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Predicting molecular properties is essential for drug discovery, and computational methods can greatly enhance this process. Molecular graphs have become a focus for representation learning, with Graph Neural Networks (GNNs) widely used. However, GNNs often struggle with capturing long-range dependencies. To address this, we propose MolGraph-xLSTM, a novel graph-based xLSTM model that enhances feature extraction and effectively models molecule long-range interactions. Our approach processes molecular graphs at two scales: atom-level and motif-level. For atom-level graphs, a GNN-based xLSTM framework with jumping knowledge extracts local features and aggregates multilayer information to capture both local and global patterns effectively. Motif-level graphs provide complementary structural information for a broader molecular view. Embeddings from both scales are refined via a multi-head mixture of experts (MHMoE), further enhancing expressiveness and performance. We validate MolGraph-xLSTM on 10 molecular property prediction datasets, covering both classification and regression tasks. Our model demonstrates consistent performance across all datasets, with improvements of up to 7.03% on the BBBP dataset for classification and 7.54% on the ESOL dataset for regression compared to baselines. On average, MolGraph-xLSTM achieves an AUROC improvement of 3.18\% for classification tasks and an RMSE reduction of 3.83\% across regression datasets compared to the baseline methods. These results confirm the effectiveness of our model, offering a promising solution for molecular representation learning for drug discovery.

In the $N=1$ superspace, AdS$_4$ supersymmetry is realized as the non-linear super coordinate transformations. The fermionic coordinates form a faithful non-linear representation of supersymmetry on their own. By introducing an auxiliary scalar coordinate, this representation is reformulated as a 5-dimensional linear representation, i.e., the superspinor representation. New linear representations are constructed by tensor products of multiple superspinors. Especially, the superspace bosonic coordinates are embedded in the supervector representation, which is the traceless symmetric part of the bi-superspinor representation. Based on these linear representations, the $N=1$ AdS$_4$ supergravity action can be reproduced in a manifestly supersymmetry-covariant way., Comment: 9 pages, no figures

Finding pulsars in binaries are important for measurements of the masses of neutron stars, for tests of gravity theories, and for studies of star evolution. We are carrying out the Galactic Plane Pulsar Snapshot survey (GPPS) by using the the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here we present the Keplerian parameters for 116 newly discovered pulsars in the FAST GPPS survey, and obtain timing solutions for 29 pulsars. Companions of these pulsars are He white dwarfs, CO/ONe white dwarfs, neutron stars, main sequence stars and ultra light objects or even planets. Our observations uncover eclipses of 8 binary systems. The optical counterpart for the companion of PSR J1908+1036 is identified. The Post-Keplerian parameter $\dot{\omega}$ for the double neutron star systems PSR J0528+3529 and J1844-0128 have been measured, with which the total masses of the binary systems are determined., Comment: 19+16 pages, 11+3 figures, 7+1 tables, published in RAA

As irregularly structured data representations, graphs have received a large amount of attention in recent years and have been widely applied to various real-world scenarios such as social, traffic, and energy settings. Compared to non-graph algorithms, numerous graph-based methodologies benefited from the strong power of graphs for representing high-dimensional and non-Euclidean data. In the field of Graph Signal Processing (GSP), analogies of classical signal processing concepts, such as shifting, convolution, filtering, and transformations are developed. However, many GSP techniques usually postulate the graph is static in both signal and typology. This assumption hinders the effectiveness of GSP methodologies as the assumption ignores the time-varying properties in numerous real-world systems. For example, in the traffic network, the signal on each node varies over time and contains underlying temporal correlation and patterns worthy of analysis. To tackle this challenge, more and more work are being done recently to investigate the processing of time-varying graph signals. They cope with time-varying challenges from three main directions: 1) graph time-spectral filtering, 2) multi-variate time-series forecasting, and 3) spatiotemporal graph data mining by neural networks, where non-negligible progress has been achieved. Despite the success of signal processing and learning over time-varying graphs, there is no survey to compare and conclude the current methodology for GSP and graph learning. To compensate for this, in this paper, we aim to review the development and recent progress on signal processing and learning over time-varying graphs, and compare their advantages and disadvantages from both the methodological and experimental side, to outline the challenges and potential research directions for future research.

