In this paper, we introduce a novel method for comparing 3D point clouds, a critical task in various machine learning applications. By interpreting point clouds as samples from underlying probability density functions, the statistical manifold structure is given to the space of point clouds. This manifold structure will help us to use the information geometric tools to analyze the point clouds. Our method uses the Gaussian Mixture Model (GMM) to find the probability density functions and the Modified Symmetric KL divergence to measure how similar the corresponding probability density functions are. This method of comparing the point clouds takes care of the geometry of the objects represented by the point clouds. To demonstrate the effectiveness of our approach, we take up five distinct case studies:(i) comparison of basic geometric shapes, (ii) comparison of 3D human body shapes within the MP FAUST dataset, (iii) comparison of animal shapes, (iv) comparison of human and animal datasets and (v) comparison of audio signals.

Polar fields at the minimum of a sunspot cycle -- which are a manifestation of the radial component of the Sun's poloidal field -- are deemed to be the best indicator of the strength of the toroidal component, and hence the amplitude of the future sunspot cycle. However, the Sun's polar magnetic fields are difficult to constrain with ground-based or space-based observations from near the plane-of-ecliptic. In this context, polar filaments -- dark, elongated structures that overlie polarity inversion lines -- are known to offer critical insights into solar polar field dynamics. Through investigations of the long-term evolution of polar filament areas and length acquired from the Meudon Observatory and complimentary solar surface flux transport simulations, here we establish the common physical foundation connecting the Babcock-Leighton solar dynamo mechanism of solar polar field reversal and build-up with the origin and evolution of polar filaments. We discover a new relationship connecting the residual filament area of adjacent solar cycles with the amplitude of the next sunspot cycle -- which can serve as a new tool for solar cycle forecasts -- advancing the forecast window to earlier than polar field based precursors. We conclude that polar filament properties encapsulate the physics of interaction of the poloidal magnetic field of the previous and current sunspot cycles, the resultant of which is the net poloidal magnetic field at the end of the current cycle, thus encoding as a precursor the strength of the upcoming solar cycle.

We present results from the search for an isotropic gravitational-wave background using Advanced LIGO and Advanced Virgo data from O1 through O4a, the first part of the fourth observing run. This background is the accumulated signal from unresolved sources throughout cosmic history and encodes information about the merger history of compact binaries throughout the Universe, as well as exotic physics and potentially primordial processes from the early cosmos. Our cross-correlation analysis reveals no statistically significant background signal, enabling us to constrain several theoretical scenarios. For compact binary coalescences which approximately follow a 2/3 power-law spectrum, we constrain the fractional energy density to $\Omega_{\rm GW}(25{\rm Hz})\leq 2.0\times 10^{-9}$ (95% cred.), a factor of 1.7 improvement over previous results. Scale-invariant backgrounds are constrained to $\Omega_{\rm GW}(25{\rm Hz})\leq 2.8\times 10^{-9}$, representing a 2.1x sensitivity gain. We also place new limits on gravity theories predicting non-standard polarization modes and confirm that terrestrial magnetic noise sources remain below detection threshold. Combining these spectral limits with population models for GWTC-4, the latest gravitational-wave event catalog, we find our constraints remain above predicted merger backgrounds but are approaching detectability. The joint analysis combining the background limits shown here with the GWTC-4 catalog enables improved inference of the binary black hole merger rate evolution across cosmic time. Employing GWTC-4 inference results and standard modeling choices, we estimate that the total background arising from compact binary coalescences is $\Omega_{\rm CBC}(25{\rm Hz})={0.9^{+1.1}_{-0.5}\times 10^{-9}}$ at 90% confidence, where the largest contribution is due to binary black holes only, $\Omega_{\rm BBH}(25{\rm Hz})=0.8^{+1.1}_{-0.5}\times 10^{-9}$.

In this paper, a distance between the Gaussian Mixture Models(GMMs) is obtained based on an embedding of the K-component Gaussian Mixture Model into the manifold of the symmetric positive definite matrices. Proof of embedding of K-component GMMs into the manifold of symmetric positive definite matrices is given and shown that it is a submanifold. Then, proved that the manifold of GMMs with the pullback of induced metric is isometric to the submanifold with the induced metric. Through this embedding we obtain a general lower bound for the Fisher-Rao metric. This lower bound is a distance measure on the manifold of GMMs and we employ it for the similarity measure of GMMs. The effectiveness of this framework is demonstrated through an experiment on standard machine learning benchmarks, achieving accuracy of 98%, 92%, and 93.33% on the UIUC, KTH-TIPS, and UMD texture recognition datasets respectively.

