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The abelian $(p+1)$-form gauge field is inherently coupled to the $p$-brane worldvolume. After quantization, the corresponding $p$-form gauge transformation is associated with the local phase ambiguity of the $p$-brane wave functional. In essence, the $p$-form gauge symmetry can be realized as a special construction of the generic 0-form gauge symmetry in the functional space of $p$-brane configurations. The non-abelian generalization is straightforward in the functional space language. To simplify the analysis, we further introduce a toy model where the infinite dimensional functional space of $p$-brane configurations is replaced by a finite dimensional matrix space. After taking the symmetric trace in the matrix model, the original discussions of the $p$-form gauge symmetry can be inherited by the toy model., Comment: 9 pages, no figures

In this paper, we propose a novel framework that leverages large language models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passing scheme. For each missing node, its neighbors and previous estimates are fed into and processed by LLM to infer the missing observations. Tested on the task of the online prediction of wind-speed graph signals, our model outperforms online graph filtering algorithms in terms of accuracy, demonstrating the potential of LLMs in effectively addressing partially observed signals in graphs.

This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise removal on time-varying graph signals. Unlike conventional Graph Signal Processing methods that apply the same filter to the entire graph, the spatiotemporal updates of GSAMP employ a distinct approach that utilizes localized computations at each node. This update is based on an adaptive solution obtained from an optimization problem designed to minimize the discrepancy between observed and estimated values. GSAMP effectively processes real-world, time-varying graph signals under Gaussian and impulsive noise conditions.

The assumption of using a static graph to represent multivariate time-varying signals oversimplifies the complexity of modeling their interactions over time. We propose a Dynamic Multi-hop model that captures dynamic interactions among time-varying node signals, while also accounting for time-varying edge signals, by extracting latent edges through topological diffusion and edge pruning. The resulting graphs are time-varying and sparse, capturing key dynamic node interactions and representing signal diffusion to both near and distant neighbors over time. The Dynamic Multi-hop Estimation algorithm is further proposed, accurately representing the interaction dynamics among node signals while enabling adaptive estimation of time-varying multivariate signals spatially and temporally. The Dynamic Multi-hop Estimation is evaluated under two real-world datasets of brain network and stock market for the online estimation of partially observed time-varying signals corrupted by noise.

In the presence of impulsive noise, and missing observations, accurate online prediction of time-varying graph signals poses a crucial challenge in numerous application domains. We propose the Adaptive Least Mean $p^{th}$ Power Graph Neural Networks (LMP-GNN), a universal framework combining adaptive filter and graph neural network for online graph signal estimation. LMP-GNN retains the advantage of adaptive filtering in handling noise and missing observations as well as the online update capability. The incorporated graph neural network within the LMP-GNN can train and update filter parameters online instead of predefined filter parameters in previous methods, outputting more accurate prediction results. The adaptive update scheme of the LMP-GNN follows the solution of a $l_p$-norm optimization, rooting to the minimum dispersion criterion, and yields robust estimation results for time-varying graph signals under impulsive noise. A special case of LMP-GNN named the Sign-GNN is also provided and analyzed, Experiment results on two real-world datasets of temperature graph and traffic graph under four different noise distributions prove the effectiveness and robustness of our proposed LMP-GNN.

Efficient and robust prediction of graph signals is challenging when the signals are under impulsive noise and have missing data. Exploiting graph signal processing (GSP) and leveraging the simplicity of the classical adaptive sign algorithm, we propose an adaptive algorithm on graphs named the Graph Normalized Sign (GNS). GNS approximated a normalization term into the update, therefore achieving faster convergence and lower error compared to previous adaptive GSP algorithms. In the task of the online prediction of multivariate temperature data under impulsive noise, GNS outputs fast and robust predictions.

Graph Neural Networks have a limitation of solely processing features on graph nodes, neglecting data on high-dimensional structures such as edges and triangles. Simplicial Convolutional Neural Networks (SCNN) represent higher-order structures using simplicial complexes to break this limitation albeit still lacking time efficiency. In this paper, we propose a novel neural network architecture on simplicial complexes named Binarized Simplicial Convolutional Neural Networks (Bi-SCNN) based on the combination of simplicial convolution with a binary-sign forward propagation strategy. The usage of the Hodge Laplacian on a binary-sign forward propagation enables Bi-SCNN to efficiently and effectively represent simplicial features that have higher-order structures than traditional graph node representations. Compared to the previous Simplicial Convolutional Neural Networks, the reduced model complexity of Bi-SCNN shortens the execution time without sacrificing the prediction performance and is less prone to the over-smoothing effect. Experimenting with real-world citation and ocean-drifter data confirmed that our proposed Bi-SCNN is efficient and accurate.

