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We present a wide-field study of the globular cluster systems (GCS) of the elliptical galaxy NGC 3640 and its companion NGC 3641, based on observations from Gemini Multi-Object Spectrograph/Gemini. NGC 3640 is a shell galaxy which presents a complex morphology, which previous studies have indicated as the sign of a recent 'dry' merger, although whether its nearest neighbour could have influenced these substructures remains an open question. In this work, we trace the spatial distribution of the globular clusters (GCs) as well as their colour distribution, finding a potential bridge of red GCs that connects NGC 3640 to its less massive companion, and signs that the blue GCs were spatially disturbed by the event that created the shells., Comment: 12 pages, 17 figures, submitted to the Open Journal of Astrophysics

In the past years, Graph Neural Networks (GNNs) have become the `de facto' standard in various deep learning domains, thanks to their flexibility in modeling real-world phenomena represented as graphs. However, the message-passing mechanism of GNNs faces challenges in learnability and expressivity, hindering high performance on heterophilic graphs, where adjacent nodes frequently have different labels. Most existing solutions addressing these challenges are primarily confined to specific benchmarks focused on node classification tasks. This narrow focus restricts the potential impact that link prediction under heterophily could offer in several applications, including recommender systems. For example, in social networks, two users may be connected for some latent reason, making it challenging to predict such connections in advance. Physics-Inspired GNNs such as GRAFF provided a significant contribution to enhance node classification performance under heterophily, thanks to the adoption of physics biases in the message-passing. Drawing inspiration from these findings, we advocate that the methodology employed by GRAFF can improve link prediction performance as well. To further explore this hypothesis, we introduce GRAFF-LP, an extension of GRAFF to link prediction. We evaluate its efficacy within a recent collection of heterophilic graphs, establishing a new benchmark for link prediction under heterophily. Our approach surpasses previous methods, in most of the datasets, showcasing a strong flexibility in different contexts, and achieving relative AUROC improvements of up to 26.7%., Comment: 7 pages, 1 figure

Ferromagnetic/Antiferromagnetic (FM/AFM) bilayers dynamics have been a recent topic of interest due to the interaction occurring at the interface, where the magnetic moments of the AFM can be imprinted into the FM, and the exchange bias field can affect these dynamics. Here, we investigate Permalloy (Py) and NiO (Py/NiO) hybrids and for comparison single Py films in the broad Py thickness range varied from few nm to 200 nm by using static (Kerr effect) and dynamic (spin waves) measurements along with micromagnetic simulations. We observe hybrid modes between uniform (ferromagnetic resonance FMR, n=0) and perpendicular standing spin waves (PSSWs, n=1, 2) and a clear enhancement of the PSSWs modes frequencies upon interfacing Py with NiO both from experiments and simulations. This enhancement becomes less pronounced as the thickness of the film increases, demonstrating its interfacial origin rooted in the exchange coupling between the FM and AFM layers. Besides, through micromagnetic simulations, we investigate and correlate changes in spatial profiles of the PSSWs with the interfacial exchange coupling. As the thickness is increased, we see that the n=1 and n=2 modes begin to couple with the fundamental FMR mode, resulting in asymmetric (with respect the Py layer center) modes. Our results suggest that PSSWs detection in a ferromagnet offers a means of probing the interfacial exchange coupling with the adjacent AFM layer. Furthermore, the controlled spatial symmetry breaking by the AFM layer enables engineering of PSSWs with different spatial profiles in the FM., Comment: 15 pages, 18 figures

The concept of superbandwidth refers to the fact that a band-limited signal can exhibit, locally, an increase of its bandwidth, i.e., an effective bandwidth greater than that predicted by its Fourier transform. In this work, we study the propagation of superbandwidth laser pulses in a dispersive medium, characterized by the group velocity dispersion. In particular, two important results arise from the analysis of the instantaneous frequency of the pulse obtained through the Wigner function distribution: First, it can be observed local oscillations of the electric field which are beyond the Fourier spectrum of the incoming pulse. Second, for a range of values of the pulse synthesis parameters, surprisingly, the dynamics of the instantaneous frequency within certain temporal regions, corresponds to that of a pulse propagating in a medium with a group velocity dispersion of opposite sign. This phenomenon is intrinsic to the special characteristics of the pulse and not to the dispersive properties of the medium., Comment: 7 pages, 6 figures

Graph Neural Networks (GNNs) have become essential for studying complex data, particularly when represented as graphs. Their value is underpinned by their ability to reflect the intricacies of numerous areas, ranging from social to biological networks. GNNs can grapple with non-linear behaviors, emerging patterns, and complex connections; these are also typical characteristics of complex systems. The renormalization group (RG) theory has emerged as the language for studying complex systems. It is recognized as the preferred lens through which to study complex systems, offering a framework that can untangle their intricate dynamics. Despite the clear benefits of integrating RG theory with GNNs, no existing methods have ventured into this promising territory. This paper proposes a new approach that applies RG theory to devise a novel graph rewiring to improve GNNs' performance on graph-related tasks. We support our proposal with extensive experiments on standard benchmarks and baselines. The results demonstrate the effectiveness of our method and its potential to remedy the current limitations of GNNs. Finally, this paper marks the beginning of a new research direction. This path combines the theoretical foundations of RG, the magnifying glass of complex systems, with the structural capabilities of GNNs. By doing so, we aim to enhance the potential of GNNs in modeling and unraveling the complexities inherent in diverse systems.

