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The fractal and phase transitional properties of each type of pions (i.e. $\pi^{\pm,0}$) through one-dimensional $\eta-$space, at an energy of $\sqrt{s}=13~$TeV, have been studied with the help of the Scaled Factorial Moment (SFM) framework. To generate simulated data sets for $pp$ collisions under the minimum bias (MB) condition at $\sqrt{s}=13~$TeV, we have employed the Monte Carlo-based event simulator PYTHIA. Various parameters such as the Levy index $(\mu)$, degree of multifractality $(r)$, anomalous fractal dimension $(d_q)$, multifractal specific heat $(c)$ and critical exponent $(\nu)$ have been calculated. To study the Bose Einstein(BE) effect due to identical particles (here pions) we have also derived these parameters for mixed pion pairs (i.e. $\{\pi^{+},\pi^{-}\}$, $\{\pi^{+},\pi^{0}\}$ and $\{\pi^{-},\pi^{0}\}$) and we find that the effects of identical particles weakened for the mixture with respect to the individual distributions. The quest for the quark-hadron phase transition has also been conducted within the framework of the Ginzburg-Landau (GL) theory of second-order phase transition. Analysis revealed that for PYTHIA-generated MB events, there is a clear indication of the quark-hadron phase transition according to the GL theory. Furthermore, the values of the multifractal specific heat ($c$) for each $\pi^{+}, \pi^{-}, \pi^{0}$ and the mixture pair data sets of pions generated by PYTHIA model at MB condition, indicate a transition from multifractality to monofractality in $pp$ collisions at $\sqrt{s}=13~$TeV., Comment: 19 pages, 10 Figures ( Total 18 Figures with sub-figures)

Accurate localization is crucial for various applications, including autonomous vehicles and next-generation wireless networks. However, the reliability and precision of Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are compromised by multi-path errors and non-line-of-sight scenarios. This paper presents a novel approach to enhance GPS accuracy by combining visual data from RGB cameras with wireless signals captured at millimeter-wave (mmWave) and sub-terahertz (sub-THz) basestations. We propose a sensing-aided framework for (i) site-specific GPS data characterization and (ii) GPS position de-noising that utilizes multi-modal visual and wireless information. Our approach is validated in a realistic Vehicle-to-Infrastructure (V2I) scenario using a comprehensive real-world dataset, demonstrating a substantial reduction in localization error to sub-meter levels. This method represents a significant advancement in achieving precise localization, particularly beneficial for high-mobility applications in 5G and beyond networks., Comment: Datasets and code files are available on the DeepSense website: https://deepsense6g.net/. To appear in IEEE GLOBECOM 2024

The notion of quasi-Poisson $G$-spaces with $D/G$-valued moment maps was introduced by Alekseev and Kosmann-Schwarzbach in 1999. Our main result is a \emph{Lifting Theorem}, establishing a bijective correspondence between the categories of quasi-Poisson $G$-spaces with $D/G$-valued moment maps and of quasi-Poisson $G\times G$-spaces with $D$-valued moment maps. Using this result, we give simple constructions of fusion and conjugation for these spaces, and new examples coming from moduli spaces., Comment: 42 pages

The road towards the discovery of new, useful materials is full of imperfections. Machine learning, which is currently the power horse of material discovery when it works in concert with density functional theory, has been able to accelerate the discovery of new materials for various applications by learning the properties of known, stable materials to infer the properties of unknown, deformed materials. Physics-informed machine learning (PIML) is particularly believed to bridge the gap between known materials and the virtually infinite space of crystal structures with imperfections such as defects, grain boundaries, composition disorders and others. State of the art PIML models struggle to bridge this gap, however. In this work we show that a critical correction of the physics underpinning a PIML, our direct integration of external potential (DIEP) method, which mimics the integration of the external potential term in typical density functional theory calculations, improves the generalisability of the model to a range of imperfect structures, including diamond defects. By training DIEP to predict the potential energy surface, we demonstrate the ability of the model in predicting the onset of fracture of pristine and defective carbon nanotubes., Comment: 14 pages, 4 figures

Let E and E' be elliptic curves over Q with complex multiplication by the ring of integers of an imaginary quadratic field K and let Y=Kum(ExE') be the minimal desingularisation of the quotient of ExE' by the action of -1. We study the Brauer groups of such surfaces Y and use them to furnish new examples of transcendental Brauer-Manin obstructions to weak approximation., Comment: 48 pages, comments welcome!

3D reconstruction of hand-object manipulations is important for emulating human actions. Most methods dealing with challenging object manipulation scenarios, focus on hands reconstruction in isolation, ignoring physical and kinematic constraints due to object contact. Some approaches produce more realistic results by jointly reconstructing 3D hand-object interactions. However, they focus on coarse pose estimation or rely upon known hand and object shapes. We propose the first approach for realistic 3D hand-object shape and pose reconstruction from a single depth map. Unlike previous work, our voxel-based reconstruction network regresses the vertex coordinates of a hand and an object and reconstructs more realistic interaction. Our pipeline additionally predicts voxelized hand-object shapes, having a one-to-one mapping to the input voxelized depth. Thereafter, we exploit the graph nature of the hand and object shapes, by utilizing the recent GraFormer network with positional embedding to reconstruct shapes from template meshes. In addition, we show the impact of adding another GraFormer component that refines the reconstructed shapes based on the hand-object interactions and its ability to reconstruct more accurate object shapes. We perform an extensive evaluation on the HO-3D and DexYCB datasets and show that our method outperforms existing approaches in hand reconstruction and produces plausible reconstructions for the objects

The mass spectrum of different meson particles is generated using an effective Lagrangian of the extended linear-sigma model (eLSM) for scalar and pseudoscalar meson fields and quark flavors, up, down, strange, and charm. Analytical formulas for the masses of scalar, pseudoscalar, vector, and axialvector meson states are derived assuming global chiral symmetry. The various eLSM parameters are analytically deduced and numerically computed. This enables accurate estimations of the masses of sixteen non-charmed and thirteen charmed meson states at vanishing temperature. The comparison of these results to recent compilation of the particle data group (PDG) allows to draw the conclusion that the masses of sixteen non-charmed and thirteen charmed meson states calculated in the eLSM are in good agreement with the PDG. This shows that the eLSM, with its configurations and parameters, is an effective theoretical framework for determining the mass spectra of various non-charmed and charmed meson states, particularly at vanishing temperature., Comment: 17 pages, 2 tables, no figures to be published in MPDI Particles