The paper introduces an efficient implementation of the Deutsch-Jozsa and Bernstein-Vazirani algorithm using the single-particle discrete-time quantum walk. We also provide a detailed optical framework with specific optical components to achieve these implementations in the photonic quantum walk scheme by simultaneously exploiting both polarization and path degrees of freedom. These implementations demonstrate improved resource efficiency while maintaining the exponential speedup characteristic of both algorithms. This work contributes to the growing field of universal quantum computing using single particle discrete-time quantum walk.

We present a comprehensive study of the gas kinematics associated with density structures at different spatial scales in the filamentary infrared dark cloud, G034.43+00.24 (G34). This study makes use of the H13CO+ (1-0) molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS) survey, which has spatial and velocity resolution of 0.04 pc and 0.2 km/s, respectively. Several tens of dendrogram structures have been extracted in the position-position-velocity space of H13CO+, which include 21 small-scale leaves and 20 larger-scale branches. Overall, their gas motions are supersonic but they exhibit the interesting behavior where leaves tend to be less dynamically supersonic than the branches. For the larger-scale, branch structures, the observed velocity-size relation (i.e., velocity variation/dispersion versus size) are seen to follow the Larson scaling exponent while the smaller-scale, leaf structures show a systematic deviation and display a steeper slope. We argue that the origin of the observed kinematics of the branch structures is likely to be a combination of turbulence and gravity-driven ordered gas flows. In comparison, gravity-driven chaotic gas motion is likely at the level of small-scale leaf structures. The results presented in our previous paper and this current follow-up study suggest that the main driving mechanism for mass accretion/inflow observed in G34 varies at different spatial scales. We therefore conclude that a scale-dependent combined effect of turbulence and gravity is essential to explain the star-formation processes in G34.

The present work develops a rigorous framework for collective dipole oscillations in dense dielectric media by extending the Lorentz oscillator model to include both quadratic (second order) and cubic (third order) nonlinear restoring forces. Dipole-dipole interactions are incorporated via the dyadic Greens function, leading to a nonlocal description of the medium. Starting from the generalised oscillator equation, the system is expressed in matrix form with an effective stiffness matrix. Using a harmonic steady state ansatz, nonlinear terms are approximated in the frequency domain. Diagonalisation provides collective normal modes, which serve as a basis for perturbative expansion zeroth order captures the linear response, while higher orders describe nonlinear corrections. The polarisation is expressed through corrected mode amplitudes, yielding an effective nonlinear nonlocal susceptibility kernel. Finally, the scalar Greens function formalism is applied to derive the structured output field. These approaches bridge microscopic dipole dynamics with macroscopic optical propagation, offering key insights into nonlinear light matter interaction and turbulence influenced field evolution.

This work presents a rigorous statistical and geometric framework for analyzing turbulence-impacted beam propagation and image topology with results obtained using a PMMA slab. The approach models beam intensity distributions as n-dimensional data set represented through Gaussian Mixture Models (GMMs), embedding them into the manifold of Symmetric Positive Definite (SPD) matrices. By employing information geometric tools, geodesic distances, and affine-invariant Riemannian metrics, we establish a principled methodology for quantifying similarity and dissimilarity among beam images. Experimental results demonstrate topological distance trends, distance statistics, and correlation measures for different turbulence scenarios, including polarized and unpolarized cases. Histograms of distance statistics reveal stable statistical features, with correlation coefficients highlighting the turbulence-induced variability in PMMA-based beam propagation. The framework not only provides a systematic foundation for analyzing optical beam statistics under turbulence but also opens avenues for advanced applications such as deep learning-based feature reduction, image compression, and secure free-space optical (FSO) communication. Future directions include refining the GMM-EM based distance measures, comparative scatter analysis, and developing robust statistical tools for turbulence imaging. Overall, this study bridges theoretical modeling, experimental validation, and potential technological applications in adaptive and applied optics.

There is growing evidence that high-mass star formation and hub-filament systems (HFS) are intricately linked. The gas kinematics along the filaments and the forming high-mass star(s) in the central hub are in excellent agreement with the new generation of global hierarchical high-mass star formation models. In this paper, we present an observational investigation of a typical HFS cloud, G310.142+0.758 (G310 hereafter) which reveals unambiguous evidence of mass inflow from the cloud scale via the filaments onto the forming protostar(s) at the hub conforming with the model predictions. Continuum and molecular line data from the ATOMS and MALT90 surveys are used that cover different spatial scales. Three filaments (with total mass $5.7\pm1.1\times 10^3~M_{\odot}$) are identified converging toward the central hub region where several signposts of high-mass star formation have been observed. The hub region contains a massive clump ($1280\pm260~M_{\odot}$) harbouring a central massive core. Additionally, five outflow lobes are associated with the central massive core implying a forming cluster. The observed large-scale, smooth and coherent velocity gradients from the cloud down to the core scale, and the signatures of infall motion seen in the central massive clump and core, clearly unveil a nearly-continuous, multi-scale mass accretion/transfer process at a similar mass infall rate of $\sim 10^{-3}~M_{\odot}~yr^{-1}$ over all scales, feeding the central forming high-mass protostar(s) in the G310 HFS cloud.