Fast and efficient switching of nanomagnets is one of the main challenges in the development of future magnetic memories. We numerically investigate the evolution of the static and dynamic spin wave (SW) magnetization in short (50-400 nm length and 120 nm diameter) cylindrical ferromagnetic nanowires, where competing single vortex (SV) and vortex domain wall with a Bloch point (BP-DW) magnetization configurations could be formed. For a limited nanowire length range (between 150 and 300 nm) we demonstrate a reversible microwave field induced (forward) and opposite spin currents (backwards) transitions between the topologically different SV and BP-DW states. By tuning the nanowire length, excitation frequency, the microwave pulse duration and the spin current values we show that the optimum (low power) manipulation of the BP-DW could be reached by a microwave excitation tuned to the main SW mode and for nanowire lengths around 230-250 nm, where single vortex domain wall magnetization reversal via nucleation and propagation of SV-DW takes place. An analytical model for dynamics of the Bloch point provides an estimation of the gyrotropic mode frequency close the one obtained via micromagnetic simulations. A practical implementation of the method on a device has been proposed involving microwave excitation and the generation of the opposite spin currents via spin orbit torque. Our findings open a new pathway for the creation of unforeseen topological magnetic memories.

Superconductor-ferromagnet tunnel junctions demonstrate giant thermoelectric effects which are being exploited to engineer ultra-sensitive terahertz radiation detectors. Here, we experimentally observe the recently predicted complete magnetic control over thermoelectric effects in a superconducting spin valve, including the dependence of its sign on the magnetic state of the spin valve. The description of the experimental results is improved by the introduction of an interfacial domain wall in the spin filter layer interfacing the superconductor. Surprisingly, the application of high in-plane magnetic fields induces a double sign inversion of the thermoelectric effect, which exhibits large values even at applied fields twice the superconducting critical field.

Models describing long wave-short wave resonant interactions have many physical applications from fluid dynamics to plasma physics. We consider here the Yajima-Oikawa-Newell (YON) model, which has been recently introduced combining the interaction terms of two long wave-short wave, integrable models, one proposed by Yajima-Oikawa, and the other one by Newell. The new YON model contains two arbitrary coupling constants and it is still integrable - in the sense of possessing a Lax pair - for any values of these coupling constants. It reduces to the Yajima-Oikawa or the Newell systems for special choices of these two parameters. We construct families of periodic and solitary wave solutions, which display the generation of very long waves. We also compute the explicit expression of a number of conservation laws.

Giant thermoelectric effects are known to arise at the interface between superconductors and strongly polarized ferromagnets, enabling the construction of efficient thermoelectric generators. We predict that the thermopower of such a generator can be completely controlled by a magnetic input signal: Not only can the thermopower be toggled on and off by rotating a magnet, but it can even be entirely reversed. This in situ control diverges from conventional thermoelectrics, where the thermopower is usually fixed by the device design., Comment: Significantly expanded manuscript: 8 pages, 6 figures, 29 subfigures

We prove first that the realization $A_{\min}$ of $A:=\mathrm{div}(Q\nabla)-V$ in $L^2(\mathbb{R}^d)$ with unbounded coefficients generates a symmetric sub-Markovian and ultracontractive semigroup on $L^2(\mathbb{R}^d)$ which coincides on $L^2(\mathbb{R}^d)\cap C_b(\mathbb{R}^d)$ with the minimal semigroup generated by a realization of $A$ on $C_b(\mathbb{R}^d)$. Moreover, using time dependent Lyapunov functions, we prove pointwise upper bounds for the heat kernel of $A$ and deduce some spectral properties of $A_{\min}$ in the case of polynomially and exponentially diffusion and potential coefficients., Comment: 21 pages, no figures

