or Markovian master equation. In this paper, we construct the non-Markovian Redfield master equation for a quantum scalar field that interacts with a second field through a bilinear or nonlinear interaction on a Minkowski background. We use the resulting master equation to set up coupled differential equations that can be solved to obtain the equal-time two-point function of the system field. We show how the equations simplify under various approximations including the Markovian limit, and argue that the Redfield equation-based solution provides a perturbative resummation to the standard second order Dyson series result. For the bilinear interaction, we explicitly show that the Redfield solution is closer to the exact solution compared to the perturbation theory-based one. Further, the environment correlation function is oscillatory and non-decaying in this case, making the Markovian master equation a poor approximation. For the nonlinear interaction, on the other hand, the environment correlation function is sharply peaked and the Redfield solution matches that obtained using a Markovian master equation in the late-time limit., Comment: 19 pages, 6 figures

Visual Inspection with Acetic Acid (VIA) remains the most feasible cervical cancer screening test in resource-constrained settings of low- and middle-income countries (LMICs), which are often performed screening camps or primary/community health centers by nurses instead of the preferred but unavailable expert Gynecologist. To address the highly subjective nature of the test, various handheld devices integrating cameras or smartphones have been recently explored to capture cervical images during VIA and aid decision-making via telemedicine or AI models. Most studies proposing AI models retrospectively use a relatively small number of already collected images from specific devices, digital cameras, or smartphones; the challenges and protocol for quality image acquisition during VIA in resource-constrained camp settings, challenges in getting gold standard, data imbalance, etc. are often overlooked. We present a novel approach and describe the end-to-end design process to build a robust smartphone-based AI-assisted system that does not require buying a separate integrated device: the proposed protocol for quality image acquisition in resource-constrained settings, dataset collected from 1,430 women during VIA performed by nurses in screening camps, preprocessing pipeline, and training and evaluation of a deep-learning-based classification model aimed to identify (pre)cancerous lesions. Our work shows that the readily available smartphones and a suitable protocol can capture the cervix images with the required details for the VIA test well; the deep-learning-based classification model provides promising results to assist nurses in VIA screening; and provides a direction for large-scale data collection and validation in resource-constrained settings.

Context: Molecular clouds are the prime locations of star formation. These clouds contain filamentary structures and cores which are crucial in the formation of young stars. Aims: In this work, we aim to quantify the physical properties of structural characteristics within the molecular cloud L1251 to better understand the initial conditions for star formation. Methods: We applied the getsf algorithm to identify cores and filaments within the molecular cloud L1251 using the Herschel multiband dust continuum image, enabling us to measure their respective physical properties. Additionally, we utilized an enhanced differential term algorithm to produce high-resolution temperature maps and column density maps with a resolution of ${13.5}''$. Results: We identified 122 cores in the region. Out of them, 23 are protostellar cores, 13 are robust prestellar cores, 32 are candidate prestellar cores (including 13 robust prestellar cores and 19 strictly candidate prestellar cores), and 67 are unbound starless cores. getsf also found 147 filament structures in the region. Statistical analysis of the physical properties (mass (M), temperature (T), size, and core brightness (hereafter, we are using the word luminosity (L)) for the core brightness) of obtained cores shows a negative correlation between core mass and temperature and a positive correlation between (M/L) and (M/T). Analysis of the filaments gives a median width of 0.14 pc and no correlation between width and length. Out of those 122 cores, 92 are present in filaments (75.4%) and the remaining were outside them. Out of the cores present in filaments, 57 (62%) cores are present in supercritical filaments ($M_{\rm line}>16M_{\odot }/{\rm pc}$)., Comment: 15 pages, 20 figures, 2 tables, accepted for publication in JAA

We propose a spectropolarimeter a covering wavelength range of 3200--7000 {\AA} [3200{\AA} chosen as lower limit to go to the atmospheric cut-off. It's ``needed" for some Serkowski curves and would make the instrument even more unique] for a 2-4~m class telescope. In this article, we discuss the science cases which will be covered with this proposed instrument. The technical requirements and analysis plan for each science case is also discussed. This spectropolarimeter targeting exciting galactic and extra-galactic research, will be unique instrument on a 2-4~m facilities., Comment: 12 pages, 3 tables, 3 Figures