We study the dynamics of a three-dimensional generalization of Kitaev's honeycomb lattice spin model (defined on the hyperhoneycomb lattice) subjected to a harmonic driving of $J_z$, one of the three types of spin-couplings in the Hamiltonian. Using numerical solutions supported by analytical calculations based on a rotating wave approximation, we find that the system responds nonmonotonically to variations in the frequency $\omega$ (while keeping the driving amplitude $J$ fixed) and undergoes dynamical freezing, where at specific values of $\omega$, it gets almost completely locked in the initial state throughout the evolution. However, this freezing occurs only when a constant bias is present in the driving, i.e., when $J_z = J'+ J\cos{\omega t}$, with $J'\neq 0$. Consequently, the bias acts as a switch that triggers the freezing phenomenon. Dynamical freezing has been previously observed in other integrable systems, such as the one-dimensional transverse-field Ising model.

Recent developments in quantum technologies have enabled significant improvements in the precision of optical sensing systems. This work explores the integration of distributed quantum sensing (DQS) with optical gyroscopes to improve the estimation accuracy of angular velocity. Utilizing bright two-mode squeezed states (bTMSS), which offer high photon numbers and strong bipartite quantum correlations, we propose a novel configuration that leverages continuous-variable entanglement across multiple spatially separated optical gyroscopes. Unlike traditional quantum sensing that enhances a single sensor, our approach focuses on estimating a global phase shift corresponding to the average angular rotation across distributed optical gyroscopes with quantum-enhanced sensitivity. We analyze the phase sensitivities of different bTMSS configurations, including M mode-entangled bTMSS and separable M-bTMSS, and evaluate their performance through the quantum Cramér-Rao bound. The analysis shows that, with 5% photon loss in every channel in the system, the proposed scheme shows a sensitivity enhancement of ~9.3 dB beyond the shot-noise limit, with an initial squeezing of ~9.8 dB. The present scheme has potential applications in quantum-enhanced inertial navigation and precision metrology within emerging quantum networks.

NASA's all-sky survey mission, the Transiting Exoplanet Survey Satellite (TESS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TESS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting Exoplanets using Statistical Tools (VaTEST). Our dedicated effort is focused on the confirmation and characterization of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilization of the statistical validation tool known as \texttt{TRICERATOPS}, we have successfully discovered eight potential super-Earths. These planets bear the designations: TOI-238b (1.61$^{+0.09} _{-0.10}$ R$_\oplus$), TOI-771b (1.42$^{+0.11} _{-0.09}$ R$_\oplus$), TOI-871b (1.66$^{+0.11} _{-0.11}$ R$_\oplus$), TOI-1467b (1.83$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-1739b (1.69$^{+0.10} _{-0.08}$ R$_\oplus$), TOI-2068b (1.82$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-4559b (1.42$^{+0.13} _{-0.11}$ R$_\oplus$), and TOI-5799b (1.62$^{+0.19} _{-0.13}$ R$_\oplus$). Among all these planets, six of them fall within the region known as 'keystone planets,' which makes them particularly interesting for study. Based on the location of TOI-771b and TOI-4559b below the radius valley we characterized them as likely super-Earths, though radial velocity mass measurements for these planets will provide more details about their characterization. It is noteworthy that planets within the size range investigated herein are absent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between Earth and Neptune.

Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets.

Modern cosmological research still thoroughly debates the discrepancy between local probes and the Cosmic Microwave Background observations in the Hubble constant (\texorpdfstring{$H_0$}{H0}) measurements, ranging from 4 to 6$\sigma$. In the current study, we examine this tension using the Supernovae Ia (SNe Ia) data from the Pantheon, Pantheon+ (P+), Joint Lightcurve Analysis (JLA), and Dark Energy Survey, (DES) catalogs combined together into the so-called Master Sample. The sample contains 3714 SNe Ia, and is divided all of them into redshift-ordered bins. Three binning techniques are presented: the equi-population, the moving window (MW), and the equi-spacing in the \texorpdfstring{$\log-z$}{log-z}. We perform a Markov-Chain Monte Carlo analysis (MCMC) for each bin to determine the $H_0$ value, estimating it within the standard flat \texorpdfstring{$\Lambda$CDM}{LCDM} and the \texorpdfstring{$w_{0}w_{a}$CDM}{w0waCDM} models. These \texorpdfstring{$H_0$}{H0} values are then fitted with the following phenomenological function: \texorpdfstring{$\mathcal{H}_0(z) = \tilde{H}_0 / (1 + z)^\alpha$}{H0(z) = H0tilde / (1 + z)^alpha}, where \texorpdfstring{$\tilde{H}_0$}{H0tilde} is a free parameter representing \texorpdfstring{$\mathcal{H}_0(z)$}{H0(z)} fitted in \texorpdfstring{$z=0$}{z=0}, and \texorpdfstring{$\alpha$}{alpha} is the evolutionary parameter. Our results indicate a decreasing trend characterized by \texorpdfstring{$\alpha \sim 0.01$}{alpha ~ 0.01}, whose consistency with zero ranges from $1 \sigma$ in 5 cases to 1 case at 3 $\sigma$ and 11 cases at $> 3 \sigma$ in several samples and configurations. Such a trend in the SNe Ia catalogs could be due to evolution with redshift for the astrophysical variables or unveiled selection biases. Alternatively, intrinsic physics, possibly the \texorpdfstring{$f(R)$}{f(R)} theory of gravity, could be responsible for this trend.