Globular clusters (GCs) are proxies of the formation assemblies of their host galaxies. However, few studies exist targeting GC systems of spiral galaxies up to several effective radii. Through 12-band Javalambre Photometric Local Universe Survey (J-PLUS) imaging, we study the point sources around the M81/M82/NGC3077 triplet in search of new GC candidates. We develop a tailored classification scheme to search for GC candidates based on their similarity to known GCs via a principal components analysis (PCA) projection. Our method accounts for missing data and photometric errors. We report 642 new GC candidates in a region of 3.5 deg$^2$ around the triplet, ranked according to their Gaia astrometric proper motions when available. We find tantalising evidence for an overdensity of GC candidate sources forming a bridge connecting M81 and M82. Finally, the spatial distribution of the GC candidates $(g-i)$ colours is consistent with halo/intra-cluster GCs, i.e. it gets bluer as they get further from the closest galaxy in the field. We further employ a regression-tree based model to estimate the metallicity distribution of the GC candidates based on their J-PLUS bands. The metallicity distribution of the sample candidates is broad and displays a bump towards the metal-rich end. Our list increases the population of GC candidates around the triplet by 3-fold, stresses the usefulness of multi-band surveys in finding these objects, and provides a testbed for further studies analysing their spatial distribution around nearby (spirals) galaxies., Comment: 20 pages, 18 figures, MNRAS accepted

The mass profile for the moderately bright elliptical NGC 3377 is studied through an spherical Jeans analysis, combined with a Bayesian approach. The prior distributions are generated from dark matter simulations and observational constraints. The observational data set consist of Gemini/GMOS long-slit observations aligned with the major and minor axes of the galaxy, and are supplemented with data from the literature for the diffuse stellar component, globular clusters and planetary nebulae. Although the galaxy is assumed to alternatively reside in central and satellite haloes, the comparison with literature results prefer the latter option. Several options of constant anisotropy are considered, as well as both NFW and Einasto mass profiles. The analysis points to an intermediate mass halo, presenting a virial mass around $(3.6\pm0.6)\times 10^{11}\,{\rm M_{\odot}}$., Comment: 16 pages, 10 figures, accepted to MNRAS

The current properties of globular cluster systems (GCSs) are the result of the evolution experienced by their host galaxies, which shape the richness of the GCS as well as its spatial distribution, among other features. We carry out an analysis of the projected radial distribution of globular clusters for a sample of almost 30 early-type galaxies (ETGs) of intermediate and low luminosity, located in cluster environments (Virgo, Fornax and Coma). We also include in the study six ETGs, for which the parameters of their GCS radial profiles are publicly available. The final analysis is performed on an enlarged sample (~ 100 GCSs), by adding the GCSs of ETGs from our previous paper (Paper I). Scaling relations involving different parameters of the GCSs are obtained for the whole sample and complement those obtained in Paper I. Several of such relations point to a second-order dependence on the environmental density. Finally, the results are analysed in the literature context.

Multipath transport protocols enable the concurrent use of different network paths, benefiting a fast and reliable data transmission. The scheduler of a multipath transport protocol determines how to distribute data packets over different paths. Existing multipath schedulers either conform to predefined policies or to online trained policies. The adoption of millimeter wave (mmWave) paths in 5th Generation (5G) networks and Wireless Local Area Networks (WLANs) introduces time-varying network conditions, under which the existing schedulers struggle to achieve fast and accurate adaptation. In this paper, we propose FALCON, a learning-based multipath scheduler that can adapt fast and accurately to time-varying network conditions. FALCON builds on the idea of meta-learning where offline learning is used to create a set of meta-models that represent coarse-grained network conditions, and online learning is used to bootstrap a specific model for the current fine-grained network conditions towards deriving the scheduling policy to deal with such conditions. Using trace-driven emulation experiments, we demonstrate FALCON outperforms the best state-of-the-art scheduler by up to 19.3% and 23.6% in static and mobile networks, respectively. Furthermore, we show FALCON is quite flexible to work with different types of applications such as bulk transfer and web services. Moreover, we observe FALCON has a much faster adaptation time compared to all the other learning-based schedulers, reaching almost an 8-fold speedup compared to the best of them. Finally, we have validated the emulation results in real-world settings illustrating that FALCON adapts well to the dynamicity of real networks, consistently outperforming all other schedulers.

Spin waves (SWs), being usually reflected by domain walls, could also be channeled along them. Edge domain walls yield the interesting, and potentially applicable to real devices property of broadband spin waves confinement to the edges of the structure. Here we investigate through numerical simulations the propagation of quasi one-dimensional spin waves in triangle-shaped amorphous YIG ($Y_3Fe_5O_{12}$) micron sized ferromagnets as a function of the angle aperture. The edge spin waves (ESWs) have been propagated over the corner in triangles of 2 microns side with a fixed thickness of 85 nm. Parameters such as superior vertex angle (in the range of 40$^\circ$-75$^\circ$) and applied magnetic field have been optimized in order to obtain a higher transmission coefficient of the ESWs over the triangle vertex. We observed that for a certain aperture angle for which dominated ESW frequency coincides with one of the localised DW modes, the transmission is maximized near one and the phase shift drops to $\pi/2$ indicating resonant transmission of ESWs through the upper corner. We compare the obtained results with existing theoretical models. These results could contribute to the development of novel basic elements for spin wave computing.