The entanglement properties of quantum synchronization, based on a single-ion phonon laser subjected to an external drive, have been studied. It is found that the maximum value of steady-state entanglement between the ion's internal and external states occurs near the noiseless boundary from synchronization to unsynchronization, accompanied by noticeable oscillatory behaviors during the corresponding time evolution of entanglement. In addition, the later time dynamics of entanglement also indicates the occurrence of frequency entrainment, as evidenced by the strong consistency between the bending of the observed frequency and the emergence of Liouvillian exceptional points (LEPs) in the first two eigenvalues of the Liouvillian eigenspectrum. Moreover, the emergence of LEPs, which is intimately associated with frequency entrainment, should be widely observed in quantum synchronization and can be explored in LEPs-based applications., Comment: 12 pages, 6 figures, Supplement 6 pages, 2 figures

How pulsars radiate is a long-standing problem. Detailed polarization measurements of individual pulses shed light on currently unknown emission processes. Recently, based on supersensitive observations, dwarf pulses have been recognized as weak narrow pulses often appearing during the nulling state. In this study, we report the detection of dwarf pulses from ten pulsars, PSRs B0525+21, B1237+25, J1538+2345, J1824$-$0127, J1851$-$0053, B1901+10, J1939+10, B1944+17, B2000+40 and J2112+4058, based on observations conducted with the Five-hundred-meter Aperture Spherical radio Telescope. Dwarf pulses of five pulsars are clearly discernible in the two-dimensional distribution of pulse intensity and pulse width. For the other five pulsars, PSRs J1538+2345, J1824$-$0127, J1939+10, B2000+40, and J2112+4058, only a few dwarf pulses are detected from pulse stacks. The dwarf pulses can emerge in both cone and core emission components for PSR B1237+25, and the polarization angles of these dwarf pulses are mostly in the orthogonal polarization mode of normal pulses for PSR B1944+17. In general, pulsars with detected dwarf pulses tend to be located within the "death valley" region of the distribution of pulsar periods and period derivatives., Comment: 12 pages, 24 figures

The online prediction of multivariate signals, existing simultaneously in space and time, from noisy partial observations is a fundamental task in numerous applications. We propose an efficient Neural Network architecture for the online estimation of time-varying graph signals named the Adaptive Least Mean Squares Graph Neural Networks (LMS-GNN). LMS-GNN aims to capture the time variation and bridge the cross-space-time interactions under the condition that signals are corrupted by noise and missing values. The LMS-GNN is a combination of adaptive graph filters and Graph Neural Networks (GNN). At each time step, the forward propagation of LMS-GNN is similar to adaptive graph filters where the output is based on the error between the observation and the prediction similar to GNN. The filter coefficients are updated via backpropagation as in GNN. Experimenting on real-world temperature data reveals that our LMS-GNN achieves more accurate online predictions compared to graph-based methods like adaptive graph filters and graph convolutional neural networks.

Individual subpulses of pulsars are regarded as the basic emission components, providing invaluable information to understand the radio emission process in the pulsar magnetosphere. Nevertheless, subpulses are overlapped with each other along the rotation phase for most pulsars, making it difficult to study the statistical properties of subpulses. Among the pulsars observed by the Five-hundred-meter Aperture Spherical radio Telescope, PSR B1916+14 has a large number of isolated well-resolved subpulses in the high time resolution observations, having a typical width of 0.15 ms and a high linear polarization. We find that the number distribution of subpulses contributes dominantly to the mean profile. According to the emission geometry, these emission units come from a region roughly 155 km above the polar cap in the pulsar magnetosphere, and the length scale of basic emission units is approximately 120 m. The deviations of polarization position angles for these single subpulses from the standard S-shaped curve are closely related to their fractional linear and circular polarization, and the large deviations tend to come from drifting subpulses., Comment: 11 pages, 10 figures, accepted for MNRAS after a minor revision

Quantum sensing utilizing unique quantum properties of non-Hermitian systems to realize ultra-precision measurements has been attracting increasing attention. However, the debate on whether non-Hermitian systems are superior to Hermitian counterparts in sensing remains an open question. Here, we investigate the quantum information in PT-symmetric quantum sensing utilizing two experimental schemes based on the trapped-ion platform. It turns out that the existence of advantages of non-Hermitian quantum sensing heavily depends on additional information resources carried by the extra degrees of freedom introduced to construct PT-symmetric quantum sensors. Moreover, the practical application of non-Hermitian quantum sensing with superior performance is primarily restricted by the additional resource consumption accompanied by the post-selection. Our study provides theoretical references for the construction of non-Hermitian quantum sensors with superior performance and has potential applications in research fields of quantum precision measurement and quantum information processing., Comment: 16 pages, 7 figures

Spatio-temporal estimation of signals on graph edges is challenging because most conventional Graph Signal Processing techniques are defined on the graph nodes. Leveraging the Line Graph transform, the Line Graph Least Mean Square (LGLMS) algorithm is proposed to conduct adaptive estimation of time-varying edge signals by projecting the edge signals from edge space to node space. LGLMS is an adaptive algorithm analogous to the classical LMS algorithm but applied to graph edges. Unlike edge-specific methods, LGLMS retains all GSP concepts and techniques originally designed for graph nodes, without the need for redefinition on the edges. Experimenting with transportation graphs and meteorological graphs, with the signal observations having noisy and missing values, we confirmed that LGLMS is suitable for the online prediction of time-varying edge signals.