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the most sensitive telescope at the $L$-band (1.0-1.5 GHz) and has been used to carry out the FAST Galactic Plane Pulsar Snapshot (GPPS) survey in the last 5 yr. Up to now, the survey has covered one-fourth of the planned areas within $\pm10^{\circ}$ from the Galactic plane visible by FAST, and discovered 751 pulsars. After the first publication of the discovery of 201 pulsars and one rotating radio transient (RRAT) in 2021 and 76 RRATs in 2023, here we report the discovery of 473 new pulsars from the FAST GPPS survey, including 137 new millisecond pulsars and 30 new RRATs. We find 34 millisecond pulsars discovered by the GPPS survey which can be timed with a precision better than 3 $\mu$s by using FAST 15 minute observations and can be used for pulsar timing arrays. The GPPS survey has discovered eight pulsars with periods greater than 10 s including one with 29.77 s. The integrated profiles of pulsars and individual pulses of RRATs are presented. During the FAST GPPS survey, we also detected previously known pulsars and updated parameters for 52 pulsars. In addition, we discovered two fast radio bursts plus one probable case with high dispersion measures indicating their extragalactic origin., Comment: 19 pages, 16 figures and 8 tables. Published in RAA

Magnetars are slowly rotating, highly magnetized young neutron stars that can show transient radio phenomena for radio pulses and fast radio bursts. We conducted radio observations of from two magnetars SGR$~$J1935+2154 and 3XMM$~$J185246.6+003317 and a high-magnetic field pulsar PSR$~$J1846$-$0258 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We performed single pulse and periodicity searches and did not detect radio signals from them. From the piggyback data recorded by other FAST telescope beams when we observed the magnetar SGR$~$1935+2154, we serendipitously discovered a new radio pulsar, PSR$~$J1935+2200. We carried out the follow-up observations and obtained the timing solution based on these new observations and the archive FAST data. PSR$~$J1935+2200 is an isolated old pulsar, with a spin period of $0.91$s, a spin-period derivative of $9.19 \times 10^{-15}$~s~s$^{-1}$, and a characteristic age of $1.57$ Myr. It is a weak pulsar with a flux density of 9.8 $\mu$Jy at 1.25 GHz. Discovery of a new pulsar from the long FAST observations of 30 minutes implies that there may be more weak older pulsars in the Galactic disk to be discovered., Comment: 7 pages, 3 figures and 3 tables. Published in RAA

This work introduces the LLM Online Spatial-temporal Reconstruction (LLM-OSR) framework, which integrates Graph Signal Processing (GSP) and Large Language Models (LLMs) for online spatial-temporal signal reconstruction. The LLM-OSR utilizes a GSP-based spatial-temporal signal handler to enhance graph signals and employs LLMs to predict missing values based on spatiotemporal patterns. The performance of LLM-OSR is evaluated on traffic and meteorological datasets under varying Gaussian noise levels. Experimental results demonstrate that utilizing GPT-4-o mini within the LLM-OSR is accurate and robust under Gaussian noise conditions. The limitations are discussed along with future research insights, emphasizing the potential of combining GSP techniques with LLMs for solving spatiotemporal prediction tasks.

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.

We study the decay behaviors of the fully-bottom tetraquark states within the diquark-antidiquark picture, and calculate their relative branching ratios through the Fierz rearrangement. Our results suggest that the $C=+$ states can be searched for in the $\mu^+ \mu^- \Upsilon(1S)$ and $\mu^+ \mu^- \Upsilon(2S)$ channels with the relative branching ratio $\mathcal{B}(X \to \mu\mu \Upsilon(2S)) / \mathcal{B}(X \to \mu \mu \Upsilon(1S)) \approx 0.4$. Our results also suggest that the $C=-$ states can be searched for in the $\mu^+ \mu^- \eta_b(1S)$ and $\mu^+ \mu^- \eta_b(2S)$ channels with the similar relative branching ratio $\mathcal{B}(X \to \mu\mu \eta_b(2S)) / \mathcal{B}(X \to \mu \mu \eta_b(1S)) \approx 0.4$. We also reanalysis the fully-charm tetraquark states, and study the $X(6900)$ decay into the $J/\psi \psi(2S)$ channel to obtain the relative branching ratio $\mathcal{B}(X \to J/\psi \psi(2S)) / \mathcal{B}(X \to J/\psi J/\psi) \approx 0.1$., Comment: 12 pages, 2 figure, 4 tables, revised version to be published in PRD

