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Recent advances in large multimodal models (LMMs) have recognized fine-grained grounding as an imperative factor of visual understanding and dialogue. However, the benefits of such representation in LMMs are limited to the natural image domain, and these models perform poorly for remote sensing (RS). The distinct overhead viewpoint, scale variation, and presence of small objects in high-resolution RS imagery present a unique challenge in region-level comprehension. Moreover, the development of the grounding conversation capability of LMMs within RS is hindered by the lack of granular, RS domain-specific grounded data. Addressing these limitations, we propose GeoPixel - the first end-to-end high resolution RS-LMM that supports pixel-level grounding. This capability allows fine-grained visual perception by generating interleaved masks in conversation. GeoPixel supports up to 4K HD resolution in any aspect ratio, ideal for high-precision RS image analysis. To support the grounded conversation generation (GCG) in RS imagery, we curate a visually grounded dataset GeoPixelD through a semi-automated pipeline that utilizes set-of-marks prompting and spatial priors tailored for RS data to methodically control the data generation process. GeoPixel demonstrates superior performance in pixel-level comprehension, surpassing existing LMMs in both single-target and multi-target segmentation tasks. Our methodological ablation studies validate the effectiveness of each component in the overall architecture. Our code and data will be publicly released.

This article explores the motion of massive particles in the gravitational field of a modified gravity (MOG) black hole (BH), characterized by the parameter $\alpha$. Using the Hamiltonian formalism, the geodesic equations and the effective potential governing particle trajectories are derived. Key features, including the innermost stable circular orbit (ISCO) and the innermost bound circular orbit (IBCO), are analyzed, revealing their dependence on the particle's energy, angular momentum, and the MOG parameter. In the extremal case, where $\alpha=-1$, the event horizon merges with the Cauchy horizon, forming a distinctive BH configuration. Numerical methods are employed to compute periodic orbits in this spacetime, with a comparison drawn to the Schwarzschild BH. The findings indicate that for $\alpha>0$, periodic orbits around Schwarzschild-MOG BH exhibit lower energy requirements than those in Schwarzschild spacetime, whereas for $-1<\alpha<0$, the energy requirements are higher. Precessing orbits near periodic trajectories are also examined, offering insights into their complex dynamical behavior. Finally, the gravitational wave (GW) radiation from the periodic orbits of a test particle around the Schwarzschild-MOG BH is examined, generating intricate waveforms that provide insights into the gravitational structure of the system., Comment: 23 pages, 13 figures. To appear in Physics of the Dark Universe

Development of quantum materials and tailoring of their functional properties is a fundamental interest in materials science. Here we propose a new design concept for robust quantum anomalous Hall effect via entropy engineering in 2D magnets. As a prototypical example, configurational entropy of monolayer transition metal trihalide VCl$_3$ is manipulated by incorporating four different transition-metal cations [Ti,Cr,Fe,Co] in the honeycomb structure made of vanadium, such that all the in-plane mirror symmetries, inversion and/or roto-inversion are broken. Monolayer VCl$_3$ is a ferromagnetic Dirac half-metal in which spin-polarized Dirac dispersion at valley momenta is accompanied by bulk states at the $\Gamma$-point and thus the spin-orbit interaction driven quantum anomalous Hall phase does not exhibit fully gapped bulk band dispersion. Entropy-driven bandstructure renormalization, especially band flattening in combination with red and blue shifts at different momenta of the Brillouin zone and crystal-field effects, transforms Dirac half-metal to a Dirac spin gapless semiconductor and leads to a robust quantum anomalous Hall phase with fully gapped bulk band dispersion, and thus, a purely topological edge state transport without mixing with dissipative bulk channels. These findings provide a paradigm to design entropy-driven 2D materials for the realization of robust quantum anomalous Hall effect and quantum device applications.

We measure the growth of cosmic density fluctuations on large scales and across the redshift range $0.3

The main objective of this article is to investigate the viability of bouncing cosmological scenarios using different forms of scale factors with perfect matter configuration in the framework of extended symmetric teleparallel theory. This modified proposal is defined by the function $f(Q,T)$, where $Q$ characterizes non-metricity and $T$ denotes the trace of energy-momentum tensor. We investigate the modified field equations of this theory using different parametric values of the Hubble parameter and non-metricity to derive viable solutions. These solutions are relevant in various cosmological bounce models such as symmetric-bounce, super-bounce, oscillatory-bounce, matter-bounce and exponential-bounce models. Furthermore, we examine the behavior of energy density and pressure to analyze the characteristics of dark energy. A comprehensive analysis is also conducted to explore the behavior of the equation of state parameter and deceleration parameter to examine the evolutionary eras of the cosmos. Our findings show that the $f(Q,T)$ gravity describes the cosmic expansion in the vicinity of the bouncing point during the early and late times of cosmic evolution., Comment: 31 pages, 23 figures, The European Physical Journal C (Accepted)