Molecular clouds are prime locations to study the process of star formation. These clouds contain filamentary structures and cores, which are crucial sites for the formation of young stars. The star-formation process has been investigated using various techniques, including polarimetry for tracing magnetic fields. In this small review-cum-short report, we put together the efforts (mainly from the Indian community) to understand the roles of turbulence and magnetic fields in star formation. These are two components of the ISM competing against gravity, which is primarily responsible for the collapse of gas to form stars. We also include attempts made using simulations of molecular clouds to study this competition. Studies on feedback and magnetic fields are combined and listed to understand the importance of the interaction between two energies in setting the current observed star formation efficiency. We have listed available and upcoming facilities with the polarization capabilities needed to trace magnetic fields. We have also stated the importance of ongoing and desired collaborations between Indian communities and facilities abroad to shed more light on the roles of turbulence and magnetic fields in the process of star formation., Comment: 12 pages, 2 figures, 1 table, accepted in Journal of Astrophysics and Astronomy

While stellar processes are believed to be the main source of feedback in dwarf galaxies, the accumulating discoveries of AGN in dwarf galaxies over recent years arouse the interest to also consider AGN feedback in them. Fast, AGN-driven outflows, a major mechanism of AGN feedback, have indeed been discovered in dwarf galaxies and may be powerful enough to provide feedback to their dwarf hosts. In this paper, we search for outflows traced by the blueshifted ultraviolet absorption features in three dwarf galaxies with AGN from the sample examined in our previous ground-based study. We confirm outflows traced by blueshifted absorption features in two objects and tentatively detect an outflow in the third object. In one object where the outflow is clearly detected in multiple species, photoionization modeling suggests that this outflow is located $\sim$0.5 kpc from the AGN, implying a galactic-scale impact. This outflow is much faster and possesses higher kinetic energy outflow rate than starburst-driven outflows in sources with similar star formation rates, and is likely energetic enough to provide negative feedback to its host galaxy as predicted by simulations. Much broader ($\sim$4000 km s$^{-1}$) absorption features are also discovered in this object which may have the same origin as that of broad absorption lines in quasars. Additionally, strong He II $\lambda$1640 emission is detected in both objects where the transition falls in the wavelength coverage, and is consistent with an AGN origin. In one of these two objects, blueshifted He II emission line is clearly detected, likely tracing a highly-ionized AGN wind., Comment: 22 pages, 14 figures, 2 tables, accepted for publication in ApJ

Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information retrieval is less well understood. In this paper we explore the feasibility of LLMs as a mechanism for quantitative knowledge retrieval to aid data analysis tasks such as elicitation of prior distributions for Bayesian models and imputation of missing data. We present a prompt engineering framework, treating an LLM as an interface to a latent space of scientific literature, comparing responses in different contexts and domains against more established approaches. Implications and challenges of using LLMs as 'experts' are discussed., Comment: 13 pages plus supplementary materials

Fairness and privacy are two important values machine learning (ML) practitioners often seek to operationalize in models. Fairness aims to reduce model bias for social/demographic sub-groups. Privacy via differential privacy (DP) mechanisms, on the other hand, limits the impact of any individual's training data on the resulting model. The trade-offs between privacy and fairness goals of trustworthy ML pose a challenge to those wishing to address both. We show that DP amplifies gender, racial, and religious bias when fine-tuning large language models (LLMs), producing models more biased than ones fine-tuned without DP. We find the cause of the amplification to be a disparity in convergence of gradients across sub-groups. Through the case of binary gender bias, we demonstrate that Counterfactual Data Augmentation (CDA), a known method for addressing bias, also mitigates bias amplification by DP. As a consequence, DP and CDA together can be used to fine-tune models while maintaining both fairness and privacy.