In this paper, we introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP techniques, including bandlimited filtering and node sampling, to estimate sampled graph signals under impulsive noise. Different from least-squares-based algorithms, such as the adaptive GSP Least Mean Squares (GLMS) algorithm and the normalized GLMS (GNLMS) algorithm, the GNLMP algorithm has the ability to reconstruct a graph signal that is corrupted by non-Gaussian noise with heavy-tailed characteristics. Compared to the recently introduced adaptive GSP least mean pth power (GLMP) algorithm, the GNLMP algorithm reduces the number of iterations to converge to a steady graph signal. The convergence condition of the GNLMP algorithm is derived, and the ability of the GNLMP algorithm to process multidimensional time-varying graph signals with multiple features is demonstrated as well. Simulations show the performance of the GNLMP algorithm in estimating steady-state and time-varying graph signals is faster than GLMP and more robust in comparison to GLMS and GNLMS.

Efficient and robust online processing technique of irregularly structured data is crucial in the current era of data abundance. In this paper, we propose a graph/network version of the classical adaptive Sign algorithm for online graph signal estimation under impulsive noise. Recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity. The Graph-Sign algorithm proposed in this work is based on the minimum dispersion criterion and therefore impulsive noise does not hinder its estimation quality. Unlike the recently proposed graph adaptive least mean p-th power algorithm, our Graph-Sign algorithm can operate without prior knowledge of the noise distribution. The proposed Graph-Sign algorithm has a faster run time because of its low computational complexity compared to the existing adaptive graph signal processing algorithms. Experimenting on steady-state and time-varying graph signals estimation utilizing spectral properties of bandlimitedness and sampling, the Graph-Sign algorithm demonstrates fast, stable, and robust graph signal estimation performance under impulsive noise modeled by alpha stable, Cauchy, Student's t, or Laplace distributions.

The ability to analyse and differentiate network protocol traffic is crucial for network resource management to provide differentiated services by Telcos. Automated Protocol Analysis (APA) is crucial to significantly improve efficiency and reduce reliance on human experts. There are numerous automated state-of-the-art unsupervised methods for clustering unknown protocols in APA. However, many such methods have not been sufficiently explored using diverse test datasets. Thus failing to demonstrate their robustness to generalise. This study proposed a comprehensive framework to evaluate various combinations of feature extraction and clustering methods in APA. It also proposed a novel approach to automate selection of dataset dependent model parameters for feature extraction, resulting in improved performance. Promising results of a novel field-based tokenisation approach also led to our proposal of a novel automated hybrid approach for feature extraction and clustering of unknown protocols in APA. Our proposed hybrid approach performed the best in 7 out of 9 of the diverse test datasets, thus displaying the robustness to generalise across diverse unknown protocols. It also outperformed the unsupervised clustering technique in state-of-the-art open-source APA tool, NETZOB in all test datasets., Comment: Accepted to the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021)

The coexistence of neutron-neutron (n-n), proton-proton (p-p), and neutron-proton (n-p) pairings is investigated by adopting an effective density-dependent contact pairing potential. These three types of pairings can coexist only if the n-p pairing is stronger than the n-n and p-p pairings for isospin asymmetric nuclear matter. In addition, the existence of n-n and p-p pairs might enhance n-p pairings in asymmetric nuclear matter when the n-p pairing strength is significantly stronger than the n-n and p-p ones. Conversely, the n-p pairing is reduced by the n-n and p-p pairs when the n-p pairing interaction approaches n-n and p-p pairings.

Discovery of pulsars is one of the main goals for large radio telescopes. The Five-hundred-meter Aperture Spherical radio Telescope (FAST), that incorporates an L-band 19-beam receiver with a system temperature of about 20~K, is the most sensitive radio telescope utilized for discovering pulsars. We designed the {\it snapshot} observation mode for a FAST key science project, the Galactic Plane Pulsar Snapshot (GPPS) survey, in which every four nearby pointings can observe {\it a cover} of a sky patch of 0.1575 square degrees through beam-switching of the L-band 19-beam receiver. The integration time for each pointing is 300 seconds so that the GPPS observations for a cover can be made in 21 minutes. The goal of the GPPS survey is to discover pulsars within the Galactic latitude of $\pm10^{\circ}$ from the Galactic plane, and the highest priority is given to the inner Galaxy within $\pm5^{\circ}$. Up to now, the GPPS survey has discovered 201 pulsars, including currently the faintest pulsars which cannot be detected by other telescopes, pulsars with extremely high dispersion measures (DMs) which challenge the currently widely used models for the Galactic electron density distribution, pulsars coincident with supernova remnants, 40 millisecond pulsars, 16 binary pulsars, some nulling and mode-changing pulsars and rotating radio transients (RRATs). The follow-up observations for confirmation of new pulsars have polarization-signals recorded for polarization profiles of the pulsars. Re-detection of previously known pulsars in the survey data also leads to significant improvements in parameters for 64 pulsars. The GPPS survey discoveries are published and will be updated at http://zmtt.bao.ac.cn/GPPS/ ., Comment: 38 pages, 22 figures. See http://www.raa-journal.org/docs/Supp/2021Newsonline.pdf for News and Views