Training deep neural networks often requires large-scale datasets, necessitating storage and processing on cloud servers due to computational constraints. The procedures must follow strict privacy regulations in domains like healthcare. Split Learning (SL), a framework that divides model layers between client(s) and server(s), is widely adopted for distributed model training. While Split Learning reduces privacy risks by limiting server access to the full parameter set, previous research has identified that intermediate outputs exchanged between server and client can compromise client's data privacy. Homomorphic encryption (HE)-based solutions exist for this scenario but often impose prohibitive computational burdens. To address these challenges, we propose CURE, a novel system based on HE, that encrypts only the server side of the model and optionally the data. CURE enables secure SL while substantially improving communication and parallelization through advanced packing techniques. We propose two packing schemes that consume one HE level for one-layer networks and generalize our solutions to n-layer neural networks. We demonstrate that CURE can achieve similar accuracy to plaintext SL while being 16x more efficient in terms of the runtime compared to the state-of-the-art privacy-preserving alternatives.

We derive new constraints on the $E_G$ statistic as a test of gravity, combining the CMB lensing map estimated from Data Release 6 (DR6) of the Atacama Cosmology Telescope with SDSS BOSS CMASS and LOWZ galaxy data. We develop an analysis pipeline to measure the cross-correlation between CMB lensing maps and galaxy data, following a blinding policy and testing the approach through null and consistency checks. By testing the equivalence of the spatial and temporal gravitational potentials, the $E_G$ statistic can distinguish $\Lambda$CDM from alternative models of gravity. We find $E_G= 0.31^{+0.06}_{-0.05}$ for ACT and CMASS data at 68.28\% confidence level, and $E_G = 0.49^{+0.14}_{-0.11}$ for ACT and LOWZ. Systematic errors are estimated to be 3\% and 4\% respectively. Including CMB lensing information from Planck PR4 results in $E_G = 0.34^{+0.05}_{-0.05}$ with CMASS and $E_G= 0.43^{+0.11}_{-0.09}$ with LOWZ. These are consistent with predictions for the $\Lambda$CDM model that best fits the Planck CMB anisotropy and SDSS BOSS BAO, where $E_G^{\rm GR} (z_{\rm eff} = 0.555) = 0.401\pm 0.005$ for CMB lensing combined with CMASS and $E_G^{\rm GR} (z_{\rm eff} = 0.316) = 0.452\pm0.005$ combined with LOWZ. We also find $E_G$ to be scale independent, with PTE $>5\%$, as predicted by general relativity. The methods developed in this work are also applicable to improved future analyses with upcoming spectroscopic galaxy samples and CMB lensing measurements., Comment: 31 pages, 18 figures, Accepted for publication in PRD

Light field (LF) imaging captures both angular and spatial light distributions, enabling advanced photographic techniques. However, micro-lens array (MLA)- based cameras face a spatial-angular resolution tradeoff due to a single shared sensor. We propose a novel light field framework for resolution enhancement, employing a modular approach. The first module generates a high-resolution, all-in-focus image. The second module, a texture transformer network, enhances the resolution of each light field perspective independently using the output of the first module as a reference image. The final module leverages light field regularity to jointly improve resolution across all LF image perspectives. Our approach demonstrates superior performance to existing methods in both qualitative and quantitative evaluations., Comment: 5 pages, 6 figures, accepted in IEEE Conference on Signal Processing and Communications Applications

Network control theory (NCT) offers a robust analytical framework for understanding the influence of network topology on dynamic behaviors, enabling researchers to decipher how certain patterns of external control measures can steer system dynamics towards desired states. Distinguished from other structure-function methodologies, NCT's predictive capabilities can be coupled with deploying Graph Neural Networks (GNNs), which have demonstrated exceptional utility in various network-based learning tasks. However, the performance of GNNs heavily relies on the expressiveness of node features, and the lack of node features can greatly degrade their performance. Furthermore, many real-world systems may lack node-level information, posing a challenge for GNNs.To tackle this challenge, we introduce a novel approach, NCT-based Enhanced Feature Augmentation (NCT-EFA), that assimilates average controllability, along with other centrality indices, into the feature augmentation pipeline to enhance GNNs performance. Our evaluation of NCT-EFA, on six benchmark GNN models across two experimental setting. solely employing average controllability and in combination with additional centrality metrics. showcases an improved performance reaching as high as 11%. Our results demonstrate that incorporating NCT into feature enrichment can substantively extend the applicability and heighten the performance of GNNs in scenarios where node-level information is unavailable., Comment: Conference paper