The AErosol RObotic NETwork (AERONET), established in 1993 with limited global sites, has grown to over 900 locations, providing three decades of continuous aerosol data. While earlier studies based on shorter time periods (10-12 years) and fewer sites (approximately 250) made significant contributions to aerosol research, the vast AERONET dataset (1993-2023) calls for a comprehensive reevaluation to refine global aerosol models and improve satellite retrievals. This is particularly important in light of major environmental changes such as industrialization, land use shifts, and natural events like wildfires and dust storms. In this study, a set of fine and coarse aerosol models called AERONET-Extended (AEROEX) models are developed based on cluster analysis of 30-years AERONET data, analyzing over 202,000 samples using Gaussian Mixture Models to classify aerosol types by season and region. Aerosols are categorized into spherical, spheroidal, and mixed types using particle linear depolarization ratio and fine mode fraction. Four fine-mode aerosol models were derived based on differences in scattering and absorption properties, revealing regional/seasonal variations, particularly in North America, Europe and Asia. Additionally, two coarse-mode aerosol models were identified, separated by their absorbing properties in dust-prone and polluted regions. We performed simulation analysis showing that the new models significantly improve satellite-based aerosol optical depth retrievals compared to widely used dark target aerosol models. A global aerosol model map, generated at 1x1 degree resolution for each season using Random Forest and expert refinement, provides valuable insights for climate and atmospheric studies, improving satellite-based aerosol retrievals at global scale.

The Indian Pulsar Timing Array (InPTA) employs unique features of the upgraded Giant Metrewave Radio Telescope (uGMRT) to monitor dozens of the International Pulsar Timing Array (IPTA) millisecond pulsars (MSPs), simultaneously in the 300-500 MHz and the 1260-1460 MHz bands. This dual-band approach ensures that any frequency-dependent delays are accurately characterized, significantly improving the timing precision for pulsar observations, which is crucial for pulsar timing arrays. We present details of InPTA's second data release that involves 7 yrs of data on 27 IPTA MSPs. This includes sub-banded Times of Arrival (ToAs), Dispersion Measures (DM), and initial timing ephemerides for our MSPs. A part of this dataset, originally released in InPTA's first data release, is being incorporated into IPTA's third data release which is expected to detect and characterize nanohertz gravitational waves in the coming years. The entire dataset is reprocessed in this second data release providing some of the highest precision DM estimates so far and interesting solar wind related DM variations in some pulsars. This is likely to characterize the noise introduced by the dynamic inter-stellar ionised medium much better than the previous release thereby increasing sensitivity to any future gravitational wave search.

Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets.

We present the pulse arrival times and high-precision dispersion measure estimates for 14 millisecond pulsars observed simultaneously in the 300-500 MHz and 1260-1460 MHz frequency bands using the upgraded Giant Metrewave Radio Telescope (uGMRT). The data spans over a baseline of 3.5 years (2018-2021), and is the first official data release made available by the Indian Pulsar Timing Array collaboration. This data release presents a unique opportunity for investigating the interstellar medium effects at low radio frequencies and their impact on the timing precision of pulsar timing array experiments. In addition to the dispersion measure time series and pulse arrival times obtained using both narrowband and wideband timing techniques, we also present the dispersion measure structure function analysis for selected pulsars. Our ongoing investigations regarding the frequency dependence of dispersion measures have been discussed. Based on the preliminary analysis for five millisecond pulsars, we do not find any conclusive evidence of chromaticity in dispersion measures. Data from regular simultaneous two-frequency observations are presented for the first time in this work. This distinctive feature leads us to the highest precision dispersion measure estimates obtained so far for a subset of our sample. Simultaneous multi-band uGMRT observations in Band 3 and Band 5 are crucial for high-precision dispersion measure estimation and for the prospect of expanding the overall frequency coverage upon the combination of data from the various Pulsar Timing Array consortia in the near future. Parts of the data presented in this work are expected to be incorporated into the upcoming third data release of the International Pulsar Timing Array.