In this paper, we report two new kinds of absolute optical instruments that can make stigmatically images for geometric optics in two dimensional space. One is called the duplex Mikaelian lens, which is made by splicing two half Mikaelian lenses with different periods. The other is exponential conformal transformer of duplex Mikaelian lens with the ratio of different periods of its two half Mikaelian lenses a rational number, which we call duplex Maxwell's fish eye lens. Duplex Mikaelian lenses have continuous translation symmetry with arbitrary real number, while duplex Maxwell's fish eye lenses have continuous rotational symmetry from 0 to 2*Pi. Hence each duplex Maxwell's fish eye lens corresponds to a duplex Mikaelian lens. We further demonstrate the caustic effect of geometric optics in duplex Mikaelian lenses and duplex Maxwell's fish eye lenses. In addition, we investigate the Talbot effect of wave optics in the duplex Mikaelian lens based on numeric calculations. Our findings based on splicing and exponential conformal mapping enlarge the family of absolute optical instruments., Comment: 14 pages, 5 figures; This is an application of geometric optics when I was preparing the course of University Physics; Senlin Liu, Yuze Wu, Yi Yang, and Zichun Zhou are undergraduate students in Xiamen University Malaysia

Crystal phase is well studied and presents a periodical atom arrangement in three dimensions lattice, but the "amorphous phase" is poorly understood. Here, by starting from cage-like bicyclocalix[2]arene[2]triazines building block, a brand-new 2D MOF is constructed with extremely weak interlaminar interaction existing between two adjacent 2D-crystal layer. Inter-layer slip happens under external disturbance and leads to the loss of periodicity at one dimension in the crystal lattice, resulting in an interim phase between the crystal and amorphous phase - the chaos phase, non-periodical in microscopic scale but orderly in mesoscopic scale. This chaos phase 2D MOF is a disordered self-assembly of black-phosphorus like 3D-layer, which has excellent mechanical-strength and a thickness of 1.15 nm. The bulky 2D-MOF material is readily to be exfoliated into monolayer nanosheets in gram-scale with unprecedented evenness and homogeneity, as well as previously unattained lateral size (>10 um), which present the first mass-producible monolayer 2D material and can form wafer-scale film on substrate.

We investigated the quantum speed limit time of a qubit system with non-Hermitian detuning. Our results show that, with respect to two distinguishable states of the non-Hermitian system, the evolutionary time does not have a nonzero lower bound. And the quantum evolution of the system can be effectively accelerated by adjusting the non-Hermitian detuning parameter, as well as the quantum speed limit time can be arbitrarily small even be zero.

As a dedicated solar radio interferometer, the MingantU SpEctral RadioHeliograph (MUSER) generates massive observational data in the frequency range of 400 MHz -- 15 GHz. High-performance imaging forms a significantly important aspect of MUSER's massive data processing requirements. In this study, we implement a practical high-performance imaging pipeline for MUSER data processing. At first, the specifications of the MUSER are introduced and its imaging requirements are analyzed. Referring to the most commonly used radio astronomy software such as CASA and MIRIAD, we then implement a high-performance imaging pipeline based on the Graphics Processing Unit (GPU) technology with respect to the current operational status of the MUSER. A series of critical algorithms and their pseudo codes, i.e., detection of the solar disk and sky brightness, automatic centering of the solar disk and estimation of the number of iterations for clean algorithms, are proposed in detail. The preliminary experimental results indicate that the proposed imaging approach significantly increases the processing performance of MUSER and generates images with high-quality, which can meet the requirements of the MUSER data processing.