During observations of the Galactic Plane Pulsar Snapshot (GPPS) survey by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), varying subpulses drifting of PSR J1857+0057 is detected. The following-up observation confirms the quasi-regularly changes of the drifting rate about every 50 periods. We determine the drift rate $D$ through a linear fit to the pulse-central longitudes of subpulses in a drifting band, and determine $P_3$ from the cross-points of two fitted lines at the zero longitude for two neighbored drifting bands. The low frequency modulation of about every 50 periods is found on variations of not only pulse intensity but also drift parameters. In most of low frequency modulation cycles, the integrated pulse intensity $I$ and the absolute drift rate $|D|$ tend to increase first and then decrease, and the drifting periodicity $P_3$ varies just in the opposite. In addition, the phase-forward intensity-enhancement is observed in many modulation cycles. Based on our polarization data, the average PA curve for pulses with a smaller $|D|$ and larger $P_3$ is slightly steep in the leading edge of pulse profile compared with that of the fully averaged profile., Comment: 17 pages, 15 figures

Four pulsars, PSRs J1838+1523, J1901+0510, J1909+0007 and J1929+1844, are found to exhibit bright and weak emission states from sensitive FAST observations. New FAST observations have measured their polarization properties for the two states, and revealed that the polarization profiles, linear polarization percentage, and polarization position angle curves, as well as circular polarization percentage are partially or entirely different in the two emission states. Remarkably, PSR J1838+1523 has very different slopes for the polarization position angle curves. PSR J1901+0510 has a wider profile and a higher linear polarization in the weak state than those in the bright state. PSR J1909+0007 has very distinct polarization angle curves for the two modes. While in the case of PSR J1929+1844, the central profile component evolves with frequency in the bright state, and the senses of circular polarization are opposite in the two modes. The different polarization properties of the two emission states provide valuable insights into the physical processes and emission conditions in pulsar magnetosphere., Comment: 13 pages, 9 figures

Timing observations are crucial for determining the basic parameters of newly discovered pulsars. Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) with the L-band 19-beam receiver covering the frequency range of 1.0--1.5 GHz, the FAST Galactic Plane Pulsar Snapshot (GPPS) Survey has discovered more than 600 faint pulsars with flux densities of only a few or a few tens of $\mu$Jy at 1.25 GHz. To obtain accurate position, spin parameters and dispersion measure of a pulsar, and to calculate derived parameters such as the characteristic age and surface magnetic field, we collect available FAST pulsar data obtained either through targeted follow-up observations or through coincidental survey observations with one of the 19 beams of the receiver. From these data we obtain time of arrival (TOA) measurements for 30 newly discovered pulsars as well as for 13 known pulsars. We demonstrate that the TOA measurements acquired by the FAST from any beams of the receiver in any observation mode (e.g. the tracking mode or the snapshot mode) can be combined to get timing solutions. We update the ephemerides of 13 previously known pulsars and obtain the first phase-coherent timing results for 30 isolated pulsars discovered in the FAST GPPS Survey. Notably, PSR J1904+0853 is an isolated millisecond pulsar, PSR J1906+0757 is a disrupted recycled pulsar, and PSR J1856+0211 has a long period of 9.89 s that can constrain pulsar death lines. Based on these timing solutions, all available FAST data have been added together to obtain the best pulse profiles for these pulsars.

Previous studies have identified two emission modes in PSR B1859+07: a normal mode that has three prominent components in the average profile, with the trailing one being the brightest, and an anomalous mode (i.e. the A mode) where emissions seem to be shifted to an earlier phase. Within the normal mode, further analysis has revealed the presence of two sub-modes, i.e. the cW mode and cB mode, where the central component can appear either weak or bright. As for the anomalous mode, a new bright component emerges in the advanced phase while the bright trailing component in the normal mode disappears. New observations of PSR B1859+07 by using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) have revealed the existence of a previously unknown emission mode, dubbed as the Af mode. In this mode, all emission components seen in the normal and anomalous modes are detected. Notably, the mean polarization profiles of both the A and Af modes exhibit an orthogonal polarization angle jump in the bright leading component. The polarization angles for the central component in the original normal mode follow two distinct orthogonal polarization modes in the A and Af modes respectively. The polarization angles for the trailing component show almost the same but a small systematic shift in the A and Af modes, roughly following the values for the cW and cB modes. Those polarization features of this newly detected emission mode imply that the anomalous mode A of PSR B1859+07 is not a result of ``phase shift" or ``swooshes" of normal components, but simply a result of the varying intensities of different profile components. Additionally, subpulse drifting has been detected in the leading component of the Af mode., Comment: 8 pages, 5 figures, accepted for publication in RAA