Background: The past few years have seen a tremendous increase in the size and complexity of datasets. Scientific and clinical studies must to incorporate datasets that cross multiple spatial and temporal scales to describe a particular phenomenon. The storage and accessibility of these heterogeneous datasets in a way that is useful to researchers and yet extensible to new data types is a major challenge. Methods: In order to overcome these obstacles, we propose the use of data primitives as a common currency between analytical methods. The four data primitives we have identified are time series, text, annotated graph and triangulated mesh, with associated metadata. Using only data primitives to store data and as algorithm input, output, and intermediate results, promotes interoperability, scalability, and reproducibility in scientific studies. Results: Data primitives were used in a multi-omic, multi-scale systems biology study of malaria infection in non-human primates to perform many types of integrative analysis quickly and efficiently. Conclusions: Using data primitives as a common currency for both data storage and for cross talk between analytical methods enables the analysis of complex multi-omic, multi-scale datasets in a reproducible modular fashion., Comment: 10 pages, 3 figures

Characterization of host responses associated with severe malaria through an integrative approach is necessary to understand the dynamics of a \textit{Plasmodium cynomolgi} infection. In this study, we conducted temporal immune profiling, cytokine profiling and transcriptomic analysis of five \textit{Macaca mulatta} infected with \textit{P. cynomolgi}. This experiment resulted in two severe infections, and two mild infections. Our analysis reveals that differential transcriptional up-regulation of genes linked with response to pathogen-associated molecular pattern (PAMP) and pro-inflammatory cytokines is characteristic of hosts experiencing severe malaria. Furthermore, our analysis discovered associations of transcriptional differential regulation unique to severe hosts with specific cellular and cytokine responses. The combined data provide a molecular and cellular basis for the development of severe malaria during \textit{P. cynomolgi} infection., Comment: 10 pages, 8 figures

We derived an ordinary differential equation model to capture the disease dynamics during blood-stage malaria. The model was directly derived from an earlier age-structured partial differential equation model. The original model was simplified due to experimental constraints. Here we calibrated the simplified model with experimental data using a multiple objective genetic algorithm. Through the calibration process, we quantified the removal of healthy red blood cells and the the preferential infection of reticulocytes during \textit{Plamodium cynomolgi} infection of \textit{Macaca mulatta}. The calibration of our model also revealed the existence of host erythropoietic response prior to blood stage infection., Comment: 17 pages, 14 figures

The goals of the Triple Aim of health care and the goals of P4 medicine outline objectives that require a significant health informatics component. However, the goals do not provide specifications about how all of the new individual patient data will be combined in meaningful ways and with data from other sources, like epidemiological data, to promote the health of individuals and society. We seem to have more data than ever before but few resources and means to use it efficiently. We need a general, extensible solution that integrates and homogenizes data of disparate origin, incompatible formats, and multiple spatial and temporal scales. To address this problem, we introduce the Scientific Knowledge Extraction from Data (SKED) architecture, as a technology-agnostic framework to minimize the overhead of data integration, permit reuse of analytical pipelines, and guarantee reproducible quantitative results. The SKED architecture consists of a Resource Allocation Service to locate resources, and the definition of data primitives to simplify and harmonize data. SKED allows automated knowledge discovery and provides a platform for the realization of the major goals of modern health care., Comment: 8 pages, 4 figures

Quantification of system-wide perturbations from time series -omic data (i.e. a large number of variables with multiple measures in time) provides the basis for many downstream hypothesis generating tools. Here we propose a method, Massively Parallel Analysis of Time Series (MPATS) that can be applied to quantify transcriptome-wide perturbations. The proposed method characterizes each individual time series through its $\ell_1$ distance to every other time series. Application of MPATS to compare biological conditions produces a ranked list of time series based on their magnitude of differences in their $\ell_1$ representation, which then can be further interpreted through enrichment analysis. The performance of MPATS was validated through its application to a study of IFN$\alpha$ dendritic cell responses to viral and bacterial infection. In conjunction with Gene Set Enrichment Analysis (GSEA), MPATS produced consistently identified signature gene sets of anti-bacterial and anti-viral response. Traditional methods such as EDGE and GSEA Time Series (GSEA-TS) failed to identify the relevant signature gene sets. Furthermore, the results of MPATS highlighted the crucial functional difference between STAT1/STAT2 during anti-viral and anti-bacterial response. In our simulation study, MPATS exhibited acceptable performance with small group size (n = 3), when the appropriate effect size is considered. This method can be easily adopted for other -omic data types., Comment: 18 pages, 8 figures

We establish a construction of the bulk local operators in AdS by considering CFT at finite energy scale. Without assuming any prior knowledge about the bulk, the normalizable solution to the bulk free field equation automatically appears in the field theory arguments. In the radial quantization formalism, we find a properly regularized version of our initial construction. Possible generalizations beyond pure AdS are also discussed., Comment: 21 pages, 1 figure

We investigate the dynamics of quantum discord and entanglement for two central spin qubits coupled to an isotropic Lipkin-Meshkov-Glick bath. It is found that both quantum discord and entanglement have quite distinct behaviors with respect to the two different phases of the bath. In the case of the symmetry broken phase bath, quantum discord and entanglement can remain as constant. In the case of the symmetric phase bath, quantum discord and entanglement always periodically oscillate with time. The critical point of quantum phase transition of the bath can be revealed clearly by the distinct behaviors of quantum correlations. Furthermore, it is observed that quantum discord is significantly enhanced during the evolution while entanglement periodically vanishes.

We study the dynamics of quantum discord and entanglement for two spin qubits coupled to a spin chain with Dzyaloshinsky-Moriya (DM) interaction. We numerically and analytically investigate the time evolution of quantum discord and entanglement for two-qubit initially prepared in a class of $X-$structure state. In the case of evolution from a pure state, quantum correlations decay to zero in a very short time at the critical point of the environment. In the case of evolution from a mixed state, It is found that quantum discord may get maximized due to the quantum critical behavior of the environment while entanglement vanishes under the same condition. Moreover, we observed sudden transition between classical and quantum decoherence when single qubit interacts with the environment. The effects of DM interaction on quantum correlations are also considered and revealed in the two cases. It can enhance the decay of quantum correlations and its effect on quantum correlations can be strengthened by anisotropy parameter.

We investigate the pairwise thermal quantum discord in a three-qubit XXZ model with Dzyaloshinskii-Moriya (DM) interaction. We find that the DM interaction can increase quantum discord to a fixed value in the anti- ferromagnetic system, but decreases quantum discord to a minimum first, then increases it to a fixed value in the ferromagnetic system. Abrupt change of quantum discord is observed, which indicates the abrupt change of groundstate. Dynamics of pairwise thermal quantum discord is also considered. We show that thermal discord vanishes in asymptotic limit regardless of its initial values, while thermal entanglement suddenly disappears at finite time., Comment: 6 pages, 6 figures

We have carried out a systematic study of the PbO-type compound FeSe_{1-x}Te_x (x = 0~1), where Te substitution effect on superconductivity is investigated. It is found that superconducting transition temperature reaches a maximum of Tc=15.2K at about 50% Te substitution. The pressure-enhanced Tc of FeSe0.5Te0.5 is more than 10 times larger than that of FeSe. Interestingly, FeTe is no longer superconducting. A low temperature structural distortion changes FeTe from triclinic symmetry to orthorhombic symmetry. We believe that this structural change breaks the magnetic symmetry and suppresses superconductivity in FeTe., Comment: Some typing errors are corrected; we take out one figures, now the paper has 14 pages, 5 figures

The recent discovery of superconductivity with relatively high transition temperature Tc in the layered iron-based quaternary oxypnictides La[ O1-xFx] FeAs was a real surprise. The excitement generated can be seen by the number of subsequent works published within a very short period of time. Although there exists superconductivity in alloy that contains Fe element, LaOMPn (with M= Fe, Ni; and Pn=P and As) is the first system where Fe-element plays the key role to the occurrence of superconductivity. LaOMPn has a layered crystal structure with an Fe-based plane. It is quite natural to ask whether there exists other Fe based planar compounds that exhibit superconductivity. Here we report the observation of superconductivity with zero resistance transition temperature at 8K in the PbO-type alpha-FeSe compound. Although FeSe has been studied quite extensively, a key observation is that the clean superconducting phase exists only in those samples prepared with intentional Se deficiency. What is truly striking, is that this compound has the same, perhaps simpler, planar crystal sublattice as the layered oxypnictides. Furthermore, FeSe is, compared with LaOFeAs, much easier to handle and fabricate. In view of the abundance of compounds with PbO type structure, this result opens a new route to the search for unconventional superconductors., Comment: PDF format, 10 pages, 5 figures