Interfacial coupling between graphene and other 2D materials can give rise to intriguing physical phenomena. In particular, several theoretical studies predict that the interplay between graphene and an antiferromagnetic insulator could lead to the emergence of quantum anomalous Hall phases. However, such phases have not been observed experimentally yet, and further experimental studies are needed to reveal the interaction between graphene and antiferromagnetic insulators. Here, we report the study in heterostructures composed of graphene and the antiferromagnetic insulator MnPSe$_3$. It is found that the MnPSe$_3$ has little impact on the quantum Hall phases apart from doping graphene via interfacial charge transfer. However, the magnetic order can contribute indirectly via process like Kondo effect, as evidenced by the observed minimum in the temperature-resistance curve between 20-40 K, far below the N\'eel temperature (70 K).

We are carrying out the GPPS survey by using the FAST, the most sensitive systematic pulsar survey in the Galactic plane. In addition to about 500 pulsars already discovered through normal periodical search, we report here the discovery of 76 new transient radio sources with sporadic strong pulses, detected by using the newly developed module for a sensitive single pulse search. Their small DM values suggest that they all are the Galactic RRATs. More radio pulses have been detected from 26 transient radio sources but no periods can be found due to a limited small number of pulses from all FAST observations. The following-up observations show that 16 transient sources are newly identified as being the prototypes of RRATs with a period already determined from more detected sporadic pulses, 10 sources are extremely nulling pulsars, and 24 sources are weak pulsars with sparse strong pulses. On the other hand, 48 previously known RRATs have been detected by the FAST. Except for 1 RRAT with four pulses detected in a session of five minute observation and 4 RRATs with only one pulse detected in a session, sensitive FAST observations reveal that 43 RRATs are just generally weak pulsars with sporadic strong pulses or simply very nulling pulsars, so that the previously known RRATs always have an extreme emission state together with a normal hardly detectable weak emission state. This is echoed by the two normal pulsars J1938+2213 and J1946+1449 with occasional brightening pulses. Though strong pulses of RRATs are very outstanding in the energy distribution, their polarization angle variations follow the polarization angle curve of the averaged normal pulse profile, suggesting that the predominant sparse pulses of RRATs are emitted in the same region with the same geometry as normal weak pulsars., Comment: Published in RAA

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the most sensitive radio telescope for pulsar observations. We make polarimetric measurements of a large number of faint and distant pulsars using the FAST. We present the new measurements of Faraday rotation for 134 faint pulsars in the Galactic halo. Significant improvements are also made for some basic pulsar parameters for 15 of them. We analyse the newly determined rotation measures (RMs) for the Galactic magnetic fields by using these 134 halo pulsars, together with previously available RMs for pulsars and extragalactic radio sources and also the newly determined RMs for another 311 faint pulsars which are either newly discovered in the project of the Galactic Plane Pulsar Snapshot (GPPS) survey or previously known pulsars without RMs. The RM tomographic analysis in the first Galactic quadrant gives roughly the same field strength of around 2~$\mu$G for the large-scale toroidal halo magnetic fields. The scale height of the halo magnetic fields is found to be at least 2.7$\pm$0.3~kpc. The RM differentiation of a large number of pulsars in the Galactic disk in the Galactic longitude range of $26^{\circ}

The adaptive estimation of coexisting temporal vertex (node) and edge signals on graphs is a critical task when a change in edge signals influences the temporal dynamics of the vertex signals. However, the current Graph Signal Processing algorithms mostly consider only the signals existing on the graph vertices and have neglected the fact that signals can reside on the edges. We propose an Adaptive Joint Vertex-Edge Estimation (AJVEE) algorithm for jointly estimating time-varying vertex and edge signals through a time-varying regression, incorporating both vertex signal filtering and edge signal filtering. Accompanying AJVEE is a newly proposed Adaptive Least Mean Square procedure based on the Hodge Laplacian (ALMS-Hodge), which is inspired by classical adaptive filters combining simplicial filtering and simplicial regression. AJVEE is able to operate jointly on the vertices and edges by merging two ALMS-Hodge specified on the vertices and edges into a unified formulation. A more generalized case extending AJVEE beyond the vertices and edges is being discussed. Experimenting on real-world traffic networks and population mobility networks, we have confirmed that our proposed AJVEE algorithm could accurately and jointly track time-varying vertex and edge signals on graphs.